The simplest way to set up JGNN is to download it as JAR package from
the project's releases
and add it in a Java project's dependencies. However, those working with Maven
- or Gradle can also add JGNN's latest release as a dependency from the JitPack
+ or Gradle can also add JGNN's latest nightly release as a dependency from the JitPack
repository. Follow the link below for full instructions.
@@ -213,7 +213,7 @@
2. Quickstart
- Here we demonstrate usage of JGNN for node classification, that is, the inductive learning
- task of inferring node labels given a graph's structure, node features, and a few node labels.
+
Here we demonstrate usage of JGNN for node classification. This is an inductive learning
+ task where node labels are predicted given a graph's structure, node features, and a few known
+ labels in the graph.
Classifying graphs is also supported, but it is a harder task to explain and set up.
GNN architectures for the chosen node classification task are typically written
as message-passing mechanisms; these diffuse node representations across edges, where
@@ -264,13 +265,13 @@
2. Quickstart
classify - Adds a softmax layer tailored to classification. This also silently declares an input nodes that represents a list of node indices where the outputs should be computed.autosize - Automatically sizes matrix and vector dimensions filled by ?. This requires some input example, and here we provide a list of node identifiers, which we also make dataless (have only the correct dimensions without allocating memory). This method also checks for integrity errors in the declared architecture, such as computational paths that do not lead to an output.[Kipf and Welling, 2017] .
- Details on the symbolic parts of definitions are presented later but, for the time being, we point to usage
- of the matrix and vector builtins to inline declarations of learnable parameter for
- given dimensions and regularization.
- The builder stores internally a constructed model, obtained through modelBuilder.getModel().
+ Details on the symbolic parts of definitions are presented later but, for the time being, we point to
+ the matrix and vector Neuralang functions. These inline declarations of learnable parameter for
+ given dimensions and regularization. The builder stores internally a constructed model, and the latter
+ can be retrieved through modelBuilder.getModel().
long numSamples = dataset.samples().getSlice().size();
@@ -294,13 +295,14 @@ 2. Quickstart
accept a method chain notation to set their parameters, like the number of epochs, patience
for early stopping, the employed optimizer, and loss functions. An example is presented below,
where Adam optimization with learning rate 0.01 is performed, and a verbose
- variation of a validation loss keeps track of training progress. To run a full training process,
+ variation of a validation loss prints the progress progress. To run a full training process,
the defined strategy is passed to the model alongside input data, corresponding output data, as well
as training and validation slices.
- Notice how, before training starts, a parameter initializer is also applied on the model for a cold
+
Notice how, before training starts, a parameter initializer is also applied on the model for cold
start. Selecting an initilizer is not part of training strategies
to signify its model-dependent nature; dense layers should maintain the expected
- input variances in the output before the first epoch. Moreover,
+ input variances in the output before the first epoch, and therefore the initializer depends
+ on the type of activation functions. Moreover,
the graph's adjacency matrix and node features are already declared as constants by the
FastBuilder constructor, as node classification takes place on the same graph
with fully known node features. Instead, what is considered as inputs and outputs in this case
@@ -354,17 +356,50 @@
2. Quickstart
3. GNN Builders
- We already touched on the subject of GNN architecture builders when the Neuralang language
- was first introduced in the introductory demonstration. To recap, there are different kinds of
- builders, some of which do not implement all features of the language in favor of
- simplifying parts of architecture definitions with hard-coded Java implementations.
- Here we cover the base GNNBuilder class that can only parse simple expressions,
- the FastBuilder class that introduces node classification boilerplate code,
- and the Neuralang class that parses the full language, including function definitions
- and handling configurations through expressions.
+ We already touched on the subject of GNN model builders in the quickstart section,
+ where we saw one of them in action. However, there exist different kinds of
+ builders that offer kinds of convenience during model definition.
+ All builders support method chain notation. Currently implemented builders are:
+
+ GNNBuilder - Parses simple Neuralang expressions. Prefer using FastBuilder,
+ which offers a superset of this one's functionalities.FastBuilder - Extends the GNNBuilder class with methods that inject
+ boilerplate code often used in node classification. Use this builder of your want to keep track
+ of the whole model definition in one place within Java code.Neuralang - Extends the GNNBuilder class so that it can parse all aspects
+ of the Neuralang language, especially the expressions responsible for handling configuration.
+ Use this builder to maintain model definitions in one place (e.g., packed in one String
+ variable, or in one read file) to avoid weaving symbolic expressions in Java code.
3.1. ModelBuilder
- [Under construction]
+ This is the base model builder class that is extended by others. We describe it separately
+ because it offers a wide breadth of functionalities that other builders inherit. Before looking
+ at how to use it, we need to tackle the concept of models. These are effectively collections
+ of NNOperation instances, each of which is an operation with specified inputs and outputs.
+ Models can be manually written using Java code only. As an example, a simple model computing the expression
+ y=log(2*x+1) is implemented below. This is still readable for simple
+ expressions, but very cumbersome to handle once actual architectures are created - hence the need for
+ model builders. This guidebook does not go into details for the available types of NNOperation,
+ as they are rarely used directly in practice. To read on them, please visit JGNN's Javadoc.
+
+
+ Variable x = new Variable();
+Constant c1 = new Constant(Tensor.fromDouble(1)); // holds the constant "1"
+Constant c2 = new Constant(Tensor.fromDouble(2)); // holds the constant "2"
+NNOperation mult = new Multiply()
+ .addInput(x)
+ .addInput(c2);
+NNOperation add = new Add()
+ .addInput(mult)
+ .addInput(c1);
+NNOperation y = new Log()
+ .addInput(add);
+Model model = new Model()
+ .addInput(x)
+ .addOutput(y);
+ All Classes and Interfaces
+
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+
+
+
+
+
+
+
All Classes and Interfaces Interfaces Classes
+
+
+
+
+
+
+
+
+
+
+
Wraps a base
Tensor by traversing only its elements in a specified range (from begin, up to end-1).
+
+
+
+
Implements an accuracy
Loss of row-by-row comparisons.
+
+
+
+
Thic class implements an Adam
Optimizer as explained in the paper:
+
Kingma, Diederik P., and Jimmy Ba.
+
+
+
+
+
+
Implements a
NNOperation that creates a version of adjacency matrices
+ with column-wise attention involving neighbor similarity.
+
+
+
+
+
+
+
+
Implements a categorical cross-entropy
Loss.
+ For binary classification of one output use
BinaryCrossEntropy.
+
+
+
+
Downloads and constructs the Citeseer node classification
Dataset.
+
+
+
+
Defines a matrix whose columns are all a copy of a
Tensor.
+
+
+
+
Implements a
NNOperation that performs the operation 1-x for its simple input x.
+
+
+
+
Implements a
NNOperation that concatenates its two matrix inputs.
+
+
+
+
+
+
Downloads and constructs the Cora node classification
Dataset.
+
+
+
+
This class provides the backbone with which to define datasets.
+
+
+
+
Implements a dense
Matrix where all elements are stored in memory.
+
+
+
+
This class provides a dense
Tensor that wraps an array of doubles.
+
+
+
+
Implements a square matrix whose diagonal elements are determined by the correspond values of
+ an underlying tensor and off-diagonal elements are zero.
+
+
+
+
+
+
Implements a
NNOperation that converts its first argument to a
ColumnRepetition matrix
+ with a number of columns equal to the second argument.
+
+
+
+
+
+
+
+
Implements a
NNOperation that performs an exponential transformation of its single input.
+
+
+
+
Extends the capabilities of
LayeredBuilder to use
+ for node classification.
+
+
+
+
+
+
Implements a
NNOperation that lists the first element of the 2D matrix element iterator.
+
+
+
+
Implements a
NNOperation that performs the equivalent of TensorFlow's gather operation.
+
+
+
+
+
+
Converts back-and-forth between objects and unique ids.
+
+
+
+
Implements a
NNOperation that just transfers its single input.
+
+
+
+
This class defines an abstract interface for applying initializers to models.
+
+
+
+
+
+
+
+
Implements a
NNOperation that performs a L1 transformation of its single input
+ by row or column.
+
+
+
+
Extends the capabilities of the
ModelBuilder
+ with the ability to define multilayer (e.g.
+
+
+
+
Implements a
NNOperation that outputs the natural logarithm of its single input.
+
+
+
+
Provides computation and (partial) derivation of popular activation functions
+ and cross-entropy loss functions.
+
+
+
+
This class provides an abstract implementation of loss functions
+ to be used during
Model training.
+
+
+
+
Implements a
NNOperation that performs a leaky relu operation, where the first argument is a tensor on which
+ it is applied and the second one should be a tensor wrapping a double value (consider initializing this with as a
+
Constant holding a tensor generated with
Tensor.fromDouble(double)) where
+ the wrapped value indicates the negative region's slope.
+
+
+
+
+
+
+
+
Implements a
NNOperation that multiplies its two matrix inputs.
+
+
+
+
This class provides an abstract implementation of Matrix functionalities.
+
+
+
+
Implements a
NNOperation that performs row-wise or column-wise
+ maximum reduction on vector tensors or matrices.
+
+
+
+
Implements a
NNOperation that performs row-wise or column-wise
+ mean reduction on vector tensors or matrices.
+
+
+
+
A memory management system for thread-safe allocation and release of arrays of doubles.
+
+
+
+
+
+
This class is a way to organize
NNOperation trees into trainable machine
+ learning models.
+
+
+
+
This class and subclasses can be used to create
Model instances
+ by automatically creating and managing
NNOperation instances based on
+ textual descriptions.
+
+
+
+
This is a helper class that automates the definition of training processes of
Model instances
+ by defining the number of epochs, loss functions, number of batches and the ability to use
ThreadPool
+ for parallelized batch computations.
+
+
+
+
Implements a
NNOperation that multiplies its two inputs element-by-element.
+
+
+
+
+
+
Implements a
NNOperation that performs an exponential transformation of
+ its single input, but only on the non-zero elements.
+
+
+
+
This class defines an abstract neural network operation with forward and
+ backpropagation capabilities.
+
+
+
+
Implements a Normal
Distribution of given mean and standard deviation.
+
+
+
+
Provides an interface for training tensors.
+
+
+
+
Implements a
NNOperation that holds and returns a parameter tensor.
+
+
+
+
+
+
Downloads and constructs the Pubmed node classification
Dataset.
+
+
+
+
Implements an iterator that traverses a range (similar to Python's range(min, max) method).
+
+
+
+
Implements an iterator that traverses a two-dimensional range (min, max) x (min2, max2).
+
+
+
+
+
+
+
+
Implements a
NNOperation that performs a relu transformation of its single input first introduced by
+
Hahnloser, Richard HR, Rahul Sarpeshkar, Misha A.
+
+
+
+
Implements a
NNOperation that converts its first argument to a
ColumnRepetition matrix
+ with a number of columns equal to the second argument.
+
+
+
+
Implements a
Matrix whose elements are all equals.
+
+
+
+
This class provides
Tensor whose elements are all equal.
+
+
+
+
+
+
Defines a matrix whose rows are all a copy of a
Tensor.
+
+
+
+
Implements a
NNOperation that performs a sigmoid transformation of its single input.
+
+
+
+
This class provices an interface with which to define data slices,
+ for instance to sample labels.
+
+
+
+
Implements a
NNOperation that performs row-wise or column-wise
+ softmax on vector tensors or matrices.
+
+
+
+
+
+
+
+
A sparse
Matrix that allocates memory only for non-zero elements.
+
+
+
Deprecated.
+
+
+
+
+
This class provides a sparse
Tensor with many zero elements.
+
+
+
+
Implements a
NNOperation that performs row-wise or column-wise
+ sum reduction on vector tensors or matrices.
+
+
+
+
Implements a
NNOperation that performs a tanh transformation of its single input.
+
+
+
+
This class provides a native java implementation of Tensor functionalities.
+
+
+
+
This class provides thread execution pool utilities while keeping track of thread
+ identifiers for use by thread-specific
NNOperation.
+
+
+
+
Implements a
NNOperation that lists the second element of the 2D matrix element iterator.
+
+
+
+
Implements a
NNOperation that performs matrix transposition.
+
+
+
+
Generates a transposed version of a base matrix, with which it shares elements.
+
+
+
+
+
+
+
+
This class describes a broad class of
Initializer strategies, in which
+ dense neural layer initialization is controlled so that variance is mostly preserved from
+ inputs to outputs to avoid vanishing or exploding gradients in the first training
+ runs.
+
+
+
+
Implements a dense
Matrix where all elements are stored in memory.
+
+
+
+
This class provides a dense
Tensor that wraps an array of doubles.
+
+
+
+
Implements a
Loss that wraps other losses and outputs their value during training to an output stream
+ (to
System.out by default).
+
+
+
+
Wraps a list of tensors into a matrix with the tensors as columns.
+
+
+
+
Wraps a list of tensors into a matrix with the tensors as rows.
+
+
+
+
+
+
+
+
+
+
All Packages
+
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+Package Summary
+
+
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+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Deprecated List
+
+JavaScript is disabled on your browser.
+
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+
+
+
+
+
+
+
Deprecated Methods
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Deprecated Constructors
+
+
+
+
+
+
API Help
+
+JavaScript is disabled on your browser.
+
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+
+JavaDoc Help
+
+
+
Navigation
+Starting from the
Overview page, you can browse the documentation using the links in each page, and in the navigation bar at the top of each page. The
Index and Search box allow you to navigate to specific declarations and summary pages, including:
All Packages ,
All Classes and Interfaces
+
+Search
+You can search for definitions of modules, packages, types, fields, methods, system properties and other terms defined in the API, using some or all of the name, optionally using "camelCase" abbreviations. For example:
+
+j.l.obj will match "java.lang.Object"InpStr will match "java.io.InputStream"HM.cK will match "java.util.HashMap.containsKey(Object)"
+Refer to the Javadoc Search Specification for a full description of search features.
+
+
+
Kinds of Pages
+The following sections describe the different kinds of pages in this collection.
+
+Overview
+The Overview page is the front page of this API document and provides a list of all packages with a summary for each. This page can also contain an overall description of the set of packages.
+
+
+Package
+Each package has a page that contains a list of its classes and interfaces, with a summary for each. These pages may contain the following categories:
+
+Interfaces
+Classes
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+Exceptions
+Errors
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+
+
+
+Class or Interface
+Each class, interface, nested class and nested interface has its own separate page. Each of these pages has three sections consisting of a declaration and description, member summary tables, and detailed member descriptions. Entries in each of these sections are omitted if they are empty or not applicable.
+
+Class Inheritance Diagram
+Direct Subclasses
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+
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+Note: Annotation interfaces have required and optional elements, but not methods. Only enum classes have enum constants. The components of a record class are displayed as part of the declaration of the record class. Properties are a feature of JavaFX.
+The summary entries are alphabetical, while the detailed descriptions are in the order they appear in the source code. This preserves the logical groupings established by the programmer.
+
+
+Other Files
+Packages and modules may contain pages with additional information related to the declarations nearby.
+
+
+Use
+Each documented package, class and interface has its own Use page. This page describes what packages, classes, methods, constructors and fields use any part of the given class or package. Given a class or interface A, its Use page includes subclasses of A, fields declared as A, methods that return A, and methods and constructors with parameters of type A. You can access this page by first going to the package, class or interface, then clicking on the USE link in the navigation bar.
+
+
+Tree (Class Hierarchy)
+There is a Class Hierarchy page for all packages, plus a hierarchy for each package. Each hierarchy page contains a list of classes and a list of interfaces. Classes are organized by inheritance structure starting with java.lang.Object. Interfaces do not inherit from java.lang.Object.
+
+When viewing the Overview page, clicking on TREE displays the hierarchy for all packages.
+When viewing a particular package, class or interface page, clicking on TREE displays the hierarchy for only that package.
+
+
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+Deprecated API
+The Deprecated API page lists all of the API that have been deprecated. A deprecated API is not recommended for use, generally due to shortcomings, and a replacement API is usually given. Deprecated APIs may be removed in future implementations.
+
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+All Packages
+The All Packages page contains an alphabetic index of all packages contained in the documentation.
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+All Classes and Interfaces
+The All Classes and Interfaces page contains an alphabetic index of all classes and interfaces contained in the documentation, including annotation interfaces, enum classes, and record classes.
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A-Index
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+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+A
+
+abs() - Method in class mklab.JGNN.core.Tensor
+Computes the absolute value of tensor elements.
+
+abs() - Method in class mklab.JGNN.core.tensor.DenseTensor
+accessCol(long) - Method in class mklab.JGNN.core.Matrix
+Retrieves the given column as a tensor.
+
+accessCol(long) - Method in class mklab.JGNN.core.matrix.WrapCols
+AccessCol - Class in mklab.JGNN.core.matrix
+
+
+AccessCol(Matrix, long) - Constructor for class mklab.JGNN.core.matrix.AccessCol
+Instantiates a see-through access of a matrix column.
+
+accessColumns() - Method in class mklab.JGNN.core.Matrix
+Organizes specific matrix columns to a list of tensors that share entries.
+
+accessColumns(long...) - Method in class mklab.JGNN.core.Matrix
+Organizes specific matrix columns to a list of tensors that share entries.
+
+accessColumns(Iterable<Long>) - Method in class mklab.JGNN.core.Matrix
+Organizes some matrix columns to a list of tensors that share entries.
+
+accessColumns(Tensor) - Method in class mklab.JGNN.core.Matrix
+Organizes matrix columns to a list of tensors that share entries.
+
+accessDim(long, String) - Method in class mklab.JGNN.core.Matrix
+Retrieves either the given row or column as a trensor.
+
+accessRow(long) - Method in class mklab.JGNN.core.Matrix
+Retrieves the given row as a tensor.
+
+accessRow(long) - Method in class mklab.JGNN.core.matrix.WrapRows
+AccessRow - Class in mklab.JGNN.core.matrix
+
+
+AccessRow(Matrix, long) - Constructor for class mklab.JGNN.core.matrix.AccessRow
+Instantiates a see-through access of a matrix row.
+
+accessRows() - Method in class mklab.JGNN.core.Matrix
+Organizes matrix rows to a list of tensors that share entries.
+
+accessRows(long...) - Method in class mklab.JGNN.core.Matrix
+Organizes specific matrix rows to a list of tensors that share entries.
+
+accessRows(Iterable<Long>) - Method in class mklab.JGNN.core.Matrix
+Organizes some matrix rows to a list of tensors that share entries.
+
+accessRows(Tensor) - Method in class mklab.JGNN.core.Matrix
+Organizes specific matrix rows to a list of tensors that share entries.
+
+accessSubtensor(long) - Method in class mklab.JGNN.core.Tensor
+Wraps a range of elements within a tensor
+ without allocating memory anew.
+
+accessSubtensor(long, long) - Method in class mklab.JGNN.core.Tensor
+Wraps a range of elements within a tensor
+ without allocating memory anew.
+
+AccessSubtensor - Class in mklab.JGNN.core.tensor
+Wraps a base
Tensor by traversing only its elements in a specified range (from begin, up to end-1).
+AccessSubtensor(Tensor, long) - Constructor for class mklab.JGNN.core.tensor.AccessSubtensor
+Instantiates a see-through access of a tensor elements.
+
+AccessSubtensor(Tensor, long, long) - Constructor for class mklab.JGNN.core.tensor.AccessSubtensor
+Instantiates a see-through access of a tensor elements.
+
+Accuracy - Class in mklab.JGNN.nn.loss
+Implements an accuracy
Loss of row-by-row comparisons.
+Accuracy() - Constructor for class mklab.JGNN.nn.loss.Accuracy
+Instantiates a row-by-row
Accuracy loss.
+Adam - Class in mklab.JGNN.nn.optimizers
+Thic class implements an Adam
Optimizer as explained in the paper:
+
Kingma, Diederik P., and Jimmy Ba.
+Adam() - Constructor for class mklab.JGNN.nn.optimizers.Adam
+Initializes an NDAdam instance of an
Adam optimizer with the default parameters recommended by the papers.
+Adam(boolean, double) - Constructor for class mklab.JGNN.nn.optimizers.Adam
+Initializes an
Adam optimizer with the default parameters recommended by the papers
+ but allows for the specification of the learning rate and whether NDAdam or simple Adam
+ is used.
+Adam(boolean, double, double, double) - Constructor for class mklab.JGNN.nn.optimizers.Adam
+Initializes an NDAdam instance of an
Adam optimizer with the default parameters with custom parameters.
+Adam(boolean, double, double, double, double) - Constructor for class mklab.JGNN.nn.optimizers.Adam
+Adam(double) - Constructor for class mklab.JGNN.nn.optimizers.Adam
+Initializes an NDAdam instance of an
Adam optimizer with the default parameters recommended by the papers
+ but allows for the specification of the learning rate.
+add(double) - Method in class mklab.JGNN.core.Tensor
+add(double) - Method in class mklab.JGNN.core.tensor.DenseTensor
+add(Tensor) - Method in class mklab.JGNN.core.Tensor
+add(Tensor) - Method in class mklab.JGNN.core.tensor.DenseTensor
+add(Tensor) - Method in class mklab.JGNN.core.tensor.VectorizedTensor
+Add - Class in mklab.JGNN.nn.operations
+
+
+Add() - Constructor for class mklab.JGNN.nn.operations.Add
+addInput(Variable) - Method in class mklab.JGNN.nn.Model
+Adds to the model's inputs the provided
Variable.
+addInput(NNOperation) - Method in class mklab.JGNN.nn.inputs.Parameter
+addInput(NNOperation) - Method in class mklab.JGNN.nn.NNOperation
+addOutput(NNOperation) - Method in class mklab.JGNN.nn.Model
+Adds to the model's output the output of the provided operation.
+
+aggregate(LSTM) - Method in class mklab.JGNN.nn.operations.LSTM
+allocate(int, Object) - Static method in class mklab.JGNN.core.Memory
+apply(Model) - Method in class mklab.JGNN.nn.Initializer
+Applies the initializer to a given model's parameters.
+
+apply(Model) - Method in class mklab.JGNN.nn.initializers.VariancePreservingInitializer
+argmax() - Method in class mklab.JGNN.core.Tensor
+Computes the position of the maximum tensor element.
+
+argmin() - Method in class mklab.JGNN.core.Tensor
+Computes the position of the minimum tensor element.
+
+asColumn() - Method in class mklab.JGNN.core.Tensor
+Accesses the tensor through a single-column matrix with the tensor as the only row.
+
+asRow() - Method in class mklab.JGNN.core.Tensor
+Accesses the tensor through a single-row matrix with the tensor as the only column.
+
+assertBackwardValidity() - Method in class mklab.JGNN.adhoc.ModelBuilder
+
+
+assertFinite() - Method in class mklab.JGNN.core.Tensor
+Asserts that the tensor holds only finite values.
+
+assertMatching(Tensor) - Method in class mklab.JGNN.core.Tensor
+Asserts that the tensor's dimensions match with another tensor.
+
+assertSize(long) - Method in class mklab.JGNN.core.Tensor
+
+
+assign(Tensor) - Method in class mklab.JGNN.core.Tensor
+Performs a sparse assignment.
+
+asTensor() - Method in class mklab.JGNN.core.Slice
+Creates a dense tensor holding the slice's identifiers.
+
+asTransposed() - Method in class mklab.JGNN.core.Matrix
+Creates a transposed version of the matrix that accesses the same elements (thus, editing one
+ edits the other) without allocating additional memory.
+
+asTransposed() - Method in class mklab.JGNN.core.matrix.TransposedMatrix
+Attention - Class in mklab.JGNN.nn.operations
+Implements a
NNOperation that creates a version of adjacency matrices
+ with column-wise attention involving neighbor similarity.
+Attention() - Constructor for class mklab.JGNN.nn.operations.Attention
+autosize(List<Tensor>) - Method in class mklab.JGNN.adhoc.ModelBuilder
+autosize(Tensor...) - Method in class mklab.JGNN.adhoc.ModelBuilder
+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+
+
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+
+JavaScript is disabled on your browser.
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diff --git a/docs/javadoc/index-files/index-11.html b/docs/javadoc/index-files/index-11.html
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+
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diff --git a/docs/javadoc/index-files/index-12.html b/docs/javadoc/index-files/index-12.html
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+
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+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+M
+
+matmul(Matrix) - Method in class mklab.JGNN.core.matrix.DenseMatrix
+matmul(Matrix) - Method in class mklab.JGNN.core.Matrix
+Performs the matrix multiplication of this*with and the recipient.
+
+matmul(Matrix) - Method in class mklab.JGNN.core.matrix.VectorizedMatrix
+matmul(Matrix, boolean, boolean) - Method in class mklab.JGNN.core.matrix.DenseMatrix
+matmul(Matrix, boolean, boolean) - Method in class mklab.JGNN.core.Matrix
+Can be used to perform fast computation of the matrix multiplications
+
this*with,
+
this.transposed()*with
+
this*with.transposed(),
+
this.transposed()*with.transposed()
+
while avoiding the overhead of calling
+
Matrix.transposed().
+matmul(Matrix, boolean, boolean) - Method in class mklab.JGNN.core.matrix.VectorizedMatrix
+MatMul - Class in mklab.JGNN.nn.operations
+Implements a
NNOperation that multiplies its two matrix inputs.
+MatMul() - Constructor for class mklab.JGNN.nn.operations.MatMul
+Matrix - Class in mklab.JGNN.core
+This class provides an abstract implementation of Matrix functionalities.
+
+max() - Method in class mklab.JGNN.core.Tensor
+Computes the maximum tensor element.
+
+Max - Class in mklab.JGNN.nn.pooling
+Implements a
NNOperation that performs row-wise or column-wise
+ maximum reduction on vector tensors or matrices.
+Max() - Constructor for class mklab.JGNN.nn.pooling.Max
+Max(boolean) - Constructor for class mklab.JGNN.nn.pooling.Max
+Mean - Class in mklab.JGNN.nn.pooling
+Implements a
NNOperation that performs row-wise or column-wise
+ mean reduction on vector tensors or matrices.
+Mean() - Constructor for class mklab.JGNN.nn.pooling.Mean
+Mean(boolean) - Constructor for class mklab.JGNN.nn.pooling.Mean
+Memory - Class in mklab.JGNN.core
+A memory management system for thread-safe allocation and release of arrays of doubles.
+
+Memory() - Constructor for class mklab.JGNN.core.Memory
+Memory.Scope - Class in mklab.JGNN.core
+min() - Method in class mklab.JGNN.core.Tensor
+Computes the minimum tensor element.
+
+mklab.JGNN.adhoc - package mklab.JGNN.adhoc
+mklab.JGNN.adhoc.datasets - package mklab.JGNN.adhoc.datasets
+mklab.JGNN.adhoc.parsers - package mklab.JGNN.adhoc.parsers
+mklab.JGNN.core.distribution - package mklab.JGNN.core.distribution
+mklab.JGNN.core.empy - package mklab.JGNN.core.empy
+mklab.JGNN.core.matrix - package mklab.JGNN.core.matrix
+mklab.JGNN.core.tensor - package mklab.JGNN.core.tensor
+mklab.JGNN.nn - package mklab.JGNN.nn
+mklab.JGNN.nn.activations - package mklab.JGNN.nn.activations
+mklab.JGNN.nn.initializers - package mklab.JGNN.nn.initializers
+mklab.JGNN.nn.inputs - package mklab.JGNN.nn.inputs
+mklab.JGNN.nn.loss - package mklab.JGNN.nn.loss
+mklab.JGNN.nn.loss.report - package mklab.JGNN.nn.loss.report
+mklab.JGNN.nn.operations - package mklab.JGNN.nn.operations
+mklab.JGNN.nn.optimizers - package mklab.JGNN.nn.optimizers
+mklab.JGNN.nn.pooling - package mklab.JGNN.nn.pooling
+Model - Class in mklab.JGNN.nn
+This class is a way to organize
NNOperation trees into trainable machine
+ learning models.
+Model() - Constructor for class mklab.JGNN.nn.Model
+Deprecated.
+
+
+
+ModelBuilder - Class in mklab.JGNN.adhoc
+This class and subclasses can be used to create
Model instances
+ by automatically creating and managing
NNOperation instances based on
+ textual descriptions.
+ModelBuilder() - Constructor for class mklab.JGNN.adhoc.ModelBuilder
+ModelBuilder(Model) - Constructor for class mklab.JGNN.adhoc.ModelBuilder
+ModelTraining - Class in mklab.JGNN.nn
+This is a helper class that automates the definition of training processes of
Model instances
+ by defining the number of epochs, loss functions, number of batches and the ability to use
ThreadPool
+ for parallelized batch computations.
+ModelTraining() - Constructor for class mklab.JGNN.nn.ModelTraining
+multiply(double) - Method in class mklab.JGNN.core.tensor.DenseTensor
+multiply(double) - Method in class mklab.JGNN.core.Tensor
+multiply(double) - Method in class mklab.JGNN.core.tensor.VectorizedTensor
+multiply(Tensor) - Method in class mklab.JGNN.core.tensor.DenseTensor
+multiply(Tensor) - Method in class mklab.JGNN.core.Tensor
+multiply(Tensor) - Method in class mklab.JGNN.core.tensor.VectorizedTensor
+Multiply - Class in mklab.JGNN.nn.operations
+Implements a
NNOperation that multiplies its two inputs element-by-element.
+Multiply() - Constructor for class mklab.JGNN.nn.operations.Multiply
+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+
+
N-Index
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/index-files/index-14.html b/docs/javadoc/index-files/index-14.html
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--- /dev/null
+++ b/docs/javadoc/index-files/index-14.html
@@ -0,0 +1,89 @@
+
+
+
+
+O-Index
+
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+
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+
+
diff --git a/docs/javadoc/index-files/index-15.html b/docs/javadoc/index-files/index-15.html
new file mode 100644
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--- /dev/null
+++ b/docs/javadoc/index-files/index-15.html
@@ -0,0 +1,228 @@
+
+
+
+
+P-Index
+
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+
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+
+
+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+P
+
+param(String, double, Tensor) - Method in class mklab.JGNN.adhoc.ModelBuilder
+Declares a learnable Paramater component with the given name,
+ learning L2 regularization, and initial value.
+
+param(String, double, Tensor) - Method in class mklab.JGNN.adhoc.parsers.FastBuilder
+param(String, double, Tensor) - Method in class mklab.JGNN.adhoc.parsers.LayeredBuilder
+param(String, Tensor) - Method in class mklab.JGNN.adhoc.ModelBuilder
+Declares a learnable mklab.JGNN.nn.inputs.Paramater component with the given name,
+ zero regularization, and initial value.
+
+param(String, Tensor) - Method in class mklab.JGNN.adhoc.parsers.FastBuilder
+param(String, Tensor) - Method in class mklab.JGNN.adhoc.parsers.LayeredBuilder
+Parameter - Class in mklab.JGNN.nn.inputs
+Implements a
NNOperation that holds and returns a parameter tensor.
+Parameter(Tensor) - Constructor for class mklab.JGNN.nn.inputs.Parameter
+Parameter(Tensor, double) - Constructor for class mklab.JGNN.nn.inputs.Parameter
+parse(String) - Method in class mklab.JGNN.adhoc.parsers.Neuralang
+parse(Path) - Method in class mklab.JGNN.adhoc.parsers.Neuralang
+persist() - Method in class mklab.JGNN.core.empy.EmptyMatrix
+persist() - Method in class mklab.JGNN.core.empy.EmptyTensor
+persist() - Method in class mklab.JGNN.core.matrix.AccessCol
+persist() - Method in class mklab.JGNN.core.matrix.AccessRow
+persist() - Method in class mklab.JGNN.core.matrix.ColumnRepetition
+persist() - Method in class mklab.JGNN.core.matrix.DenseMatrix
+persist() - Method in class mklab.JGNN.core.matrix.Diagonal
+persist() - Method in class mklab.JGNN.core.matrix.RepeatMatrix
+persist() - Method in class mklab.JGNN.core.matrix.RowRepetition
+persist() - Method in class mklab.JGNN.core.matrix.SparseMatrix
+persist() - Method in class mklab.JGNN.core.matrix.SparseSymmetric
+Deprecated.
+
+persist() - Method in class mklab.JGNN.core.matrix.TransposedMatrix
+persist() - Method in class mklab.JGNN.core.matrix.VectorizedMatrix
+persist() - Method in class mklab.JGNN.core.matrix.WrapCols
+persist() - Method in class mklab.JGNN.core.matrix.WrapRows
+persist() - Method in class mklab.JGNN.core.tensor.AccessSubtensor
+persist() - Method in class mklab.JGNN.core.tensor.DenseTensor
+persist() - Method in class mklab.JGNN.core.Tensor
+Deprecated.
+
+
+
+persist() - Method in class mklab.JGNN.core.tensor.RepeatTensor
+persist() - Method in class mklab.JGNN.core.tensor.SparseTensor
+persist() - Method in class mklab.JGNN.core.tensor.VectorizedTensor
+predict(List<Tensor>) - Method in class mklab.JGNN.nn.Model
+Forward run of the model given a list of input tensors.
+
+predict(Tensor...) - Method in class mklab.JGNN.nn.Model
+Forward run of the model given an array of input tensors.
+
+predict(Tensor[]) - Method in class mklab.JGNN.nn.operations.LSTM
+PRelu - Class in mklab.JGNN.nn.activations
+PRelu() - Constructor for class mklab.JGNN.nn.activations.PRelu
+print() - Method in class mklab.JGNN.adhoc.ModelBuilder
+printState() - Method in class mklab.JGNN.adhoc.ModelBuilder
+Pubmed - Class in mklab.JGNN.adhoc.datasets
+Downloads and constructs the Pubmed node classification
Dataset.
+Pubmed() - Constructor for class mklab.JGNN.adhoc.datasets.Pubmed
+put(int, double) - Method in class mklab.JGNN.core.tensor.DenseTensor
+put(int, double) - Method in class mklab.JGNN.core.tensor.VectorizedTensor
+put(long, double) - Method in class mklab.JGNN.core.empy.EmptyMatrix
+put(long, double) - Method in class mklab.JGNN.core.empy.EmptyTensor
+put(long, double) - Method in class mklab.JGNN.core.matrix.AccessCol
+put(long, double) - Method in class mklab.JGNN.core.matrix.AccessRow
+put(long, double) - Method in class mklab.JGNN.core.matrix.ColumnRepetition
+put(long, double) - Method in class mklab.JGNN.core.matrix.DenseMatrix
+put(long, double) - Method in class mklab.JGNN.core.matrix.Diagonal
+put(long, double) - Method in class mklab.JGNN.core.matrix.RepeatMatrix
+put(long, double) - Method in class mklab.JGNN.core.matrix.RowRepetition
+put(long, double) - Method in class mklab.JGNN.core.matrix.SparseMatrix
+put(long, double) - Method in class mklab.JGNN.core.matrix.SparseSymmetric
+Deprecated.
+
+put(long, double) - Method in class mklab.JGNN.core.matrix.TransposedMatrix
+put(long, double) - Method in class mklab.JGNN.core.matrix.VectorizedMatrix
+put(long, double) - Method in class mklab.JGNN.core.matrix.WrapCols
+put(long, double) - Method in class mklab.JGNN.core.matrix.WrapRows
+put(long, double) - Method in class mklab.JGNN.core.tensor.AccessSubtensor
+put(long, double) - Method in class mklab.JGNN.core.tensor.DenseTensor
+put(long, double) - Method in class mklab.JGNN.core.Tensor
+Assign a value to a tensor element.
+
+put(long, double) - Method in class mklab.JGNN.core.tensor.RepeatTensor
+put(long, double) - Method in class mklab.JGNN.core.tensor.SparseTensor
+put(long, double) - Method in class mklab.JGNN.core.tensor.VectorizedTensor
+put(long, long, double) - Method in class mklab.JGNN.core.Matrix
+Stores values at matrix elements.
+
+putAdd(int, double) - Method in class mklab.JGNN.core.tensor.DenseTensor
+putAdd(int, double) - Method in class mklab.JGNN.core.tensor.VectorizedTensor
+putAdd(long, double) - Method in class mklab.JGNN.core.Tensor
+Add a value to a tensor element.
+
+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+
+
R-Index
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+R
+
+range(double, double) - Method in class mklab.JGNN.core.Slice
+Performs the
Slice.range(int, int) operation
+ while replacing values of
from and
end
+ with
(int)(from*size()) and
(int)(end*size())
+ so that fractional ranges can be obtained.
+range(int, int) - Method in class mklab.JGNN.core.Slice
+Obtains the identifiers in a given range of the (shuffled) slice.
+
+Range - Class in mklab.JGNN.core.util
+Implements an iterator that traverses a range (similar to Python's range(min, max) method).
+
+Range(long, long) - Constructor for class mklab.JGNN.core.util.Range
+Range2D - Class in mklab.JGNN.core.util
+Implements an iterator that traverses a two-dimensional range (min, max) x (min2, max2).
+
+Range2D(long, long, long, long) - Constructor for class mklab.JGNN.core.util.Range2D
+Reduce - Class in mklab.JGNN.nn.operations
+Reduce() - Constructor for class mklab.JGNN.nn.operations.Reduce
+register(double[]) - Method in class mklab.JGNN.core.Memory.Scope
+Regularization - Class in mklab.JGNN.nn.optimizers
+
+
+Regularization(Optimizer, double) - Constructor for class mklab.JGNN.nn.optimizers.Regularization
+
+
+release() - Method in class mklab.JGNN.core.empy.EmptyMatrix
+release() - Method in class mklab.JGNN.core.empy.EmptyTensor
+release() - Method in class mklab.JGNN.core.matrix.AccessCol
+release() - Method in class mklab.JGNN.core.matrix.AccessRow
+release() - Method in class mklab.JGNN.core.matrix.ColumnRepetition
+release() - Method in class mklab.JGNN.core.matrix.DenseMatrix
+release() - Method in class mklab.JGNN.core.matrix.Diagonal
+release() - Method in class mklab.JGNN.core.matrix.RepeatMatrix
+release() - Method in class mklab.JGNN.core.matrix.RowRepetition
+release() - Method in class mklab.JGNN.core.matrix.SparseMatrix
+release() - Method in class mklab.JGNN.core.matrix.SparseSymmetric
+Deprecated.
+
+release() - Method in class mklab.JGNN.core.matrix.TransposedMatrix
+release() - Method in class mklab.JGNN.core.matrix.VectorizedMatrix
+release() - Method in class mklab.JGNN.core.matrix.WrapCols
+release() - Method in class mklab.JGNN.core.matrix.WrapRows
+release() - Method in class mklab.JGNN.core.tensor.AccessSubtensor
+release() - Method in class mklab.JGNN.core.tensor.DenseTensor
+release() - Method in class mklab.JGNN.core.Tensor
+Deprecated.
+
+
+
+release() - Method in class mklab.JGNN.core.tensor.RepeatTensor
+release() - Method in class mklab.JGNN.core.tensor.SparseTensor
+release() - Method in class mklab.JGNN.core.tensor.VectorizedTensor
+release(double[]) - Static method in class mklab.JGNN.core.Memory
+relu(double) - Static method in interface mklab.JGNN.core.util.Loss
+The relu activation x if x > 0, 0 otherwise
+
+relu(Tensor) - Static method in interface mklab.JGNN.core.util.Loss
+
+
+Relu - Class in mklab.JGNN.nn.activations
+Implements a
NNOperation that performs a relu transformation of its single input first introduced by
+
Hahnloser, Richard HR, Rahul Sarpeshkar, Misha A.
+Relu() - Constructor for class mklab.JGNN.nn.activations.Relu
+reluDerivative(double) - Static method in interface mklab.JGNN.core.util.Loss
+
+
+reluDerivative(Tensor) - Static method in interface mklab.JGNN.core.util.Loss
+
+
+rememberAs(String) - Method in class mklab.JGNN.adhoc.parsers.FastBuilder
+Remembers the last layer's output per a given identifier so that {layerId}
+ within future
FastBuilder.layer(String) definitions is made to refer to the
+ current layer.
+rememberAs(String) - Method in class mklab.JGNN.adhoc.parsers.LayeredBuilder
+Sets the current layer identifier to a specific symbol layerId
+ so that future usage of {layerId} is automatically replaced with
+ the identifier.
+
+Repeat - Class in mklab.JGNN.nn.operations
+Implements a
NNOperation that converts its first argument to a
ColumnRepetition matrix
+ with a number of columns equal to the second argument.
+Repeat() - Constructor for class mklab.JGNN.nn.operations.Repeat
+RepeatMatrix - Class in mklab.JGNN.core.matrix
+Implements a
Matrix whose elements are all equals.
+RepeatMatrix(double, long, long) - Constructor for class mklab.JGNN.core.matrix.RepeatMatrix
+Generates a dense matrix with the designated number of rows and columns.
+
+RepeatTensor - Class in mklab.JGNN.core.tensor
+This class provides
Tensor whose elements are all equal.
+RepeatTensor(double, long) - Constructor for class mklab.JGNN.core.tensor.RepeatTensor
+reset() - Method in class mklab.JGNN.nn.loss.report.VerboseLoss
+reset() - Method in interface mklab.JGNN.nn.Optimizer
+Resets (and lets the garbage collector free) optimizer memory.
+
+reset() - Method in class mklab.JGNN.nn.optimizers.Adam
+reset() - Method in class mklab.JGNN.nn.optimizers.BatchOptimizer
+reset() - Method in class mklab.JGNN.nn.optimizers.GradientDescent
+reset() - Method in class mklab.JGNN.nn.optimizers.Regularization
+Reshape - Class in mklab.JGNN.nn.operations
+
+
+Reshape(long, long) - Constructor for class mklab.JGNN.nn.operations.Reshape
+RowRepetition - Class in mklab.JGNN.core.matrix
+Defines a matrix whose rows are all a copy of a
Tensor.
+RowRepetition(Tensor, long) - Constructor for class mklab.JGNN.core.matrix.RowRepetition
+Instantiates a matrix repeating a tensor to be treated as a row.
+
+run(List<Tensor>) - Method in class mklab.JGNN.nn.NNOperation
+Performs a forward pass in the operation without inducing any kind of
+ learning or storing the outcome .
+
+run(Tensor...) - Method in class mklab.JGNN.nn.NNOperation
+Performs a forward pass in the operation without inducing any kind of
+ learning or storing the outcome .
+
+runModel(ArrayList<Tensor>) - Method in class mklab.JGNN.adhoc.ModelBuilder
+This is a wrapper for
getModel().predict(inputs)
+
without returning output values (use
ModelBuilder.get(String)
+ afterwards to view outputs.
+runModel(Tensor...) - Method in class mklab.JGNN.adhoc.ModelBuilder
+This is a wrapper for
getModel().predict(inputs)
+
without returning output values (use
ModelBuilder.get(String)
+ afterwards to view outputs.
+runPrediction() - Method in class mklab.JGNN.nn.NNOperation
+runPredictionAndAutosize() - Method in class mklab.JGNN.nn.NNOperation
+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+
+
S-Index
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+S
+
+sample() - Method in class mklab.JGNN.core.distribution.Normal
+sample() - Method in interface mklab.JGNN.core.Distribution
+Retrieves a new sample from the distribution.
+
+sample() - Method in class mklab.JGNN.core.distribution.Uniform
+samples() - Method in class mklab.JGNN.adhoc.Dataset
+
+
+samplesAsFeatures() - Method in class mklab.JGNN.core.Slice
+Constructs a column matrix holding identifiers in
+ the range 0,1,..
Slice.size()-1 so that the pattern
+
slice.samplesAsFeatures().accessRows(slice.range(from, end))
+ retrieves one-element tensors holding
+
slice[from], slice[from+1], ...
+save(Path) - Method in class mklab.JGNN.adhoc.ModelBuilder
+scope() - Static method in class mklab.JGNN.core.Memory
+selfAbs() - Method in class mklab.JGNN.core.tensor.DenseTensor
+selfAbs() - Method in class mklab.JGNN.core.Tensor
+Performs in-memory set of each element to its absolute value.
+
+selfAdd(double) - Method in class mklab.JGNN.core.tensor.DenseTensor
+selfAdd(double) - Method in class mklab.JGNN.core.Tensor
+Performs in-memory addition to the Tensor, storing the result in itself.
+
+selfAdd(Tensor) - Method in class mklab.JGNN.core.tensor.DenseTensor
+selfAdd(Tensor) - Method in class mklab.JGNN.core.Tensor
+Performs in-memory addition to the Tensor, storing the result in itself.
+
+selfAdd(Tensor) - Method in class mklab.JGNN.core.tensor.VectorizedTensor
+selfAdd(Tensor, double) - Method in class mklab.JGNN.core.Tensor
+Performs in-memory weighted addition to the Tensor, storing the result in itself.
+
+selfExpMinusOne() - Method in class mklab.JGNN.core.tensor.DenseTensor
+selfExpMinusOne() - Method in class mklab.JGNN.core.Tensor
+Sets the exponential minus 1 of tensor elements.
+
+selfInverse() - Method in class mklab.JGNN.core.tensor.DenseTensor
+selfInverse() - Method in class mklab.JGNN.core.Tensor
+Performs in-memory the inverse of each non-zero element.
+
+selfLog() - Method in class mklab.JGNN.core.tensor.DenseTensor
+selfLog() - Method in class mklab.JGNN.core.Tensor
+Performs in-memory set of each element to the logarithm of its absolute value.
+
+selfMultiply(double) - Method in class mklab.JGNN.core.tensor.DenseTensor
+selfMultiply(double) - Method in class mklab.JGNN.core.Tensor
+Performs in-memory multiplication on the Tensor, storing the result to itself.
+
+selfMultiply(double) - Method in class mklab.JGNN.core.tensor.VectorizedTensor
+selfMultiply(Tensor) - Method in class mklab.JGNN.core.tensor.DenseTensor
+selfMultiply(Tensor) - Method in class mklab.JGNN.core.Tensor
+Performs in-memory multiplication on the Tensor, storing the result in itself .
+
+selfMultiply(Tensor) - Method in class mklab.JGNN.core.tensor.VectorizedTensor
+selfNegative() - Method in class mklab.JGNN.core.tensor.DenseTensor
+selfNegative() - Method in class mklab.JGNN.core.Tensor
+Performs in-memory set of each element to the negative of itself.
+
+selfSqrt() - Method in class mklab.JGNN.core.tensor.DenseTensor
+selfSqrt() - Method in class mklab.JGNN.core.Tensor
+Performs in-memory set of each element to the square root of its absolute value.
+
+selfSubtract(Tensor) - Method in class mklab.JGNN.core.tensor.DenseTensor
+selfSubtract(Tensor) - Method in class mklab.JGNN.core.Tensor
+Performs in-memory subtraction from the Tensor, storing the result in itself.
+
+selfSubtract(Tensor) - Method in class mklab.JGNN.core.tensor.VectorizedTensor
+set(Tensor) - Method in class mklab.JGNN.nn.inputs.Parameter
+Forcefully sets the parameter's value tensor to the desired value.
+
+setColName(String) - Method in class mklab.JGNN.core.Matrix
+Sets a name for the matrix's column dimension.
+
+setDescription(String) - Method in class mklab.JGNN.nn.NNOperation
+setDeviation(double) - Method in class mklab.JGNN.core.distribution.Normal
+setDeviation(double) - Method in interface mklab.JGNN.core.Distribution
+Sets the standard deviation of the distribution.
+
+setDeviation(double) - Method in class mklab.JGNN.core.distribution.Uniform
+setDiagonal(long, double) - Method in class mklab.JGNN.core.Matrix
+Sets the matrix's specified diagonal elements to a given value.
+
+setDimensionName(String) - Method in class mklab.JGNN.core.Tensor
+Sets a name for the tensor's one dimension.
+
+setDimensionName(String) - Method in class mklab.JGNN.nn.pooling.Sort
+setDimensionName(String, String) - Method in class mklab.JGNN.adhoc.IdConverter
+Sets dimension names for one-hot encodings.
+
+setDimensionName(String, String) - Method in class mklab.JGNN.core.Matrix
+Sets a name for the matrix's row and column dimensions.
+
+setDimensionName(String, String) - Method in class mklab.JGNN.nn.operations.Reshape
+setDimensionName(Tensor) - Method in class mklab.JGNN.core.Matrix
+setDimensionName(Tensor) - Method in class mklab.JGNN.core.Tensor
+Fills in dimension names per an example Tensor.isMatching(mklab.JGNN.core.Tensor) tensor.
+
+setEnabled(boolean) - Method in class mklab.JGNN.nn.operations.Dropout
+setEpochs(int) - Method in class mklab.JGNN.nn.ModelTraining
+Sets the maximum number of epochs for which training runs.
+
+setInterval(int) - Method in class mklab.JGNN.nn.loss.report.VerboseLoss
+Changes on which epochs the loss should be reported.
+
+setKey(K) - Method in class mklab.JGNN.core.util.FastEntry
+setLoss(Loss) - Method in class mklab.JGNN.nn.ModelTraining
+Set
+
+setMainDiagonal(double) - Method in class mklab.JGNN.core.Matrix
+Sets the matrix's specified main diagonal elements to a given value value.
+
+setMean(double) - Method in class mklab.JGNN.core.distribution.Normal
+setMean(double) - Method in interface mklab.JGNN.core.Distribution
+Sets the mean of the distribution.
+
+setMean(double) - Method in class mklab.JGNN.core.distribution.Uniform
+setMeanReduction(boolean) - Method in class mklab.JGNN.nn.loss.CategoricalCrossEntropy
+Sets the reduction mechanism of categorical cross entropy.
+
+setNumBatches(int) - Method in class mklab.JGNN.nn.ModelTraining
+Sets the number of batches training data slices should be split into.
+
+setOptimizer(Optimizer) - Method in class mklab.JGNN.nn.ModelTraining
+Sets an
Optimizer instance to controls parameter updates during training.
+setParallelizedStochasticGradientDescent(boolean) - Method in class mklab.JGNN.nn.ModelTraining
+Sets whether the training strategy should reflect stochastic
+ gradient descent by randomly sampling from the training dataset to obtain data samples.
+
+setPatience(int) - Method in class mklab.JGNN.nn.ModelTraining
+Sets the patience of the training strategy that performs early stopping.
+
+setRange(double, double) - Method in class mklab.JGNN.core.distribution.Uniform
+Sets the random of the uniform distribution.
+
+setRowName(String) - Method in class mklab.JGNN.core.Matrix
+Sets a name for the matrix's row dimension.
+
+setSeed(long) - Method in class mklab.JGNN.core.distribution.Normal
+setSeed(long) - Method in interface mklab.JGNN.core.Distribution
+Sets the distribution's seed.
+
+setSeed(long) - Method in class mklab.JGNN.core.distribution.Uniform
+setStream(PrintStream) - Method in class mklab.JGNN.nn.loss.report.VerboseLoss
+Changes where the output is printed.
+
+setTo(Tensor) - Method in class mklab.JGNN.nn.inputs.Variable
+setToASymmetricNormalization() - Method in class mklab.JGNN.core.Matrix
+Sets the Matrix to its asymmetrically normalized transformation
+ by appropriately adjusting its element values.
+
+setToNormalized() - Method in class mklab.JGNN.core.Tensor
+L2-normalizes the tensor's elements.
+
+setToOnes() - Method in class mklab.JGNN.core.Tensor
+Set all tensor element values to 1.
+
+setToProbability() - Method in class mklab.JGNN.core.Tensor
+Divides the tensor's elements with their sum.
+
+setToRandom() - Method in class mklab.JGNN.core.Tensor
+Set tensor elements to random values from the uniform range [0,1]
+
+setToRandom(Distribution) - Method in class mklab.JGNN.core.Tensor
+Set tensor elements to random values by sampling them from a given
Distribution
+ instance.
+setToSymmetricNormalization() - Method in class mklab.JGNN.core.Matrix
+Sets the Matrix to its symmetrically normalized transformation
+ by appropriately adjusting its element values.
+
+setToUniform() - Method in class mklab.JGNN.core.Tensor
+
+
+setToZero() - Method in class mklab.JGNN.core.Tensor
+Set all tensor element values to 0.
+
+setValidationLoss(Loss) - Method in class mklab.JGNN.nn.ModelTraining
+setValue(V) - Method in class mklab.JGNN.core.util.FastEntry
+setVerbose(boolean) - Method in class mklab.JGNN.nn.ModelTraining
+Deprecated.
+
+
+
+setZeroCopyType(Matrix) - Method in class mklab.JGNN.core.matrix.WrapCols
+Sets a prototype matrix from which to borrow copying operations.
+
+setZeroCopyType(Matrix) - Method in class mklab.JGNN.core.matrix.WrapRows
+Sets a prototype matrix from which to borrow copying operations.
+
+shuffle() - Method in class mklab.JGNN.core.Slice
+Shuffles the slice.
+
+shuffle(int) - Method in class mklab.JGNN.core.Slice
+Shuffles the slice with a provided randomization seed.
+
+sigmoid(double) - Static method in interface mklab.JGNN.core.util.Loss
+The sigmoid function 1/(1+exp(-x)).
+
+sigmoid(Tensor) - Static method in interface mklab.JGNN.core.util.Loss
+
+
+Sigmoid - Class in mklab.JGNN.nn.activations
+Implements a
NNOperation that performs a sigmoid transformation of its single input.
+Sigmoid() - Constructor for class mklab.JGNN.nn.activations.Sigmoid
+sigmoidDerivative(double) - Static method in interface mklab.JGNN.core.util.Loss
+
+
+sigmoidDerivative(Tensor) - Static method in interface mklab.JGNN.core.util.Loss
+
+
+size() - Method in class mklab.JGNN.adhoc.IdConverter
+The number of registered identifiers.
+
+size() - Method in class mklab.JGNN.core.Slice
+Retrieves the size of the slice.
+
+size() - Method in class mklab.JGNN.core.Tensor
+Slice - Class in mklab.JGNN.core
+This class provices an interface with which to define data slices,
+ for instance to sample labels.
+
+Slice(Iterable<Long>) - Constructor for class mklab.JGNN.core.Slice
+Instantiates the data slice from a collection of element identifiers.
+
+SoftMax - Class in mklab.JGNN.nn.pooling
+Implements a
NNOperation that performs row-wise or column-wise
+ softmax on vector tensors or matrices.
+SoftMax() - Constructor for class mklab.JGNN.nn.pooling.SoftMax
+SoftMax(boolean) - Constructor for class mklab.JGNN.nn.pooling.SoftMax
+Sort - Class in mklab.JGNN.core.util
+Sort - Class in mklab.JGNN.nn.pooling
+Sort() - Constructor for class mklab.JGNN.core.util.Sort
+Sort(int) - Constructor for class mklab.JGNN.nn.pooling.Sort
+sortedIndexes(double[]) - Static method in class mklab.JGNN.core.util.Sort
+sortedIndexes(ArrayList<Double>) - Static method in class mklab.JGNN.core.util.Sort
+SparseMatrix - Class in mklab.JGNN.core.matrix
+A sparse
Matrix that allocates memory only for non-zero elements.
+SparseMatrix(long, long) - Constructor for class mklab.JGNN.core.matrix.SparseMatrix
+Generates a sparse matrix with the designated number of rows and columns.
+
+SparseSymmetric - Class in mklab.JGNN.core.matrix
+Deprecated.
+
+
+
+SparseSymmetric(long, long) - Constructor for class mklab.JGNN.core.matrix.SparseSymmetric
+Deprecated.
+Generates a symmetric matrix with the designated number of rows and columns.
+
+SparseTensor - Class in mklab.JGNN.core.tensor
+This class provides a sparse
Tensor with many zero elements.
+SparseTensor() - Constructor for class mklab.JGNN.core.tensor.SparseTensor
+SparseTensor(long) - Constructor for class mklab.JGNN.core.tensor.SparseTensor
+SPECIES - Static variable in class mklab.JGNN.core.tensor.VectorizedTensor
+sqrt() - Method in class mklab.JGNN.core.tensor.DenseTensor
+sqrt() - Method in class mklab.JGNN.core.Tensor
+Computes the square root of tensor elements.
+
+startTape() - Method in class mklab.JGNN.nn.operations.LSTM
+submit(Runnable) - Method in class mklab.JGNN.core.ThreadPool
+Submits a runnable to be executed at some future point by a thread,
+ for example via ThreadPool.getInstance().submit(new Runnable(){public void run(){...}});.
+
+subtract(Tensor) - Method in class mklab.JGNN.core.tensor.DenseTensor
+subtract(Tensor) - Method in class mklab.JGNN.core.Tensor
+subtract(Tensor) - Method in class mklab.JGNN.core.tensor.VectorizedTensor
+sum() - Method in class mklab.JGNN.core.Tensor
+Sum - Class in mklab.JGNN.nn.pooling
+Implements a
NNOperation that performs row-wise or column-wise
+ sum reduction on vector tensors or matrices.
+Sum() - Constructor for class mklab.JGNN.nn.pooling.Sum
+Sum(boolean) - Constructor for class mklab.JGNN.nn.pooling.Sum
+symmetricNormalization() - Method in class mklab.JGNN.core.Matrix
+Creates a copy of the Matrix that holds its symmetrically normalized version.
+
+
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+
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+
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+T
+
+tanh(double) - Static method in interface mklab.JGNN.core.util.Loss
+The tanh activation (exp(x)-exp(-x))/(exp(x)+exp(-x))
+
+tanh(Tensor) - Static method in interface mklab.JGNN.core.util.Loss
+
+
+Tanh - Class in mklab.JGNN.nn.activations
+Implements a
NNOperation that performs a tanh transformation of its single input.
+Tanh() - Constructor for class mklab.JGNN.nn.activations.Tanh
+tanhDerivative(double) - Static method in interface mklab.JGNN.core.util.Loss
+
+
+tanhDerivative(Tensor) - Static method in interface mklab.JGNN.core.util.Loss
+
+
+tensor - Variable in class mklab.JGNN.core.matrix.VectorizedMatrix
+Tensor - Class in mklab.JGNN.core
+This class provides a native java implementation of Tensor functionalities.
+
+Tensor(long) - Constructor for class mklab.JGNN.core.Tensor
+Construct that creates a tensor of zeros given its number of elements
+
+ThreadPool - Class in mklab.JGNN.core
+This class provides thread execution pool utilities while keeping track of thread
+ identifiers for use by thread-specific
NNOperation.
+To - Class in mklab.JGNN.nn.operations
+Implements a
NNOperation that lists the second element of the 2D matrix element iterator.
+To() - Constructor for class mklab.JGNN.nn.operations.To
+toArray() - Method in class mklab.JGNN.core.Tensor
+Retrieves a representation of the Tensor as an array of doubles.
+
+toDense() - Method in class mklab.JGNN.core.Matrix
+Creates a copy of the matrix organized as a dense matrix.
+
+toDouble() - Method in class mklab.JGNN.core.Tensor
+
+
+toNonZeroString() - Method in class mklab.JGNN.core.Matrix
+toProbability() - Method in class mklab.JGNN.core.Tensor
+toSparse() - Method in class mklab.JGNN.core.Matrix
+Creates a copy of the matrix organized as a sparse matrix.
+
+toString() - Method in class mklab.JGNN.core.Matrix
+toString() - Method in class mklab.JGNN.core.Tensor
+A string serialization of the tensor that can be used by the constructor
DenseTensor(String) to create an identical copy.
+toString() - Method in class mklab.JGNN.core.util.FastEntry
+train(Tensor[], Tensor) - Method in class mklab.JGNN.nn.operations.LSTM
+train(Loss, Optimizer, List<Tensor>, List<Tensor>) - Method in class mklab.JGNN.nn.Model
+Performs the training of #train(Optimizer, List, List, List) for unit weights.
+
+train(Loss, Optimizer, List<Tensor>, List<Tensor>, List<Tensor>) - Method in class mklab.JGNN.nn.Model
+Performs one parameter adjustment step (e.g.
+
+train(Model, Matrix, Matrix, Slice, Slice) - Method in class mklab.JGNN.nn.ModelTraining
+Trains a
Model instance based on current settings.
+train(ModelTraining, Matrix, Matrix, Slice, Slice) - Method in class mklab.JGNN.nn.Model
+
+
+trainOnOutputError(Tensor[], Tensor) - Method in class mklab.JGNN.nn.operations.LSTM
+trainTowardsZero(Optimizer, List<Tensor>) - Method in class mklab.JGNN.nn.Model
+
+
+transform(Tensor) - Method in class mklab.JGNN.core.Matrix
+Performs the linear algebra transformation A*x where A is this matrix and x a vector
+
+Transpose - Class in mklab.JGNN.nn.operations
+Implements a
NNOperation that performs matrix transposition.
+Transpose() - Constructor for class mklab.JGNN.nn.operations.Transpose
+transposed() - Method in class mklab.JGNN.core.Matrix
+Creates a transposed copy of the matrix.
+
+TransposedMatrix - Class in mklab.JGNN.core.matrix
+Generates a transposed version of a base matrix, with which it shares elements.
+
+TransposedMatrix(Matrix) - Constructor for class mklab.JGNN.core.matrix.TransposedMatrix
+traverseNonZeroElements() - Method in class mklab.JGNN.core.empy.EmptyMatrix
+traverseNonZeroElements() - Method in class mklab.JGNN.core.empy.EmptyTensor
+traverseNonZeroElements() - Method in class mklab.JGNN.core.matrix.AccessCol
+traverseNonZeroElements() - Method in class mklab.JGNN.core.matrix.AccessRow
+traverseNonZeroElements() - Method in class mklab.JGNN.core.matrix.ColumnRepetition
+traverseNonZeroElements() - Method in class mklab.JGNN.core.matrix.DenseMatrix
+traverseNonZeroElements() - Method in class mklab.JGNN.core.matrix.Diagonal
+traverseNonZeroElements() - Method in class mklab.JGNN.core.matrix.RepeatMatrix
+traverseNonZeroElements() - Method in class mklab.JGNN.core.matrix.RowRepetition
+traverseNonZeroElements() - Method in class mklab.JGNN.core.matrix.SparseMatrix
+traverseNonZeroElements() - Method in class mklab.JGNN.core.matrix.SparseSymmetric
+Deprecated.
+
+traverseNonZeroElements() - Method in class mklab.JGNN.core.matrix.TransposedMatrix
+traverseNonZeroElements() - Method in class mklab.JGNN.core.matrix.VectorizedMatrix
+traverseNonZeroElements() - Method in class mklab.JGNN.core.matrix.WrapCols
+traverseNonZeroElements() - Method in class mklab.JGNN.core.matrix.WrapRows
+traverseNonZeroElements() - Method in class mklab.JGNN.core.tensor.AccessSubtensor
+traverseNonZeroElements() - Method in class mklab.JGNN.core.tensor.DenseTensor
+traverseNonZeroElements() - Method in class mklab.JGNN.core.tensor.RepeatTensor
+traverseNonZeroElements() - Method in class mklab.JGNN.core.tensor.SparseTensor
+traverseNonZeroElements() - Method in class mklab.JGNN.core.Tensor
+Retrieves positions within the tensor that may hold non-zero elements.
+
+traverseNonZeroElements() - Method in class mklab.JGNN.core.tensor.VectorizedTensor
+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+
+
U-Index
+
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+
+
+
+
+
+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+U
+
+Uniform - Class in mklab.JGNN.core.distribution
+
+
+Uniform() - Constructor for class mklab.JGNN.core.distribution.Uniform
+Instantiates a uniform distribution that samples values from the range [0,1].
+
+Uniform(double, double) - Constructor for class mklab.JGNN.core.distribution.Uniform
+Instantiates a uniform distribution that samples values from the given range [from, to].
+
+unregister(double[]) - Method in class mklab.JGNN.core.Memory.Scope
+update(Tensor, Tensor) - Method in interface mklab.JGNN.nn.Optimizer
+In-place updates the value of a tensor given its gradient.
+
+update(Tensor, Tensor) - Method in class mklab.JGNN.nn.optimizers.Adam
+update(Tensor, Tensor) - Method in class mklab.JGNN.nn.optimizers.BatchOptimizer
+update(Tensor, Tensor) - Method in class mklab.JGNN.nn.optimizers.GradientDescent
+update(Tensor, Tensor) - Method in class mklab.JGNN.nn.optimizers.Regularization
+updateAll() - Method in class mklab.JGNN.nn.optimizers.BatchOptimizer
+Updates all tracked variables with pending batch calculations using the
+ wrapped optimizer.
+
+updateTape(Tensor, LSTM.LSTMState, Tensor) - Method in class mklab.JGNN.nn.operations.LSTM
+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+
+
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+
+JavaScript is disabled on your browser.
+
+
+
+
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+
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+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+Z
+
+zeroCopy() - Method in class mklab.JGNN.core.Matrix
+Creates a Matrix with the same class and dimensions and all element set to zero.
+
+zeroCopy() - Method in class mklab.JGNN.core.Tensor
+Creates a tensor of the same class with the same size and all element set to zero.
+
+zeroCopy(long) - Method in class mklab.JGNN.core.empy.EmptyTensor
+zeroCopy(long) - Method in class mklab.JGNN.core.matrix.AccessCol
+zeroCopy(long) - Method in class mklab.JGNN.core.matrix.AccessRow
+zeroCopy(long) - Method in class mklab.JGNN.core.Matrix
+Creates a Matrix with the same class and dimensions and all element set to zero.
+
+zeroCopy(long) - Method in class mklab.JGNN.core.tensor.AccessSubtensor
+zeroCopy(long) - Method in class mklab.JGNN.core.tensor.DenseTensor
+zeroCopy(long) - Method in class mklab.JGNN.core.tensor.RepeatTensor
+zeroCopy(long) - Method in class mklab.JGNN.core.tensor.SparseTensor
+zeroCopy(long) - Method in class mklab.JGNN.core.tensor.VectorizedTensor
+zeroCopy(long) - Method in class mklab.JGNN.core.Tensor
+Creates a tensor of the same class with a given size and all element set to zero.
+
+zeroCopy(long, long) - Method in class mklab.JGNN.core.empy.EmptyMatrix
+zeroCopy(long, long) - Method in class mklab.JGNN.core.matrix.ColumnRepetition
+zeroCopy(long, long) - Method in class mklab.JGNN.core.matrix.DenseMatrix
+zeroCopy(long, long) - Method in class mklab.JGNN.core.matrix.Diagonal
+zeroCopy(long, long) - Method in class mklab.JGNN.core.matrix.RepeatMatrix
+zeroCopy(long, long) - Method in class mklab.JGNN.core.matrix.RowRepetition
+zeroCopy(long, long) - Method in class mklab.JGNN.core.matrix.SparseMatrix
+zeroCopy(long, long) - Method in class mklab.JGNN.core.matrix.SparseSymmetric
+Deprecated.
+
+zeroCopy(long, long) - Method in class mklab.JGNN.core.matrix.TransposedMatrix
+zeroCopy(long, long) - Method in class mklab.JGNN.core.matrix.VectorizedMatrix
+zeroCopy(long, long) - Method in class mklab.JGNN.core.matrix.WrapCols
+zeroCopy(long, long) - Method in class mklab.JGNN.core.matrix.WrapRows
+zeroCopy(long, long) - Method in class mklab.JGNN.core.Matrix
+Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+zeroCopy(Tensor) - Method in class mklab.JGNN.core.Matrix
+Creates a tensor of the same class and all elements set to zero,
+ but size and dimension names are obtained from a prototype tensor.
+
+zeroCopy(Tensor) - Method in class mklab.JGNN.core.Tensor
+Creates a tensor of the same class and all elements set to zero,
+ but size and dimension names are obtained from a prototype tensor.
+
+
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+
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+
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+
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+C
+
+cast(Class<Type>) - Method in class mklab.JGNN.core.Tensor
+Performs the equivalent of Java's typecasting that fits
+ in functional interfaces.
+
+CategoricalCrossEntropy - Class in mklab.JGNN.nn.loss
+Implements a categorical cross-entropy
Loss.
+ For binary classification of one output use
BinaryCrossEntropy.
+CategoricalCrossEntropy() - Constructor for class mklab.JGNN.nn.loss.CategoricalCrossEntropy
+Initializes categorical cross entropy with 1.E-12 epsilon value.
+
+CategoricalCrossEntropy(double) - Constructor for class mklab.JGNN.nn.loss.CategoricalCrossEntropy
+Initializes categorical cross entropy with and epsilon value
+ to bound its outputs in the range [log(epsilon), -log(epsilon)] instead of (-inf, inf).
+
+Citeseer - Class in mklab.JGNN.adhoc.datasets
+Downloads and constructs the Citeseer node classification
Dataset.
+Citeseer() - Constructor for class mklab.JGNN.adhoc.datasets.Citeseer
+classes() - Method in class mklab.JGNN.adhoc.Dataset
+Retrieves a converter that maps class names to label dimentions.
+
+classify() - Method in class mklab.JGNN.adhoc.parsers.FastBuilder
+Adds a classification layer that gather the number of inputs nodes
+ and applies softmax on all of them.
+
+clearPrediction() - Method in class mklab.JGNN.nn.NNOperation
+ColumnRepetition - Class in mklab.JGNN.core.matrix
+Defines a matrix whose columns are all a copy of a
Tensor.
+ColumnRepetition(long, Tensor) - Constructor for class mklab.JGNN.core.matrix.ColumnRepetition
+Instantiates a matrix repeating a tensor to be treated as a column.
+
+Complement - Class in mklab.JGNN.nn.operations
+Implements a
NNOperation that performs the operation 1-x for its simple input x.
+Complement() - Constructor for class mklab.JGNN.nn.operations.Complement
+concat(int) - Method in class mklab.JGNN.adhoc.parsers.FastBuilder
+Concatenates horizontally the output of a number of given layers,
+ starting from the last one and going backwards.
+
+concat(int) - Method in class mklab.JGNN.adhoc.parsers.LayeredBuilder
+Concatenates horizontally the output of a number of given layers,
+ starting from the last one and going backwards.
+
+Concat - Class in mklab.JGNN.nn.operations
+Implements a
NNOperation that concatenates its two matrix inputs.
+Concat() - Constructor for class mklab.JGNN.nn.operations.Concat
+config(String, double) - Method in class mklab.JGNN.adhoc.ModelBuilder
+Declares a configuration hyperparameter, which can be used to declare
+ matrix and vector parameters during
ModelBuilder.operation(String) expressions.
+config(String, double) - Method in class mklab.JGNN.adhoc.parsers.FastBuilder
+config(String, double) - Method in class mklab.JGNN.adhoc.parsers.LayeredBuilder
+config(String, double) - Method in class mklab.JGNN.adhoc.parsers.Neuralang
+config(String, String) - Method in class mklab.JGNN.adhoc.ModelBuilder
+config(String, String) - Method in class mklab.JGNN.adhoc.parsers.FastBuilder
+configFrom(ModelBuilder) - Method in class mklab.JGNN.nn.ModelTraining
+constant(String, double) - Method in class mklab.JGNN.adhoc.ModelBuilder
+Declares a non-learnable constant component with the given name.
+
+constant(String, double) - Method in class mklab.JGNN.adhoc.parsers.FastBuilder
+constant(String, double) - Method in class mklab.JGNN.adhoc.parsers.LayeredBuilder
+constant(String, double) - Method in class mklab.JGNN.adhoc.parsers.Neuralang
+constant(String, Tensor) - Method in class mklab.JGNN.adhoc.ModelBuilder
+Declares a non-learnable constant component with the given name.
+
+constant(String, Tensor) - Method in class mklab.JGNN.adhoc.parsers.FastBuilder
+constant(String, Tensor) - Method in class mklab.JGNN.adhoc.parsers.LayeredBuilder
+constant(String, Tensor) - Method in class mklab.JGNN.adhoc.parsers.Neuralang
+Constant - Class in mklab.JGNN.nn.inputs
+
+
+Constant(Tensor) - Constructor for class mklab.JGNN.nn.inputs.Constant
+Creates a constant holding a tensor.
+
+contains(Object) - Method in class mklab.JGNN.adhoc.IdConverter
+
+
+copy() - Method in class mklab.JGNN.core.Tensor
+Creates a
Tensor.zeroCopy() and transfers to it all potentially non-zero element values.
+Cora - Class in mklab.JGNN.adhoc.datasets
+Downloads and constructs the Cora node classification
Dataset.
+Cora() - Constructor for class mklab.JGNN.adhoc.datasets.Cora
+createFirstState() - Method in class mklab.JGNN.nn.operations.LSTM
+createForwardValidity(List<Tensor>) - Method in class mklab.JGNN.adhoc.ModelBuilder
+
+
+crossEntropy(double, double) - Static method in interface mklab.JGNN.core.util.Loss
+A cross entropy loss for one sample computes as -label*log(output) -(1-label)*log(1-output).
+
+crossEntropyDerivative(double, double) - Static method in interface mklab.JGNN.core.util.Loss
+
+
+crossEntropyDerivativeCategorical(double, double) - Static method in interface mklab.JGNN.core.util.Loss
+The derivative of the #crossEntropyCategorical(double, double) loss.
+
+crossEntropySigmoidDerivative(double, double) - Static method in interface mklab.JGNN.core.util.Loss
+The derivative of crossEntropy(sigmoid(x), label) with respect to x.
+
+crossEntropyTanhDerivative(double, double) - Static method in interface mklab.JGNN.core.util.Loss
+The derivative of crossEntropy(tanh(x), label) with respect to x.
+
+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+
+
D-Index
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/index-files/index-5.html b/docs/javadoc/index-files/index-5.html
new file mode 100644
index 00000000..7464c91f
--- /dev/null
+++ b/docs/javadoc/index-files/index-5.html
@@ -0,0 +1,134 @@
+
+
+
+
+E-Index
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+E
+
+EmptyMatrix - Class in mklab.JGNN.core.empy
+EmptyMatrix(long, long) - Constructor for class mklab.JGNN.core.empy.EmptyMatrix
+EmptyTensor - Class in mklab.JGNN.core.empy
+EmptyTensor() - Constructor for class mklab.JGNN.core.empy.EmptyTensor
+EmptyTensor(long) - Constructor for class mklab.JGNN.core.empy.EmptyTensor
+endTape() - Method in class mklab.JGNN.nn.operations.LSTM
+enter() - Method in class mklab.JGNN.core.Memory.Scope
+estimateNumNonZeroElements() - Method in class mklab.JGNN.core.matrix.AccessCol
+estimateNumNonZeroElements() - Method in class mklab.JGNN.core.matrix.AccessRow
+estimateNumNonZeroElements() - Method in class mklab.JGNN.core.matrix.Diagonal
+estimateNumNonZeroElements() - Method in class mklab.JGNN.core.matrix.SparseMatrix
+estimateNumNonZeroElements() - Method in class mklab.JGNN.core.matrix.TransposedMatrix
+estimateNumNonZeroElements() - Method in class mklab.JGNN.core.matrix.WrapCols
+estimateNumNonZeroElements() - Method in class mklab.JGNN.core.matrix.WrapRows
+estimateNumNonZeroElements() - Method in class mklab.JGNN.core.Tensor
+Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+
+estimateNumNonZeroElements() - Method in class mklab.JGNN.core.tensor.SparseTensor
+evaluate(Tensor, Tensor) - Method in class mklab.JGNN.nn.loss.Accuracy
+evaluate(Tensor, Tensor) - Method in class mklab.JGNN.nn.loss.BinaryCrossEntropy
+evaluate(Tensor, Tensor) - Method in class mklab.JGNN.nn.loss.CategoricalCrossEntropy
+evaluate(Tensor, Tensor) - Method in class mklab.JGNN.nn.Loss
+Provides a numerical evaluation of a loss function, so that
+ lower values correspond to better predictions.
+
+evaluate(Tensor, Tensor) - Method in class mklab.JGNN.nn.loss.report.VerboseLoss
+exit() - Method in class mklab.JGNN.core.Memory.Scope
+Exp - Class in mklab.JGNN.nn.activations
+Implements a
NNOperation that performs an exponential transformation of its single input.
+Exp() - Constructor for class mklab.JGNN.nn.activations.Exp
+expMinusOne() - Method in class mklab.JGNN.core.tensor.DenseTensor
+expMinusOne() - Method in class mklab.JGNN.core.Tensor
+Computes the exponential minus 1 of tensor elements.
+
+external(Tensor, Tensor) - Static method in class mklab.JGNN.core.Matrix
+Produces the external product of two tensors.
+
+eye(long) - Static method in class mklab.JGNN.core.Matrix
+Creates a sparse unit matrix.
+
+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+
+
F-Index
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+F
+
+FastBuilder - Class in mklab.JGNN.adhoc.parsers
+Extends the capabilities of
LayeredBuilder to use
+ for node classification.
+FastBuilder() - Constructor for class mklab.JGNN.adhoc.parsers.FastBuilder
+Deprecated.
+
+
+
+FastBuilder(Matrix, Matrix) - Constructor for class mklab.JGNN.adhoc.parsers.FastBuilder
+Creates a graph neural network builder from an
+ normalized adjacency matrix and a node feature matrix.
+
+FastEntry <K ,V > - Class in mklab.JGNN.core.util
+FastEntry() - Constructor for class mklab.JGNN.core.util.FastEntry
+FastEntry(K, V) - Constructor for class mklab.JGNN.core.util.FastEntry
+features() - Method in class mklab.JGNN.adhoc.Dataset
+Retrieves the dataset's feature matrix.
+
+forward(List<Tensor>) - Method in class mklab.JGNN.nn.activations.L1
+forward(List<Tensor>) - Method in class mklab.JGNN.nn.operations.Attention
+forward(List<Tensor>) - Method in class mklab.JGNN.nn.pooling.Mean
+forward(List<Tensor>) - Method in class mklab.JGNN.nn.pooling.Sum
+From - Class in mklab.JGNN.nn.operations
+Implements a
NNOperation that lists the first element of the 2D matrix element iterator.
+From() - Constructor for class mklab.JGNN.nn.operations.From
+fromDouble(double) - Static method in class mklab.JGNN.core.Matrix
+Converts a given value to a JGNN-compatible 1x1 matrix.
+
+fromDouble(double) - Static method in class mklab.JGNN.core.Tensor
+Converts a given value to a JGNN-compatible dense tensor.
+
+fromRange(long) - Static method in class mklab.JGNN.core.Tensor
+Creates a dense tensor holding the desired range [0, 1, ..., end-1].
+
+fromRange(long, long) - Static method in class mklab.JGNN.core.Tensor
+Creates a dense tensor holding the desired range [start, start+1, ..., end-1].
+
+function(String, String) - Method in class mklab.JGNN.adhoc.ModelBuilder
+function(String, String) - Method in class mklab.JGNN.adhoc.parsers.FastBuilder
+futureConfigs(String, Function<Integer, Double>, int) - Method in class mklab.JGNN.adhoc.parsers.FastBuilder
+
+
+futureConfigs(String, Function<Integer, Double>, int) - Method in class mklab.JGNN.adhoc.parsers.LayeredBuilder
+
+
+futureConstants(String, Function<Integer, Double>, int) - Method in class mklab.JGNN.adhoc.parsers.FastBuilder
+
+
+futureConstants(String, Function<Integer, Double>, int) - Method in class mklab.JGNN.adhoc.parsers.LayeredBuilder
+
+
+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+
+
G-Index
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+G
+
+Gather - Class in mklab.JGNN.nn.operations
+Implements a
NNOperation that performs the equivalent of TensorFlow's gather operation.
+Gather() - Constructor for class mklab.JGNN.nn.operations.Gather
+get() - Method in class mklab.JGNN.nn.inputs.Parameter
+Gets sets the parameter's value tensor
+
+get(int) - Method in class mklab.JGNN.core.tensor.DenseTensor
+get(int) - Method in class mklab.JGNN.core.tensor.VectorizedTensor
+get(long) - Method in class mklab.JGNN.adhoc.IdConverter
+Retrieves the object corresponding to a given identifier.
+
+get(long) - Method in class mklab.JGNN.core.empy.EmptyMatrix
+get(long) - Method in class mklab.JGNN.core.empy.EmptyTensor
+get(long) - Method in class mklab.JGNN.core.matrix.AccessCol
+get(long) - Method in class mklab.JGNN.core.matrix.AccessRow
+get(long) - Method in class mklab.JGNN.core.matrix.ColumnRepetition
+get(long) - Method in class mklab.JGNN.core.matrix.DenseMatrix
+get(long) - Method in class mklab.JGNN.core.matrix.Diagonal
+get(long) - Method in class mklab.JGNN.core.matrix.RepeatMatrix
+get(long) - Method in class mklab.JGNN.core.matrix.RowRepetition
+get(long) - Method in class mklab.JGNN.core.matrix.SparseMatrix
+get(long) - Method in class mklab.JGNN.core.matrix.SparseSymmetric
+Deprecated.
+
+get(long) - Method in class mklab.JGNN.core.matrix.TransposedMatrix
+get(long) - Method in class mklab.JGNN.core.matrix.VectorizedMatrix
+get(long) - Method in class mklab.JGNN.core.matrix.WrapCols
+get(long) - Method in class mklab.JGNN.core.matrix.WrapRows
+get(long) - Method in class mklab.JGNN.core.tensor.AccessSubtensor
+get(long) - Method in class mklab.JGNN.core.tensor.DenseTensor
+get(long) - Method in class mklab.JGNN.core.Tensor
+Retrieves the value of a tensor element at a given position.
+
+get(long) - Method in class mklab.JGNN.core.tensor.RepeatTensor
+get(long) - Method in class mklab.JGNN.core.tensor.SparseTensor
+get(long) - Method in class mklab.JGNN.core.tensor.VectorizedTensor
+get(long, long) - Method in class mklab.JGNN.core.Matrix
+Retrieves the value stored at a matrix element.
+
+get(String) - Method in class mklab.JGNN.adhoc.ModelBuilder
+Retrieves the
NNOperation registered with the provided
+ name, for example to investigates its value.
+getColName() - Method in class mklab.JGNN.core.Matrix
+getCols() - Method in class mklab.JGNN.core.Matrix
+Retrieves the number of columns of a matrix.
+
+getColumn() - Method in class mklab.JGNN.core.matrix.ColumnRepetition
+Retrieves the wrapped column tensor.
+
+getConfigOrDefault(String, double) - Method in class mklab.JGNN.adhoc.ModelBuilder
+getConfigOrDefault(String, int) - Method in class mklab.JGNN.adhoc.ModelBuilder
+getCurrentThreadId() - Static method in class mklab.JGNN.core.ThreadPool
+Retrieves a unique integer indicating the currently running thread.
+
+getDepthLastOperations() - Method in class mklab.JGNN.nn.Model
+Retrieves a list of operations by traversing the model's execution
+ graph with the depth-first algorithm in the inverse edge
+ order (starting from the outputs).
+
+getDescription() - Method in class mklab.JGNN.nn.NNOperation
+getDeviation() - Method in interface mklab.JGNN.core.Distribution
+Retrieves the distribution's standard deviation.
+
+getDeviation() - Method in class mklab.JGNN.core.distribution.Normal
+getDeviation() - Method in class mklab.JGNN.core.distribution.Uniform
+getDimensionName() - Method in class mklab.JGNN.core.Tensor
+getDimensionSize(String) - Method in class mklab.JGNN.core.Matrix
+Retrieves the value of the dimension with the given name.
+
+getExecutionGraphDot() - Method in class mklab.JGNN.adhoc.ModelBuilder
+Exports the builded model's execution graph into a .dot format
+ representation.
+
+getId(Object) - Method in class mklab.JGNN.adhoc.IdConverter
+Retrieves an identifier.
+
+getInputs() - Method in class mklab.JGNN.nn.Model
+Retrieves a list of model inputs.
+
+getInputs() - Method in class mklab.JGNN.nn.NNOperation
+Retrieves a list of input operations within a model's execution graph.
+
+getInstance() - Static method in class mklab.JGNN.core.ThreadPool
+Retrieves the singleton
ThreadPool instance used by JGNN.
+getKey() - Method in class mklab.JGNN.core.util.FastEntry
+getLastTapeError() - Method in class mklab.JGNN.nn.NNOperation
+getMean() - Method in interface mklab.JGNN.core.Distribution
+Retrieves the distribution's mean.
+
+getMean() - Method in class mklab.JGNN.core.distribution.Normal
+getMean() - Method in class mklab.JGNN.core.distribution.Uniform
+getMemory() - Method in class mklab.JGNN.nn.operations.LSTM.LSTMState
+getModel() - Method in class mklab.JGNN.adhoc.ModelBuilder
+Retrieves the model currently built by the builder.
+
+getNonLinearity(int, double, double) - Method in class mklab.JGNN.nn.activations.Exp
+getNonLinearity(int, double, double) - Method in class mklab.JGNN.nn.activations.LRelu
+getNonLinearity(int, double, double) - Method in class mklab.JGNN.nn.activations.NExp
+getNonLinearity(int, double, double) - Method in class mklab.JGNN.nn.activations.PRelu
+getNonLinearity(int, double, double) - Method in class mklab.JGNN.nn.activations.Relu
+getNonLinearity(int, double, double) - Method in class mklab.JGNN.nn.activations.Sigmoid
+getNonLinearity(int, double, double) - Method in class mklab.JGNN.nn.activations.Tanh
+getNonLinearity(int, double, double) - Method in class mklab.JGNN.nn.NNOperation
+
+
+getNonLinearity(int, double, double) - Method in class mklab.JGNN.nn.operations.MatMul
+getNonLinearity(int, double, double) - Method in class mklab.JGNN.nn.operations.Multiply
+getNonZeroElements() - Method in class mklab.JGNN.core.Tensor
+
+
+getNonZeroEntries() - Method in class mklab.JGNN.core.empy.EmptyMatrix
+getNonZeroEntries() - Method in class mklab.JGNN.core.matrix.ColumnRepetition
+getNonZeroEntries() - Method in class mklab.JGNN.core.matrix.DenseMatrix
+getNonZeroEntries() - Method in class mklab.JGNN.core.matrix.Diagonal
+getNonZeroEntries() - Method in class mklab.JGNN.core.Matrix
+Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+getNonZeroEntries() - Method in class mklab.JGNN.core.matrix.RepeatMatrix
+getNonZeroEntries() - Method in class mklab.JGNN.core.matrix.RowRepetition
+getNonZeroEntries() - Method in class mklab.JGNN.core.matrix.SparseMatrix
+getNonZeroEntries() - Method in class mklab.JGNN.core.matrix.SparseSymmetric
+Deprecated.
+
+getNonZeroEntries() - Method in class mklab.JGNN.core.matrix.TransposedMatrix
+getNonZeroEntries() - Method in class mklab.JGNN.core.matrix.VectorizedMatrix
+getNonZeroEntries() - Method in class mklab.JGNN.core.matrix.WrapCols
+getNonZeroEntries() - Method in class mklab.JGNN.core.matrix.WrapRows
+getOptimizer() - Method in class mklab.JGNN.nn.operations.LSTM
+getOrCreateId(Object) - Method in class mklab.JGNN.adhoc.IdConverter
+Retrieves an identifier for a given object, creating one if none exists.
+
+getOutput() - Method in class mklab.JGNN.nn.operations.LSTM.LSTMState
+getOutputs() - Method in class mklab.JGNN.nn.Model
+Retrieves a list of model outputs.
+
+getOutputs() - Method in class mklab.JGNN.nn.NNOperation
+Retrieves a list of output operations within a model's execution graph.
+
+getParameters() - Method in class mklab.JGNN.nn.Model
+Retrieves a list of all parameters eventually leading to the model's outputs.
+
+getPrediction() - Method in class mklab.JGNN.nn.NNOperation
+getRowName() - Method in class mklab.JGNN.core.Matrix
+getRows() - Method in class mklab.JGNN.core.Matrix
+Retrieves the number of rows of a matrix.
+
+getSimpleDescription() - Method in class mklab.JGNN.nn.NNOperation
+Provides a simple description to show when drawing .dot format diagrams.
+
+getSimpleDescription() - Method in class mklab.JGNN.nn.operations.Reshape
+getSlice() - Method in class mklab.JGNN.adhoc.IdConverter
+Returns a slice of all registered identifiers.
+
+getValue() - Method in class mklab.JGNN.core.util.FastEntry
+GradientDescent - Class in mklab.JGNN.nn.optimizers
+
+
+GradientDescent(double) - Constructor for class mklab.JGNN.nn.optimizers.GradientDescent
+
+
+GradientDescent(double, double) - Constructor for class mklab.JGNN.nn.optimizers.GradientDescent
+
+
+graph() - Method in class mklab.JGNN.adhoc.Dataset
+Retrieves the dataset's graph.
+
+
+A B C D E F G H I K L M N O P R S T U V W X Z All Classes and Interfaces | All Packages
+
+
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+
+JavaScript is disabled on your browser.
+
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diff --git a/docs/javadoc/index-files/index-9.html b/docs/javadoc/index-files/index-9.html
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+
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+
+I-Index
+
+JavaScript is disabled on your browser.
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+
+
+
+
+Overview
+
+JavaScript is disabled on your browser.
+
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+
+
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+
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+
+
+
+
+
+
+
+
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+
+
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+
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+
+
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+
+
+
+
+
+
+
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+
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+
)","u":"accessColumns(java.lang.Iterable)"},{"p":"mklab.JGNN.core","c":"Matrix","l":"accessColumns(long...)"},{"p":"mklab.JGNN.core","c":"Matrix","l":"accessColumns(Tensor)","u":"accessColumns(mklab.JGNN.core.Tensor)"},{"p":"mklab.JGNN.core","c":"Matrix","l":"accessDim(long, String)","u":"accessDim(long,java.lang.String)"},{"p":"mklab.JGNN.core","c":"Matrix","l":"accessRow(long)"},{"p":"mklab.JGNN.core.matrix","c":"WrapRows","l":"accessRow(long)"},{"p":"mklab.JGNN.core.matrix","c":"AccessRow","l":"AccessRow(Matrix, 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long)","u":"zeroCopy(long,long)"},{"p":"mklab.JGNN.core.matrix","c":"ColumnRepetition","l":"zeroCopy(long, long)","u":"zeroCopy(long,long)"},{"p":"mklab.JGNN.core.matrix","c":"DenseMatrix","l":"zeroCopy(long, long)","u":"zeroCopy(long,long)"},{"p":"mklab.JGNN.core.matrix","c":"Diagonal","l":"zeroCopy(long, long)","u":"zeroCopy(long,long)"},{"p":"mklab.JGNN.core.matrix","c":"RepeatMatrix","l":"zeroCopy(long, long)","u":"zeroCopy(long,long)"},{"p":"mklab.JGNN.core.matrix","c":"RowRepetition","l":"zeroCopy(long, long)","u":"zeroCopy(long,long)"},{"p":"mklab.JGNN.core.matrix","c":"SparseMatrix","l":"zeroCopy(long, long)","u":"zeroCopy(long,long)"},{"p":"mklab.JGNN.core.matrix","c":"SparseSymmetric","l":"zeroCopy(long, long)","u":"zeroCopy(long,long)"},{"p":"mklab.JGNN.core.matrix","c":"TransposedMatrix","l":"zeroCopy(long, long)","u":"zeroCopy(long,long)"},{"p":"mklab.JGNN.core.matrix","c":"VectorizedMatrix","l":"zeroCopy(long, long)","u":"zeroCopy(long,long)"},{"p":"mklab.JGNN.core.matrix","c":"WrapCols","l":"zeroCopy(long, long)","u":"zeroCopy(long,long)"},{"p":"mklab.JGNN.core.matrix","c":"WrapRows","l":"zeroCopy(long, long)","u":"zeroCopy(long,long)"},{"p":"mklab.JGNN.core","c":"Matrix","l":"zeroCopy(long, long)","u":"zeroCopy(long,long)"},{"p":"mklab.JGNN.core","c":"Matrix","l":"zeroCopy(Tensor)","u":"zeroCopy(mklab.JGNN.core.Tensor)"},{"p":"mklab.JGNN.core","c":"Tensor","l":"zeroCopy(Tensor)","u":"zeroCopy(mklab.JGNN.core.Tensor)"}];updateSearchResults();
\ No newline at end of file
diff --git a/docs/javadoc/mklab/JGNN/adhoc/Dataset.html b/docs/javadoc/mklab/JGNN/adhoc/Dataset.html
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@@ -0,0 +1,256 @@
+
+
+
+
+Dataset
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+Direct Known Subclasses:
+Citeseer Cora Pubmed
+public class Dataset
+
extends Object This class provides the backbone with which to define datasets.
+ It provides common operations for downloading and importing data.
+
+Author:
+Emmanouil Krasanakis
+See Also:
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Retrieves a converter that maps class names to label dimentions.
+
+
+
+
+
Retrieves the dataset's feature matrix.
+
+
+
+
+
Retrieves the dataset's graph.
+
+
+
+
+
Retrieves the dataset's sample labels in one-hot encoding.
+
+
+
+
+
Retrieves a converter that maps samples to long identifiers that match them to
+ rows of
features(),
labels(), and
graph() matrices.
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Dataset
+public Dataset ()
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+samples
+
+Retrieves a converter that maps samples to long identifiers that match them to
+ rows of
features(),
labels(), and
graph() matrices.
+ For example, a list of all node ids can be obtained per
+
dataset.samples().getIds()
+Returns:
+A IdConverter
+
+
+
+
+
+classes
+
+Retrieves a converter that maps class names to label dimentions.
+ For example, the prediction for one sample can be converted to its name
+ per dataset.classes().get(prediction.argmax()).
+
+Returns:
+An IdConverter
+
+
+
+
+
+features
+
+Retrieves the dataset's feature matrix.
+
+Returns:
+A nodes x features Matrix
+
+
+
+
+
+labels
+
+Retrieves the dataset's sample labels in one-hot encoding.
+
+Returns:
+A nodes x classes Matrix
+
+
+
+
+
+graph
+
+Retrieves the dataset's graph.
+
+Returns:
+A Matrixnull if the dataset is feature-only.
+
+
+
+
+
+
+
+
+
+
+
+
IdConverter
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+public class IdConverter
+
extends Object Converts back-and-forth between objects and unique ids.
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
boolean
+
+
+
+
+
+
Retrieves the object corresponding to a given identifier.
+
+
long
+
+
+
Retrieves an identifier.
+
+
long
+
+
+
Retrieves an identifier for a given object, creating one if none exists.
+
+
+
+
+
Returns a slice of all registered identifiers.
+
+
+
+
+
Sets dimension names for one-hot encodings.
+
+
long
+
+
+
The number of registered identifiers.
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+IdConverter
+public IdConverter ()
+
+
+
+
+
+IdConverter
+public IdConverter (List <?> objects)
+Parameters:
+objects - A list of objects.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+setDimensionName
+
+Sets dimension names for one-hot encodings.
+
+Parameters:
+nodeDimensionName - The dimension name for traversing nodes (e.g. "node").featureDimensionName - The dimension name for traversing features (e.g. "label").Returns:
+this instance
+
+
+
+
+getOrCreateId
+public long getOrCreateId (Object object) Retrieves an identifier for a given object, creating one if none exists.
+
+Parameters:
+object - The object for which to obtain an identifier.Returns:
+A long identifier.
+See Also:
+
+
+
+
+
+
+
+
+get
+
+Retrieves the object corresponding to a given identifier.
+
+Parameters:
+id - The identifier of the object.Returns:
+The object.
+
+
+
+
+
+getId
+public long getId (Object object) Retrieves an identifier.
+
+Parameters:
+object - An object with a registered identifier.Returns:
+A long identifier.
+Throws:
+Exception See Also:
+
+
+
+
+
+
+
+
+size
+public long size ()
+The number of registered identifiers.
+
+Returns:
+A long value.
+
+
+
+
+
+contains
+public boolean contains (Object object)
+Parameters:
+object - An object to check if it exists.Returns:
+A boolean value.
+
+
+
+
+
+getSlice
+
+Returns a slice of all registered identifiers.
+ The slice is persistent across multiple calls to this method, but is
+ instantiated anew after
getOrCreateId(Object) registers a new
+ object (but not if it retrieves an existing object).
+Returns:
+A Slice
+
+
+
+
+
+
+
+
+
+
+
+
ModelBuilder
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+Direct Known Subclasses:
+FastBuilder LayeredBuilder Neuralang
+public class ModelBuilder
+
extends Object This class and subclasses can be used to create
Model instances
+ by automatically creating and managing
NNOperation instances based on
+ textual descriptions.
+Author:
+Emmanouil Krasanakis
+See Also:
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Static Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Declares a configuration hyperparameter, which can be used to declare
+ matrix and vector parameters during
operation(String) expressions.
+
+
+
+
+
+
+
+
Declares a non-learnable constant component with the given name.
+
+
+
+
+
Declares a non-learnable constant component with the given name.
+
+
+
+
+
+
+
+
Creates a description of the builded model's internal execution graph.
+
+
+
+
+
+
+
+
Retrieves the
NNOperation registered with the provided
+ name, for example to investigates its value.
+
+
double
+
+
+
int
+
+
+
+
+
+
Exports the builded model's execution graph into a .dot format
+ representation.
+
+
+
+
+
Retrieves the model currently built by the builder.
+
+
boolean
+
+
+
Checks whether the builder has added to its managed model a component of
+ the given name.
+
+
+
+
+
+
+
+
Parses one or more operations split by new line characters or ;
+ to add to the execution graph.
+
+
+
+
+
Declares the component with the given name an output of the
+ managed model.
+
+
+
+
+
Declares a learnable Paramater component with the given name,
+ learning L2 regularization, and initial value.
+
+
+
+
+
Declares a learnable mklab.JGNN.nn.inputs.Paramater component with the given name,
+ zero regularization, and initial value.
+
+
+
+
+
+
+
+
+
+
+
This is a wrapper for
getModel().predict(inputs)
+
without returning output values (use
get(String)
+ afterwards to view outputs.
+
+
+
+
+
This is a wrapper for
getModel().predict(inputs)
+
without returning output values (use
get(String)
+ afterwards to view outputs.
+
+
+
+
+
+
+
+
Declares a component with the given name to be used as an input
+ of the managed model.
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+ModelBuilder
+public ModelBuilder ()
+
+
+
+
+ModelBuilder
+public ModelBuilder (Model model)
+
+
+
+
+
+
+
+Method Details
+
+
+
+getModel
+
+Retrieves the model currently built by the builder.
+ This can changed depending on additional building method calls.
+
+Returns:
+A Model
+
+
+
+
+
+
+
+
+
+
+
+hasComponent
+public boolean hasComponent (String name) Checks whether the builder has added to its managed model a component of
+ the given name.
+
+Parameters:
+name - The component name to check for.Returns:
+a boolean value
+
+
+
+
+
+var
+
+Declares a component with the given name to be used as an input
+ of the managed model.
+
+Parameters:
+name - The name of the component.Returns:
+The builder's instance.
+
+
+
+
+
+out
+
+Declares the component with the given name an output of the
+ managed model. The component should have already been assigned a value.
+ To output complex expressions use
operation(String)
+ to define them first.
+Parameters:
+name - A component name.Returns:
+The builder's instance.
+
+
+
+
+
+param
+
+Declares a learnable Paramater component with the given name,
+ learning L2 regularization, and initial value.
+
+Parameters:
+name - The name to be assigned to the new component.regularization - The regularization value. Zero corresponds to no regularization.
+ Typically, this is non-negative.value - The initial value to be assigned to the parameter. Exact values
+ can be overridden by neural initialization strategies, but an initial value
+ should be declared nonetheless to determine the parameter type and allocate
+ any necessary memory.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+config
+
+Declares a configuration hyperparameter, which can be used to declare
+ matrix and vector parameters during
operation(String) expressions.
+ For in-expression use of hyperparameters, delcare them with
constant(String, double).
+Parameters:
+name - The name of the configuration hyperparameter.value - The value to be assigned to the hyperparameter.
+ Typically, provide a long number.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+getConfigOrDefault
+public int getConfigOrDefault (String name,
+ int defaultValue)
+
+
+
+getConfigOrDefault
+public double getConfigOrDefault (String name,
+ double defaultValue)
+
+
+
+param
+
+Declares a learnable mklab.JGNN.nn.inputs.Paramater component with the given name,
+ zero regularization, and initial value.
+
+Parameters:
+name - The name to be assigned to the new component.value - The initial value to be assigned to the parameter. Exact values
+ can be overridden by neural initialization strategies, but an initial value
+ should be declared nonetheless to determine the parameter type and allocate
+ any necessary memory.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+constant
+
+Declares a non-learnable constant component with the given name.
+ This can be used in computations. To edit the constant's values,
+ use
get(String) to retrieve the respective component.
+Parameters:
+name - The name of the constant component.value - A double value to assign to the constant.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+constant
+
+Declares a non-learnable constant component with the given name.
+ This can be used in computations. To edit the constant's values,
+ use
get(String) to retrieve the respective component.
+Parameters:
+name - The name of the constant component.value - A Tensor value to assign to the constant.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+get
+
+Retrieves the
NNOperation registered with the provided
+ name, for example to investigates its value.
+Parameters:
+name - The name of the component.Returns:
+A NNOperation
+
+
+
+
+
+runModel
+
+This is a wrapper for
getModel().predict(inputs)
+
without returning output values (use
get(String)
+ afterwards to view outputs.
+Parameters:
+inputs - A variable number of Tensor inputs.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+runModel
+
+This is a wrapper for
getModel().predict(inputs)
+
without returning output values (use
get(String)
+ afterwards to view outputs.
+Parameters:
+inputs - A list of Tensor inputs.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+operation
+
+Parses one or more operations split by new line characters or ;
+ to add to the execution graph. All operations should assign a
+ value to a new component name and comprise operators and functions.
+ For a detailed description of the domain-specific language this
+ method accepts, please refer to the library's
+
+ online documentation .
+Parameters:
+desc - The operation to parse.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+createForwardValidity
+
+
+
+Returns:
+The builder's instance.
+Throws:
+RuntimeException
+
+
+
+
+assertBackwardValidity
+
+
+
+Returns:
+The builder's instance.
+Throws:
+RuntimeException
+
+
+
+
+describe
+
+Creates a description of the builded model's internal execution graph.
+
+Returns:
+A String.
+See Also:
+
+
+
+
+
+
+
+
+getExecutionGraphDot
+public String getExecutionGraphDot ()
Exports the builded model's execution graph into a .dot format
+ representation.
+
+Returns:
+A String to be pasted into GraphViz for visualization.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.adhoc.Dataset
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
class
+
+
+
Downloads and constructs the Citeseer node classification
Dataset.
+
+
class
+
+
+
Downloads and constructs the Cora node classification
Dataset.
+
+
class
+
+
+
Downloads and constructs the Pubmed node classification
Dataset.
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.adhoc.IdConverter
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Retrieves a converter that maps class names to label dimentions.
+
+
+
+
+
+
+
+
Sets dimension names for one-hot encodings.
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.adhoc.ModelBuilder
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Declares a configuration hyperparameter, which can be used to declare
+ matrix and vector parameters during
operation(String) expressions.
+
+
+
+
+
+
+
+
Declares a non-learnable constant component with the given name.
+
+
+
+
+
Declares a non-learnable constant component with the given name.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Parses one or more operations split by new line characters or ;
+ to add to the execution graph.
+
+
+
+
+
Declares the component with the given name an output of the
+ managed model.
+
+
+
+
+
Declares a learnable Paramater component with the given name,
+ learning L2 regularization, and initial value.
+
+
+
+
+
Declares a learnable mklab.JGNN.nn.inputs.Paramater component with the given name,
+ zero regularization, and initial value.
+
+
+
+
+
+
+
+
+
+
+
This is a wrapper for
getModel().predict(inputs)
+
without returning output values (use
get(String)
+ afterwards to view outputs.
+
+
+
+
+
This is a wrapper for
getModel().predict(inputs)
+
without returning output values (use
get(String)
+ afterwards to view outputs.
+
+
+
+
+
+
+
+
Declares a component with the given name to be used as an input
+ of the managed model.
+
+
+
+
+
+
+
+
+
+
+
+
class
+
+
+
Extends the capabilities of
LayeredBuilder to use
+ for node classification.
+
+
class
+
+
+
Extends the capabilities of the
ModelBuilder
+ with the ability to define multilayer (e.g.
+
+
class
+
+
+
+
+
+
+
+
+
+
+
+
Citeseer
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+public class Citeseer
+
extends Dataset Downloads and constructs the Citeseer node classification
Dataset.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Citeseer
+public Citeseer ()
+
+
+
+
+
+
+
+
+
+
+
Cora
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Downloads and constructs the Cora node classification
Dataset.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Pubmed
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+public class Pubmed
+
extends Dataset Downloads and constructs the Pubmed node classification
Dataset.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Pubmed
+public Pubmed ()
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.adhoc.datasets.Citeseer
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.adhoc.datasets.Citeseer
+
+
Uses of Class mklab.JGNN.adhoc.datasets.Cora
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.adhoc.datasets.Cora
+
+
Uses of Class mklab.JGNN.adhoc.datasets.Pubmed
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.adhoc.datasets.Pubmed
+
+
mklab.JGNN.adhoc.datasets
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+package mklab.JGNN.adhoc.datasets
+
+
+
+
+
+
+
+
Classes
+
+
+
+
+
+
Downloads and constructs the Citeseer node classification
Dataset.
+
+
+
+
Downloads and constructs the Cora node classification
Dataset.
+
+
+
+
Downloads and constructs the Pubmed node classification
Dataset.
+
+
+
+
+
+
+
+
mklab.JGNN.adhoc.datasets Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+Class Hierarchy
+
+java.lang.Object
+
+mklab.JGNN.adhoc.Dataset
+
+mklab.JGNN.adhoc.datasets.Citeseer
+mklab.JGNN.adhoc.datasets.Cora
+mklab.JGNN.adhoc.datasets.Pubmed
+
+
+
+
+
+
+
+
+
Uses of Package mklab.JGNN.adhoc.datasets
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.adhoc.datasets
+
+
mklab.JGNN.adhoc
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+package mklab.JGNN.adhoc
+
+
+
+
+
+
+
+
Classes
+
+
+
+
+
+
This class provides the backbone with which to define datasets.
+
+
+
+
Converts back-and-forth between objects and unique ids.
+
+
+
+
This class and subclasses can be used to create
Model instances
+ by automatically creating and managing
NNOperation instances based on
+ textual descriptions.
+
+
+
+
+
+
+
+
mklab.JGNN.adhoc Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/adhoc/package-use.html b/docs/javadoc/mklab/JGNN/adhoc/package-use.html
new file mode 100644
index 00000000..3c1498b4
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/adhoc/package-use.html
@@ -0,0 +1,138 @@
+
+
+
+
+Uses of Package mklab.JGNN.adhoc
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Converts back-and-forth between objects and unique ids.
+
+
+
+
This class and subclasses can be used to create
Model instances
+ by automatically creating and managing
NNOperation instances based on
+ textual descriptions.
+
+
+
+
+
+
+
+
+
+
+
+
This class provides the backbone with which to define datasets.
+
+
+
+
+
+
+
+
+
+
+
+
This class and subclasses can be used to create
Model instances
+ by automatically creating and managing
NNOperation instances based on
+ textual descriptions.
+
+
+
+
+
+
+
+
+
+
+
+
This class and subclasses can be used to create
Model instances
+ by automatically creating and managing
NNOperation instances based on
+ textual descriptions.
+
+
+
+
+
+
+
+
FastBuilder
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Extends the capabilities of
LayeredBuilder to use
+ for node classification. It accepts the adjacency graph in the constructor,
+ to be used with the symbol
A in operations or layer definitions,
+ and node features.
+Author:
+Emmanouil Krasanakis
+See Also:
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+
+
Creates a graph neural network builder from an
+ normalized adjacency matrix and a node feature matrix.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Adds a classification layer that gather the number of inputs nodes
+ and applies softmax on all of them.
+
+
+
+
+
Concatenates horizontally the output of a number of given layers,
+ starting from the last one and going backwards.
+
+
+
+
+
Declares a configuration hyperparameter, which can be used to declare
+ matrix and vector parameters during
ModelBuilder.operation(String) expressions.
+
+
+
+
+
+
+
+
Declares a non-learnable constant component with the given name.
+
+
+
+
+
Declares a non-learnable constant component with the given name.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Parses one or more operations split by new line characters or ;
+ to add to the execution graph.
+
+
+
+
+
Declares a learnable Paramater component with the given name,
+ learning L2 regularization, and initial value.
+
+
+
+
+
Declares a learnable mklab.JGNN.nn.inputs.Paramater component with the given name,
+ zero regularization, and initial value.
+
+
+
+
+
Remembers the last layer's output per a given identifier so that {layerId}
+ within future
layer(String) definitions is made to refer to the
+ current layer.
+
+
+
+
+
Methods inherited from class mklab.JGNN.adhoc.ModelBuilder
+
assertBackwardValidity , autosize , autosize , createForwardValidity , describe , get , getConfigOrDefault , getConfigOrDefault , getExecutionGraphDot , getModel , hasComponent , load , out , print , printState , runModel , runModel , save , var
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+FastBuilder
+public FastBuilder ()
+Deprecated.
+
+
+
+
+
+
+FastBuilder
+
+Creates a graph neural network builder from an
+ normalized adjacency matrix and a node feature matrix.
+
+Parameters:
+adjacency - The pre-normalized adjacency matrix.features - The node feature matrix.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+rememberAs
+
+Remembers the last layer's output per a given identifier so that {layerId}
+ within future
layer(String) definitions is made to refer to the
+ current layer.
+Parameters:
+layerId - An identifier to remember the last layer's output as.Returns:
+The model builder.
+
+
+
+
+
+layer
+
+
+
+Parameters:
+expression - A parsable expression.Returns:
+this builder.See Also:
+
+
+
+
+
+
+
+
+classify
+
+Adds a classification layer that gather the number of inputs nodes
+ and applies softmax on all of them.
+
+Returns:
+this builder.
+
+
+
+
+layerRepeat
+
+Repeats a
layer(String) definition a number of times.
+ Ideal for building deep architectures.
+Parameters:
+expression - The expression to repeat for each layer.times - The number of times to repeat the expression.Returns:
+this builder.See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+config
+
+
+
+
+Overrides:
+config ModelBuilder Parameters:
+name - The name of the configuration hyperparameter.value - The value to be assigned to the hyperparameter.
+ Typically, provide a long number.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+param
+
+
+Declares a learnable mklab.JGNN.nn.inputs.Paramater component with the given name,
+ zero regularization, and initial value.
+
+Overrides:
+param ModelBuilder Parameters:
+name - The name to be assigned to the new component.value - The initial value to be assigned to the parameter. Exact values
+ can be overridden by neural initialization strategies, but an initial value
+ should be declared nonetheless to determine the parameter type and allocate
+ any necessary memory.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+constant
+
+
+Declares a non-learnable constant component with the given name.
+ This can be used in computations. To edit the constant's values,
+ use
ModelBuilder.get(String) to retrieve the respective component.
+Overrides:
+constant ModelBuilder Parameters:
+name - The name of the constant component.value - A double value to assign to the constant.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+constant
+
+
+Declares a non-learnable constant component with the given name.
+ This can be used in computations. To edit the constant's values,
+ use
ModelBuilder.get(String) to retrieve the respective component.
+Overrides:
+constant ModelBuilder Parameters:
+name - The name of the constant component.value - A Tensor value to assign to the constant.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+param
+
+
+Declares a learnable Paramater component with the given name,
+ learning L2 regularization, and initial value.
+
+Overrides:
+param ModelBuilder Parameters:
+name - The name to be assigned to the new component.regularization - The regularization value. Zero corresponds to no regularization.
+ Typically, this is non-negative.value - The initial value to be assigned to the parameter. Exact values
+ can be overridden by neural initialization strategies, but an initial value
+ should be declared nonetheless to determine the parameter type and allocate
+ any necessary memory.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+operation
+
+
+Parses one or more operations split by new line characters or ;
+ to add to the execution graph. All operations should assign a
+ value to a new component name and comprise operators and functions.
+ For a detailed description of the domain-specific language this
+ method accepts, please refer to the library's
+
+ online documentation .
+Overrides:
+operation ModelBuilder Parameters:
+desc - The operation to parse.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+futureConfigs
+
+
+
+Parameters:
+config - The configuration symbols (these should involve {l}).func - A lambda Java function to calculate the configuration's value. This takes
+ as input an integer (starting from 0 for the current layer) and adds one for each
+ subsequently declared symbol.depth - The number of future layers expected to use the symbols.Returns:
+this builder.See Also:
+
+
+
+
+
+
+
+
+futureConstants
+
+
+
+Parameters:
+constantName - The configuration symbols (these should involve {l}).func - A lambda Java function to calculate the constant's value. This takes
+ as input an integer (starting from 0 for the current layer) and adds one for each
+ subsequently declared symbol.depth - The number of future layers expected to use the constant.Returns:
+this builder.See Also:
+
+
+
+
+
+
+
+
+concat
+
+Concatenates horizontally the output of a number of given layers,
+ starting from the last one and going backwards. (For concatenation
+ of specific layers just use concat within normal operations.)
+
+Parameters:
+depth - The number of given layers to concatenate.Returns:
+this builder.
+
+
+
+
+
+
+
+
+
+
+
LayeredBuilder
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Extends the capabilities of the
ModelBuilder
+ with the ability to define multilayer (e.g. deep) neural architectures.
+ The symbols
{l} and
{l+1} are replaced in all expressions
+ with appropriate layer identifiers (these increase by one each time a new
+
layer(String) is defined.
+See Also:
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Instantiates a layered builder with input name h0.
+
+
+
+
Instantiates a layered builder with the given symbol as an input name.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Concatenates horizontally the output of a number of given layers,
+ starting from the last one and going backwards.
+
+
+
+
+
Declares a configuration hyperparameter, which can be used to declare
+ matrix and vector parameters during
ModelBuilder.operation(String) expressions.
+
+
+
+
+
Declares a non-learnable constant component with the given name.
+
+
+
+
+
Declares a non-learnable constant component with the given name.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Parses one or more operations split by new line characters or ;
+ to add to the execution graph.
+
+
+
+
+
Declares the component with the given name an output of the
+ managed model.
+
+
+
+
+
Declares a learnable Paramater component with the given name,
+ learning L2 regularization, and initial value.
+
+
+
+
+
Declares a learnable mklab.JGNN.nn.inputs.Paramater component with the given name,
+ zero regularization, and initial value.
+
+
+
+
+
Sets the current layer identifier to a specific symbol layerId
+ so that future usage of {layerId} is automatically replaced with
+ the identifier.
+
+
+
+
+
Declares a component with the given name to be used as an input
+ of the managed model.
+
+
+
+
+
Methods inherited from class mklab.JGNN.adhoc.ModelBuilder
+
assertBackwardValidity , autosize , autosize , config , createForwardValidity , describe , function , get , getConfigOrDefault , getConfigOrDefault , getExecutionGraphDot , getModel , hasComponent , load , print , printState , runModel , runModel , save
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+LayeredBuilder
+public LayeredBuilder ()
+Instantiates a layered builder with input name
h0. This can be
+ used by future expressions involving
h{l}. You can add more
+ architecture inputs normally with
var(String).
+See Also:
+
+
+
+
+
+
+
+
+LayeredBuilder
+public LayeredBuilder (String inputName) Instantiates a layered builder with the given symbol as an input name.
+ If you plan to immediately use a
layer(String) expression
+ that involves
X{l}, where
X is some symbol,
+ set
X0 as the architecture's input. You can add more
+ architecture inputs normally with
var(String).
+Parameters:
+inputName - The symbol to use as the built architecture's input.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+var
+
+
+Declares a component with the given name to be used as an input
+ of the managed model.
+
+Overrides:
+var ModelBuilder Parameters:
+inputName - The name of the component.Returns:
+The builder's instance.
+
+
+
+
+
+rememberAs
+
+Sets the current layer identifier to a specific symbol layerId
+ so that future usage of {layerId} is automatically replaced with
+ the identifier.
+
+Parameters:
+layerId - The symbol to set to the current layer identifier.Returns:
+this layer builder.
+
+
+
+
+layer
+
+
+
+Parameters:
+expression - A parsable expression.Returns:
+this layer builder.See Also:
+
+
+
+
+
+
+
+
+layerRepeat
+
+Repeats a
layer(String) definition a number of times.
+ Ideal for building deep architectures.
+Parameters:
+expression - The expression to repeat for each layer.times - The number of times to repeat the expression.Returns:
+this layer builder.See Also:
+
+
+
+
+
+
+
+
+config
+
+
+
+
+Overrides:
+config ModelBuilder Parameters:
+name - The name of the configuration hyperparameter.value - The value to be assigned to the hyperparameter.
+ Typically, provide a long number.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+param
+
+
+Declares a learnable mklab.JGNN.nn.inputs.Paramater component with the given name,
+ zero regularization, and initial value.
+
+Overrides:
+param ModelBuilder Parameters:
+name - The name to be assigned to the new component.value - The initial value to be assigned to the parameter. Exact values
+ can be overridden by neural initialization strategies, but an initial value
+ should be declared nonetheless to determine the parameter type and allocate
+ any necessary memory.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+constant
+
+
+Declares a non-learnable constant component with the given name.
+ This can be used in computations. To edit the constant's values,
+ use
ModelBuilder.get(String) to retrieve the respective component.
+Overrides:
+constant ModelBuilder Parameters:
+name - The name of the constant component.value - A double value to assign to the constant.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+constant
+
+
+Declares a non-learnable constant component with the given name.
+ This can be used in computations. To edit the constant's values,
+ use
ModelBuilder.get(String) to retrieve the respective component.
+Overrides:
+constant ModelBuilder Parameters:
+name - The name of the constant component.value - A Tensor value to assign to the constant.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+param
+
+
+Declares a learnable Paramater component with the given name,
+ learning L2 regularization, and initial value.
+
+Overrides:
+param ModelBuilder Parameters:
+name - The name to be assigned to the new component.regularization - The regularization value. Zero corresponds to no regularization.
+ Typically, this is non-negative.value - The initial value to be assigned to the parameter. Exact values
+ can be overridden by neural initialization strategies, but an initial value
+ should be declared nonetheless to determine the parameter type and allocate
+ any necessary memory.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+operation
+
+
+Parses one or more operations split by new line characters or ;
+ to add to the execution graph. All operations should assign a
+ value to a new component name and comprise operators and functions.
+ For a detailed description of the domain-specific language this
+ method accepts, please refer to the library's
+
+ online documentation .
+Overrides:
+operation ModelBuilder Parameters:
+desc - The operation to parse.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+out
+
+
+Declares the component with the given name an output of the
+ managed model. The component should have already been assigned a value.
+ To output complex expressions use
ModelBuilder.operation(String)
+ to define them first.
+Overrides:
+out ModelBuilder Parameters:
+expression - A component name.Returns:
+The builder's instance.
+
+
+
+
+
+concat
+
+Concatenates horizontally the output of a number of given layers,
+ starting from the last one and going backwards. (For concatenation
+ of specific layers just use concat within normal operations.)
+
+Parameters:
+depth - The number of given layers to concatenate.Returns:
+this layer builder.
+
+
+
+
+futureConfigs
+
+
+
+Parameters:
+config - The configuration symbols (these should involve {l}).func - A lambda Java function to calculate the configuration's value. This takes
+ as input an integer (starting from 0 for the current layer) and adds one for each
+ subsequently declared symbol.depth - The number of future layers expected to use the symbols.Returns:
+this layer builder.See Also:
+
+
+
+
+
+
+
+
+futureConstants
+
+
+
+Parameters:
+constantName - The configuration symbols (these should involve {l}).func - A lambda Java function to calculate the constant's value. This takes
+ as input an integer (starting from 0 for the current layer) and adds one for each
+ subsequently declared symbol.depth - The number of future layers expected to use the constant.Returns:
+this layer builder.See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Neuralang
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Declares a configuration hyperparameter, which can be used to declare
+ matrix and vector parameters during
ModelBuilder.operation(String) expressions.
+
+
+
+
+
Declares a non-learnable constant component with the given name.
+
+
+
+
+
Declares a non-learnable constant component with the given name.
+
+
+
+
+
+
+
+
+
+
+
Declares a component with the given name to be used as an input
+ of the managed model.
+
+
+
+
+
Methods inherited from class mklab.JGNN.adhoc.ModelBuilder
+
assertBackwardValidity , autosize , autosize , config , createForwardValidity , describe , function , get , getConfigOrDefault , getConfigOrDefault , getExecutionGraphDot , getModel , hasComponent , load , operation , out , param , param , print , printState , runModel , runModel , save
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Neuralang
+public Neuralang ()
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+config
+
+
+
+
+Overrides:
+config ModelBuilder Parameters:
+name - The name of the configuration hyperparameter.value - The value to be assigned to the hyperparameter.
+ Typically, provide a long number.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+constant
+
+
+Declares a non-learnable constant component with the given name.
+ This can be used in computations. To edit the constant's values,
+ use
ModelBuilder.get(String) to retrieve the respective component.
+Overrides:
+constant ModelBuilder Parameters:
+name - The name of the constant component.value - A Tensor value to assign to the constant.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+constant
+
+
+Declares a non-learnable constant component with the given name.
+ This can be used in computations. To edit the constant's values,
+ use
ModelBuilder.get(String) to retrieve the respective component.
+Overrides:
+constant ModelBuilder Parameters:
+name - The name of the constant component.value - A double value to assign to the constant.Returns:
+The builder's instance.
+See Also:
+
+
+
+
+
+
+
+
+var
+
+
+Declares a component with the given name to be used as an input
+ of the managed model.
+
+Overrides:
+var ModelBuilder Parameters:
+var - The name of the component.Returns:
+The builder's instance.
+
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.adhoc.parsers.FastBuilder
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Adds a classification layer that gather the number of inputs nodes
+ and applies softmax on all of them.
+
+
+
+
+
Concatenates horizontally the output of a number of given layers,
+ starting from the last one and going backwards.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Remembers the last layer's output per a given identifier so that {layerId}
+ within future
layer(String) definitions is made to refer to the
+ current layer.
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.adhoc.parsers.LayeredBuilder
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
LayeredBuilder. concat (int depth)
+
+
Concatenates horizontally the output of a number of given layers,
+ starting from the last one and going backwards.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Sets the current layer identifier to a specific symbol layerId
+ so that future usage of {layerId} is automatically replaced with
+ the identifier.
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.adhoc.parsers.Neuralang
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
mklab.JGNN.adhoc.parsers
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+package mklab.JGNN.adhoc.parsers
+
+
+
+
+
+
+
+
Classes
+
+
+
+
+
+
Extends the capabilities of
LayeredBuilder to use
+ for node classification.
+
+
+
+
Extends the capabilities of the
ModelBuilder
+ with the ability to define multilayer (e.g.
+
+
+
+
+
+
+
+
+
+
mklab.JGNN.adhoc.parsers Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/adhoc/parsers/package-use.html b/docs/javadoc/mklab/JGNN/adhoc/parsers/package-use.html
new file mode 100644
index 00000000..c2a3314f
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/adhoc/parsers/package-use.html
@@ -0,0 +1,91 @@
+
+
+
+
+Uses of Package mklab.JGNN.adhoc.parsers
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Extends the capabilities of
LayeredBuilder to use
+ for node classification.
+
+
+
+
Extends the capabilities of the
ModelBuilder
+ with the ability to define multilayer (e.g.
+
+
+
+
+
+
+
+
+
+
Distribution
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+All Known Implementing Classes:
+Normal Uniform
+public interface Distribution
+
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Abstract Methods
+
+
+
+
+
+
double
+
+
+
Retrieves the distribution's standard deviation.
+
+
double
+
+
+
Retrieves the distribution's mean.
+
+
double
+
+
+
Retrieves a new sample from the distribution.
+
+
+
+
+
Sets the standard deviation of the distribution.
+
+
+
+
+
Sets the mean of the distribution.
+
+
+
+
+
Sets the distribution's seed.
+
+
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+setSeed
+
+Sets the distribution's seed. This should yield reproducible sampling.
+
+Parameters:
+seed - The distribution's new seed.Returns:
+this Distribution.
+
+
+
+
+sample
+double sample ()
+Retrieves a new sample from the distribution.
+
+Returns:
+A double value.
+
+
+
+
+
+setMean
+
+Sets the mean of the distribution.
+
+Parameters:
+mean - The new mean.Returns:
+this Distribution.
+
+
+
+
+setDeviation
+
+Sets the standard deviation of the distribution.
+
+Parameters:
+std - The new standard deviation.Returns:
+this Distribution.
+
+
+
+
+getMean
+double getMean ()
+Retrieves the distribution's mean.
+
+Returns:
+The mean value.
+
+
+
+
+
+getDeviation
+double getDeviation ()
+Retrieves the distribution's standard deviation.
+
+Returns:
+The standard deviation.
+
+
+
+
+
+
+
+
+
+
+
+
Matrix
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+
+Direct Known Subclasses:
+ColumnRepetition DenseMatrix Diagonal EmptyMatrix RepeatMatrix RowRepetition SparseMatrix SparseSymmetric TransposedMatrix VectorizedMatrix WrapCols WrapRows
+public abstract class Matrix
+
extends Tensor This class provides an abstract implementation of Matrix functionalities.
+ Matrices inherit
Tensor operations, such as addition,
+ element-by-element multiplication, randomizing them and producing zero copies.
+ Additionally, matrix multiplication, transposition and access operations are
+ provided.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Method Summary
+
+
All Methods Static Methods Instance Methods Abstract Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Retrieves the given column as a tensor.
+
+
+
+
+
Organizes specific matrix columns to a list of tensors that share entries.
+
+
+
+
+
Organizes specific matrix columns to a list of tensors that share entries.
+
+
+
+
+
Organizes some matrix columns to a list of tensors that share entries.
+
+
+
+
+
Organizes matrix columns to a list of tensors that share entries.
+
+
+
+
+
Retrieves either the given row or column as a trensor.
+
+
+
+
+
Retrieves the given row as a tensor.
+
+
+
+
+
Organizes matrix rows to a list of tensors that share entries.
+
+
+
+
+
Organizes specific matrix rows to a list of tensors that share entries.
+
+
+
+
+
Organizes some matrix rows to a list of tensors that share entries.
+
+
+
+
+
Organizes specific matrix rows to a list of tensors that share entries.
+
+
+
+
+
Creates a transposed version of the matrix that accesses the same elements (thus, editing one
+ edits the other) without allocating additional memory.
+
+
+
+
+
Describes the type, size and other characteristics of the tensor.
+
+
+
+
+
Produces the external product of two tensors.
+
+
+
+
+
Creates a sparse unit matrix.
+
+
+
+
+
Converts a given value to a JGNN-compatible 1x1 matrix.
+
+
double
+
get (long row,
+ long col)
+
+
Retrieves the value stored at a matrix element.
+
+
+
+
+
long
+
+
+
Retrieves the number of columns of a matrix.
+
+
long
+
+
+
Retrieves the value of the dimension with the given name.
+
+
+
+
+
Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+
+
+
+
long
+
+
+
Retrieves the number of rows of a matrix.
+
+
+
+
+
Performs the matrix multiplication of this*with and the recipient.
+
+
+
matmul (Matrix with,
+ boolean transposeSelf,
+ boolean transposeWith)
+
+
Can be used to perform fast computation of the matrix multiplications
+
this*with,
+
this.transposed()*with
+
this*with.transposed(),
+
this.transposed()*with.transposed()
+
while avoiding the overhead of calling
+
transposed().
+
+
+
+
+
Produces a mask that indicates the non-zero elements of the matrix.
+
+
+
put (long row,
+ long col,
+ double value)
+
+
Stores values at matrix elements.
+
+
+
+
+
Sets a name for the matrix's column dimension.
+
+
+
+
+
Sets the matrix's specified diagonal elements to a given value.
+
+
+
+
+
Sets a name for the matrix's row and column dimensions.
+
+
+
+
+
Fills in dimension names per an example Tensor.isMatching(mklab.JGNN.core.Tensor) tensor.
+
+
+
+
+
Sets the matrix's specified main diagonal elements to a given value value.
+
+
+
+
+
Sets a name for the matrix's row dimension.
+
+
+
+
+
Sets the Matrix to its asymmetrically normalized transformation
+ by appropriately adjusting its element values.
+
+
+
+
+
Sets the Matrix to its symmetrically normalized transformation
+ by appropriately adjusting its element values.
+
+
+
+
+
Creates a copy of the Matrix that holds its symmetrically normalized version.
+
+
+
+
+
Creates a copy of the matrix organized as a dense matrix.
+
+
+
+
+
+
+
+
Creates a copy of the matrix organized as a sparse matrix.
+
+
+
+
+
A string serialization of the tensor that can be used by the constructor
DenseTensor(String) to create an identical copy.
+
+
+
+
+
Performs the linear algebra transformation A*x where A is this matrix and x a vector
+
+
+
+
+
Creates a transposed copy of the matrix.
+
+
+
+
+
Creates a Matrix with the same class and dimensions and all element set to zero.
+
+
+
+
+
Creates a Matrix with the same class and dimensions and all element set to zero.
+
+
+
+
+
Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+
+
+
+
Creates a tensor of the same class and all elements set to zero,
+ but size and dimension names are obtained from a prototype tensor.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , dot , dot , estimateNumNonZeroElements , expMinusOne , fromRange , fromRange , get , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , persist , put , putAdd , release , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability , traverseNonZeroElements
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+
+
+
+
+
+
+setDimensionName
+
+Sets a name for the matrix's row and column dimensions. If set, names are checked for
+ compatibility during matrix operations.
+
+Parameters:
+rowName - The new row name or null to remove current name.colName - The new column name or null to remove current name.Returns:
+this Matrix instance.See Also:
+
+
+
+
+
+
+
+
+setRowName
+
+Sets a name for the matrix's row dimension. If set, names are checked for
+ compatibility during matrix operations.
+
+Parameters:
+rowName - The new row name or null to remove current name.Returns:
+this Matrix instance.See Also:
+
+
+
+
+
+
+
+
+setColName
+
+Sets a name for the matrix's column dimension. If set, names are checked for
+ compatibility during matrix operations.
+
+Parameters:
+colName - The new column name or null to remove current name.Returns:
+this Matrix instance.See Also:
+
+
+
+
+
+
+
+
+getNonZeroEntries
+
+Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+Returns:
+An Entry iterable.
+See Also:
+
+
+
+
+
+
+
+
+setDimensionName
+
+Description copied from class: Tensor Fills in dimension names per an example Tensor.isMatching(mklab.JGNN.core.Tensor) tensor. This appropriately fills in dimension
+ names of inherited classes too, such as matrices. Effectively, this method automatically infers
+ dimension names during operations.
+
+Overrides:
+setDimensionName Tensor Parameters:
+other - The tensor from which to retrieve dimension names.Returns:
+this Tensor instance.
+
+
+
+
+zeroCopy
+
+Creates a Matrix with the same class and dimensions and all element set to zero.
+
+Overrides:
+zeroCopy Tensor Returns:
+A Matrix with the same class and dimensions.
+See Also:
+
+
+
+
+
+
+
+
+zeroCopy
+public Tensor zeroCopy (long size) Creates a Matrix with the same class and dimensions and all element set to zero. This
+ checks that the copy has a total number of elements equal to the given size.
+
+Specified by:
+zeroCopy Tensor Parameters:
+size - The desired size of the matrix.Returns:
+A Matrix with the same class and dimensions.
+Throws:
+RuntimeException See Also:
+
+
+
+
+
+
+
+
+zeroCopy
+
+Creates a tensor of the same class and all elements set to zero,
+ but size and dimension names are obtained from a prototype tensor.
+
+Overrides:
+zeroCopy Tensor
+
+
+
+
+zeroCopy
+public abstract Matrix zeroCopy (long rows,
+ long cols) Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+Parameters:
+rows - The number of rows of the matrix.cols - The number of columns of the matrix.Returns:
+A Matrix of the same class.
+See Also:
+
+
+
+
+
+
+
+
+getRows
+public long getRows ()
+Retrieves the number of rows of a matrix.
+
+Returns:
+The number of rows.
+See Also:
+
+
+
+
+
+
+
+
+getCols
+public long getCols ()
+Retrieves the number of columns of a matrix.
+
+Returns:
+The number of columns.
+See Also:
+
+
+
+
+
+
+
+
+getDimensionSize
+public long getDimensionSize (String name) Retrieves the value of the dimension with the given name.
+
+Parameters:
+name - The given name.Returns:
+Either the number of rows or the number of cols, depending on which dimension
+ the given name matches,
+Throws:
+RuntimeException See Also:
+
+
+
+
+
+
+
+
+get
+public double get (long row,
+ long col)
+Retrieves the value stored at a matrix element.
+
+Parameters:
+row - The element's row.col - The element's column.Returns:
+The value corresponding to the element (row, col).
+
+
+
+
+
+put
+public Matrix put (long row,
+ long col,
+ double value) Stores values at matrix elements.
+
+Parameters:
+row - The element's row.col - The element's column.value - The value to store.Returns:
+this Matrix instance.
+
+
+
+
+transposed
+
+Creates a transposed copy of the matrix.
+ Note: Contrary to typical tensor operations, in-place transposition is not supported.
+ However, related methods can help avoid explicit transposition without allocating more
+ memory.
+
+Returns:
+A transposed copy of the matrix.
+See Also:
+
+
+
+
+
+
+
+
+asTransposed
+
+Creates a transposed version of the matrix that accesses the same elements (thus, editing one
+ edits the other) without allocating additional memory.
+
+Returns:
+A TransposedMatrix
+
+
+
+
+
+
+
+
+matmul
+
+Performs the matrix multiplication of this*with and the recipient.
+
+Parameters:
+with - The matrix to multiply with.Returns:
+A matrix that stores the outcome of the multiplication.
+See Also:
+
+
+
+
+
+
+
+
+matmul
+public Matrix matmul (Matrix with,
+ boolean transposeSelf,
+ boolean transposeWith) Can be used to perform fast computation of the matrix multiplications
+
this*with,
+
this.transposed()*with
+
this*with.transposed(),
+
this.transposed()*with.transposed()
+
while avoiding the overhead of calling
+
transposed(). In this first of those cases, this operation
+ becomes equivalent to
matmul(Matrix).
+Parameters:
+with - The matrix to multiply with.transposeSelf - Whether this matrix should be transposed before multiplication.transposeWith - Whether the multiplied with matrix should be transposed before multiplication.Returns:
+A matrix that stores the outcome of the multiplication.
+See Also:
+
+
+
+
+
+
+
+
+external
+
+Produces the external product of two tensors.
+ This is equivalent but faster to calling matmul(horizontal.asColum(), vertical.asRow()).
+
+Parameters:
+horizontal - The first tensor.vertical - The second tensor.Returns:
+A dense matrix holding the external product.
+
+
+
+
+
+describe
+
+Description copied from class: Tensor Describes the type, size and other characteristics of the tensor.
+
+Overrides:
+describe Tensor Returns:
+A String description.
+
+
+
+
+
+onesMask
+
+Produces a mask that indicates the non-zero elements of the matrix.
+ Element's correspond to the matrix's whose non-zero ones are set to 1.
+
+Returns:
+A matrix of the same dimensions.
+
+
+
+
+
+symmetricNormalization
+public Matrix symmetricNormalization ()
Creates a copy of the Matrix that holds its symmetrically normalized version.
+
+Returns:
+A new Matrix of the same dimensions.
+See Also:
+
+
+
+
+
+
+
+
+setMainDiagonal
+public Matrix setMainDiagonal (double value) Sets the matrix's specified main diagonal elements to a given value value.
+
+Parameters:
+value - The value to set to the diagonal's elements.Returns:
+this Matrix instance.See Also:
+
+
+
+
+
+
+
+
+setDiagonal
+public Matrix setDiagonal (long diagonal,
+ double value) Sets the matrix's specified diagonal elements to a given value.
+
+Parameters:
+diagonal - Which diagonal to set. 0 is the main diagonalvalue - The value to set to the diagonal's elements.Returns:
+this Matrix instance.See Also:
+
+
+
+
+
+
+
+
+setToSymmetricNormalization
+public Matrix setToSymmetricNormalization ()
Sets the Matrix to its symmetrically normalized transformation
+ by appropriately adjusting its element values.
+
+Returns:
+this Matrix instance.See Also:
+
+
+
+
+
+
+
+
+setToASymmetricNormalization
+public Matrix setToASymmetricNormalization ()
Sets the Matrix to its asymmetrically normalized transformation
+ by appropriately adjusting its element values.
+
+Returns:
+this Matrix instance.See Also:
+
+
+
+
+
+
+
+
+accessDim
+
+Retrieves either the given row or column as a trensor.
+
+Parameters:
+index - The dimension index to access.name - The dimension's name.Returns:
+Either a AccessRowAccessCol
+See Also:
+
+
+
+
+
+
+
+
+accessRow
+public Tensor accessRow (long row) Retrieves the given row as a tensor. Editing the result
+ also edits the original matrix.
+ No new memory is allocated for matrix values.
+
+Parameters:
+row - The given row.Returns:
+An AccessRow
+See Also:
+
+
+
+
+
+
+
+
+accessCol
+public Tensor accessCol (long col) Retrieves the given column as a tensor. Editing the result
+ also edits the original matrix.
+ No new memory is allocated for matrix values.
+
+Parameters:
+col - The given column.Returns:
+An AccessCol
+See Also:
+
+
+
+
+
+
+
+
+toString
+
+Description copied from class: Tensor A string serialization of the tensor that can be used by the constructor
DenseTensor(String) to create an identical copy.
+Overrides:
+toString Tensor Returns:
+A serialization of the tensor.
+
+
+
+
+
+toNonZeroString
+public String toNonZeroString ()
+
+
+
+accessRows
+
+Organizes matrix rows to a list of tensors that share entries.
+ This operation does not allocate memory for matrix elements and editing
+ tensor elements edits the original matrix's elements.
+
+Returns:
+A list of AccessRow
+See Also:
+
+
+
+
+
+
+
+
+accessColumns
+
+Organizes specific matrix columns to a list of tensors that share entries.
+ This operation does not allocate memory for matrix elements and editing
+ tensor elements edits the original matrix's elements.
+
+Returns:
+A list of AccessCol
+See Also:
+
+
+
+
+
+
+
+
+accessRows
+public Matrix accessRows (long... rows) Organizes specific matrix rows to a list of tensors that share entries.
+ This operation does not allocate memory for matrix elements and editing
+ tensor elements edits the original matrix's elements.
+
+Parameters:
+rows - An array of rows to access.Returns:
+A list of AccessRow
+See Also:
+
+
+
+
+
+
+
+
+accessColumns
+public Matrix accessColumns (long... cols) Organizes specific matrix columns to a list of tensors that share entries.
+ This operation does not allocate memory for matrix elements and editing
+ tensor elements edits the original matrix's elements.
+
+Parameters:
+cols - An array of columns to access.Returns:
+A list of AccessCol
+See Also:
+
+
+
+
+
+
+
+
+accessRows
+
+Organizes specific matrix rows to a list of tensors that share entries.
+ This operation does not allocate memory for matrix elements and editing
+ tensor elements edits the original matrix's elements.
+
+Parameters:
+rows - A tensor whose values hold the rows to access.Returns:
+A list of AccessRow
+See Also:
+
+
+
+
+
+
+
+
+accessColumns
+
+Organizes matrix columns to a list of tensors that share entries.
+ This operation does not allocate memory for matrix elements and editing
+ tensor elements edits the original matrix's elements.
+
+Parameters:
+cols - A tensor whose values hold the columns to access.Returns:
+A list of AccessCol
+See Also:
+
+
+
+
+
+
+
+
+accessRows
+
+Organizes some matrix rows to a list of tensors that share entries.
+ This operation does not allocate memory for matrix elements and editing
+ tensor elements edits the original matrix's elements.
+
+Parameters:
+rowIds - The rows to access.Returns:
+A list of AccessRow
+See Also:
+
+
+
+
+
+
+
+
+accessColumns
+
+Organizes some matrix columns to a list of tensors that share entries.
+ This operation does not allocate memory for matrix elements and editing
+ tensor elements edits the original matrix's elements.
+
+Parameters:
+colIds - The columns to access.Returns:
+A list of AccessCol
+See Also:
+
+
+
+
+
+
+
+
+fromDouble
+public static Matrix fromDouble (double value) Converts a given value to a JGNN-compatible 1x1 matrix.
+
+Parameters:
+value - A given value.Returns:
+a Matrix holding the given value
+See Also:
+
+
+
+
+
+
+
+
+toDense
+
+Creates a copy of the matrix organized as a dense matrix.
+
+Returns:
+A DenseMatrix
+
+
+
+
+
+toSparse
+
+Creates a copy of the matrix organized as a sparse matrix.
+
+Returns:
+A SparseMatrix
+
+
+
+
+
+eye
+
+Creates a sparse unit matrix.
+
+Parameters:
+dims - The dimensions of the unit matrix.Returns:
+A sparse matrix.
+
+
+
+
+
+
+
+
+
+
+
+
Memory.Scope
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+Enclosing class:
+Memory
+public static class Memory.Scope
+
extends Object
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
void
+
+
+
void
+
+
+
void
+
+
+
void
+
+
+
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+enter
+public void enter ()
+
+
+
+
+exit
+public void exit ()
+
+
+
+
+register
+public void register (double[] value)
+
+
+
+
+unregister
+public void unregister (double[] value)
+
+
+
+
+
+
+
+
+
+
+
Memory
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+public class Memory
+
extends Object A memory management system for thread-safe allocation and release of arrays of doubles.
+ Soft references to allocated arrays kept so that released ones can be reused by future
+ allocation calls without explicitly initializing memory.
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Nested Class Summary
+Nested Classes
+
+
+
+
+
static class
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Static Methods Concrete Methods
+
+
+
+
+
+
static double[]
+
+
+
static void
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Memory
+public Memory ()
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+
+
+
+allocate
+public static double[] allocate (int length,
+ Object boundTo)
+
+
+
+release
+public static void release (double[] value)
+
+
+
+
+
+
+
+
+
+
+
Slice
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+This class provices an interface with which to define data slices,
+ for instance to sample labels.
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Instantiates the data slice from a collection of element identifiers.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Creates a dense tensor holding the slice's identifiers.
+
+
+
+
+
+
range (double from,
+ double end)
+
+
Performs the
range(int, int) operation
+ while replacing values of
from and
end
+ with
(int)(from*size()) and
(int)(end*size())
+ so that fractional ranges can be obtained.
+
+
+
range (int from,
+ int end)
+
+
Obtains the identifiers in a given range of the (shuffled) slice.
+
+
+
+
+
Constructs a column matrix holding identifiers in
+ the range 0,1,..
size()-1 so that the pattern
+
slice.samplesAsFeatures().accessRows(slice.range(from, end))
+ retrieves one-element tensors holding
+
slice[from], slice[from+1], ...
+
+
+
+
+
+
+
+
Shuffles the slice with a provided randomization seed.
+
+
int
+
+
+
Retrieves the size of the slice.
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Slice
+
+Instantiates the data slice from a collection of element identifiers.
+
+Parameters:
+collection - An iterable of longs.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+shuffle
+
+Shuffles the slice.
+
+Returns:
+this slice.See Also:
+
+
+
+
+
+
+
+
+shuffle
+public Slice shuffle (int seed) Shuffles the slice with a provided randomization seed.
+
+Parameters:
+seed - The seed to shuffle with.Returns:
+this slice.See Also:
+
+
+
+
+
+
+
+
+range
+public Slice range (int from,
+ int end) Obtains the identifiers in a given range of the (shuffled) slice.
+
+Parameters:
+from - The beginning of the identifiers' position in the slice.end - The end (non-inclusive) of the identifiers' position in the slice.Returns:
+A new Slice instance holding the position identifiers in this one's given range.
+See Also:
+
+
+
+
+
+
+
+
+samplesAsFeatures
+public Matrix samplesAsFeatures ()
Constructs a column matrix holding identifiers in
+ the range 0,1,..
size()-1 so that the pattern
+
slice.samplesAsFeatures().accessRows(slice.range(from, end))
+ retrieves one-element tensors holding
+
slice[from], slice[from+1], ... slice[end].
+ The constructed matrix is typically used as node identifier data.
+
+ This is different than
asTensor().
+Returns:
+A Matrix
+
+
+
+
+
+range
+public Slice range (double from,
+ double end) Performs the
range(int, int) operation
+ while replacing values of
from and
end
+ with
(int)(from*size()) and
(int)(end*size())
+ so that fractional ranges can be obtained. For example,
+ you can call
slice.shuffle().range(0.5, 1) to obtain a
+ random subset of the slice's identifiers.
+Parameters:
+from - An integer at least 1 or a double in the range [0,1).end - An integer greater than 1 or a double in the range [0,1].Returns:
+A new Slice instance holding the position identifiers in this one's given range.
+See Also:
+
+
+
+
+
+
+
+
+size
+public int size ()
+Retrieves the size of the slice.
+
+Returns:
+An integer.
+
+
+
+
+
+
+
+
+asTensor
+
+Creates a dense tensor holding the slice's identifiers.
+
+Returns:
+A DenseTensor
+
+
+
+
+
+
+
+
+
+
+
+
Tensor
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Field Summary
+Fields
+
+
+
+
+
static boolean
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Construct that creates a tensor of zeros given its number of elements
+
+
+
+
+
+
+Method Summary
+
+
All Methods Static Methods Instance Methods Abstract Methods Concrete Methods Deprecated Methods
+
+
+
+
+
+
+
+
+
Computes the absolute value of tensor elements.
+
+
+
+
+
Wraps a range of elements within a tensor
+ without allocating memory anew.
+
+
+
+
+
Wraps a range of elements within a tensor
+ without allocating memory anew.
+
+
+
+
+
+
+
+
long
+
+
+
Computes the position of the maximum tensor element.
+
+
long
+
+
+
Computes the position of the minimum tensor element.
+
+
+
+
+
Accesses the tensor through a single-column matrix with the tensor as the only row.
+
+
+
+
+
Accesses the tensor through a single-row matrix with the tensor as the only column.
+
+
void
+
+
+
Asserts that the tensor holds only finite values.
+
+
+
+
+
Asserts that the tensor's dimensions match with another tensor.
+
+
void
+
+
+
Asserts that the tensor's
size() matches the given size.
+
+
+
+
+
Performs a sparse assignment.
+
+
<Type> Type
+
+
+
Performs the equivalent of Java's typecasting that fits
+ in functional interfaces.
+
+
+
+
+
Creates a
zeroCopy() and transfers to it all potentially non-zero element values.
+
+
double
+
+
+
+
+
+
Describes the type, size and other characteristics of the tensor.
+
+
double
+
+
+
Performs the dot product between this and another tensor.
+
+
double
+
+
+
Performs the triple dot product between this and two other tensors.
+
+
long
+
+
+
Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+
+
+
+
+
Computes the exponential minus 1 of tensor elements.
+
+
+
+
+
Converts a given value to a JGNN-compatible dense tensor.
+
+
+
+
+
Creates a dense tensor holding the desired range [0, 1, ..., end-1].
+
+
+
+
+
Creates a dense tensor holding the desired range [start, start+1, ..., end-1].
+
+
abstract double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Computes the logarithm of tensor elements.
+
+
double
+
+
+
Computes the maximum tensor element.
+
+
double
+
+
+
Computes the minimum tensor element.
+
+
+
+
+
+
+
+
+
+
+
Computes the negative of tensor elements.
+
+
double
+
+
+
+
+
+
abstract void
+
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
+
putAdd (long pos,
+ double value)
+
+
Add a value to a tensor element.
+
+
abstract void
+
+
+
+
+
+
Performs in-memory set of each element to its absolute value.
+
+
+
+
+
Performs in-memory addition to the Tensor, storing the result in itself.
+
+
+
+
+
Performs in-memory addition to the Tensor, storing the result in itself.
+
+
+
+
+
Performs in-memory weighted addition to the Tensor, storing the result in itself.
+
+
+
+
+
Sets the exponential minus 1 of tensor elements.
+
+
+
+
+
Performs in-memory the inverse of each non-zero element.
+
+
+
+
+
Performs in-memory set of each element to the logarithm of its absolute value.
+
+
+
+
+
Performs in-memory multiplication on the Tensor, storing the result to itself.
+
+
+
+
+
Performs in-memory multiplication on the Tensor, storing the result in itself .
+
+
+
+
+
Performs in-memory set of each element to the negative of itself.
+
+
+
+
+
Performs in-memory set of each element to the square root of its absolute value.
+
+
+
+
+
Performs in-memory subtraction from the Tensor, storing the result in itself.
+
+
+
+
+
Sets a name for the tensor's one dimension.
+
+
+
+
+
Fills in dimension names per an example isMatching(mklab.JGNN.core.Tensor) tensor.
+
+
+
+
+
L2-normalizes the tensor's elements.
+
+
+
+
+
Set all tensor element values to 1.
+
+
+
+
+
Divides the tensor's elements with their sum.
+
+
+
+
+
Set tensor elements to random values from the uniform range [0,1]
+
+
+
+
+
Set tensor elements to random values by sampling them from a given
Distribution
+ instance.
+
+
+
+
+
Set all tensor element values to 1/
size()
+
+
+
+
+
Set all tensor element values to 0.
+
+
long
+
+
+
+
+
+
Computes the square root of tensor elements.
+
+
+
+
+
double
+
+
+
double[]
+
+
+
Retrieves a representation of the Tensor as an array of doubles.
+
+
double
+
+
+
Converts a tensor of
size()==1 to double.
+
+
+
+
+
+
+
+
A string serialization of the tensor that can be used by the constructor
DenseTensor(String) to create an identical copy.
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a tensor of the same class with the same size and all element set to zero.
+
+
+
+
+
Creates a tensor of the same class with a given size and all element set to zero.
+
+
+
+
+
Creates a tensor of the same class and all elements set to zero,
+ but size and dimension names are obtained from a prototype tensor.
+
+
+
+
+
+
+
+
+
+
+
+
+Field Details
+
+
+
+vectorization
+public static boolean vectorization
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Tensor
+public Tensor (long size)
+Construct that creates a tensor of zeros given its number of elements
+
+Parameters:
+size - The number of tensor elements
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+setDimensionName
+
+Sets a name for the tensor's one dimension. If set, names are checked for
+ compatibility during operations, so that tensors laying across different dimensions
+ do not match. Removed dimension names are matched to anything.
+
+Parameters:
+dimensionName - The new row name or null to remove current name.Returns:
+this Tensor instance.See Also:
+
+
+
+
+
+
+
+
+getDimensionName
+public String getDimensionName ()
+
+
+
+setToRandom
+
+Set tensor elements to random values from the uniform range [0,1]
+
+Returns:
+this Tensor instance.
+
+
+
+
+setToRandom
+
+Set tensor elements to random values by sampling them from a given
Distribution
+ instance.
+Parameters:
+distribution - The distribution instance to sample from.Returns:
+this Tensor instance.
+
+
+
+
+assertFinite
+public void assertFinite ()
+Asserts that the tensor holds only finite values. Helps catch errors
+ early on and avoid misidentifying models as high quality by comparing
+ desired outcomes with NaN when in reality they pass through infinity and hence
+ don't converge.
+
+Throws:
+RuntimeException
+
+
+
+
+release
+public abstract void release ()
+Deprecated.
+
+
+If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+See Also:
+
+
+
+
+
+
+
+
+persist
+public abstract void persist ()
+Deprecated.
+
+
+If supported by the subclassed tensor, invalidates calls to
+
release() so that memory is a de-allocated only when
+ object references expire.
+See Also:
+
+
+
+
+
+
+
+
+put
+public abstract Tensor put (long pos,
+ double value) Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.Throws:
+RuntimeException size()
+
+
+
+
+get
+public abstract double get (long pos)
+Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+Throws:
+RuntimeException size()
+
+
+
+
+putAdd
+public Tensor putAdd (long pos,
+ double value) Add a value to a tensor element.
+
+Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.See Also:
+
+
+
+
+
+
+
+
+size
+public long size ()
+
+Returns:
+The number of tensor elements
+
+
+
+
+
+assertSize
+public void assertSize (long size)
+Asserts that the tensor's
size() matches the given size.
+Parameters:
+size - The size the tensor should matchThrows:
+RuntimeException
+
+
+
+
+assertMatching
+
+Asserts that the tensor's dimensions match with another tensor. This check can be made
+ more complex by derived classes, but for a base Tensor instance it is equivalent
assertSize(long).
+ This method calls
isMatching(Tensor) to compare the tensors and throws an exception
+ if it returns false.
+Parameters:
+other - The other tensor to compare with.Returns:
+this Tensor instance.
+
+
+
+
+zeroCopy
+
+Creates a tensor of the same class with the same size and all element set to zero.
+
+Returns:
+A tensor with the same size.
+See Also:
+
+
+
+
+
+
+
+
+zeroCopy
+
+Creates a tensor of the same class and all elements set to zero,
+ but size and dimension names are obtained from a prototype tensor.
+
+
+
+
+setDimensionName
+
+Fills in dimension names per an example isMatching(mklab.JGNN.core.Tensor) tensor. This appropriately fills in dimension
+ names of inherited classes too, such as matrices. Effectively, this method automatically infers
+ dimension names during operations.
+
+Parameters:
+other - The tensor from which to retrieve dimension names.Returns:
+this Tensor instance.
+
+
+
+
+zeroCopy
+public abstract Tensor zeroCopy (long size) Creates a tensor of the same class with a given size and all element set to zero.
+
+Parameters:
+size - The size of the new tensor.Returns:
+A new tensor.
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+getNonZeroElements
+
+Retrieves an iterable that wraps
traverseNonZeroElements().
+ For the time being,
this is returned by implementing Iterable,
+ but this only serves the practical purpose of avoiding to instantiate
+ a new object in case many tensors are used.
+Returns:
+An iterable of tensor positions.
+
+
+
+
+
+estimateNumNonZeroElements
+public long estimateNumNonZeroElements ()
+Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+ Basically, this quantity is proportional to the allocated memory.
+
+Returns:
+A long number equal to or less to the tensor size.
+See Also:
+
+
+
+
+
+
+
+
+density
+public double density ()
+Provides the memory allocation density of
getNonZeroElements()
+ compare to the size of the tensor. 1 indicates fully dense tensors,
+ and lower values sparser data.
+Returns:
+A double in the range [0,1].
+
+
+
+
+
+traverseNonZeroElements
+
+Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+copy
+
+Creates a
zeroCopy() and transfers to it all potentially non-zero element values.
+Returns:
+a copy of the Tensor with the same size and contents
+See Also:
+
+
+
+
+
+
+
+
+assign
+
+Performs a sparse assignment.
+
+Parameters:
+tensor - The tensor whose elements to copy (it's not affected).Returns:
+this Tensor instance.See Also:
+
+
+
+
+
+
+
+
+add
+
+
+Parameters:
+tensor - The tensor to add withReturns:
+a new Tensor that stores the outcome of addition
+
+
+
+
+
+add
+public Tensor add (double value)
+Parameters:
+value - The value to add to each elementReturns:
+a new Tensor that stores the outcome of addition
+
+
+
+
+
+selfAdd
+
+Performs in-memory addition to the Tensor, storing the result in itself.
+
+Parameters:
+tensor - The tensor to add (it's not affected).Returns:
+this Tensor instance.
+
+
+
+
+selfAdd
+
+Performs in-memory weighted addition to the Tensor, storing the result in itself.
+
+Parameters:
+tensor - The tensor to add (it's not affected).weight - The weight to multiply the added tensor's elements with during addition.Returns:
+this Tensor instance.
+
+
+
+
+selfAdd
+public Tensor selfAdd (double value) Performs in-memory addition to the Tensor, storing the result in itself.
+
+Parameters:
+value - The value to add to each tensor element.Returns:
+this Tensor instance.
+
+
+
+
+subtract
+
+
+Parameters:
+tensor - The tensor to subtractReturns:
+a new Tensor that stores the outcome of subtraction
+
+
+
+
+
+selfSubtract
+
+Performs in-memory subtraction from the Tensor, storing the result in itself.
+
+Parameters:
+tensor - The tensor to subtract (it's not affected).Returns:
+this Tensor instance.
+
+
+
+
+multiply
+
+
+Parameters:
+tensor - The tensor to perform element-wise multiplication with.Returns:
+A new Tensor that stores the outcome of the multiplication.
+
+
+
+
+
+selfMultiply
+
+Performs in-memory multiplication on the Tensor, storing the result in itself .
+
+Parameters:
+tensor - The tensor to perform element-wise multiplication with (it's not affected).Returns:
+this Tensor instance.
+
+
+
+
+multiply
+public Tensor multiply (double value)
+Parameters:
+value - A number to multiply all tensor elements with.Returns:
+A new Tensor that stores the outcome of the multiplication.
+
+
+
+
+
+selfMultiply
+public Tensor selfMultiply (double value) Performs in-memory multiplication on the Tensor, storing the result to itself.
+
+Parameters:
+value - A number to multiply all tensor elements with.Returns:
+this Tensor instance.
+
+
+
+
+sqrt
+
+Computes the square root of tensor elements.
+
+Returns:
+A new Tensor that stores the outcome of finding the absolute square root of each element.
+
+
+
+
+
+selfSqrt
+
+Performs in-memory set of each element to the square root of its absolute value.
+
+Returns:
+this Tensor instance.
+
+
+
+
+expMinusOne
+
+Computes the exponential minus 1 of tensor elements.
+
+Returns:
+A new Tensor that stores the outcome of finding the operation on each element.
+
+
+
+
+
+selfExpMinusOne
+public Tensor selfExpMinusOne ()
Sets the exponential minus 1 of tensor elements.
+
+Returns:
+this Tensor instance.
+
+
+
+
+log
+
+Computes the logarithm of tensor elements.
+
+Returns:
+A new Tensor that stores the outcome of finding the logarithm of the absolute of each element.
+
+
+
+
+
+selfLog
+
+Performs in-memory set of each element to the logarithm of its absolute value.
+
+Returns:
+this Tensor instance.
+
+
+
+
+negative
+
+Computes the negative of tensor elements.
+
+Returns:
+A new Tensor that stores the outcome of finding the negative of each element.
+
+
+
+
+
+selfNegative
+
+Performs in-memory set of each element to the negative of itself.
+
+Returns:
+this Tensor instance.
+
+
+
+
+abs
+
+Computes the absolute value of tensor elements.
+
+Returns:
+A new Tensor that stores the outcome of finding the absolute value of each element.
+
+
+
+
+
+selfAbs
+
+Performs in-memory set of each element to its absolute value.
+
+Returns:
+this Tensor instance.
+
+
+
+
+inverse
+
+
+Returns:
+A new Tensor with inversed each non-zero element.
+
+
+
+
+
+selfInverse
+
+Performs in-memory the inverse of each non-zero element.
+
+Returns:
+this Tensor instance.
+
+
+
+
+dot
+public double dot (Tensor tensor) Performs the dot product between this and another tensor.
+
+Parameters:
+tensor - The tensor with which to find the product.Returns:
+The dot product between the tensors.
+
+
+
+
+
+dot
+
+Performs the triple dot product between this and two other tensors.
+
+Parameters:
+tensor1 - The firth other tensor with which to find the product.tensor2 - The second other tensor with which to find the product.Returns:
+The triple dot product between the tensors.
+
+
+
+
+
+norm
+public double norm ()
+
+Returns:
+The L2 norm of the tensor
+
+
+
+
+
+sum
+public double sum ()
+
+Returns:
+The sum of tensor elements
+
+
+
+
+
+accessSubtensor
+public Tensor accessSubtensor (long from) Wraps a range of elements within a tensor
+ without allocating memory anew. Editing the returned
+ tensor also affects the original one and conversely.
+ The elements are accessed so that the starting position
+ is accessed at position 0 of the starting tensor.
+
+Parameters:
+from - The starting position of the subtensor till its end.Returns:
+An AccessSubtensor
+See Also:
+
+
+
+
+
+
+
+
+accessSubtensor
+public Tensor accessSubtensor (long from,
+ long to) Wraps a range of elements within a tensor
+ without allocating memory anew. Editing the returned
+ tensor also affects the original one and conversely.
+ The elements are accessed so that the starting position
+ is accessed at position 0 of the starting tensor. Accessing
+ stops up to but not including the end poisition,
+ so that accessSubtensor(0, size()) is
+ a see-through copy of the original tensor.
+
+Parameters:
+from - The starting position of the subtensor.to - The end position of the subtensor that is not included.Returns:
+An AccessSubtensor
+See Also:
+
+
+
+
+
+
+
+
+max
+public double max ()
+Computes the maximum tensor element. If the tensor has zero
size(),
+ this returns
Double.NEGATIVE_INFINITY.
+Returns:
+The maximum tensor element
+See Also:
+
+
+
+
+
+
+
+
+argmax
+public long argmax ()
+Computes the position of the maximum tensor element. If the tensor has zero
size(),
+ this returns
-1.
+Returns:
+The position of the maximum tensor element
+See Also:
+
+
+
+
+
+
+
+
+min
+public double min ()
+Computes the minimum tensor element. If the tensor has zero
size(),
+ this returns
Double.POSITIVE_INFINITY.
+Returns:
+The minimum tensor element
+See Also:
+
+
+
+
+
+
+
+
+argmin
+public long argmin ()
+Computes the position of the minimum tensor element. If the tensor has zero
size(),
+ this returns
-1.
+Returns:
+The position of the minimum tensor element
+See Also:
+
+
+
+
+
+
+
+
+toString
+
+A string serialization of the tensor that can be used by the constructor
DenseTensor(String) to create an identical copy.
+Overrides:
+toString Object Returns:
+A serialization of the tensor.
+
+
+
+
+
+normalized
+
+
+Returns:
+A copy of the tensor on which L2 normalization has been performed.
+See Also:
+
+
+
+
+
+
+
+
+toProbability
+
+
+Returns:
+A copy of the tensor on which division with the sum has been performed
+ (if the tensor contains no negative elements, this is equivalent to L1 normalization)
+See Also:
+
+
+
+
+
+
+
+
+setToNormalized
+public Tensor setToNormalized ()
L2-normalizes the tensor's elements. Does nothing if the
norm() is zero.
+Returns:
+this Tensor instance.See Also:
+
+
+
+
+
+
+
+
+setToProbability
+public Tensor setToProbability ()
Divides the tensor's elements with their sum. Does nothing if the
sum() is zero.
+Returns:
+this Tensor instance.See Also:
+
+
+
+
+
+
+
+
+
+
+
+setToOnes
+
+Set all tensor element values to 1.
+
+Returns:
+this Tensor instance.
+
+
+
+
+setToZero
+
+Set all tensor element values to 0.
+
+Returns:
+this Tensor instance.
+
+
+
+
+toArray
+public double[] toArray ()
+Retrieves a representation of the Tensor as an array of doubles.
+
+Returns:
+An array of doubles
+
+
+
+
+
+fromDouble
+public static Tensor fromDouble (double value) Converts a given value to a JGNN-compatible dense tensor.
+
+Parameters:
+value - A given value.Returns:
+a Tensor holding the given value
+See Also:
+
+
+
+
+
+
+
+
+fromRange
+public static Tensor fromRange (long start,
+ long end) Creates a dense tensor holding the desired range [start, start+1, ..., end-1].
+ This allocates a new tensor.
+
+Parameters:
+start - The start of the range.end - The end of the range.Returns:
+A DenseTensor
+See Also:
+
+
+
+
+
+
+
+
+fromRange
+public static Tensor fromRange (long end) Creates a dense tensor holding the desired range [0, 1, ..., end-1].
+ This allocates a new tensor.
+
+Parameters:
+end - The end of the range.Returns:
+A DenseTensor
+See Also:
+
+
+
+
+
+
+
+
+toDouble
+public double toDouble ()
+Converts a tensor of
size()==1 to double. Throws an exception otherwise.
+Returns:
+A double.
+Throws:
+RuntimeException See Also:
+
+
+
+
+
+
+
+
+asColumn
+
+Accesses the tensor through a single-column matrix with the tensor as the only row.
+ Editing the returned matrix also edits the original tensor.
+ No new memory is allocated for tensor values.
+
+Returns:
+A WrapCols
+See Also:
+
+
+
+
+
+
+
+
+asRow
+
+Accesses the tensor through a single-row matrix with the tensor as the only column.
+ Editing the returned matrix also edits the original tensor.
+ No new memory is allocated for tensor values.
+
+Returns:
+A WrapRows
+See Also:
+
+
+
+
+
+
+
+
+describe
+
+Describes the type, size and other characteristics of the tensor.
+
+Returns:
+A String description.
+
+
+
+
+
+cast
+public <Type> Type cast (Class <Type> type) Performs the equivalent of Java's typecasting that fits
+ in functional interfaces.
+
+Type Parameters:
+Type - The automatically inferred type of the class.Parameters:
+type - The class to cast to.Returns:
+this Tensor instance typecast to the given type.
+
+
+
+
+
+
+
+
+
+
+
ThreadPool
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+public class ThreadPool
+
extends Object This class provides thread execution pool utilities while keeping track of thread
+ identifiers for use by thread-specific
NNOperation.
+ Threads scheduling relies on Java's
ThreadPoolExecutor.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Method Summary
+
+
All Methods Static Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Retrieves a unique integer indicating the currently running thread.
+
+
+
+
+
Retrieves the singleton
ThreadPool instance used by JGNN.
+
+
void
+
+
+
Submits a runnable to be executed at some future point by a thread,
+ for example via ThreadPool.getInstance().submit(new Runnable(){public void run(){...}});.
+
+
void
+
+
+
Waits until all threads in the pool have finished.
+
+
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+getInstance
+
+Retrieves the singleton
ThreadPool instance used by JGNN.
+Returns:
+A ThreadPool
+
+
+
+
+
+submit
+
+Submits a runnable to be executed at some future point by a thread,
+ for example via ThreadPool.getInstance().submit(new Runnable(){public void run(){...}});.
+
+Parameters:
+runnable - A Java RunnableSee Also:
+
+
+
+
+
+
+
+
+getCurrentThreadId
+public static Integer getCurrentThreadId ()
Retrieves a unique integer indicating the currently running thread.
+
+Returns:
+An integer id.
+
+
+
+
+
+waitForConclusion
+public void waitForConclusion ()
+Waits until all threads in the pool have finished. This concludes only
+ if all submitted runnable conclude.
+
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Uses of Interface mklab.JGNN.core.Distribution
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Sets the standard deviation of the distribution.
+
+
+
+
+
Sets the mean of the distribution.
+
+
+
+
+
Sets the distribution's seed.
+
+
+
+
+
+
+
+
+
Set tensor elements to random values by sampling them from a given
Distribution
+ instance.
+
+
+
+
+
+
+
+
+
+
+
+
class
+
+
+
Implements a Normal
Distribution of given mean and standard deviation.
+
+
class
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.core.Matrix
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Retrieves the dataset's feature matrix.
+
+
+
+
+
Retrieves the dataset's graph.
+
+
+
+
+
Retrieves the dataset's sample labels in one-hot encoding.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Creates a graph neural network builder from an
+ normalized adjacency matrix and a node feature matrix.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Organizes specific matrix columns to a list of tensors that share entries.
+
+
+
+
+
Organizes matrix columns to a list of tensors that share entries.
+
+
+
+
+
Organizes specific matrix rows to a list of tensors that share entries.
+
+
+
+
+
Organizes specific matrix rows to a list of tensors that share entries.
+
+
+
+
+
Creates a transposed version of the matrix that accesses the same elements (thus, editing one
+ edits the other) without allocating additional memory.
+
+
+
+
+
Produces the external product of two tensors.
+
+
+
+
+
Converts a given value to a JGNN-compatible 1x1 matrix.
+
+
+
+
+
Performs the matrix multiplication of this*with and the recipient.
+
+
+
Matrix. matmul (Matrix with,
+ boolean transposeSelf,
+ boolean transposeWith)
+
+
Can be used to perform fast computation of the matrix multiplications
+
this*with,
+
this.transposed()*with
+
this*with.transposed(),
+
this.transposed()*with.transposed()
+
while avoiding the overhead of calling
+
transposed().
+
+
+
+
+
Produces a mask that indicates the non-zero elements of the matrix.
+
+
+
Matrix. put (long row,
+ long col,
+ double value)
+
+
Stores values at matrix elements.
+
+
+
+
+
Constructs a column matrix holding identifiers in
+ the range 0,1,..
Slice.size()-1 so that the pattern
+
slice.samplesAsFeatures().accessRows(slice.range(from, end))
+ retrieves one-element tensors holding
+
slice[from], slice[from+1], ...
+
+
+
+
+
Sets a name for the matrix's column dimension.
+
+
+
+
+
Sets the matrix's specified diagonal elements to a given value.
+
+
+
+
+
Sets a name for the matrix's row and column dimensions.
+
+
+
+
+
+
+
+
Sets the matrix's specified main diagonal elements to a given value value.
+
+
+
+
+
Sets a name for the matrix's row dimension.
+
+
+
+
+
Sets the Matrix to its asymmetrically normalized transformation
+ by appropriately adjusting its element values.
+
+
+
+
+
Sets the Matrix to its symmetrically normalized transformation
+ by appropriately adjusting its element values.
+
+
+
+
+
Creates a copy of the Matrix that holds its symmetrically normalized version.
+
+
+
+
+
Creates a copy of the matrix organized as a dense matrix.
+
+
+
+
+
Creates a copy of the matrix organized as a sparse matrix.
+
+
+
+
+
Creates a transposed copy of the matrix.
+
+
+
+
+
Creates a Matrix with the same class and dimensions and all element set to zero.
+
+
+
+
+
Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+
+
+
+
+
+
+
+
Performs the matrix multiplication of this*with and the recipient.
+
+
+
Matrix. matmul (Matrix with,
+ boolean transposeSelf,
+ boolean transposeWith)
+
+
Can be used to perform fast computation of the matrix multiplications
+
this*with,
+
this.transposed()*with
+
this*with.transposed(),
+
this.transposed()*with.transposed()
+
while avoiding the overhead of calling
+
transposed().
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
EmptyMatrix. zeroCopy (long rows,
+ long cols)
+
+
+
+
+
+
+
+
+
+
+
+
class
+
+
+
Defines a matrix whose columns are all a copy of a
Tensor.
+
+
class
+
+
+
Implements a dense
Matrix where all elements are stored in memory.
+
+
class
+
+
+
Implements a square matrix whose diagonal elements are determined by the correspond values of
+ an underlying tensor and off-diagonal elements are zero.
+
+
class
+
+
+
Implements a
Matrix whose elements are all equals.
+
+
class
+
+
+
Defines a matrix whose rows are all a copy of a
Tensor.
+
+
class
+
+
+
A sparse
Matrix that allocates memory only for non-zero elements.
+
+
class
+
+
+
class
+
+
+
Generates a transposed version of a base matrix, with which it shares elements.
+
+
class
+
+
+
Implements a dense
Matrix where all elements are stored in memory.
+
+
class
+
+
+
Wraps a list of tensors into a matrix with the tensors as columns.
+
+
class
+
+
+
Wraps a list of tensors into a matrix with the tensors as rows.
+
+
+
+
+
+
+
+
+
+
+
+
+
DenseMatrix. matmul (Matrix with,
+ boolean transposeThis,
+ boolean transposeWith)
+
+
+
+
+
+
VectorizedMatrix. matmul (Matrix with,
+ boolean transposeThis,
+ boolean transposeWith)
+
+
+
ColumnRepetition. zeroCopy (long rows,
+ long cols)
+
+
+
DenseMatrix. zeroCopy (long rows,
+ long cols)
+
+
+
Diagonal. zeroCopy (long rows,
+ long cols)
+
+
+
RepeatMatrix. zeroCopy (long rows,
+ long cols)
+
+
+
RowRepetition. zeroCopy (long rows,
+ long cols)
+
+
+
SparseMatrix. zeroCopy (long rows,
+ long cols)
+
+
+
SparseSymmetric. zeroCopy (long rows,
+ long cols)
+
+
+
TransposedMatrix. zeroCopy (long rows,
+ long cols)
+
+
+
VectorizedMatrix. zeroCopy (long rows,
+ long cols)
+
+
+
WrapCols. zeroCopy (long rows,
+ long cols)
+
+
+
WrapRows. zeroCopy (long rows,
+ long cols)
+
+
+
+
+
+
+
+
+
+
DenseMatrix. matmul (Matrix with,
+ boolean transposeThis,
+ boolean transposeWith)
+
+
+
+
+
+
VectorizedMatrix. matmul (Matrix with,
+ boolean transposeThis,
+ boolean transposeWith)
+
+
+
+
+
Sets a prototype matrix from which to borrow copying operations.
+
+
+
+
+
Sets a prototype matrix from which to borrow copying operations.
+
+
+
+
+
+
+
+
+
Instantiates a see-through access of a matrix column.
+
+
+
+
+
Instantiates a see-through access of a matrix row.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Trains a
Model instance based on current settings.
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.core.Memory.Scope
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/core/class-use/Memory.html b/docs/javadoc/mklab/JGNN/core/class-use/Memory.html
new file mode 100644
index 00000000..015e7767
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/core/class-use/Memory.html
@@ -0,0 +1,59 @@
+
+
+
+
+Uses of Class mklab.JGNN.core.Memory
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.Memory
+
+
Uses of Class mklab.JGNN.core.Slice
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Returns a slice of all registered identifiers.
+
+
+
+
+
+
+
+
+
+
+
+
+
Slice. range (double from,
+ double end)
+
+
Performs the
range(int, int) operation
+ while replacing values of
from and
end
+ with
(int)(from*size()) and
(int)(end*size())
+ so that fractional ranges can be obtained.
+
+
+
Slice. range (int from,
+ int end)
+
+
Obtains the identifiers in a given range of the (shuffled) slice.
+
+
+
+
+
+
+
+
Shuffles the slice with a provided randomization seed.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Trains a
Model instance based on current settings.
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.core.Tensor
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Declares a non-learnable constant component with the given name.
+
+
+
+
+
Declares a learnable Paramater component with the given name,
+ learning L2 regularization, and initial value.
+
+
+
+
+
Declares a learnable mklab.JGNN.nn.inputs.Paramater component with the given name,
+ zero regularization, and initial value.
+
+
+
+
+
This is a wrapper for
getModel().predict(inputs)
+
without returning output values (use
ModelBuilder.get(String)
+ afterwards to view outputs.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
This is a wrapper for
getModel().predict(inputs)
+
without returning output values (use
ModelBuilder.get(String)
+ afterwards to view outputs.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
class
+
+
+
This class provides an abstract implementation of Matrix functionalities.
+
+
+
+
+
+
+
+
+
Computes the absolute value of tensor elements.
+
+
+
+
+
Retrieves the given column as a tensor.
+
+
+
+
+
Retrieves either the given row or column as a trensor.
+
+
+
+
+
Retrieves the given row as a tensor.
+
+
+
+
+
Wraps a range of elements within a tensor
+ without allocating memory anew.
+
+
+
+
+
Wraps a range of elements within a tensor
+ without allocating memory anew.
+
+
+
+
+
+
+
+
+
+
+
Asserts that the tensor's dimensions match with another tensor.
+
+
+
+
+
Performs a sparse assignment.
+
+
+
+
+
Creates a dense tensor holding the slice's identifiers.
+
+
+
+
+
Creates a
zeroCopy() and transfers to it all potentially non-zero element values.
+
+
+
+
+
Computes the exponential minus 1 of tensor elements.
+
+
+
+
+
Converts a given value to a JGNN-compatible dense tensor.
+
+
+
+
+
Creates a dense tensor holding the desired range [0, 1, ..., end-1].
+
+
+
+
+
Creates a dense tensor holding the desired range [start, start+1, ..., end-1].
+
+
+
+
+
+
+
+
Computes the logarithm of tensor elements.
+
+
+
+
+
+
+
+
+
+
+
Computes the negative of tensor elements.
+
+
+
+
+
+
Tensor. put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
+
Tensor. putAdd (long pos,
+ double value)
+
+
Add a value to a tensor element.
+
+
+
+
+
Performs in-memory set of each element to its absolute value.
+
+
+
+
+
Performs in-memory addition to the Tensor, storing the result in itself.
+
+
+
+
+
Performs in-memory addition to the Tensor, storing the result in itself.
+
+
+
+
+
Performs in-memory weighted addition to the Tensor, storing the result in itself.
+
+
+
+
+
Sets the exponential minus 1 of tensor elements.
+
+
+
+
+
Performs in-memory the inverse of each non-zero element.
+
+
+
+
+
Performs in-memory set of each element to the logarithm of its absolute value.
+
+
+
+
+
Performs in-memory multiplication on the Tensor, storing the result to itself.
+
+
+
+
+
Performs in-memory multiplication on the Tensor, storing the result in itself .
+
+
+
+
+
Performs in-memory set of each element to the negative of itself.
+
+
+
+
+
Performs in-memory set of each element to the square root of its absolute value.
+
+
+
+
+
Performs in-memory subtraction from the Tensor, storing the result in itself.
+
+
+
+
+
Sets a name for the tensor's one dimension.
+
+
+
+
+
Fills in dimension names per an example isMatching(mklab.JGNN.core.Tensor) tensor.
+
+
+
+
+
L2-normalizes the tensor's elements.
+
+
+
+
+
Set all tensor element values to 1.
+
+
+
+
+
Divides the tensor's elements with their sum.
+
+
+
+
+
Set tensor elements to random values from the uniform range [0,1]
+
+
+
+
+
Set tensor elements to random values by sampling them from a given
Distribution
+ instance.
+
+
+
+
+
Set all tensor element values to 1/
size()
+
+
+
+
+
Set all tensor element values to 0.
+
+
+
+
+
Computes the square root of tensor elements.
+
+
+
+
+
+
+
+
+
+
+
Performs the linear algebra transformation A*x where A is this matrix and x a vector
+
+
+
+
+
Creates a Matrix with the same class and dimensions and all element set to zero.
+
+
+
+
+
Creates a tensor of the same class and all elements set to zero,
+ but size and dimension names are obtained from a prototype tensor.
+
+
+
+
+
Creates a tensor of the same class with the same size and all element set to zero.
+
+
+
+
+
Creates a tensor of the same class with a given size and all element set to zero.
+
+
+
+
+
Creates a tensor of the same class and all elements set to zero,
+ but size and dimension names are obtained from a prototype tensor.
+
+
+
+
+
+
+
+
+
Organizes specific matrix columns to a list of tensors that share entries.
+
+
+
+
+
Organizes some matrix columns to a list of tensors that share entries.
+
+
+
+
+
Organizes matrix rows to a list of tensors that share entries.
+
+
+
+
+
Organizes some matrix rows to a list of tensors that share entries.
+
+
+
+
+
+
+
+
+
Organizes matrix columns to a list of tensors that share entries.
+
+
+
+
+
Organizes specific matrix rows to a list of tensors that share entries.
+
+
+
+
+
+
+
+
Asserts that the tensor's dimensions match with another tensor.
+
+
+
+
+
Performs a sparse assignment.
+
+
double
+
+
+
Performs the dot product between this and another tensor.
+
+
double
+
+
+
Performs the triple dot product between this and two other tensors.
+
+
+
+
+
Produces the external product of two tensors.
+
+
+
+
+
+
+
+
Performs in-memory addition to the Tensor, storing the result in itself.
+
+
+
+
+
Performs in-memory weighted addition to the Tensor, storing the result in itself.
+
+
+
+
+
Performs in-memory multiplication on the Tensor, storing the result in itself .
+
+
+
+
+
Performs in-memory subtraction from the Tensor, storing the result in itself.
+
+
+
+
+
+
+
+
Fills in dimension names per an example isMatching(mklab.JGNN.core.Tensor) tensor.
+
+
+
+
+
+
+
+
Performs the linear algebra transformation A*x where A is this matrix and x a vector
+
+
+
+
+
Creates a tensor of the same class and all elements set to zero,
+ but size and dimension names are obtained from a prototype tensor.
+
+
+
+
+
Creates a tensor of the same class and all elements set to zero,
+ but size and dimension names are obtained from a prototype tensor.
+
+
+
+
+
+
+
+
+
+
+
+
class
+
+
+
class
+
+
+
+
+
+
+
+
EmptyMatrix. put (long pos,
+ double value)
+
+
+
EmptyTensor. put (long pos,
+ double value)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
class
+
+
+
class
+
+
+
class
+
+
+
Defines a matrix whose columns are all a copy of a
Tensor.
+
+
class
+
+
+
Implements a dense
Matrix where all elements are stored in memory.
+
+
class
+
+
+
Implements a square matrix whose diagonal elements are determined by the correspond values of
+ an underlying tensor and off-diagonal elements are zero.
+
+
class
+
+
+
Implements a
Matrix whose elements are all equals.
+
+
class
+
+
+
Defines a matrix whose rows are all a copy of a
Tensor.
+
+
class
+
+
+
A sparse
Matrix that allocates memory only for non-zero elements.
+
+
class
+
+
+
class
+
+
+
Generates a transposed version of a base matrix, with which it shares elements.
+
+
class
+
+
+
Implements a dense
Matrix where all elements are stored in memory.
+
+
class
+
+
+
Wraps a list of tensors into a matrix with the tensors as columns.
+
+
class
+
+
+
Wraps a list of tensors into a matrix with the tensors as rows.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Retrieves the wrapped column tensor.
+
+
+
AccessCol. put (long pos,
+ double value)
+
+
+
AccessRow. put (long pos,
+ double value)
+
+
+
ColumnRepetition. put (long pos,
+ double value)
+
+
+
DenseMatrix. put (long pos,
+ double value)
+
+
+
Diagonal. put (long pos,
+ double value)
+
+
+
RepeatMatrix. put (long pos,
+ double value)
+
+
+
RowRepetition. put (long pos,
+ double value)
+
+
+
SparseMatrix. put (long pos,
+ double value)
+
+
+
SparseSymmetric. put (long pos,
+ double value)
+
+
+
TransposedMatrix. put (long pos,
+ double value)
+
+
+
VectorizedMatrix. put (long pos,
+ double value)
+
+
+
WrapCols. put (long pos,
+ double value)
+
+
+
WrapRows. put (long pos,
+ double value)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Instantiates a matrix repeating a tensor to be treated as a column.
+
+
+
+
+
Instantiates a matrix repeating a tensor to be treated as a row.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
class
+
+
+
Wraps a base
Tensor by traversing only its elements in a specified range (from begin, up to end-1).
+
+
class
+
+
+
This class provides a dense
Tensor that wraps an array of doubles.
+
+
class
+
+
+
This class provides
Tensor whose elements are all equal.
+
+
class
+
+
+
This class provides a sparse
Tensor with many zero elements.
+
+
class
+
+
+
This class provides a dense
Tensor that wraps an array of doubles.
+
+
+
+
+
+
+
+
+
+
DenseTensor. add (double value)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
AccessSubtensor. put (long pos,
+ double value)
+
+
+
DenseTensor. put (int pos,
+ double value)
+
+
+
DenseTensor. put (long pos,
+ double value)
+
+
+
RepeatTensor. put (long pos,
+ double value)
+
+
+
SparseTensor. put (long pos,
+ double value)
+
+
+
VectorizedTensor. put (int pos,
+ double value)
+
+
+
VectorizedTensor. put (long pos,
+ double value)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Instantiates a see-through access of a tensor elements.
+
+
+
+
+
Instantiates a see-through access of a tensor elements.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Provides the derivative of a loss function at its evaluation point.
+
+
+
+
+
+
+
+
+
+
+
Performs a forward pass in the operation without inducing any kind of
+ learning or storing the outcome .
+
+
+
+
+
Performs a forward pass in the operation without inducing any kind of
+ learning or storing the outcome .
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Forward run of the model given a list of input tensors.
+
+
+
+
+
Forward run of the model given an array of input tensors.
+
+
+
+
+
Performs the training of #train(Optimizer, List, List, List) for unit weights.
+
+
+
+
+
Performs one parameter adjustment step (e.g.
+
+
+
+
+
+
+
+
+
Provides the derivative of a loss function at its evaluation point.
+
+
abstract double
+
+
+
Provides a numerical evaluation of a loss function, so that
+ lower values correspond to better predictions.
+
+
+
+
+
Forward run of the model given an array of input tensors.
+
+
+
+
+
Performs a forward pass in the operation without inducing any kind of
+ learning or storing the outcome .
+
+
void
+
+
+
In-place updates the value of a tensor given its gradient.
+
+
+
+
+
+
+
+
+
Forward run of the model given a list of input tensors.
+
+
+
+
+
Performs a forward pass in the operation without inducing any kind of
+ learning or storing the outcome .
+
+
+
+
+
Performs the training of #train(Optimizer, List, List, List) for unit weights.
+
+
+
+
+
Performs one parameter adjustment step (e.g.
+
+
double
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
double
+
+
+
double
+
+
+
double
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
double
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
double
+
+
+
void
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
void
+
+
+
void
+
+
+
void
+
+
+
void
+
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.core.ThreadPool
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Retrieves the singleton
ThreadPool instance used by JGNN.
+
+
+
+
+
+
+
+
Normal
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Distribution
+Implements a Normal
Distribution of given mean and standard deviation.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Instantiates a normal distribution with zero mean and standard deviation equal to 1.
+
+
Normal (double mean,
+ double std)
+
+
Instantiates a normal distribution with a given mean and standard deviation.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
+
+
Retrieves the distribution's standard deviation.
+
+
double
+
+
+
Retrieves the distribution's mean.
+
+
double
+
+
+
Retrieves a new sample from the distribution.
+
+
+
+
+
Sets the standard deviation of the distribution.
+
+
+
+
+
Sets the mean of the distribution.
+
+
+
+
+
Sets the distribution's seed.
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Normal
+public Normal ()
+Instantiates a normal distribution with zero mean and standard deviation equal to 1.
+
+
+
+
+Normal
+public Normal (double mean,
+ double std)
+Instantiates a normal distribution with a given mean and standard deviation.
+
+Parameters:
+mean - The distibution's mean.std - The distribution's standard deviation.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+setSeed
+public Normal setSeed (long seed) Sets the distribution's seed. This should yield reproducible sampling.
+
+Specified by:
+setSeed Distribution Parameters:
+seed - The distribution's new seed.Returns:
+this Distribution.
+
+
+
+
+setMean
+public Normal setMean (double mean) Sets the mean of the distribution.
+
+Specified by:
+setMean Distribution Parameters:
+mean - The new mean.Returns:
+this Distribution.
+
+
+
+
+setDeviation
+public Normal setDeviation (double std) Sets the standard deviation of the distribution.
+
+Specified by:
+setDeviation Distribution Parameters:
+std - The new standard deviation.Returns:
+this Distribution.
+
+
+
+
+getMean
+public double getMean ()
+
+Retrieves the distribution's mean.
+
+Specified by:
+getMean Distribution Returns:
+The mean value.
+
+
+
+
+
+getDeviation
+public double getDeviation ()
+
+Retrieves the distribution's standard deviation.
+
+Specified by:
+getDeviation Distribution Returns:
+The standard deviation.
+
+
+
+
+
+sample
+public double sample ()
+
+Retrieves a new sample from the distribution.
+
+Specified by:
+sample Distribution Returns:
+A double value.
+
+
+
+
+
+
+
+
+
+
+
+
Uniform
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Distribution
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Instantiates a uniform distribution that samples values from the range [0,1].
+
+
+
+
Instantiates a uniform distribution that samples values from the given range [from, to].
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
+
+
Retrieves the distribution's standard deviation.
+
+
double
+
+
+
Retrieves the distribution's mean.
+
+
double
+
+
+
Retrieves a new sample from the distribution.
+
+
+
+
+
Sets the standard deviation of the distribution.
+
+
+
+
+
Sets the mean of the distribution.
+
+
+
+
+
Sets the random of the uniform distribution.
+
+
+
+
+
Sets the distribution's seed.
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Uniform
+public Uniform ()
+Instantiates a uniform distribution that samples values from the range [0,1].
+
+
+
+
+Uniform
+public Uniform (double from,
+ double to)
+Instantiates a uniform distribution that samples values from the given range [from, to].
+
+Parameters:
+from - The minimum value of the distribution.to - The maximum value of the distribution.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+setRange
+public Uniform setRange (double from,
+ double to) Sets the random of the uniform distribution.
+
+Parameters:
+from - The range's start.to - The range's end.Returns:
+this Distribution.
+
+
+
+
+setSeed
+
+
+Sets the distribution's seed. This should yield reproducible sampling.
+
+Specified by:
+setSeed Distribution Parameters:
+seed - The distribution's new seed.Returns:
+this Distribution.
+
+
+
+
+setMean
+public Uniform setMean (double mean) Sets the mean of the distribution.
+
+Specified by:
+setMean Distribution Parameters:
+mean - The new mean.Returns:
+this Distribution.
+
+
+
+
+setDeviation
+public Uniform setDeviation (double std) Sets the standard deviation of the distribution.
+
+Specified by:
+setDeviation Distribution Parameters:
+std - The new standard deviation.Returns:
+this Distribution.
+
+
+
+
+getMean
+public double getMean ()
+
+Retrieves the distribution's mean.
+
+Specified by:
+getMean Distribution Returns:
+The mean value.
+
+
+
+
+
+getDeviation
+public double getDeviation ()
+
+Retrieves the distribution's standard deviation.
+
+Specified by:
+getDeviation Distribution Returns:
+The standard deviation.
+
+
+
+
+
+sample
+public double sample ()
+
+Retrieves a new sample from the distribution.
+
+Specified by:
+sample Distribution Returns:
+A double value.
+
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.core.distribution.Normal
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.core.distribution.Uniform
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Uniform. setRange (double from,
+ double to)
+
+
Sets the random of the uniform distribution.
+
+
+
+
+
+
+
+
+
+
+
mklab.JGNN.core.distribution
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+package mklab.JGNN.core.distribution
+
+
+
+
+
+
+
+
Classes
+
+
+
+
+
+
Implements a Normal
Distribution of given mean and standard deviation.
+
+
+
+
+
+
+
+
+
+
mklab.JGNN.core.distribution Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/core/distribution/package-use.html b/docs/javadoc/mklab/JGNN/core/distribution/package-use.html
new file mode 100644
index 00000000..0e4af0ce
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/core/distribution/package-use.html
@@ -0,0 +1,87 @@
+
+
+
+
+Uses of Package mklab.JGNN.core.distribution
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Implements a Normal
Distribution of given mean and standard deviation.
+
+
+
+
+
+
+
+
+
+
EmptyMatrix
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class EmptyMatrix
+
extends Matrix
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
+
+
+
Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Matrix
+
accessCol , accessColumns , accessColumns , accessColumns , accessColumns , accessDim , accessRow , accessRows , accessRows , accessRows , accessRows , asTransposed , describe , external , eye , fromDouble , get , getColName , getCols , getDimensionSize , getRowName , getRows , matmul , matmul , onesMask , put , setColName , setDiagonal , setDimensionName , setDimensionName , setMainDiagonal , setRowName , setToASymmetricNormalization , setToSymmetricNormalization , symmetricNormalization , toDense , toNonZeroString , toSparse , toString , transform , transposed , zeroCopy , zeroCopy , zeroCopy
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , dot , dot , estimateNumNonZeroElements , expMinusOne , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+EmptyMatrix
+public EmptyMatrix (long rows,
+ long cols)
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+getNonZeroEntries
+
+Description copied from class: Matrix Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+Specified by:
+getNonZeroEntries Matrix Returns:
+An Entry iterable.
+See Also:
+
+
+
+
+
+
+
+
+zeroCopy
+public Matrix zeroCopy (long rows,
+ long cols) Description copied from class: Matrix Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+Specified by:
+zeroCopy Matrix Parameters:
+rows - The number of rows of the matrix.cols - The number of columns of the matrix.Returns:
+A Matrix of the same class.
+See Also:
+
+
+
+
+
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+put
+public Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+get
+public double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+
+
+
+
+
+
+
EmptyTensor
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class EmptyTensor
+
extends Tensor
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a tensor of the same class with a given size and all element set to zero.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , describe , dot , dot , estimateNumNonZeroElements , expMinusOne , fromDouble , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability , toString , zeroCopy , zeroCopy
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+EmptyTensor
+public EmptyTensor ()
+
+
+
+
+EmptyTensor
+public EmptyTensor (long size)
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+put
+public Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+get
+public double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+zeroCopy
+public Tensor zeroCopy (long size) Description copied from class: Tensor Creates a tensor of the same class with a given size and all element set to zero.
+
+Specified by:
+zeroCopy Tensor Parameters:
+size - The size of the new tensor.Returns:
+A new tensor.
+See Also:
+
+
+
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.core.empy.EmptyMatrix
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.empy.EmptyMatrix
+
+
Uses of Class mklab.JGNN.core.empy.EmptyTensor
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.empy.EmptyTensor
+
+
mklab.JGNN.core.empy
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+package mklab.JGNN.core.empy
+
+
+
+
mklab.JGNN.core.empy Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/core/empy/package-use.html b/docs/javadoc/mklab/JGNN/core/empy/package-use.html
new file mode 100644
index 00000000..25c0b7e8
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/core/empy/package-use.html
@@ -0,0 +1,59 @@
+
+
+
+
+Uses of Package mklab.JGNN.core.empy
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.empy
+
+
AccessCol
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class AccessCol
+
extends Tensor Accesses a column of a
Matrix as if it were a dense
Tensor.
+ Prefer using
Matrix.accessCol(long), which wraps usage
+ of this class. Instances of this class share elements with the matrix which
+ they access and do
not allocate new memory.
+Author:
+Emmanouil Krasanakis
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Instantiates a see-through access of a matrix column.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
long
+
+
+
Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+
+
double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a tensor of the same class with a given size and all element set to zero.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , describe , dot , dot , expMinusOne , fromDouble , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability , toString , zeroCopy , zeroCopy
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+AccessCol
+public AccessCol (Matrix matrix,
+ long col) Instantiates a see-through access of a matrix column.
+
+Parameters:
+matrix - The base matrix.col - Which column to access.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+estimateNumNonZeroElements
+public long estimateNumNonZeroElements ()
+Description copied from class: Tensor Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+ Basically, this quantity is proportional to the allocated memory.
+
+Overrides:
+estimateNumNonZeroElements Tensor Returns:
+A long number equal to or less to the tensor size.
+See Also:
+
+
+
+
+
+
+
+
+put
+public Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+get
+public double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+zeroCopy
+public Tensor zeroCopy (long size) Description copied from class: Tensor Creates a tensor of the same class with a given size and all element set to zero.
+
+Specified by:
+zeroCopy Tensor Parameters:
+size - The size of the new tensor.Returns:
+A new tensor.
+See Also:
+
+
+
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
AccessRow
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class AccessRow
+
extends Tensor Accesses a row of a
Matrix as if it were a dense
Tensor.
+ Prefer using
Matrix.accessRow(long), which wraps usage
+ of this class. Instances of this class share elements with the matrix which
+ they access and do
not allocate new memory.
+Author:
+Emmanouil Krasanakis
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Instantiates a see-through access of a matrix row.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
long
+
+
+
Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+
+
double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a tensor of the same class with a given size and all element set to zero.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , describe , dot , dot , expMinusOne , fromDouble , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability , toString , zeroCopy , zeroCopy
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+AccessRow
+public AccessRow (Matrix matrix,
+ long row) Instantiates a see-through access of a matrix row.
+
+Parameters:
+matrix - The base matrix.row - Which row to access.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+estimateNumNonZeroElements
+public long estimateNumNonZeroElements ()
+Description copied from class: Tensor Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+ Basically, this quantity is proportional to the allocated memory.
+
+Overrides:
+estimateNumNonZeroElements Tensor Returns:
+A long number equal to or less to the tensor size.
+See Also:
+
+
+
+
+
+
+
+
+put
+public Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+get
+public double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+zeroCopy
+public Tensor zeroCopy (long size) Description copied from class: Tensor Creates a tensor of the same class with a given size and all element set to zero.
+
+Specified by:
+zeroCopy Tensor Parameters:
+size - The size of the new tensor.Returns:
+A new tensor.
+See Also:
+
+
+
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
ColumnRepetition
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class ColumnRepetition
+
extends Matrix Defines a matrix whose columns are all a copy of a
Tensor.
+ To avoid potential confusion, setting element values (and all supported operations) throws
+ an exception.
+Author:
+Emmanouil Krasanakis
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Instantiates a matrix repeating a tensor to be treated as a column.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
+
+
+
Retrieves the wrapped column tensor.
+
+
+
+
+
Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Matrix
+
accessCol , accessColumns , accessColumns , accessColumns , accessColumns , accessDim , accessRow , accessRows , accessRows , accessRows , accessRows , asTransposed , describe , external , eye , fromDouble , get , getColName , getCols , getDimensionSize , getRowName , getRows , matmul , matmul , onesMask , put , setColName , setDiagonal , setDimensionName , setDimensionName , setMainDiagonal , setRowName , setToASymmetricNormalization , setToSymmetricNormalization , symmetricNormalization , toDense , toNonZeroString , toSparse , toString , transform , transposed , zeroCopy , zeroCopy , zeroCopy
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , dot , dot , estimateNumNonZeroElements , expMinusOne , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+ColumnRepetition
+public ColumnRepetition (long times,
+ Tensor column) Instantiates a matrix repeating a tensor to be treated as a column.
+
+Parameters:
+times - The number of times the column should be repeated.column - The column Tensor
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+getColumn
+
+Retrieves the wrapped column tensor.
+
+Returns:
+The wrapped Tensor
+
+
+
+
+
+zeroCopy
+public Matrix zeroCopy (long rows,
+ long cols) Description copied from class: Matrix Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+Specified by:
+zeroCopy Matrix Parameters:
+rows - The number of rows of the matrix.cols - The number of columns of the matrix.Returns:
+A Matrix of the same class.
+See Also:
+
+
+
+
+
+
+
+
+put
+public Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+get
+public double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+getNonZeroEntries
+
+Description copied from class: Matrix Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+Specified by:
+getNonZeroEntries Matrix Returns:
+An Entry iterable.
+See Also:
+
+
+
+
+
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
DenseMatrix
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class DenseMatrix
+
extends Matrix Implements a dense
Matrix where all elements are stored in memory.
+ For matrices with more than MAXINT number of elements or many zeros use the
SparseMatrix
+ structure.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Generates a dense matrix with the designated number of rows and columns.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
+
+
+
Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+
+
+
+
Performs the matrix multiplication of this*with and the recipient.
+
+
+
matmul (Matrix with,
+ boolean transposeThis,
+ boolean transposeWith)
+
+
Can be used to perform fast computation of the matrix multiplications
+
this*with,
+
this.transposed()*with
+
this*with.transposed(),
+
this.transposed()*with.transposed()
+
while avoiding the overhead of calling
+
Matrix.transposed().
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Matrix
+
accessCol , accessColumns , accessColumns , accessColumns , accessColumns , accessDim , accessRow , accessRows , accessRows , accessRows , accessRows , asTransposed , describe , external , eye , fromDouble , get , getColName , getCols , getDimensionSize , getRowName , getRows , onesMask , put , setColName , setDiagonal , setDimensionName , setDimensionName , setMainDiagonal , setRowName , setToASymmetricNormalization , setToSymmetricNormalization , symmetricNormalization , toDense , toNonZeroString , toSparse , toString , transform , transposed , zeroCopy , zeroCopy , zeroCopy
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , dot , dot , estimateNumNonZeroElements , expMinusOne , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+DenseMatrix
+public DenseMatrix (long rows,
+ long cols)
+Generates a dense matrix with the designated number of rows and columns.
+
+Parameters:
+rows - The number of rows.cols - The number of columns.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+zeroCopy
+public Matrix zeroCopy (long rows,
+ long cols) Description copied from class: Matrix Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+Specified by:
+zeroCopy Matrix Parameters:
+rows - The number of rows of the matrix.cols - The number of columns of the matrix.Returns:
+A Matrix of the same class.
+See Also:
+
+
+
+
+
+
+
+
+put
+public Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+get
+public double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+getNonZeroEntries
+
+Description copied from class: Matrix Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+Specified by:
+getNonZeroEntries Matrix Returns:
+An Entry iterable.
+See Also:
+
+
+
+
+
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+matmul
+
+Description copied from class: Matrix Performs the matrix multiplication of this*with and the recipient.
+
+Overrides:
+matmul Matrix Parameters:
+with - The matrix to multiply with.Returns:
+A matrix that stores the outcome of the multiplication.
+See Also:
+
+
+
+
+
+
+
+
+matmul
+public Matrix matmul (Matrix with,
+ boolean transposeThis,
+ boolean transposeWith) Description copied from class: Matrix Can be used to perform fast computation of the matrix multiplications
+
this*with,
+
this.transposed()*with
+
this*with.transposed(),
+
this.transposed()*with.transposed()
+
while avoiding the overhead of calling
+
Matrix.transposed(). In this first of those cases, this operation
+ becomes equivalent to
Matrix.matmul(Matrix).
+Overrides:
+matmul Matrix Parameters:
+with - The matrix to multiply with.transposeThis - Whether this matrix should be transposed before multiplication.transposeWith - Whether the multiplied with matrix should be transposed before multiplication.Returns:
+A matrix that stores the outcome of the multiplication.
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Diagonal
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class Diagonal
+
extends Matrix Implements a square matrix whose diagonal elements are determined by the correspond values of
+ an underlying tensor and off-diagonal elements are zero. Elements are shared between the matrix
+ and its diagonal tensor. This structure is similar to a sparse matrix.
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
long
+
+
+
Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+
+
double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
+
+
+
Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Matrix
+
accessCol , accessColumns , accessColumns , accessColumns , accessColumns , accessDim , accessRow , accessRows , accessRows , accessRows , accessRows , asTransposed , describe , external , eye , fromDouble , get , getColName , getCols , getDimensionSize , getRowName , getRows , matmul , matmul , onesMask , put , setColName , setDiagonal , setDimensionName , setDimensionName , setMainDiagonal , setRowName , setToASymmetricNormalization , setToSymmetricNormalization , symmetricNormalization , toDense , toNonZeroString , toSparse , toString , transform , transposed , zeroCopy , zeroCopy , zeroCopy
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , dot , dot , expMinusOne , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+estimateNumNonZeroElements
+public long estimateNumNonZeroElements ()
+Description copied from class: Tensor Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+ Basically, this quantity is proportional to the allocated memory.
+
+Overrides:
+estimateNumNonZeroElements Tensor Returns:
+A long number equal to or less to the tensor size.
+See Also:
+
+
+
+
+
+
+
+
+getNonZeroEntries
+
+Description copied from class: Matrix Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+Specified by:
+getNonZeroEntries Matrix Returns:
+An Entry iterable.
+See Also:
+
+
+
+
+
+
+
+
+zeroCopy
+public Matrix zeroCopy (long rows,
+ long cols) Description copied from class: Matrix Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+Specified by:
+zeroCopy Matrix Parameters:
+rows - The number of rows of the matrix.cols - The number of columns of the matrix.Returns:
+A Matrix of the same class.
+See Also:
+
+
+
+
+
+
+
+
+put
+public Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+get
+public double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
RepeatMatrix
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class RepeatMatrix
+
extends Matrix Implements a
Matrix whose elements are all equals.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Generates a dense matrix with the designated number of rows and columns.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
+
+
+
Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Matrix
+
accessCol , accessColumns , accessColumns , accessColumns , accessColumns , accessDim , accessRow , accessRows , accessRows , accessRows , accessRows , asTransposed , describe , external , eye , fromDouble , get , getColName , getCols , getDimensionSize , getRowName , getRows , matmul , matmul , onesMask , put , setColName , setDiagonal , setDimensionName , setDimensionName , setMainDiagonal , setRowName , setToASymmetricNormalization , setToSymmetricNormalization , symmetricNormalization , toDense , toNonZeroString , toSparse , toString , transform , transposed , zeroCopy , zeroCopy , zeroCopy
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , dot , dot , estimateNumNonZeroElements , expMinusOne , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+RepeatMatrix
+public RepeatMatrix (double value,
+ long rows,
+ long cols)
+Generates a dense matrix with the designated number of rows and columns.
+
+Parameters:
+rows - The number of rows.cols - The number of columns.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+zeroCopy
+public Matrix zeroCopy (long rows,
+ long cols) Description copied from class: Matrix Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+Specified by:
+zeroCopy Matrix Parameters:
+rows - The number of rows of the matrix.cols - The number of columns of the matrix.Returns:
+A Matrix of the same class.
+See Also:
+
+
+
+
+
+
+
+
+put
+public Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+get
+public double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+getNonZeroEntries
+
+Description copied from class: Matrix Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+Specified by:
+getNonZeroEntries Matrix Returns:
+An Entry iterable.
+See Also:
+
+
+
+
+
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
RowRepetition
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class RowRepetition
+
extends Matrix Defines a matrix whose rows are all a copy of a
Tensor.
+ To avoid potential confusion, setting element values (and all supported operations) throws
+ an exception.
+Author:
+Emmanouil Krasanakis
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Instantiates a matrix repeating a tensor to be treated as a row.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
+
+
+
Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Matrix
+
accessCol , accessColumns , accessColumns , accessColumns , accessColumns , accessDim , accessRow , accessRows , accessRows , accessRows , accessRows , asTransposed , describe , external , eye , fromDouble , get , getColName , getCols , getDimensionSize , getRowName , getRows , matmul , matmul , onesMask , put , setColName , setDiagonal , setDimensionName , setDimensionName , setMainDiagonal , setRowName , setToASymmetricNormalization , setToSymmetricNormalization , symmetricNormalization , toDense , toNonZeroString , toSparse , toString , transform , transposed , zeroCopy , zeroCopy , zeroCopy
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , dot , dot , estimateNumNonZeroElements , expMinusOne , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+RowRepetition
+public RowRepetition (Tensor row,
+ long times) Instantiates a matrix repeating a tensor to be treated as a row.
+
+Parameters:
+column - The row Tensortimes - The number of times the row should be repeated.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+zeroCopy
+public Matrix zeroCopy (long rows,
+ long cols) Description copied from class: Matrix Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+Specified by:
+zeroCopy Matrix Parameters:
+rows - The number of rows of the matrix.cols - The number of columns of the matrix.Returns:
+A Matrix of the same class.
+See Also:
+
+
+
+
+
+
+
+
+put
+public Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+get
+public double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+getNonZeroEntries
+
+Description copied from class: Matrix Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+Specified by:
+getNonZeroEntries Matrix Returns:
+An Entry iterable.
+See Also:
+
+
+
+
+
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
SparseMatrix
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class SparseMatrix
+
extends Matrix A sparse
Matrix that allocates memory only for non-zero elements. Operations
+ that involve all matrix elements are slower compared to a
DenseMatrix.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Generates a sparse matrix with the designated number of rows and columns.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Describes the type, size and other characteristics of the tensor.
+
+
long
+
+
+
Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+
+
double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
+
+
+
Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Matrix
+
accessCol , accessColumns , accessColumns , accessColumns , accessColumns , accessDim , accessRow , accessRows , accessRows , accessRows , accessRows , asTransposed , external , eye , fromDouble , get , getColName , getCols , getDimensionSize , getRowName , getRows , matmul , matmul , onesMask , put , setColName , setDiagonal , setDimensionName , setDimensionName , setMainDiagonal , setRowName , setToASymmetricNormalization , setToSymmetricNormalization , symmetricNormalization , toDense , toNonZeroString , toSparse , toString , transform , transposed , zeroCopy , zeroCopy , zeroCopy
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , dot , dot , expMinusOne , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+SparseMatrix
+public SparseMatrix (long rows,
+ long cols)
+Generates a sparse matrix with the designated number of rows and columns.
+
+Parameters:
+rows - The number of rows.cols - The number of columns.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+zeroCopy
+public Matrix zeroCopy (long rows,
+ long cols) Description copied from class: Matrix Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+Specified by:
+zeroCopy Matrix Parameters:
+rows - The number of rows of the matrix.cols - The number of columns of the matrix.Returns:
+A Matrix of the same class.
+See Also:
+
+
+
+
+
+
+
+
+put
+public Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+get
+public double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+describe
+
+Description copied from class: Tensor Describes the type, size and other characteristics of the tensor.
+
+Overrides:
+describe Matrix Returns:
+A String description.
+
+
+
+
+
+estimateNumNonZeroElements
+public long estimateNumNonZeroElements ()
+Description copied from class: Tensor Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+ Basically, this quantity is proportional to the allocated memory.
+
+Overrides:
+estimateNumNonZeroElements Tensor Returns:
+A long number equal to or less to the tensor size.
+See Also:
+
+
+
+
+
+
+
+
+getNonZeroEntries
+
+Description copied from class: Matrix Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+Specified by:
+getNonZeroEntries Matrix Returns:
+An Entry iterable.
+See Also:
+
+
+
+
+
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
SparseSymmetric
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class SparseSymmetric
+
extends Matrix Deprecated.
+
+
+Defines a
SparseMatrix that is constrained to be symmetric
+ in that it returns the sum of values put on elements (i,j) and (j ,i).
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Deprecated.
+
Generates a symmetric matrix with the designated number of rows and columns.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods Deprecated Methods
+
+
+
+
+
+
+
+
+
Deprecated.
+
Describes the type, size and other characteristics of the tensor.
+
+
double
+
+
+
Deprecated.
+
Retrieves the value of a tensor element at a given position.
+
+
+
+
+
Deprecated.
+
Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+
void
+
+
+
Deprecated.
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (long pos,
+ double value)
+
+
Deprecated.
+
Assign a value to a tensor element.
+
+
void
+
+
+
Deprecated.
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Deprecated.
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Deprecated.
+
Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Matrix
+
accessCol , accessColumns , accessColumns , accessColumns , accessColumns , accessDim , accessRow , accessRows , accessRows , accessRows , accessRows , asTransposed , external , eye , fromDouble , get , getColName , getCols , getDimensionSize , getRowName , getRows , matmul , matmul , onesMask , put , setColName , setDiagonal , setDimensionName , setDimensionName , setMainDiagonal , setRowName , setToASymmetricNormalization , setToSymmetricNormalization , symmetricNormalization , toDense , toNonZeroString , toSparse , toString , transform , transposed , zeroCopy , zeroCopy , zeroCopy
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , dot , dot , estimateNumNonZeroElements , expMinusOne , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+SparseSymmetric
+public SparseSymmetric (long rows,
+ long cols)
+Deprecated.
+Generates a symmetric matrix with the designated number of rows and columns.
+
+Parameters:
+rows - The number of rows.cols - The number of columns.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+zeroCopy
+public Matrix zeroCopy (long rows,
+ long cols) Deprecated.
+Description copied from class: Matrix Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+Specified by:
+zeroCopy Matrix Parameters:
+rows - The number of rows of the matrix.cols - The number of columns of the matrix.Returns:
+A Matrix of the same class.
+See Also:
+
+
+
+
+
+
+
+
+put
+public Tensor put (long pos,
+ double value) Deprecated.
+Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+get
+public double get (long pos)
+Deprecated.
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+traverseNonZeroElements
+
+Deprecated.
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+describe
+
+Deprecated.
+Description copied from class: Tensor Describes the type, size and other characteristics of the tensor.
+
+Overrides:
+describe Matrix Returns:
+A String description.
+
+
+
+
+
+getNonZeroEntries
+
+Deprecated.
+Description copied from class: Matrix Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+Specified by:
+getNonZeroEntries Matrix Returns:
+An Entry iterable.
+See Also:
+
+
+
+
+
+
+
+
+release
+public void release ()
+Deprecated.
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Deprecated.
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
TransposedMatrix
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class TransposedMatrix
+
extends Matrix Generates a transposed version of a base matrix, with which it shares elements.
+ This avoids excessive memory allocation and can be used to quickly perform
+ operations with a transposed version of a matrix. Prefer using
+
Matrix.asTransposed(), which wraps usage of this class.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Creates a transposed version of the matrix that accesses the same elements (thus, editing one
+ edits the other) without allocating additional memory.
+
+
+
+
+
Describes the type, size and other characteristics of the tensor.
+
+
long
+
+
+
Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+
+
double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
+
+
+
Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Matrix
+
accessCol , accessColumns , accessColumns , accessColumns , accessColumns , accessDim , accessRow , accessRows , accessRows , accessRows , accessRows , external , eye , fromDouble , get , getColName , getCols , getDimensionSize , getRowName , getRows , matmul , matmul , onesMask , put , setColName , setDiagonal , setDimensionName , setDimensionName , setMainDiagonal , setRowName , setToASymmetricNormalization , setToSymmetricNormalization , symmetricNormalization , toDense , toNonZeroString , toSparse , toString , transform , transposed , zeroCopy , zeroCopy , zeroCopy
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , dot , dot , expMinusOne , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+TransposedMatrix
+public TransposedMatrix (Matrix matrix)
+
+
+
+
+
+
+
+Method Details
+
+
+
+estimateNumNonZeroElements
+public long estimateNumNonZeroElements ()
+Description copied from class: Tensor Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+ Basically, this quantity is proportional to the allocated memory.
+
+Overrides:
+estimateNumNonZeroElements Tensor Returns:
+A long number equal to or less to the tensor size.
+See Also:
+
+
+
+
+
+
+
+
+getNonZeroEntries
+
+Description copied from class: Matrix Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+Specified by:
+getNonZeroEntries Matrix Returns:
+An Entry iterable.
+See Also:
+
+
+
+
+
+
+
+
+zeroCopy
+public Matrix zeroCopy (long rows,
+ long cols) Description copied from class: Matrix Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+Specified by:
+zeroCopy Matrix Parameters:
+rows - The number of rows of the matrix.cols - The number of columns of the matrix.Returns:
+A Matrix of the same class.
+See Also:
+
+
+
+
+
+
+
+
+put
+public Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+get
+public double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+asTransposed
+
+Description copied from class: Matrix Creates a transposed version of the matrix that accesses the same elements (thus, editing one
+ edits the other) without allocating additional memory.
+
+Overrides:
+asTransposed Matrix Returns:
+A TransposedMatrix
+
+
+
+
+
+describe
+
+Description copied from class: Tensor Describes the type, size and other characteristics of the tensor.
+
+Overrides:
+describe Matrix Returns:
+A String description.
+
+
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
VectorizedMatrix
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class VectorizedMatrix
+
extends Matrix Implements a dense
Matrix where all elements are stored in memory.
+ For matrices with more than MAXINT number of elements or many zeros use the
SparseMatrix
+ structure.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Field Summary
+Fields
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Generates a dense matrix with the designated number of rows and columns.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
+
+
+
Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+
+
+
+
Performs the matrix multiplication of this*with and the recipient.
+
+
+
matmul (Matrix with,
+ boolean transposeThis,
+ boolean transposeWith)
+
+
Can be used to perform fast computation of the matrix multiplications
+
this*with,
+
this.transposed()*with
+
this*with.transposed(),
+
this.transposed()*with.transposed()
+
while avoiding the overhead of calling
+
Matrix.transposed().
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Matrix
+
accessCol , accessColumns , accessColumns , accessColumns , accessColumns , accessDim , accessRow , accessRows , accessRows , accessRows , accessRows , asTransposed , describe , external , eye , fromDouble , get , getColName , getCols , getDimensionSize , getRowName , getRows , onesMask , put , setColName , setDiagonal , setDimensionName , setDimensionName , setMainDiagonal , setRowName , setToASymmetricNormalization , setToSymmetricNormalization , symmetricNormalization , toDense , toNonZeroString , toSparse , toString , transform , transposed , zeroCopy , zeroCopy , zeroCopy
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , dot , dot , estimateNumNonZeroElements , expMinusOne , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+VectorizedMatrix
+public VectorizedMatrix (long rows,
+ long cols)
+Generates a dense matrix with the designated number of rows and columns.
+
+Parameters:
+rows - The number of rows.cols - The number of columns.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+zeroCopy
+public Matrix zeroCopy (long rows,
+ long cols) Description copied from class: Matrix Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+Specified by:
+zeroCopy Matrix Parameters:
+rows - The number of rows of the matrix.cols - The number of columns of the matrix.Returns:
+A Matrix of the same class.
+See Also:
+
+
+
+
+
+
+
+
+put
+public Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+get
+public double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+getNonZeroEntries
+
+Description copied from class: Matrix Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+Specified by:
+getNonZeroEntries Matrix Returns:
+An Entry iterable.
+See Also:
+
+
+
+
+
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+matmul
+
+Description copied from class: Matrix Performs the matrix multiplication of this*with and the recipient.
+
+Overrides:
+matmul Matrix Parameters:
+with - The matrix to multiply with.Returns:
+A matrix that stores the outcome of the multiplication.
+See Also:
+
+
+
+
+
+
+
+
+matmul
+public Matrix matmul (Matrix with,
+ boolean transposeThis,
+ boolean transposeWith) Description copied from class: Matrix Can be used to perform fast computation of the matrix multiplications
+
this*with,
+
this.transposed()*with
+
this*with.transposed(),
+
this.transposed()*with.transposed()
+
while avoiding the overhead of calling
+
Matrix.transposed(). In this first of those cases, this operation
+ becomes equivalent to
Matrix.matmul(Matrix).
+Overrides:
+matmul Matrix Parameters:
+with - The matrix to multiply with.transposeThis - Whether this matrix should be transposed before multiplication.transposeWith - Whether the multiplied with matrix should be transposed before multiplication.Returns:
+A matrix that stores the outcome of the multiplication.
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
WrapCols
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class WrapCols
+
extends Matrix Wraps a list of tensors into a matrix with the tensors as columns.
+ Does not allocate additional elements. Editing the matrix edits
+ the original tensors and conversely.
+
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Retrieves the given column as a tensor.
+
+
long
+
+
+
Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+
+
double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
+
+
+
Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Sets a prototype matrix from which to borrow copying operations.
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Matrix
+
accessColumns , accessColumns , accessColumns , accessColumns , accessDim , accessRow , accessRows , accessRows , accessRows , accessRows , asTransposed , describe , external , eye , fromDouble , get , getColName , getCols , getDimensionSize , getRowName , getRows , matmul , matmul , onesMask , put , setColName , setDiagonal , setDimensionName , setDimensionName , setMainDiagonal , setRowName , setToASymmetricNormalization , setToSymmetricNormalization , symmetricNormalization , toDense , toNonZeroString , toSparse , toString , transform , transposed , zeroCopy , zeroCopy , zeroCopy
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , dot , dot , expMinusOne , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+WrapCols
+public WrapCols (Tensor ... cols)
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+estimateNumNonZeroElements
+public long estimateNumNonZeroElements ()
+Description copied from class: Tensor Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+ Basically, this quantity is proportional to the allocated memory.
+
+Overrides:
+estimateNumNonZeroElements Tensor Returns:
+A long number equal to or less to the tensor size.
+See Also:
+
+
+
+
+
+
+
+
+setZeroCopyType
+
+Sets a prototype matrix from which to borrow copying operations.
+
+Parameters:
+zeroCopyType - A MatrixzeroCopy(long, long)Returns:
+this object
+
+
+
+
+zeroCopy
+public Matrix zeroCopy (long rows,
+ long cols) Description copied from class: Matrix Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+Specified by:
+zeroCopy Matrix Parameters:
+rows - The number of rows of the matrix.cols - The number of columns of the matrix.Returns:
+A Matrix of the same class.
+See Also:
+
+
+
+
+
+
+
+
+put
+public Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+get
+public double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+getNonZeroEntries
+
+Description copied from class: Matrix Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+Specified by:
+getNonZeroEntries Matrix Returns:
+An Entry iterable.
+See Also:
+
+
+
+
+
+
+
+
+accessCol
+public Tensor accessCol (long col) Description copied from class: Matrix Retrieves the given column as a tensor. Editing the result
+ also edits the original matrix.
+ No new memory is allocated for matrix values.
+
+Overrides:
+accessCol Matrix Parameters:
+col - The given column.Returns:
+An AccessCol
+See Also:
+
+
+
+
+
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
WrapRows
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class WrapRows
+
extends Matrix Wraps a list of tensors into a matrix with the tensors as rows.
+ Does not allocate additional elements. Editing the matrix edits
+ the original tensors and conversely.
+
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Retrieves the given row as a tensor.
+
+
long
+
+
+
Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+
+
double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
+
+
+
Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Sets a prototype matrix from which to borrow copying operations.
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Matrix
+
accessCol , accessColumns , accessColumns , accessColumns , accessColumns , accessDim , accessRows , accessRows , accessRows , accessRows , asTransposed , describe , external , eye , fromDouble , get , getColName , getCols , getDimensionSize , getRowName , getRows , matmul , matmul , onesMask , put , setColName , setDiagonal , setDimensionName , setDimensionName , setMainDiagonal , setRowName , setToASymmetricNormalization , setToSymmetricNormalization , symmetricNormalization , toDense , toNonZeroString , toSparse , toString , transform , transposed , zeroCopy , zeroCopy , zeroCopy
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , dot , dot , expMinusOne , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+WrapRows
+public WrapRows (Tensor ... rows)
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+estimateNumNonZeroElements
+public long estimateNumNonZeroElements ()
+Description copied from class: Tensor Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+ Basically, this quantity is proportional to the allocated memory.
+
+Overrides:
+estimateNumNonZeroElements Tensor Returns:
+A long number equal to or less to the tensor size.
+See Also:
+
+
+
+
+
+
+
+
+setZeroCopyType
+
+Sets a prototype matrix from which to borrow copying operations.
+
+Parameters:
+zeroCopyType - A MatrixzeroCopy(long, long)Returns:
+this object
+
+
+
+
+zeroCopy
+public Matrix zeroCopy (long rows,
+ long cols) Description copied from class: Matrix Creates a matrix of the same class and all element set to zero, but with
+ a given number of rows and columns.
+
+Specified by:
+zeroCopy Matrix Parameters:
+rows - The number of rows of the matrix.cols - The number of columns of the matrix.Returns:
+A Matrix of the same class.
+See Also:
+
+
+
+
+
+
+
+
+put
+public Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+get
+public double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+getNonZeroEntries
+
+Description copied from class: Matrix Retrieves an iterable that traverses (row, col) entry pairs
+ of non zero entries.
+
+Specified by:
+getNonZeroEntries Matrix Returns:
+An Entry iterable.
+See Also:
+
+
+
+
+
+
+
+
+accessRow
+public Tensor accessRow (long row) Description copied from class: Matrix Retrieves the given row as a tensor. Editing the result
+ also edits the original matrix.
+ No new memory is allocated for matrix values.
+
+Overrides:
+accessRow Matrix Parameters:
+row - The given row.Returns:
+An AccessRow
+See Also:
+
+
+
+
+
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.core.matrix.AccessCol
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.matrix.AccessCol
+
+
Uses of Class mklab.JGNN.core.matrix.AccessRow
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.matrix.AccessRow
+
+
Uses of Class mklab.JGNN.core.matrix.ColumnRepetition
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.matrix.ColumnRepetition
+
+
Uses of Class mklab.JGNN.core.matrix.DenseMatrix
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.matrix.DenseMatrix
+
+
Uses of Class mklab.JGNN.core.matrix.Diagonal
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.matrix.Diagonal
+
+
Uses of Class mklab.JGNN.core.matrix.RepeatMatrix
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.matrix.RepeatMatrix
+
+
Uses of Class mklab.JGNN.core.matrix.RowRepetition
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.matrix.RowRepetition
+
+
Uses of Class mklab.JGNN.core.matrix.SparseMatrix
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Creates a sparse unit matrix.
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.core.matrix.SparseSymmetric
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.matrix.SparseSymmetric
+
+
Uses of Class mklab.JGNN.core.matrix.TransposedMatrix
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.matrix.TransposedMatrix
+
+
Uses of Class mklab.JGNN.core.matrix.VectorizedMatrix
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.matrix.VectorizedMatrix
+
+
Uses of Class mklab.JGNN.core.matrix.WrapCols
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Accesses the tensor through a single-column matrix with the tensor as the only row.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Sets a prototype matrix from which to borrow copying operations.
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.core.matrix.WrapRows
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Accesses the tensor through a single-row matrix with the tensor as the only column.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Sets a prototype matrix from which to borrow copying operations.
+
+
+
+
+
+
+
+
mklab.JGNN.core.matrix
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+package mklab.JGNN.core.matrix
+
+
+
+
+
+
+
+
Classes
+
+
+
+
+
+
+
+
+
+
Defines a matrix whose columns are all a copy of a
Tensor.
+
+
+
+
Implements a dense
Matrix where all elements are stored in memory.
+
+
+
+
Implements a square matrix whose diagonal elements are determined by the correspond values of
+ an underlying tensor and off-diagonal elements are zero.
+
+
+
+
Implements a
Matrix whose elements are all equals.
+
+
+
+
Defines a matrix whose rows are all a copy of a
Tensor.
+
+
+
+
A sparse
Matrix that allocates memory only for non-zero elements.
+
+
+
Deprecated.
+
+
+
+
+
Generates a transposed version of a base matrix, with which it shares elements.
+
+
+
+
Implements a dense
Matrix where all elements are stored in memory.
+
+
+
+
Wraps a list of tensors into a matrix with the tensors as columns.
+
+
+
+
Wraps a list of tensors into a matrix with the tensors as rows.
+
+
+
+
+
+
+
+
mklab.JGNN.core.matrix Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/core/matrix/package-use.html b/docs/javadoc/mklab/JGNN/core/matrix/package-use.html
new file mode 100644
index 00000000..7b16a886
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/core/matrix/package-use.html
@@ -0,0 +1,110 @@
+
+
+
+
+Uses of Package mklab.JGNN.core.matrix
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
A sparse
Matrix that allocates memory only for non-zero elements.
+
+
+
+
Wraps a list of tensors into a matrix with the tensors as columns.
+
+
+
+
Wraps a list of tensors into a matrix with the tensors as rows.
+
+
+
+
+
+
+
+
+
+
+
+
Wraps a list of tensors into a matrix with the tensors as columns.
+
+
+
+
Wraps a list of tensors into a matrix with the tensors as rows.
+
+
+
+
+
+
+
+
mklab.JGNN.core
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+package mklab.JGNN.core
+
+
+
+
+
+
+
+
All Classes and Interfaces Interfaces Classes
+
+
+
+
+
+
+
+
+
This class provides an abstract implementation of Matrix functionalities.
+
+
+
+
A memory management system for thread-safe allocation and release of arrays of doubles.
+
+
+
+
+
+
This class provices an interface with which to define data slices,
+ for instance to sample labels.
+
+
+
+
This class provides a native java implementation of Tensor functionalities.
+
+
+
+
This class provides thread execution pool utilities while keeping track of thread
+ identifiers for use by thread-specific
NNOperation.
+
+
+
+
+
+
+
+
+
mklab.JGNN.core Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/core/package-use.html b/docs/javadoc/mklab/JGNN/core/package-use.html
new file mode 100644
index 00000000..caed193e
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/core/package-use.html
@@ -0,0 +1,362 @@
+
+
+
+
+Uses of Package mklab.JGNN.core
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
This class provides an abstract implementation of Matrix functionalities.
+
+
+
+
This class provices an interface with which to define data slices,
+ for instance to sample labels.
+
+
+
+
This class provides a native java implementation of Tensor functionalities.
+
+
+
+
+
+
+
+
+
+
+
+
This class provides an abstract implementation of Matrix functionalities.
+
+
+
+
This class provides a native java implementation of Tensor functionalities.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
This class provides an abstract implementation of Matrix functionalities.
+
+
+
+
+
+
This class provices an interface with which to define data slices,
+ for instance to sample labels.
+
+
+
+
This class provides a native java implementation of Tensor functionalities.
+
+
+
+
This class provides thread execution pool utilities while keeping track of thread
+ identifiers for use by thread-specific
NNOperation.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
This class provides an abstract implementation of Matrix functionalities.
+
+
+
+
This class provides a native java implementation of Tensor functionalities.
+
+
+
+
+
+
+
+
+
+
+
+
This class provides an abstract implementation of Matrix functionalities.
+
+
+
+
This class provides a native java implementation of Tensor functionalities.
+
+
+
+
+
+
+
+
+
+
+
+
This class provides a native java implementation of Tensor functionalities.
+
+
+
+
+
+
+
+
+
+
+
+
This class provides a native java implementation of Tensor functionalities.
+
+
+
+
+
+
+
+
+
+
+
+
This class provides an abstract implementation of Matrix functionalities.
+
+
+
+
This class provices an interface with which to define data slices,
+ for instance to sample labels.
+
+
+
+
This class provides a native java implementation of Tensor functionalities.
+
+
+
+
+
+
+
+
+
+
+
+
This class provides a native java implementation of Tensor functionalities.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
This class provides a native java implementation of Tensor functionalities.
+
+
+
+
+
+
+
+
+
+
+
+
This class provides a native java implementation of Tensor functionalities.
+
+
+
+
+
+
+
+
+
+
+
+
This class provides a native java implementation of Tensor functionalities.
+
+
+
+
+
+
+
+
+
+
+
+
This class provides a native java implementation of Tensor functionalities.
+
+
+
+
+
+
+
+
+
+
+
+
This class provides a native java implementation of Tensor functionalities.
+
+
+
+
+
+
+
+
AccessSubtensor
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class AccessSubtensor
+
extends Tensor Wraps a base
Tensor by traversing only its elements in a specified range (from begin, up to end-1).
+ Although in principle it does not require a specific type of base tensor, it is created with optimized
+
DenseTensor operations in mind. That is, it implements
traverseNonZeroElements() as a
Range.
+ This class's
Tensor.zeroCopy() is marked as unimplemented by throwing an exception, which will also make dependent
+ operations fail. However, it makes sense that members of this class are only used to access (or modify) the subtensor.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Instantiates a see-through access of a tensor elements.
+
+
+
+
Instantiates a see-through access of a tensor elements.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a tensor of the same class with a given size and all element set to zero.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , describe , dot , dot , estimateNumNonZeroElements , expMinusOne , fromDouble , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability , toString , zeroCopy , zeroCopy
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+AccessSubtensor
+public AccessSubtensor (Tensor baseTensor,
+ long begin) Instantiates a see-through access of a tensor elements.
+
+Parameters:
+baseTensor - The base tensor whose elements to access.begin - The first element to access. (This is retrieved by get(0).)
+
+
+
+
+AccessSubtensor
+public AccessSubtensor (Tensor baseTensor,
+ long begin,
+ long end) Instantiates a see-through access of a tensor elements.
+
+Parameters:
+baseTensor - The base tensor whose elements to access.begin - The first element to access. (This is retrieved by get(0).)end - The non-inclusive last element. (The subtensor has size begin-end.)
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+put
+public Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+get
+public double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+zeroCopy
+public Tensor zeroCopy (long size) Description copied from class: Tensor Creates a tensor of the same class with a given size and all element set to zero.
+
+Specified by:
+zeroCopy Tensor Parameters:
+size - The size of the new tensor.Returns:
+A new tensor.
+See Also:
+
+
+
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
DenseTensor
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class DenseTensor
+
extends Tensor This class provides a dense
Tensor that wraps an array of doubles.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Field Summary
+Fields
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+
+
+
+
Constructs a dense tensor holding zero values.
+
+
+
+
Reconstructs a serialized Tensor (i.e.
+
+
+
+
Constructs a dense tensor from an iterator holding
+ that outputs its values.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Computes the absolute value of tensor elements.
+
+
+
+
+
+
+
+
+
+
+
Computes the exponential minus 1 of tensor elements.
+
+
final double
+
+
+
final double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
+
+
+
+
+
+
Computes the logarithm of tensor elements.
+
+
+
+
+
+
+
+
+
+
+
Computes the negative of tensor elements.
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (int pos,
+ double value)
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
final void
+
putAdd (int pos,
+ double value)
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Performs in-memory set of each element to its absolute value.
+
+
+
+
+
Performs in-memory addition to the Tensor, storing the result in itself.
+
+
+
+
+
Performs in-memory addition to the Tensor, storing the result in itself.
+
+
+
+
+
Sets the exponential minus 1 of tensor elements.
+
+
+
+
+
Performs in-memory the inverse of each non-zero element.
+
+
+
+
+
Performs in-memory set of each element to the logarithm of its absolute value.
+
+
+
+
+
Performs in-memory multiplication on the Tensor, storing the result to itself.
+
+
+
+
+
Performs in-memory multiplication on the Tensor, storing the result in itself .
+
+
+
+
+
Performs in-memory set of each element to the negative of itself.
+
+
+
+
+
Performs in-memory set of each element to the square root of its absolute value.
+
+
+
+
+
Performs in-memory subtraction from the Tensor, storing the result in itself.
+
+
+
+
+
Computes the square root of tensor elements.
+
+
+
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a tensor of the same class with a given size and all element set to zero.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Tensor
+
accessSubtensor , accessSubtensor , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , describe , dot , dot , estimateNumNonZeroElements , fromDouble , fromRange , fromRange , getDimensionName , getNonZeroElements , iterator , max , min , norm , normalized , putAdd , selfAdd , setDimensionName , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sum , toArray , toDouble , toProbability , toString , zeroCopy , zeroCopy
+
+
+
+
+
+
+
+
+Field Details
+
+
+
+values
+public double[] values
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+DenseTensor
+
+Constructs a dense tensor from an iterator holding
+ that outputs its values. Tensor size is equal to the
+ number of extracted values.
+
+Parameters:
+iterator - The iterator to obtain values from.
+
+
+
+
+DenseTensor
+public DenseTensor (double... values)
+
+
+
+
+DenseTensor
+public DenseTensor (long size)
+Constructs a dense tensor holding zero values.
+
+Parameters:
+size - The size of the tensor.
+
+
+
+
+DenseTensor
+public DenseTensor (String expr)
+Parameters:
+expr - A serialized tensorThrows:
+IllegalArgumentException
+
+
+
+
+DenseTensor
+public DenseTensor ()
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+put
+public final Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+put
+public final Tensor put (int pos,
+ double value)
+
+
+
+putAdd
+public final void putAdd (int pos,
+ double value)
+
+
+
+
+get
+public final double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+get
+public final double get (int pos)
+
+
+
+
+zeroCopy
+public Tensor zeroCopy (long size) Description copied from class: Tensor Creates a tensor of the same class with a given size and all element set to zero.
+
+Specified by:
+zeroCopy Tensor Parameters:
+size - The size of the new tensor.Returns:
+A new tensor.
+See Also:
+
+
+
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+add
+
+
+Overrides:
+add Tensor Parameters:
+tensor - The tensor to add withReturns:
+a new Tensor that stores the outcome of addition
+
+
+
+
+
+add
+public Tensor add (double value)
+Overrides:
+add Tensor Parameters:
+value - The value to add to each elementReturns:
+a new Tensor that stores the outcome of addition
+
+
+
+
+
+selfAdd
+
+Description copied from class: Tensor Performs in-memory addition to the Tensor, storing the result in itself.
+
+Overrides:
+selfAdd Tensor Parameters:
+tensor - The tensor to add (it's not affected).Returns:
+this Tensor instance.
+
+
+
+
+selfAdd
+public Tensor selfAdd (double value) Description copied from class: Tensor Performs in-memory addition to the Tensor, storing the result in itself.
+
+Overrides:
+selfAdd Tensor Parameters:
+value - The value to add to each tensor element.Returns:
+this Tensor instance.
+
+
+
+
+subtract
+
+
+Overrides:
+subtract Tensor Parameters:
+tensor - The tensor to subtractReturns:
+a new Tensor that stores the outcome of subtraction
+
+
+
+
+
+selfSubtract
+
+Description copied from class: Tensor Performs in-memory subtraction from the Tensor, storing the result in itself.
+
+Overrides:
+selfSubtract Tensor Parameters:
+tensor - The tensor to subtract (it's not affected).Returns:
+this Tensor instance.
+
+
+
+
+multiply
+
+
+Overrides:
+multiply Tensor Parameters:
+tensor - The tensor to perform element-wise multiplication with.Returns:
+A new Tensor that stores the outcome of the multiplication.
+
+
+
+
+
+multiply
+public Tensor multiply (double value)
+Overrides:
+multiply Tensor Parameters:
+value - A number to multiply all tensor elements with.Returns:
+A new Tensor that stores the outcome of the multiplication.
+
+
+
+
+
+selfMultiply
+
+Description copied from class: Tensor Performs in-memory multiplication on the Tensor, storing the result in itself .
+
+Overrides:
+selfMultiply Tensor Parameters:
+tensor - The tensor to perform element-wise multiplication with (it's not affected).Returns:
+this Tensor instance.
+
+
+
+
+selfMultiply
+public Tensor selfMultiply (double value) Description copied from class: Tensor Performs in-memory multiplication on the Tensor, storing the result to itself.
+
+Overrides:
+selfMultiply Tensor Parameters:
+value - A number to multiply all tensor elements with.Returns:
+this Tensor instance.
+
+
+
+
+sqrt
+
+Description copied from class: Tensor Computes the square root of tensor elements.
+
+Overrides:
+sqrt Tensor Returns:
+A new Tensor that stores the outcome of finding the absolute square root of each element.
+
+
+
+
+
+selfSqrt
+
+Description copied from class: Tensor Performs in-memory set of each element to the square root of its absolute value.
+
+Overrides:
+selfSqrt Tensor Returns:
+this Tensor instance.
+
+
+
+
+expMinusOne
+
+Description copied from class: Tensor Computes the exponential minus 1 of tensor elements.
+
+Overrides:
+expMinusOne Tensor Returns:
+A new Tensor that stores the outcome of finding the operation on each element.
+
+
+
+
+
+selfExpMinusOne
+public Tensor selfExpMinusOne ()
Description copied from class: Tensor Sets the exponential minus 1 of tensor elements.
+
+Overrides:
+selfExpMinusOne Tensor Returns:
+this Tensor instance.
+
+
+
+
+log
+
+Description copied from class: Tensor Computes the logarithm of tensor elements.
+
+Overrides:
+log Tensor Returns:
+A new Tensor that stores the outcome of finding the logarithm of the absolute of each element.
+
+
+
+
+
+selfLog
+
+Description copied from class: Tensor Performs in-memory set of each element to the logarithm of its absolute value.
+
+Overrides:
+selfLog Tensor Returns:
+this Tensor instance.
+
+
+
+
+negative
+
+Description copied from class: Tensor Computes the negative of tensor elements.
+
+Overrides:
+negative Tensor Returns:
+A new Tensor that stores the outcome of finding the negative of each element.
+
+
+
+
+
+selfNegative
+
+Description copied from class: Tensor Performs in-memory set of each element to the negative of itself.
+
+Overrides:
+selfNegative Tensor Returns:
+this Tensor instance.
+
+
+
+
+abs
+
+Description copied from class: Tensor Computes the absolute value of tensor elements.
+
+Overrides:
+abs Tensor Returns:
+A new Tensor that stores the outcome of finding the absolute value of each element.
+
+
+
+
+
+selfAbs
+
+Description copied from class: Tensor Performs in-memory set of each element to its absolute value.
+
+Overrides:
+selfAbs Tensor Returns:
+this Tensor instance.
+
+
+
+
+inverse
+
+
+Overrides:
+inverse Tensor Returns:
+A new Tensor with inversed each non-zero element.
+
+
+
+
+
+selfInverse
+
+Description copied from class: Tensor Performs in-memory the inverse of each non-zero element.
+
+Overrides:
+selfInverse Tensor Returns:
+this Tensor instance.
+
+
+
+
+
+
+
+
+
+
+
RepeatTensor
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class RepeatTensor
+
extends Tensor This class provides
Tensor whose elements are all equal.
+ Due to uncertain usage, its
put(long, double) operation is unsupported and throws a corresponding exception.
+ Otherwise, instances of this class behave similarly to
DenseTensor but permanently allocate only O(1) memory.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
final double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a tensor of the same class with a given size and all element set to zero.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , describe , dot , dot , estimateNumNonZeroElements , expMinusOne , fromDouble , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability , toString , zeroCopy , zeroCopy
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+RepeatTensor
+public RepeatTensor (double value,
+ long length)
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+put
+public final Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+get
+public final double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+zeroCopy
+public Tensor zeroCopy (long size) Description copied from class: Tensor Creates a tensor of the same class with a given size and all element set to zero.
+
+Specified by:
+zeroCopy Tensor Parameters:
+size - The size of the new tensor.Returns:
+A new tensor.
+See Also:
+
+
+
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
SparseTensor
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class SparseTensor
+
extends Tensor This class provides a sparse
Tensor with many zero elements.
+ Critically, it implements a
traverseNonZeroElements() method
+ that provides the positions of only non-zero elements to speed up computations.
+
+ Speed ups are expected mostly for operations between sparse tensors,
+ when sparse tensors are added or subtracted TO tense ones and when
+ sparse tensors are multiplied WITH dense ones.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
long
+
+
+
Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+
+
final double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a tensor of the same class with a given size and all element set to zero.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , describe , dot , dot , expMinusOne , fromDouble , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , multiply , multiply , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfMultiply , selfMultiply , selfNegative , selfSqrt , selfSubtract , setDimensionName , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , subtract , sum , toArray , toDouble , toProbability , toString , zeroCopy , zeroCopy
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+SparseTensor
+public SparseTensor (long length)
+
+
+
+
+SparseTensor
+public SparseTensor ()
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+put
+public final Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+get
+public final double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+zeroCopy
+public Tensor zeroCopy (long size) Description copied from class: Tensor Creates a tensor of the same class with a given size and all element set to zero.
+
+Specified by:
+zeroCopy Tensor Parameters:
+size - The size of the new tensor.Returns:
+A new tensor.
+See Also:
+
+
+
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+estimateNumNonZeroElements
+public long estimateNumNonZeroElements ()
+Description copied from class: Tensor Provides an estimation for the non-zero number of elements stored in the tensor,
+ where this number is equal to the size for dense tensors, but equal to the actual
+ number of non-zero elements for sparse tensors.
+ Basically, this quantity is proportional to the allocated memory.
+
+Overrides:
+estimateNumNonZeroElements Tensor Returns:
+A long number equal to or less to the tensor size.
+See Also:
+
+
+
+
+
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
VectorizedTensor
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >
+public class VectorizedTensor
+
extends Tensor This class provides a dense
Tensor that wraps an array of doubles.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Field Summary
+Fields
+
+
+
+
+
+
+
+
double[]
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+
+
+
+
Constructs a dense tensor holding zero values.
+
+
+
+
Reconstructs a serialized Tensor (i.e.
+
+
+
+
Constructs a dense tensor from an iterator holding that outputs its values.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
final double
+
+
+
final double
+
+
+
Retrieves the value of a tensor element at a given position.
+
+
+
+
+
+
+
+
void
+
+
+
If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+
+
+
put (int pos,
+ double value)
+
+
+
put (long pos,
+ double value)
+
+
Assign a value to a tensor element.
+
+
final void
+
putAdd (int pos,
+ double value)
+
+
void
+
+
+
If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up.
+
+
+
+
+
Performs in-memory addition to the Tensor, storing the result in itself.
+
+
+
+
+
Performs in-memory multiplication on the Tensor, storing the result to itself.
+
+
+
+
+
Performs in-memory multiplication on the Tensor, storing the result in itself .
+
+
+
+
+
Performs in-memory subtraction from the Tensor, storing the result in itself.
+
+
+
+
+
+
+
+
Retrieves positions within the tensor that may hold non-zero elements.
+
+
+
+
+
Creates a tensor of the same class with a given size and all element set to zero.
+
+
+
+
+
Methods inherited from class mklab.JGNN.core.Tensor
+
abs , accessSubtensor , accessSubtensor , add , argmax , argmin , asColumn , asRow , assertFinite , assertMatching , assertSize , assign , cast , copy , density , describe , dot , dot , estimateNumNonZeroElements , expMinusOne , fromDouble , fromRange , fromRange , getDimensionName , getNonZeroElements , inverse , iterator , log , max , min , negative , norm , normalized , putAdd , selfAbs , selfAdd , selfAdd , selfExpMinusOne , selfInverse , selfLog , selfNegative , selfSqrt , setDimensionName , setDimensionName , setToNormalized , setToOnes , setToProbability , setToRandom , setToRandom , setToUniform , setToZero , size , sqrt , sum , toArray , toDouble , toProbability , toString , zeroCopy , zeroCopy
+
+
+
+
+
+
+
+
+Field Details
+
+
+
+values
+public double[] values
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+VectorizedTensor
+
+Constructs a dense tensor from an iterator holding that outputs its values.
+ Tensor size is equal to the number of extracted values.
+
+Parameters:
+iterator - The iterator to obtain values from.
+
+
+
+
+VectorizedTensor
+public VectorizedTensor (double... values)
+
+
+
+
+VectorizedTensor
+public VectorizedTensor (long size)
+Constructs a dense tensor holding zero values.
+
+Parameters:
+size - The size of the tensor.
+
+
+
+
+VectorizedTensor
+public VectorizedTensor (String expr)
+Parameters:
+expr - A serialized tensorThrows:
+IllegalArgumentException
+
+
+
+
+VectorizedTensor
+public VectorizedTensor ()
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+put
+public final Tensor put (long pos,
+ double value) Description copied from class: Tensor Assign a value to a tensor element. All tensor operations use this function to wrap
+ element assignments.
+
+Specified by:
+put Tensor Parameters:
+pos - The position of the tensor elementvalue - The value to assignReturns:
+this Tensor instance.
+
+
+
+
+put
+public final Tensor put (int pos,
+ double value)
+
+
+
+putAdd
+public final void putAdd (int pos,
+ double value)
+
+
+
+
+get
+public final double get (long pos)
+Description copied from class: Tensor Retrieves the value of a tensor element at a given position. All tensor operations use this function to wrap
+ element retrieval.
+
+Specified by:
+get Tensor Parameters:
+pos - The position of the tensor elementReturns:
+The value of the tensor element
+
+
+
+
+
+get
+public final double get (int pos)
+
+
+
+
+zeroCopy
+public Tensor zeroCopy (long size) Description copied from class: Tensor Creates a tensor of the same class with a given size and all element set to zero.
+
+Specified by:
+zeroCopy Tensor Parameters:
+size - The size of the new tensor.Returns:
+A new tensor.
+See Also:
+
+
+
+
+
+
+
+
+traverseNonZeroElements
+
+Description copied from class: Tensor Retrieves positions within the tensor that may hold non-zero elements.
+ This guarantees that
all non-zero elements positions are traversed
+ but
some of the returned positions could hold zero elements .
+ For example,
DenseTensor traverses all
+ of its elements this way, whereas
SparseTensor
+ indeed traverses only non-zero elements.
+Specified by:
+traverseNonZeroElements Tensor Returns:
+An iterator that traverses positions within the tensor.
+
+
+
+
+
+release
+public void release ()
+Description copied from class: Tensor If the subclassed tensor allows it, release all memory it takes up
+ so that the garbage collector will eventually clean it up. This
+ memory will be released anyway by Java once there are no more
+ references to the object.
+
+Specified by:
+release Tensor See Also:
+
+
+
+
+
+
+
+
+persist
+public void persist ()
+Description copied from class: Tensor If supported by the subclassed tensor, invalidates calls to
+
Tensor.release() so that memory is a de-allocated only when
+ object references expire.
+Specified by:
+persist Tensor See Also:
+
+
+
+
+
+
+
+
+add
+
+
+Overrides:
+add Tensor Parameters:
+tensor - The tensor to add withReturns:
+a new Tensor that stores the outcome of addition
+
+
+
+
+
+selfAdd
+
+Description copied from class: Tensor Performs in-memory addition to the Tensor, storing the result in itself.
+
+Overrides:
+selfAdd Tensor Parameters:
+tensor - The tensor to add (it's not affected).Returns:
+this Tensor instance.
+
+
+
+
+subtract
+
+
+Overrides:
+subtract Tensor Parameters:
+tensor - The tensor to subtractReturns:
+a new Tensor that stores the outcome of subtraction
+
+
+
+
+
+selfSubtract
+
+Description copied from class: Tensor Performs in-memory subtraction from the Tensor, storing the result in itself.
+
+Overrides:
+selfSubtract Tensor Parameters:
+tensor - The tensor to subtract (it's not affected).Returns:
+this Tensor instance.
+
+
+
+
+multiply
+
+
+Overrides:
+multiply Tensor Parameters:
+tensor - The tensor to perform element-wise multiplication with.Returns:
+A new Tensor that stores the outcome of the multiplication.
+
+
+
+
+
+selfMultiply
+
+Description copied from class: Tensor Performs in-memory multiplication on the Tensor, storing the result in itself .
+
+Overrides:
+selfMultiply Tensor Parameters:
+tensor - The tensor to perform element-wise multiplication with (it's not affected).Returns:
+this Tensor instance.
+
+
+
+
+multiply
+public Tensor multiply (double value)
+Overrides:
+multiply Tensor Parameters:
+value - A number to multiply all tensor elements with.Returns:
+A new Tensor that stores the outcome of the multiplication.
+
+
+
+
+
+selfMultiply
+public Tensor selfMultiply (double value) Description copied from class: Tensor Performs in-memory multiplication on the Tensor, storing the result to itself.
+
+Overrides:
+selfMultiply Tensor Parameters:
+value - A number to multiply all tensor elements with.Returns:
+this Tensor instance.
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.core.tensor.AccessSubtensor
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.tensor.AccessSubtensor
+
+
Uses of Class mklab.JGNN.core.tensor.DenseTensor
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.tensor.DenseTensor
+
+
Uses of Class mklab.JGNN.core.tensor.RepeatTensor
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.tensor.RepeatTensor
+
+
Uses of Class mklab.JGNN.core.tensor.SparseTensor
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.tensor.SparseTensor
+
+
Uses of Class mklab.JGNN.core.tensor.VectorizedTensor
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/core/tensor/package-summary.html b/docs/javadoc/mklab/JGNN/core/tensor/package-summary.html
new file mode 100644
index 00000000..5daa3c18
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/core/tensor/package-summary.html
@@ -0,0 +1,121 @@
+
+
+
+
+mklab.JGNN.core.tensor
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+package mklab.JGNN.core.tensor
+
+
+
+
+
+
+
+
Classes
+
+
+
+
+
+
Wraps a base
Tensor by traversing only its elements in a specified range (from begin, up to end-1).
+
+
+
+
This class provides a dense
Tensor that wraps an array of doubles.
+
+
+
+
This class provides
Tensor whose elements are all equal.
+
+
+
+
This class provides a sparse
Tensor with many zero elements.
+
+
+
+
This class provides a dense
Tensor that wraps an array of doubles.
+
+
+
+
+
+
+
+
mklab.JGNN.core.tensor Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/core/tensor/package-use.html b/docs/javadoc/mklab/JGNN/core/tensor/package-use.html
new file mode 100644
index 00000000..1eb3c150
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/core/tensor/package-use.html
@@ -0,0 +1,83 @@
+
+
+
+
+Uses of Package mklab.JGNN.core.tensor
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
This class provides a dense
Tensor that wraps an array of doubles.
+
+
+
+
+
+
+
+
FastEntry
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Map.Entry <K,V>
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
+
+
+
void
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+FastEntry
+public FastEntry ()
+
+
+
+
+FastEntry
+public FastEntry (K key,
+ V value)
+
+
+
+
+
+
+
+Method Details
+
+
+
+
+
+
+
+
+
+
+
+
+setKey
+public void setKey (K key)
+
+
+
+
+
+
+
+
+
+
+
+
+
Loss
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+public interface Loss
+Provides computation and (partial) derivation of popular activation functions
+ and cross-entropy loss functions.
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Method Summary
+
+
Static Methods
+
+
+
+
+
static double
+
+
+
A cross entropy loss for one sample computes as -label*log(output) -(1-label)*log(1-output).
+
+
static double
+
+
+
static double
+
+
+
The derivative of the #crossEntropyCategorical(double, double) loss.
+
+
static double
+
+
+
The derivative of crossEntropy(sigmoid(x), label) with respect to x.
+
+
static double
+
+
+
The derivative of crossEntropy(tanh(x), label) with respect to x.
+
+
static double
+
+
+
The relu activation x if x > 0, 0 otherwise
+
+
+
+
+
static double
+
+
+
+
+
+
static double
+
+
+
The sigmoid function 1/(1+exp(-x)).
+
+
+
+
+
static double
+
+
+
+
+
+
static double
+
+
+
The tanh activation (exp(x)-exp(-x))/(exp(x)+exp(-x))
+
+
+
+
+
static double
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+sigmoid
+static double sigmoid (double x)
+The sigmoid function 1/(1+exp(-x)).
+
+Parameters:
+x - The activation of the sigmoid function.Returns:
+The sigmoid value.
+See Also:
+
+
+
+
+
+
+
+
+tanh
+static double tanh (double x)
+The tanh activation (exp(x)-exp(-x))/(exp(x)+exp(-x))
+
+Parameters:
+x - The activation of the tanh function.Returns:
+The tanh value.
+See Also:
+
+
+
+
+
+
+
+
+relu
+static double relu (double x)
+The relu activation x if x > 0, 0 otherwise
+
+Parameters:
+x - The activation of the relu function.Returns:
+The relu value.
+See Also:
+
+
+
+
+
+
+
+
+sigmoidDerivative
+static double sigmoidDerivative (double x)
+
+
+Parameters:
+x - The activation of the sigmoid function.Returns:
+The sigmoid derivative's value.
+See Also:
+
+
+
+
+
+
+
+
+tanhDerivative
+static double tanhDerivative (double x)
+
+
+Parameters:
+x - The activation of the tanh function.Returns:
+The tanh derivative's value.
+See Also:
+
+
+
+
+
+
+
+
+reluDerivative
+static double reluDerivative (double x)
+
+
+Parameters:
+x - The activation of the relu function.Returns:
+The relu derivative's value.
+See Also:
+
+
+
+
+
+
+
+
+crossEntropy
+static double crossEntropy (double output,
+ double label)
+A cross entropy loss for one sample computes as -label*log(output) -(1-label)*log(1-output). To avoid producing invalid
+ values, an eps of 1.E-12 is used to constraint the cross entropy in the range [-12, 12].
+
+Parameters:
+output - The output of a prediction task. Should lie in the range [0,1]label - The desired label of the prediction task. Should assume binary values 0 or 1Returns:
+The cross entropy value.
+Throws:
+IllegalArgumentException
+
+
+
+
+crossEntropyDerivative
+static double crossEntropyDerivative (double output,
+ double label)
+The derivative of the
crossEntropy(double, double) loss. To avoid producing invalid
+ values, an eps of 1.E-12 is used to constraint the cross entropy in the range [-12, 12], which results
+ to this derivative being constrained in the range [-1.E12, 1.E12].
+Parameters:
+output - The output of a prediction task. Should lie in the range [0,1]label - The desired label of the prediction task. Should assume binary values 0 or 1Returns:
+The cross entropy derivative's value.
+Throws:
+IllegalArgumentException
+
+
+
+
+crossEntropyDerivativeCategorical
+static double crossEntropyDerivativeCategorical (double output,
+ double label)
+The derivative of the #crossEntropyCategorical(double, double) loss. To avoid producing invalid
+ values, an eps of 1.E-12 is used to constraint the cross entropy in the range [-12, 12], which results
+ to this derivative being constrained in the range [-1.E12, 1.E12].
+
+Parameters:
+output - The output of a prediction task. Should lie in the range [0,1]label - The desired label of the prediction task. Should assume binary values 0 or 1Returns:
+The cross entropy derivative's value.
+Throws:
+IllegalArgumentException
+
+
+
+
+crossEntropySigmoidDerivative
+static double crossEntropySigmoidDerivative (double x,
+ double label)
+The derivative of crossEntropy(sigmoid(x), label) with respect to x. This function can avoid
+ using an eps and is hence more precise than the expression
+ crossEntropyDerivative(sigmoid(x), label)*sigmoidDerivative(x).
+
+Parameters:
+x - The activation of the sigmoid function.label - The desired label of the prediction task. Should assume binary values 0 or 1Returns:
+The cross entropy partial derivative with respect to the activation passed to an intermediate sigmoid transformation.
+Throws:
+IllegalArgumentException
+
+
+
+
+crossEntropyTanhDerivative
+static double crossEntropyTanhDerivative (double x,
+ double label)
+The derivative of crossEntropy(tanh(x), label) with respect to x. This function calculates
+ crossEntropyDerivative(tanh(x), label)*tanhDerivative(x).
+
+Parameters:
+x - The activation of the tanh function.label - The desired label of the prediction task. Should assume binary values 0 or 1Returns:
+The cross entropy partial derivative with respect to the activation passed to an intermediate tanh transformation.
+
+
+
+
+
+sigmoid
+
+
+
+Parameters:
+x - The activation tensor of the sigmoid function.Returns:
+The tensor of sigmoid values.
+
+
+
+
+
+tanh
+
+
+
+Parameters:
+x - The activation tensor of the tanh function.Returns:
+The tensor of tanh values.
+
+
+
+
+
+relu
+
+
+
+Parameters:
+x - The activation tensor of the relu function.Returns:
+The tensor of relu values.
+
+
+
+
+
+sigmoidDerivative
+
+
+
+Parameters:
+x - The activation tensor of the sigmoid function.Returns:
+The tensor of sigmoid derivative values.
+
+
+
+
+
+tanhDerivative
+
+
+
+Parameters:
+x - The activation tensor of the tanh function.Returns:
+The tensor of tanh derivative values.
+
+
+
+
+
+reluDerivative
+
+
+
+Parameters:
+x - The activation tensor of the relu function.Returns:
+The tensor of relu derivative values.
+
+
+
+
+
+
+
+
+
+
+
+
Range
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Long >Iterator <Long >
+Implements an iterator that traverses a range (similar to Python's range(min, max) method).
+ It is often used by
Tensor derived classes to traverse through all
+ element positions in sequential order.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
Range (long min,
+ long max)
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
boolean
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Range
+public Range (long min,
+ long max)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Range2D
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Iterable <Map.Entry <Long ,Long >>Iterator <Map.Entry <Long ,Long >>
+Implements an iterator that traverses a two-dimensional range (min, max) x (min2, max2).
+ It is often used by
Matrix instances to traverse through all element positions.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
Range2D (long min,
+ long max,
+ long min2,
+ long max2)
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
boolean
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Range2D
+public Range2D (long min,
+ long max,
+ long min2,
+ long max2)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Sort
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+public class Sort
+
extends Object
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Static Methods Concrete Methods
+
+
+
+
+
+
static int[]
+
+
+
static int[]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+sortedIndexes
+public static int[] sortedIndexes (double[] A)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.core.util.FastEntry
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.util.FastEntry
+
+
Uses of Interface mklab.JGNN.core.util.Loss
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.util.Loss
+
+
Uses of Class mklab.JGNN.core.util.Range
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.util.Range
+
+
Uses of Class mklab.JGNN.core.util.Range2D
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.util.Range2D
+
+
Uses of Class mklab.JGNN.core.util.Sort
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.util.Sort
+
+
mklab.JGNN.core.util
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+package mklab.JGNN.core.util
+
+
+
+
+
+
+
+
All Classes and Interfaces Interfaces Classes
+
+
+
+
+
+
+
+
+
Provides computation and (partial) derivation of popular activation functions
+ and cross-entropy loss functions.
+
+
+
+
Implements an iterator that traverses a range (similar to Python's range(min, max) method).
+
+
+
+
Implements an iterator that traverses a two-dimensional range (min, max) x (min2, max2).
+
+
+
+
+
+
+
+
+
+
+
mklab.JGNN.core.util Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+Interface Hierarchy
+
+mklab.JGNN.core.util.Loss
+
+
+
+
+
Uses of Package mklab.JGNN.core.util
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.core.util
+
+
Initializer
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+Direct Known Subclasses:
+VariancePreservingInitializer
+public abstract class Initializer
+
extends Object This class defines an abstract interface for applying initializers to models.
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Abstract Methods
+
+
+
+
+
+
+
+
+
Applies the initializer to a given model's parameters.
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Initializer
+public Initializer ()
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+apply
+
+Applies the initializer to a given model's parameters.
+
+Parameters:
+model - The given model.Returns:
+The given model after parameters are initialized.
+
+
+
+
+
+
+
+
+
+
+
+
Loss
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Abstract Methods
+
+
+
+
+
+
+
+
+
Provides the derivative of a loss function at its evaluation point.
+
+
abstract double
+
+
+
Provides a numerical evaluation of a loss function, so that
+ lower values correspond to better predictions.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+evaluate
+public abstract double evaluate (Tensor output,
+ Tensor desired) Provides a numerical evaluation of a loss function, so that
+ lower values correspond to better predictions.
+
+Parameters:
+output - A model's estimation of true outputs.desired - The expected outputs.Returns:
+A double value (is negative if smaller
+ values are better).
+See Also:
+
+
+
+
+
+
+
+
+derivative
+
+Provides the derivative of a loss function at its evaluation point.
+
+Parameters:
+output - A model's estimation of true outputs.desired - The expected outputs.Returns:
+A Tensor compliant to the model's estimation.
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Model
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+public class Model
+
extends Object This class is a way to organize
NNOperation trees into trainable machine
+ learning models. Critically, only model inputs and outputs need to be defined. It also
+ provides methods that perform training by calling forward and backward passes.
+ Models can have multiple inputs and outputs.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Adds to the model's inputs the provided
Variable.
+
+
+
+
+
Adds to the model's output the output of the provided operation.
+
+
+
+
+
Retrieves a list of operations by traversing the model's execution
+ graph with the depth-first algorithm in the inverse edge
+ order (starting from the outputs).
+
+
+
+
+
Retrieves a list of model inputs.
+
+
+
+
+
Retrieves a list of model outputs.
+
+
+
+
+
Retrieves a list of all parameters eventually leading to the model's outputs.
+
+
+
+
+
Apply the provided initializer on the model to set first values to its
+ parameters.
+
+
+
+
+
Forward run of the model given a list of input tensors.
+
+
+
+
+
Forward run of the model given an array of input tensors.
+
+
+
+
+
Performs the training of #train(Optimizer, List, List, List) for unit weights.
+
+
+
+
+
Performs one parameter adjustment step (e.g.
+
+
+
+
+
double
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Model
+public Model ()
+Deprecated.
+
+
+Instantiates an empty model.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+init
+
+Apply the provided initializer on the model to set first values to its
+ parameters.
+
+Parameters:
+initializer - An InitializerReturns:
+The model's instance.
+
+
+
+
+
+train
+
+
+
+Parameters:
+trainer - The ModelTrainingfeatures - A training feature Matrixlabels - A training label MatrixtrainingSamples - A slice of samples to use for training.validationSamples - A slice of samples to use for validation.Returns:
+The model's instance.
+
+
+
+
+
+getDepthLastOperations
+
+Retrieves a list of operations by traversing the model's execution
+ graph with the depth-first algorithm in the
inverse edge
+ order (starting from the outputs). This can be used by
Initializer
+ classes to push non-linearities to earlier layers.
+Returns:
+A list of NNOperation
+
+
+
+
+
+getParameters
+
+Retrieves a list of all parameters eventually leading to the model's outputs.
+
+Returns:
+A list of Parameter
+
+
+
+
+
+
+
+
+addOutput
+
+Adds to the model's output the output of the provided operation.
+
+Parameters:
+output - An operation to set as an output.Returns:
+this Model instance.See Also:
+
+
+
+
+
+
+
+
+
+
+
+getOutputs
+
+Retrieves a list of model outputs. Editing this list affects
+ the model and is not recommended. Output order is based on
+ the chronological addition of outputs through
addOutput(NNOperation).
+Returns:
+A list of Variable
+See Also:
+
+
+
+
+
+
+
+
+predict
+
+Forward run of the model given an array of input tensors.
+ Wraps
predict(List).
+Parameters:
+inputs - Input tensors to be assigned to input variables.Returns:
+A list of tensors output by the model after a forward pass.
+See Also:
+
+
+
+
+
+
+
+
+predict
+
+Forward run of the model given a list of input tensors. Their order should match the order
+ of variables in
getInputs().
+Parameters:
+inputs - A list of tensors to be assigned to input variables. These should haveReturns:
+A list of tensors output by the model after a forward pass.
+Throws:
+IllegalArgumentException See Also:
+
+
+
+
+
+
+
+
+train
+
+Performs the training of #train(Optimizer, List, List, List) for unit weights.
+
+Parameters:
+loss - The loss to minimize.optimizer - The provided optimizer with which to adjust values.inputs - A list of input tensors that would be passed to a corresponding predict(List)desiredOutputs - A list of output tensors that would be ideally returned by the prediction.
+ Element values should lie in the rage [0,1].Returns:
+A list of prediction outputs (the ones computed before parameter adjustment)
+Throws:
+IllegalArgumentException getInputs()getOutputs()See Also:
+
+
+
+
+
+
+
+
+train
+
+Performs one parameter adjustment step (e.g. epoch) using
Optimizer for a cross entropy loss function
+ that compares desired outputs and the ones
predict(List) yields for the given inputs.
+Parameters:
+loss - The loss to minimize.optimizer - The provided optimizer with which to adjust values.inputs - A list of input tensors that would be passed to a corresponding predict(List)desiredOutputs - A list of output tensors that would be ideally returned by the prediction.
+ Element values should lie in the rage [0,1].weights - A list of weight tensors to be applied element-by-element on the outcome of
+ Loss#crossEntropyDerivative(double, double).Returns:
+A list of prediction outputs (the ones computed before parameter adjustment)
+Throws:
+IllegalArgumentException getInputs()getOutputs()See Also:
+
+
+
+
+
+
+
+
+trainTowardsZero
+
+
+
+Parameters:
+optimizer - The provided optimizer with which to adjust values.inputs - A list of input tensors that would be passed to a corresponding predict(List)Returns:
+The L2 loss (computed before parameter adjustment)
+Throws:
+IllegalArgumentException getInputs()getOutputs()See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
ModelTraining
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+public class ModelTraining
+
extends Object This is a helper class that automates the definition of training processes of
Model instances
+ by defining the number of epochs, loss functions, number of batches and the ability to use
ThreadPool
+ for parallelized batch computations.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods Deprecated Methods
+
+
+
+
+
+
+
+
+
+
+
+
Sets the maximum number of epochs for which training runs.
+
+
+
+
+
+
+
+
Sets the number of batches training data slices should be split into.
+
+
+
+
+
Sets an
Optimizer instance to controls parameter updates during training.
+
+
+
+
+
Sets whether the training strategy should reflect stochastic
+ gradient descent by randomly sampling from the training dataset to obtain data samples.
+
+
+
+
+
Sets the patience of the training strategy that performs early stopping.
+
+
+
+
+
+
+
+
+
+
+
Trains a
Model instance based on current settings.
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+ModelTraining
+public ModelTraining ()
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+setVerbose
+
+Deprecated.
+
+
+
+Parameters:
+verbose - Whether an error message will be printed.
+
+
+
+
+setLoss
+
+Set
+
+Parameters:
+loss - Returns:
+
+
+
+
+
+
+
+
+setOptimizer
+
+Sets an
Optimizer instance to controls parameter updates during training.
+ If the provided optimizer is not an instance of
BatchOptimizer,
+ it is forcefully wrapped by the latter. Training calls the batch optimizer's
+ update method after every batch.
+Parameters:
+optimizer - The desired optimizer.Returns:
+this model training instance.See Also:
+
+
+
+
+
+
+
+
+setNumBatches
+
+Sets the number of batches training data slices should be split into.
+
+Parameters:
+numBatches - The desired number of batches. Default is 1.Returns:
+this model training instance.See Also:
+
+
+
+
+
+
+
+
+setParallelizedStochasticGradientDescent
+public ModelTraining setParallelizedStochasticGradientDescent (boolean paralellization) Sets whether the training strategy should reflect stochastic
+ gradient descent by randomly sampling from the training dataset to obtain data samples.
+ If
true, both this feature and acceptable thread-based paralellization
+ is enabled. Parallelization makes use of JGNN's
ThreadPool.
+Parameters:
+paralellization - A boolean value indicating whether this feature is enabled.Returns:
+this model training instance.See Also:
+
+
+
+
+
+
+
+
+setEpochs
+
+Sets the maximum number of epochs for which training runs.
+ If no patience has been set, training runs for exactly this
+ number of epochs.
+
+Parameters:
+epochs - The maximum number of epochs.Returns:
+this model training instance.See Also:
+
+
+
+
+
+
+
+
+setPatience
+
+Sets the patience of the training strategy that performs early stopping.
+ If training does not encounter a smaller validation loss for this number of
+ epochs, it stops.
+
+Parameters:
+patience - The number of patience epochs. Default is Integer.MAX_VALUE to effectively disable this
+ feature and let training always reach the maximum number of set epochs.Returns:
+this model training instance.See Also:
+
+
+
+
+
+
+
+
+train
+
+Trains a
Model instance based on current settings.
+Parameters:
+model - The model instance to train.features - A matrix whose columns correspond to sample features.labels - A matrix whose columns correspond to sample (one hot) labels.trainingSamples - Which columns to select for training.Returns:
+The trained model (the same instance as the first argument).
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
NNOperation
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+Direct Known Subclasses:
+Add Attention Complement Concat Dropout Exp From Gather Identity L1 Log LRelu MatMul Max Mean Multiply NExp Parameter PRelu Reduce Relu Repeat Reshape Sigmoid SoftMax Sort Sum Tanh To Transpose
+public abstract class NNOperation
+
extends Object This class defines an abstract neural network operation with forward and
+ backpropagation capabilities. Defined operations create execution trees based
+ on input dependencies, which can then be run by
Model instances to
+ make predictions. Creating the execution tree can be done by using the
+
addInput(NNOperation) method. The correct number of inputs should be
+ added to each operation. Compliance to this rule needs to be checked by
+ individual operations during forward passes.
+ Operations are thread-safe in the sense that they store gradients for
+ backward passes on different objects across different threads. This, way
+ models can perform learning passes which are all synchronized when eventually
+ backpropagation feeds
Parameter updates to an
+
Optimizer.
+ The internal state of operations can be obtained with
+
getPrediction() to obtain their last
Tensor output (this
+ output is depends on the thread calling the operation) and
+
getLastTapeError() to obtain the last gradient obtained through
+ backpropagation.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Field Summary
+Fields
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
final void
+
+
+
+
+
+
Retrieves an concise description of the operation that shows metadata and
+ potential data descriptions processed by the current thread.
+
+
+
+
+
+
+
+
Retrieves a list of input operations within a model's execution graph.
+
+
+
+
+
double
+
getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+
+
+
+
Retrieves a list of output operations within a model's execution graph.
+
+
+
+
+
+
+
+
Provides a simple description to show when drawing .dot format diagrams.
+
+
boolean
+
+
+
Checks whether the operation's output should be cached given that it is a
+ constant.
+
+
boolean
+
+
+
Checks whether the operation yields a constant output, so that propagation
+ does not try to compute partial derivatives for it.
+
+
+
+
+
Performs a forward pass in the operation without inducing any kind of
+ learning or storing the outcome .
+
+
+
+
+
Performs a forward pass in the operation without inducing any kind of
+ learning or storing the outcome .
+
+
+
+
+
+
+
+
void
+
+
+
+
+
+
Retrieves a string that views internal data being processed by the current
+ thread, including gradients.
+
+
+
+
+
+
+
+
+
+
+
+
+Field Details
+
+
+
+debugging
+public boolean debugging
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+setDescription
+public void setDescription (String description)
+
+
+
+getDescription
+public String getDescription ()
+
+
+
+describe
+
+Retrieves an concise description of the operation that shows metadata and
+ potential data descriptions processed by the current thread.
+
+Returns:
+A String description.
+See Also:
+
+
+
+
+
+
+
+
+view
+
+Retrieves a string that views internal data being processed by the current
+ thread, including gradients. This may
+
+Returns:
+A String view.
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+getOutputs
+
+Retrieves a list of output operations within a model's execution graph.
+
+Returns:
+A list of NNOperation
+
+
+
+
+
+isConstant
+public boolean isConstant ()
+Checks whether the operation yields a constant output, so that propagation
+ does not try to compute partial derivatives for it.
+
+Returns:
+A boolean value.
+
+
+
+
+
+isCachable
+public boolean isCachable ()
+Checks whether the operation's output should be cached given that it is a
+ constant. This returns false only for randomized components that
+ yield different outputs from different inputs, such as dropouts.
+
+Returns:
+A boolean values.
+
+
+
+
+
+getNonLinearity
+public double getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+Retrieves the degree of non-linearity of the operation to be used by
+
VariancePreservingInitializer. Default is
+ one for operations like addition, multiplication, and matrix multiplication,
+ and is different only for activation functions.
+Parameters:
+inputId - The input for which the non-linearity is
+ calculated.inputMass - The fraction of (matrix) parameters affecting the
+ calculation coming from the respective input.outputNonLinearity - The output's non-linearity gain.Returns:
+double describing the non-linearity.
+
+
+
+
+clearPrediction
+public final void clearPrediction ()
+
+
+
+
+
+
+
+getLastTapeError
+public final Tensor getLastTapeError ()
+
+
+
+getPrediction
+public final Tensor getPrediction ()
+
+
+
+runPrediction
+public final Tensor runPrediction ()
+
+
+
+run
+
+Performs a forward pass in the operation
without inducing any kind of
+ learning or storing the outcome . This is just a way to replicate the
+ operation at the tensor level and does not affect or is affected by any
+ dependent inputs
addInput(mklab.JGNN.nn.NNOperation).
+Parameters:
+inputs - A list of input tensors needed by the operation.Returns:
+A Tensor with the operation's outcome.
+
+
+
+
+
+run
+
+Performs a forward pass in the operation
without inducing any kind of
+ learning or storing the outcome . This is just a way to replicate the
+ operation at the tensor level and does not affect or is affected by any
+ dependent inputs
addInput(mklab.JGNN.nn.NNOperation).
+Parameters:
+inputs - A list of input tensors needed by the operation.Returns:
+A Tensor with the operation's outcome.
+
+
+
+
+
+getSimpleDescription
+public String getSimpleDescription ()
Provides a simple description to show when drawing .dot format diagrams.
+
+Returns:
+A string description, usually the component's class name.
+
+
+
+
+
+runPredictionAndAutosize
+public Tensor runPredictionAndAutosize ()
+
+
+
+
+
+
+
+
+
+
Optimizer
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+All Known Implementing Classes:
+Adam BatchOptimizer GradientDescent Regularization
+public interface Optimizer
+Provides an interface for training tensors. Has a
reset() method that starts potential training memory from scratch.
+ Has an
update(Tensor, Tensor) method that, given a current Tensor
+ and a gradient operates on the former and adjusts its value.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Abstract Methods Default Methods
+
+
+
+
+
+
default void
+
+
+
Resets (and lets the garbage collector free) optimizer memory.
+
+
void
+
+
+
In-place updates the value of a tensor given its gradient.
+
+
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+update
+
+In-place updates the value of a tensor given its gradient.
+ Some optimizers (e.g. Adama) require the exact same tensor instance to be provided
+ so as to keep track of its optimization progress. The library makes sure to keep
+ this constraint.
+
+Parameters:
+value - The tensor to update.gradient - The tensor's gradient.
+
+
+
+
+reset
+default void reset ()
+Resets (and lets the garbage collector free) optimizer memory.
+ Should be called at the beginning of training (not after each epoch).
+
+
+
+
+
+
+
+
+
+
+
Exp
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that performs an exponential transformation of its single input.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+getNonLinearity
+public double getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+Retrieves the degree of non-linearity of the operation to be used by
+
VariancePreservingInitializer. Default is
+ one for operations like addition, multiplication, and matrix multiplication,
+ and is different only for activation functions.
+Overrides:
+getNonLinearity NNOperation Parameters:
+inputId - The input for which the non-linearity is
+ calculated.inputMass - The fraction of (matrix) parameters affecting the
+ calculation coming from the respective input.outputNonLinearity - The output's non-linearity gain.Returns:
+double describing the non-linearity.
+
+
+
+
+
+
+
+
+
+
+
L1
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that performs a L1 transformation of its single input
+ by row or column.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+
+
+
+L1
+public L1 (boolean colMode)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
LRelu
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that performs a leaky relu operation, where the first argument is a tensor on which
+ it is applied and the second one should be a tensor wrapping a double value (consider initializing this with as a
+
Constant holding a tensor generated with
Tensor.fromDouble(double)) where
+ the wrapped value indicates the negative region's slope. If the negative slope is zero, leaky relu is reduced to
Relu.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+getNonLinearity
+public double getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+Retrieves the degree of non-linearity of the operation to be used by
+
VariancePreservingInitializer. Default is
+ one for operations like addition, multiplication, and matrix multiplication,
+ and is different only for activation functions.
+Overrides:
+getNonLinearity NNOperation Parameters:
+inputId - The input for which the non-linearity is
+ calculated.inputMass - The fraction of (matrix) parameters affecting the
+ calculation coming from the respective input.outputNonLinearity - The output's non-linearity gain.Returns:
+double describing the non-linearity.
+
+
+
+
+
+
+
+
+
+
+
NExp
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that performs an exponential transformation of
+ its single input, but only on the non-zero elements.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+getNonLinearity
+public double getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+Retrieves the degree of non-linearity of the operation to be used by
+
VariancePreservingInitializer. Default is
+ one for operations like addition, multiplication, and matrix multiplication,
+ and is different only for activation functions.
+Overrides:
+getNonLinearity NNOperation Parameters:
+inputId - The input for which the non-linearity is
+ calculated.inputMass - The fraction of (matrix) parameters affecting the
+ calculation coming from the respective input.outputNonLinearity - The output's non-linearity gain.Returns:
+double describing the non-linearity.
+
+
+
+
+
+
+
+
+
+
+
PRelu
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+getNonLinearity
+public double getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+Retrieves the degree of non-linearity of the operation to be used by
+
VariancePreservingInitializer. Default is
+ one for operations like addition, multiplication, and matrix multiplication,
+ and is different only for activation functions.
+Overrides:
+getNonLinearity NNOperation Parameters:
+inputId - The input for which the non-linearity is
+ calculated.inputMass - The fraction of (matrix) parameters affecting the
+ calculation coming from the respective input.outputNonLinearity - The output's non-linearity gain.Returns:
+double describing the non-linearity.
+
+
+
+
+
+
+
+
+
+
+
Relu
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that performs a relu transformation of its single input first introduced by
+
Hahnloser, Richard HR, Rahul Sarpeshkar, Misha A. Mahowald, Rodney J. Douglas, and H. Sebastian Seung.
+ "Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit."
+ Nature 405, no. 6789 (2000): 947-951.
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+getNonLinearity
+public double getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+Retrieves the degree of non-linearity of the operation to be used by
+
VariancePreservingInitializer. Default is
+ one for operations like addition, multiplication, and matrix multiplication,
+ and is different only for activation functions.
+Overrides:
+getNonLinearity NNOperation Parameters:
+inputId - The input for which the non-linearity is
+ calculated.inputMass - The fraction of (matrix) parameters affecting the
+ calculation coming from the respective input.outputNonLinearity - The output's non-linearity gain.Returns:
+double describing the non-linearity.
+
+
+
+
+
+
+
+
+
+
+
Sigmoid
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that performs a sigmoid transformation of its single input.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Sigmoid
+public Sigmoid ()
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+getNonLinearity
+public double getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+Retrieves the degree of non-linearity of the operation to be used by
+
VariancePreservingInitializer. Default is
+ one for operations like addition, multiplication, and matrix multiplication,
+ and is different only for activation functions.
+Overrides:
+getNonLinearity NNOperation Parameters:
+inputId - The input for which the non-linearity is
+ calculated.inputMass - The fraction of (matrix) parameters affecting the
+ calculation coming from the respective input.outputNonLinearity - The output's non-linearity gain.Returns:
+double describing the non-linearity.
+
+
+
+
+
+
+
+
+
+
+
Tanh
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that performs a tanh transformation of its single input.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+getNonLinearity
+public double getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+Retrieves the degree of non-linearity of the operation to be used by
+
VariancePreservingInitializer. Default is
+ one for operations like addition, multiplication, and matrix multiplication,
+ and is different only for activation functions.
+Overrides:
+getNonLinearity NNOperation Parameters:
+inputId - The input for which the non-linearity is
+ calculated.inputMass - The fraction of (matrix) parameters affecting the
+ calculation coming from the respective input.outputNonLinearity - The output's non-linearity gain.Returns:
+double describing the non-linearity.
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.nn.activations.Exp
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.activations.Exp
+
+
Uses of Class mklab.JGNN.nn.activations.L1
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.activations.L1
+
+
Uses of Class mklab.JGNN.nn.activations.LRelu
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.activations.LRelu
+
+
Uses of Class mklab.JGNN.nn.activations.NExp
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.activations.NExp
+
+
Uses of Class mklab.JGNN.nn.activations.PRelu
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.activations.PRelu
+
+
Uses of Class mklab.JGNN.nn.activations.Relu
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.activations.Relu
+
+
Uses of Class mklab.JGNN.nn.activations.Sigmoid
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.activations.Sigmoid
+
+
Uses of Class mklab.JGNN.nn.activations.Tanh
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.activations.Tanh
+
+
mklab.JGNN.nn.activations
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+package mklab.JGNN.nn.activations
+
+
+
+
+
+
+
+
Classes
+
+
+
+
+
+
Implements a
NNOperation that performs an exponential transformation of its single input.
+
+
+
+
Implements a
NNOperation that performs a L1 transformation of its single input
+ by row or column.
+
+
+
+
Implements a
NNOperation that performs a leaky relu operation, where the first argument is a tensor on which
+ it is applied and the second one should be a tensor wrapping a double value (consider initializing this with as a
+
Constant holding a tensor generated with
Tensor.fromDouble(double)) where
+ the wrapped value indicates the negative region's slope.
+
+
+
+
Implements a
NNOperation that performs an exponential transformation of
+ its single input, but only on the non-zero elements.
+
+
+
+
+
+
Implements a
NNOperation that performs a relu transformation of its single input first introduced by
+
Hahnloser, Richard HR, Rahul Sarpeshkar, Misha A.
+
+
+
+
Implements a
NNOperation that performs a sigmoid transformation of its single input.
+
+
+
+
Implements a
NNOperation that performs a tanh transformation of its single input.
+
+
+
+
+
+
+
+
mklab.JGNN.nn.activations Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+Class Hierarchy
+
+java.lang.Object
+
+mklab.JGNN.nn.NNOperation
+
+mklab.JGNN.nn.activations.Exp
+mklab.JGNN.nn.activations.L1
+mklab.JGNN.nn.activations.LRelu
+mklab.JGNN.nn.activations.NExp
+mklab.JGNN.nn.activations.PRelu
+mklab.JGNN.nn.activations.Relu
+mklab.JGNN.nn.activations.Sigmoid
+mklab.JGNN.nn.activations.Tanh
+
+
+
+
+
+
+
+
+
Uses of Package mklab.JGNN.nn.activations
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.activations
+
+
Uses of Class mklab.JGNN.nn.Initializer
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Apply the provided initializer on the model to set first values to its
+ parameters.
+
+
+
+
+
+
+
+
+
+
+
+
class
+
+
+
class
+
+
+
class
+
+
+
This class describes a broad class of
Initializer strategies, in which
+ dense neural layer initialization is controlled so that variance is mostly preserved from
+ inputs to outputs to avoid vanishing or exploding gradients in the first training
+ runs.
+
+
class
+
+
+
class
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.nn.Loss
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Performs the training of #train(Optimizer, List, List, List) for unit weights.
+
+
+
+
+
Performs one parameter adjustment step (e.g.
+
+
+
+
+
+
+
+
+
+
+
+
class
+
+
+
Implements an accuracy
Loss of row-by-row comparisons.
+
+
class
+
+
+
class
+
+
+
Implements a categorical cross-entropy
Loss.
+ For binary classification of one output use
BinaryCrossEntropy.
+
+
+
+
+
+
+
+
+
+
+
+
class
+
+
+
Implements a
Loss that wraps other losses and outputs their value during training to an output stream
+ (to
System.out by default).
+
+
+
+
+
+
+
+
+
Instantiates a
VerboseLoss given a base loss to be wrapped.
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.nn.Model
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Retrieves the model currently built by the builder.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Adds to the model's inputs the provided
Variable.
+
+
+
+
+
Adds to the model's output the output of the provided operation.
+
+
+
+
+
Applies the initializer to a given model's parameters.
+
+
+
+
+
Apply the provided initializer on the model to set first values to its
+ parameters.
+
+
+
+
+
+
+
+
Trains a
Model instance based on current settings.
+
+
+
+
+
+
+
+
+
Applies the initializer to a given model's parameters.
+
+
+
+
+
Trains a
Model instance based on current settings.
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.nn.ModelTraining
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Sets the maximum number of epochs for which training runs.
+
+
+
+
+
+
+
+
Sets the number of batches training data slices should be split into.
+
+
+
+
+
Sets an
Optimizer instance to controls parameter updates during training.
+
+
+
+
+
Sets whether the training strategy should reflect stochastic
+ gradient descent by randomly sampling from the training dataset to obtain data samples.
+
+
+
+
+
Sets the patience of the training strategy that performs early stopping.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.nn.NNOperation
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Retrieves the
NNOperation registered with the provided
+ name, for example to investigates its value.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Retrieves a list of operations by traversing the model's execution
+ graph with the depth-first algorithm in the inverse edge
+ order (starting from the outputs).
+
+
+
+
+
Retrieves a list of input operations within a model's execution graph.
+
+
+
+
+
Retrieves a list of model outputs.
+
+
+
+
+
Retrieves a list of output operations within a model's execution graph.
+
+
+
+
+
+
+
+
+
+
+
+
Adds to the model's output the output of the provided operation.
+
+
+
+
+
+
+
+
+
+
+
+
class
+
+
+
Implements a
NNOperation that performs an exponential transformation of its single input.
+
+
class
+
+
+
Implements a
NNOperation that performs a L1 transformation of its single input
+ by row or column.
+
+
class
+
+
+
Implements a
NNOperation that performs a leaky relu operation, where the first argument is a tensor on which
+ it is applied and the second one should be a tensor wrapping a double value (consider initializing this with as a
+
Constant holding a tensor generated with
Tensor.fromDouble(double)) where
+ the wrapped value indicates the negative region's slope.
+
+
class
+
+
+
Implements a
NNOperation that performs an exponential transformation of
+ its single input, but only on the non-zero elements.
+
+
class
+
+
+
class
+
+
+
Implements a
NNOperation that performs a relu transformation of its single input first introduced by
+
Hahnloser, Richard HR, Rahul Sarpeshkar, Misha A.
+
+
class
+
+
+
Implements a
NNOperation that performs a sigmoid transformation of its single input.
+
+
class
+
+
+
Implements a
NNOperation that performs a tanh transformation of its single input.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
class
+
+
+
class
+
+
+
Implements a
NNOperation that creates a version of adjacency matrices
+ with column-wise attention involving neighbor similarity.
+
+
class
+
+
+
Implements a
NNOperation that performs the operation 1-x for its simple input x.
+
+
class
+
+
+
Implements a
NNOperation that concatenates its two matrix inputs.
+
+
class
+
+
+
Implements a
NNOperation that converts its first argument to a
ColumnRepetition matrix
+ with a number of columns equal to the second argument.
+
+
class
+
+
+
Implements a
NNOperation that lists the first element of the 2D matrix element iterator.
+
+
class
+
+
+
Implements a
NNOperation that performs the equivalent of TensorFlow's gather operation.
+
+
class
+
+
+
Implements a
NNOperation that just transfers its single input.
+
+
class
+
+
+
Implements a
NNOperation that outputs the natural logarithm of its single input.
+
+
class
+
+
+
Implements a
NNOperation that multiplies its two matrix inputs.
+
+
class
+
+
+
Implements a
NNOperation that multiplies its two inputs element-by-element.
+
+
class
+
+
+
class
+
+
+
Implements a
NNOperation that converts its first argument to a
ColumnRepetition matrix
+ with a number of columns equal to the second argument.
+
+
class
+
+
+
class
+
+
+
Implements a
NNOperation that lists the second element of the 2D matrix element iterator.
+
+
class
+
+
+
Implements a
NNOperation that performs matrix transposition.
+
+
+
+
+
+
+
+
+
+
+
+
class
+
+
+
Implements a
NNOperation that performs row-wise or column-wise
+ maximum reduction on vector tensors or matrices.
+
+
class
+
+
+
Implements a
NNOperation that performs row-wise or column-wise
+ mean reduction on vector tensors or matrices.
+
+
class
+
+
+
Implements a
NNOperation that performs row-wise or column-wise
+ softmax on vector tensors or matrices.
+
+
class
+
+
+
class
+
+
+
Implements a
NNOperation that performs row-wise or column-wise
+ sum reduction on vector tensors or matrices.
+
+
+
+
+
+
+
+
Uses of Interface mklab.JGNN.nn.Optimizer
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Sets an
Optimizer instance to controls parameter updates during training.
+
+
+
+
+
Performs the training of #train(Optimizer, List, List, List) for unit weights.
+
+
+
+
+
Performs one parameter adjustment step (e.g.
+
+
double
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
class
+
+
+
Thic class implements an Adam
Optimizer as explained in the paper:
+
Kingma, Diederik P., and Jimmy Ba.
+
+
class
+
+
+
class
+
+
+
class
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
KaimingNormal
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+KaimingNormal
+public KaimingNormal ()
+
+
+
+
+
+
+
+
+
+
+
KaimingUniform
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+KaimingUniform
+public KaimingUniform ()
+
+
+
+
+
+
+
+
+
+
+
VariancePreservingInitializer
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+Direct Known Subclasses:
+KaimingNormal KaimingUniform XavierNormal XavierUniform
+public abstract class VariancePreservingInitializer
+
extends Initializer This class describes a broad class of
Initializer strategies, in which
+ dense neural layer initialization is controlled so that variance is mostly preserved from
+ inputs to outputs to avoid vanishing or exploding gradients in the first training
+ runs.
+
+ This initializer traverses the execution tree to discover the impact of matrix parameters
+ to output variances, as eventually determined by backtracking
+
NNOperation.getNonLinearity(int, double, double) up to non-linear components,
+ where the latter are identified by the condition
getNonLinearity(0, 1, 1)!=1.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Applies the initializer to a given model's parameters.
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+VariancePreservingInitializer
+public VariancePreservingInitializer ()
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+apply
+
+
+Applies the initializer to a given model's parameters.
+
+Specified by:
+apply Initializer Parameters:
+model - The given model.Returns:
+The given model after parameters are initialized.
+
+
+
+
+
+
+
+
+
+
+
+
XavierNormal
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+XavierNormal
+public XavierNormal ()
+
+
+
+
+
+
+
+
+
+
+
XavierUniform
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+XavierUniform
+public XavierUniform ()
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.nn.initializers.KaimingNormal
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.initializers.KaimingNormal
+
+
Uses of Class mklab.JGNN.nn.initializers.KaimingUniform
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.initializers.KaimingUniform
+
+
Uses of Class mklab.JGNN.nn.initializers.VariancePreservingInitializer
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
class
+
+
+
class
+
+
+
class
+
+
+
class
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.nn.initializers.XavierNormal
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.initializers.XavierNormal
+
+
Uses of Class mklab.JGNN.nn.initializers.XavierUniform
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.initializers.XavierUniform
+
+
mklab.JGNN.nn.initializers
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+package mklab.JGNN.nn.initializers
+
+
+
+
+
+
+
+
Classes
+
+
+
+
+
+
+
+
+
+
This class describes a broad class of
Initializer strategies, in which
+ dense neural layer initialization is controlled so that variance is mostly preserved from
+ inputs to outputs to avoid vanishing or exploding gradients in the first training
+ runs.
+
+
+
+
+
+
+
+
+
+
+
+
mklab.JGNN.nn.initializers Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/nn/initializers/package-use.html b/docs/javadoc/mklab/JGNN/nn/initializers/package-use.html
new file mode 100644
index 00000000..c2e92358
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/nn/initializers/package-use.html
@@ -0,0 +1,86 @@
+
+
+
+
+Uses of Package mklab.JGNN.nn.initializers
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
This class describes a broad class of
Initializer strategies, in which
+ dense neural layer initialization is controlled so that variance is mostly preserved from
+ inputs to outputs to avoid vanishing or exploding gradients in the first training
+ runs.
+
+
+
+
+
+
+
+
Constant
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that holds a constant tensor.
+ This value *is not affected by learning* but can be manually updated with the
#setTo(Tensor) method.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Creates a constant holding a tensor.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
boolean
+
+
+
Checks whether the operation's output should be cached given that it is a
+ constant.
+
+
boolean
+
+
+
Checks whether the operation yields a constant output, so that propagation
+ does not try to compute partial derivatives for it.
+
+
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Constant
+public Constant (Tensor tensor) Creates a constant holding a tensor.
+
+Parameters:
+tensor - The held tensor.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+isConstant
+public boolean isConstant ()
+
+Checks whether the operation yields a constant output, so that propagation
+ does not try to compute partial derivatives for it.
+
+Overrides:
+isConstant Parameter Returns:
+A boolean value.
+
+
+
+
+
+isCachable
+public boolean isCachable ()
+
+Checks whether the operation's output should be cached given that it is a
+ constant. This returns false only for randomized components that
+ yield different outputs from different inputs, such as dropouts.
+
+Overrides:
+isCachable Parameter Returns:
+A boolean values.
+
+
+
+
+
+
+
+
+
+
+
+
Parameter
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+Direct Known Subclasses:
+Constant Variable
+Implements a
NNOperation that holds and returns a parameter tensor.
+
The held value is tuned by learning.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
+
+
+
Gets sets the parameter's value tensor
+
+
boolean
+
+
+
Checks whether the operation's output should be cached given that it is a
+ constant.
+
+
boolean
+
+
+
Checks whether the operation yields a constant output, so that propagation
+ does not try to compute partial derivatives for it.
+
+
+
+
+
Forcefully sets the parameter's value tensor to the desired value.
+
+
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Parameter
+public Parameter (Tensor tensor)
+
+
+
+Parameter
+public Parameter (Tensor tensor,
+ double regularization)
+
+
+
+
+
+
+
+Method Details
+
+
+
+set
+
+Forcefully sets the parameter's value tensor to the desired value.
+
+Parameters:
+tensor - The new parameter value.Returns:
+this parameter.
+
+
+
+
+get
+
+Gets sets the parameter's value tensor
+
+Returns:
+The current value Tensor
+
+
+
+
+
+
+
+
+isConstant
+public boolean isConstant ()
+
+Checks whether the operation yields a constant output, so that propagation
+ does not try to compute partial derivatives for it.
+
+Overrides:
+isConstant NNOperation Returns:
+A boolean value.
+
+
+
+
+
+isCachable
+public boolean isCachable ()
+
+Checks whether the operation's output should be cached given that it is a
+ constant. This returns false only for randomized components that
+ yield different outputs from different inputs, such as dropouts.
+
+Overrides:
+isCachable NNOperation Returns:
+A boolean values.
+
+
+
+
+
+
+
+
+
+
+
+
Variable
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
boolean
+
+
+
Checks whether the operation's output should be cached given that it is a
+ constant.
+
+
boolean
+
+
+
Checks whether the operation yields a constant output, so that propagation
+ does not try to compute partial derivatives for it.
+
+
void
+
+
+
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Variable
+public Variable ()
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+isConstant
+public boolean isConstant ()
+
+Checks whether the operation yields a constant output, so that propagation
+ does not try to compute partial derivatives for it.
+
+Overrides:
+isConstant Parameter Returns:
+A boolean value.
+
+
+
+
+
+isCachable
+public boolean isCachable ()
+
+Checks whether the operation's output should be cached given that it is a
+ constant. This returns false only for randomized components that
+ yield different outputs from different inputs, such as dropouts.
+
+Overrides:
+isCachable Parameter Returns:
+A boolean values.
+
+
+
+
+
+setTo
+public void setTo (Tensor value)
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.nn.inputs.Constant
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.inputs.Constant
+
+
Uses of Class mklab.JGNN.nn.inputs.Parameter
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Retrieves a list of all parameters eventually leading to the model's outputs.
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.nn.inputs.Variable
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Retrieves a list of model inputs.
+
+
+
+
+
+
+
+
+
Adds to the model's inputs the provided
Variable.
+
+
+
+
+
+
+
+
mklab.JGNN.nn.inputs
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+package mklab.JGNN.nn.inputs
+
+
+
+
+
+
+
+
Classes
+
+
+
+
+
+
+
+
Implements a
NNOperation that holds and returns a parameter tensor.
+
+
+
+
+
+
+
+
+
+
mklab.JGNN.nn.inputs Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/nn/inputs/package-use.html b/docs/javadoc/mklab/JGNN/nn/inputs/package-use.html
new file mode 100644
index 00000000..f11e9120
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/nn/inputs/package-use.html
@@ -0,0 +1,102 @@
+
+
+
+
+Uses of Package mklab.JGNN.nn.inputs
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Implements a
NNOperation that holds and returns a parameter tensor.
+
+
+
+
+
+
+
+
+
+
+
+
+
Accuracy
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+public class Accuracy
+
extends Loss Implements an accuracy
Loss of row-by-row comparisons.
+ Each row of the output should have the same
Tensor.argmax()
+ value as the corresponding row of desired values.
+ This comparison has no derivative.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Instantiates a row-by-row
Accuracy loss.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Provides the derivative of a loss function at its evaluation point.
+
+
double
+
+
+
Provides a numerical evaluation of a loss function, so that
+ lower values correspond to better predictions.
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Accuracy
+public Accuracy ()
+Instantiates a row-by-row
Accuracy loss.
+ For this loss, each row of the output should have the same
+ value as the corresponding row of desired values.
+ This comparison has no derivative.
+
+
+
+
+
+
+
+Method Details
+
+
+
+evaluate
+
+Description copied from class: Loss Provides a numerical evaluation of a loss function, so that
+ lower values correspond to better predictions.
+
+Specified by:
+evaluate Loss Parameters:
+output - A model's estimation of true outputs.desired - The expected outputs.Returns:
+A double value (is negative if smaller
+ values are better).
+See Also:
+
+
+
+
+
+
+
+
+derivative
+
+Description copied from class: Loss Provides the derivative of a loss function at its evaluation point.
+
+Specified by:
+derivative Loss Parameters:
+output - A model's estimation of true outputs.desired - The expected outputs.Returns:
+A Tensor compliant to the model's estimation.
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
BinaryCrossEntropy
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+public class BinaryCrossEntropy
+
extends Loss
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Initializes binary cross entropy with 1.E-12 epsilon value.
+
+
+
+
Initializes binary cross entropy with and epsilon value
+ to bound its outputs in the range [log(epsilon), -log(epsilon)] instead of (-inf, inf).
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Provides the derivative of a loss function at its evaluation point.
+
+
double
+
+
+
Provides a numerical evaluation of a loss function, so that
+ lower values correspond to better predictions.
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+BinaryCrossEntropy
+public BinaryCrossEntropy ()
+Initializes binary cross entropy with 1.E-12 epsilon value.
+ For more than one output dimensions use
CategoricalCrossEntropy()
+See Also:
+
+
+
+
+
+
+
+
+BinaryCrossEntropy
+public BinaryCrossEntropy (double epsilon)
+Initializes binary cross entropy with and epsilon value
+ to bound its outputs in the range [log(epsilon), -log(epsilon)] instead of (-inf, inf).
+ For more than one output dimensions use
CategoricalCrossEntropy(double)
+Parameters:
+epsilon - A very small positive double.See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+evaluate
+
+Description copied from class: Loss Provides a numerical evaluation of a loss function, so that
+ lower values correspond to better predictions.
+
+Specified by:
+evaluate Loss Parameters:
+output - A model's estimation of true outputs.desired - The expected outputs.Returns:
+A double value (is negative if smaller
+ values are better).
+See Also:
+
+
+
+
+
+
+
+
+derivative
+
+Description copied from class: Loss Provides the derivative of a loss function at its evaluation point.
+
+Specified by:
+derivative Loss Parameters:
+output - A model's estimation of true outputs.desired - The expected outputs.Returns:
+A Tensor compliant to the model's estimation.
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
CategoricalCrossEntropy
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+public class CategoricalCrossEntropy
+
extends Loss Implements a categorical cross-entropy
Loss.
+ For binary classification of one output use
BinaryCrossEntropy.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Initializes categorical cross entropy with 1.E-12 epsilon value.
+
+
+
+
Initializes categorical cross entropy with and epsilon value
+ to bound its outputs in the range [log(epsilon), -log(epsilon)] instead of (-inf, inf).
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Provides the derivative of a loss function at its evaluation point.
+
+
double
+
+
+
Provides a numerical evaluation of a loss function, so that
+ lower values correspond to better predictions.
+
+
+
+
+
Sets the reduction mechanism of categorical cross entropy.
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+CategoricalCrossEntropy
+public CategoricalCrossEntropy ()
+Initializes categorical cross entropy with 1.E-12 epsilon value.
+ For binary classification of one output use
BinaryCrossEntropy().
+See Also:
+
+
+
+
+
+
+
+
+CategoricalCrossEntropy
+public CategoricalCrossEntropy (double epsilon)
+Initializes categorical cross entropy with and epsilon value
+ to bound its outputs in the range [log(epsilon), -log(epsilon)] instead of (-inf, inf).
+ For binary classification of one output use
BinaryCrossEntropy(double).
+Parameters:
+epsilon - A very small positive double.See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+setMeanReduction
+
+Sets the reduction mechanism of categorical cross entropy.
+ This can be either a sum or a mean across the categorical cross entropy of all data samples.
+
+Parameters:
+meanReduction - true to perform mean reduction, false (default) for sum reduction.Returns:
+this CategoricalCrossEntropy object.
+
+
+
+
+evaluate
+
+Description copied from class: Loss Provides a numerical evaluation of a loss function, so that
+ lower values correspond to better predictions.
+
+Specified by:
+evaluate Loss Parameters:
+output - A model's estimation of true outputs.desired - The expected outputs.Returns:
+A double value (is negative if smaller
+ values are better).
+See Also:
+
+
+
+
+
+
+
+
+derivative
+
+Description copied from class: Loss Provides the derivative of a loss function at its evaluation point.
+
+Specified by:
+derivative Loss Parameters:
+output - A model's estimation of true outputs.desired - The expected outputs.Returns:
+A Tensor compliant to the model's estimation.
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.nn.loss.Accuracy
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.loss.Accuracy
+
+
Uses of Class mklab.JGNN.nn.loss.BinaryCrossEntropy
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.loss.BinaryCrossEntropy
+
+
Uses of Class mklab.JGNN.nn.loss.CategoricalCrossEntropy
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Sets the reduction mechanism of categorical cross entropy.
+
+
+
+
+
+
+
+
mklab.JGNN.nn.loss
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+package mklab.JGNN.nn.loss
+
+
+
+
+
+
+
+
Classes
+
+
+
+
+
+
Implements an accuracy
Loss of row-by-row comparisons.
+
+
+
+
+
+
Implements a categorical cross-entropy
Loss.
+ For binary classification of one output use
BinaryCrossEntropy.
+
+
+
+
+
+
+
+
mklab.JGNN.nn.loss Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/nn/loss/package-use.html b/docs/javadoc/mklab/JGNN/nn/loss/package-use.html
new file mode 100644
index 00000000..3e5da23f
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/nn/loss/package-use.html
@@ -0,0 +1,84 @@
+
+
+
+
+Uses of Package mklab.JGNN.nn.loss
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Implements a categorical cross-entropy
Loss.
+ For binary classification of one output use
BinaryCrossEntropy.
+
+
+
+
+
+
+
+
VerboseLoss
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+public class VerboseLoss
+
extends Loss Implements a
Loss that wraps other losses and outputs their value during training to an output stream
+ (to
System.out by default). This is the simplest loss wrapper to keep track of training progress.
+Author:
+Emmanouil Krasanakis
+See Also:
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Instantiates a
VerboseLoss given a base loss to be wrapped.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Provides the derivative of a loss function at its evaluation point.
+
+
double
+
+
+
Provides a numerical evaluation of a loss function, so that
+ lower values correspond to better predictions.
+
+
void
+
+
+
+
+
+
Changes on which epochs the loss should be reported.
+
+
+
+
+
Changes where the output is printed.
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+VerboseLoss
+public VerboseLoss (Loss baseLoss) Instantiates a
VerboseLoss given a base loss to be wrapped.
+ Use a method chain to modify when losses should be reported, and which
+ output stream is used.
+Parameters:
+baseLoss - See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+reset
+public void reset ()
+
+
+
+
+setInterval
+
+Changes on which epochs the loss should be reported.
+
+Parameters:
+every - The loss is reported on epochs 0, every, 2every, ... Default is 1.Returns:
+this verbose loss instance.
+
+
+
+
+setStream
+
+Changes where the output is printed.
+
+Parameters:
+out - The print stream to print to. Default is System.outReturns:
+this verbose loss instance.
+
+
+
+
+evaluate
+
+Description copied from class: Loss Provides a numerical evaluation of a loss function, so that
+ lower values correspond to better predictions.
+
+Specified by:
+evaluate Loss Parameters:
+output - A model's estimation of true outputs.desired - The expected outputs.Returns:
+A double value (is negative if smaller
+ values are better).
+See Also:
+
+
+
+
+
+
+
+
+derivative
+
+Description copied from class: Loss Provides the derivative of a loss function at its evaluation point.
+
+Specified by:
+derivative Loss Parameters:
+output - A model's estimation of true outputs.desired - The expected outputs.Returns:
+A Tensor compliant to the model's estimation.
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.nn.loss.report.VerboseLoss
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Changes on which epochs the loss should be reported.
+
+
+
+
+
Changes where the output is printed.
+
+
+
+
+
+
+
+
mklab.JGNN.nn.loss.report
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+package mklab.JGNN.nn.loss.report
+
+
+
+
+
+
+
+
Classes
+
+
+
+
+
+
Implements a
Loss that wraps other losses and outputs their value during training to an output stream
+ (to
System.out by default).
+
+
+
+
+
+
+
+
mklab.JGNN.nn.loss.report Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/nn/loss/report/package-use.html b/docs/javadoc/mklab/JGNN/nn/loss/report/package-use.html
new file mode 100644
index 00000000..e98264e4
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/nn/loss/report/package-use.html
@@ -0,0 +1,84 @@
+
+
+
+
+Uses of Package mklab.JGNN.nn.loss.report
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Implements a
Loss that wraps other losses and outputs their value during training to an output stream
+ (to
System.out by default).
+
+
+
+
+
+
+
+
Add
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
Attention
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that creates a version of adjacency matrices
+ with column-wise attention involving neighbor similarity.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Attention
+public Attention ()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Complement
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that performs the operation 1-x for its simple input x.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Complement
+public Complement ()
+
+
+
+
+
+
+
+
+
+
+
Concat
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that concatenates its two matrix inputs.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Concat
+public Concat ()
+
+
+
+
+
+
+
+
+
+
+
Dropout
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that converts its first argument to a
ColumnRepetition matrix
+ with a number of columns equal to the second argument.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
boolean
+
+
+
Checks whether the operation's output should be cached given that it is a
+ constant.
+
+
boolean
+
+
+
void
+
+
+
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Dropout
+public Dropout ()
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+isEnabled
+public boolean isEnabled ()
+
+
+
+
+setEnabled
+public void setEnabled (boolean enabled)
+
+
+
+
+isCachable
+public boolean isCachable ()
+
+Checks whether the operation's output should be cached given that it is a
+ constant. This returns false only for randomized components that
+ yield different outputs from different inputs, such as dropouts.
+
+Overrides:
+isCachable NNOperation Returns:
+A boolean values.
+
+
+
+
+
+
+
+
+
+
+
+
From
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that lists the first element of the 2D matrix element iterator.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
boolean
+
+
+
Checks whether the operation's output should be cached given that it is a
+ constant.
+
+
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+isCachable
+public boolean isCachable ()
+
+Checks whether the operation's output should be cached given that it is a
+ constant. This returns false only for randomized components that
+ yield different outputs from different inputs, such as dropouts.
+
+Overrides:
+isCachable NNOperation Returns:
+A boolean values.
+
+
+
+
+
+
+
+
+
+
+
+
Gather
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that performs the equivalent of TensorFlow's gather operation.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Gather
+public Gather ()
+
+
+
+
+
+
+
+
+
+
+
Identity
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that just transfers its single input.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Identity
+public Identity ()
+
+
+
+
+
+
+
+
+
+
+
LSTM.LSTMState
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+Enclosing class:
+LSTM
+public static class LSTM.LSTMState
+
extends Object
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+LSTMState
+public LSTMState (Tensor previousMemory,
+ Tensor previousOutput)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
LSTM
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+public class LSTM
+
extends Object
+
+
+
+
+
+Nested Class Summary
+Nested Classes
+
+
+
+
+
static class
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
void
+
+
+
+
+
+
void
+
+
+
+
+
+
+
+
+
+
+
+
void
+
+
+
double
+
+
+
void
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+LSTM
+public LSTM (Optimizer optimizer,
+ int inputSize,
+ int outputSize)
+
+
+
+
+
+
+
+Method Details
+
+
+
+
+
+
+
+
+
+
+
+
+startTape
+public void startTape ()
+
+
+
+
+
+
+
+trainOnOutputError
+public void trainOnOutputError (Tensor [] inputs,
+ Tensor outputGradient)
+
+
+
+
+
+
+
+
+
+endTape
+public void endTape ()
+
+
+
+
+aggregate
+public void aggregate (LSTM lstm)
+
+
+
+
+
+
+
+
+
+
Log
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that outputs the natural logarithm of its single input.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
MatMul
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that multiplies its two matrix inputs.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+MatMul
+public MatMul ()
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+getNonLinearity
+public double getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+Retrieves the degree of non-linearity of the operation to be used by
+
VariancePreservingInitializer. Default is
+ one for operations like addition, multiplication, and matrix multiplication,
+ and is different only for activation functions.
+Overrides:
+getNonLinearity NNOperation Parameters:
+inputId - The input for which the non-linearity is
+ calculated.inputMass - The fraction of (matrix) parameters affecting the
+ calculation coming from the respective input.outputNonLinearity - The output's non-linearity gain.Returns:
+double describing the non-linearity.
+
+
+
+
+
+
+
+
+
+
+
Multiply
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that multiplies its two inputs element-by-element.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
double
+
getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Multiply
+public Multiply ()
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+getNonLinearity
+public double getNonLinearity (int inputId,
+ double inputMass,
+ double outputNonLinearity)
+
+Retrieves the degree of non-linearity of the operation to be used by
+
VariancePreservingInitializer. Default is
+ one for operations like addition, multiplication, and matrix multiplication,
+ and is different only for activation functions.
+Overrides:
+getNonLinearity NNOperation Parameters:
+inputId - The input for which the non-linearity is
+ calculated.inputMass - The fraction of (matrix) parameters affecting the
+ calculation coming from the respective input.outputNonLinearity - The output's non-linearity gain.Returns:
+double describing the non-linearity.
+
+
+
+
+
+
+
+
+
+
+
Reduce
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Reduce
+public Reduce ()
+
+
+
+
+
+
+
+
+
+
+
Repeat
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that converts its first argument to a
ColumnRepetition matrix
+ with a number of columns equal to the second argument.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Repeat
+public Repeat ()
+
+
+
+
+
+
+
+
+
+
+
Reshape
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
+
+
+
Provides a simple description to show when drawing .dot format diagrams.
+
+
boolean
+
+
+
Checks whether the operation's output should be cached given that it is a
+ constant.
+
+
+
+
+
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Reshape
+public Reshape (long rows,
+ long cols)
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+getSimpleDescription
+public String getSimpleDescription ()
Provides a simple description to show when drawing .dot format diagrams.
+
+Overrides:
+getSimpleDescription NNOperation Returns:
+A string description, usually the component's class name.
+
+
+
+
+
+isCachable
+public boolean isCachable ()
+
+Checks whether the operation's output should be cached given that it is a
+ constant. This returns false only for randomized components that
+ yield different outputs from different inputs, such as dropouts.
+
+Overrides:
+isCachable NNOperation Returns:
+A boolean values.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
To
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that lists the second element of the 2D matrix element iterator.
+Author:
+Emmanouil Krasanakis
+See Also:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
boolean
+
+
+
Checks whether the operation's output should be cached given that it is a
+ constant.
+
+
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+isCachable
+public boolean isCachable ()
+
+Checks whether the operation's output should be cached given that it is a
+ constant. This returns false only for randomized components that
+ yield different outputs from different inputs, such as dropouts.
+
+Overrides:
+isCachable NNOperation Returns:
+A boolean values.
+
+
+
+
+
+
+
+
+
+
+
+
Transpose
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that performs matrix transposition.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Transpose
+public Transpose ()
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.nn.operations.Add
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.operations.Add
+
+
Uses of Class mklab.JGNN.nn.operations.Attention
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.operations.Attention
+
+
Uses of Class mklab.JGNN.nn.operations.Complement
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.operations.Complement
+
+
Uses of Class mklab.JGNN.nn.operations.Concat
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.operations.Concat
+
+
Uses of Class mklab.JGNN.nn.operations.Dropout
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.operations.Dropout
+
+
Uses of Class mklab.JGNN.nn.operations.From
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.operations.From
+
+
Uses of Class mklab.JGNN.nn.operations.Gather
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.operations.Gather
+
+
Uses of Class mklab.JGNN.nn.operations.Identity
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.operations.Identity
+
+
Uses of Class mklab.JGNN.nn.operations.LSTM.LSTMState
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/nn/operations/class-use/LSTM.html b/docs/javadoc/mklab/JGNN/nn/operations/class-use/LSTM.html
new file mode 100644
index 00000000..38e8a716
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/nn/operations/class-use/LSTM.html
@@ -0,0 +1,84 @@
+
+
+
+
+Uses of Class mklab.JGNN.nn.operations.LSTM
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/nn/operations/class-use/Log.html b/docs/javadoc/mklab/JGNN/nn/operations/class-use/Log.html
new file mode 100644
index 00000000..74737b8f
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/nn/operations/class-use/Log.html
@@ -0,0 +1,59 @@
+
+
+
+
+Uses of Class mklab.JGNN.nn.operations.Log
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.operations.Log
+
+
Uses of Class mklab.JGNN.nn.operations.MatMul
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.operations.MatMul
+
+
Uses of Class mklab.JGNN.nn.operations.Multiply
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.operations.Multiply
+
+
Uses of Class mklab.JGNN.nn.operations.Reduce
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.operations.Reduce
+
+
Uses of Class mklab.JGNN.nn.operations.Repeat
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.operations.Repeat
+
+
Uses of Class mklab.JGNN.nn.operations.Reshape
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/nn/operations/class-use/To.html b/docs/javadoc/mklab/JGNN/nn/operations/class-use/To.html
new file mode 100644
index 00000000..f6a05956
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/nn/operations/class-use/To.html
@@ -0,0 +1,59 @@
+
+
+
+
+Uses of Class mklab.JGNN.nn.operations.To
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.operations.To
+
+
Uses of Class mklab.JGNN.nn.operations.Transpose
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.operations.Transpose
+
+
mklab.JGNN.nn.operations
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+package mklab.JGNN.nn.operations
+
+
+
+
+
+
+
+
Classes
+
+
+
+
+
+
+
+
Implements a
NNOperation that creates a version of adjacency matrices
+ with column-wise attention involving neighbor similarity.
+
+
+
+
Implements a
NNOperation that performs the operation 1-x for its simple input x.
+
+
+
+
Implements a
NNOperation that concatenates its two matrix inputs.
+
+
+
+
Implements a
NNOperation that converts its first argument to a
ColumnRepetition matrix
+ with a number of columns equal to the second argument.
+
+
+
+
Implements a
NNOperation that lists the first element of the 2D matrix element iterator.
+
+
+
+
Implements a
NNOperation that performs the equivalent of TensorFlow's gather operation.
+
+
+
+
Implements a
NNOperation that just transfers its single input.
+
+
+
+
Implements a
NNOperation that outputs the natural logarithm of its single input.
+
+
+
+
+
+
+
+
Implements a
NNOperation that multiplies its two matrix inputs.
+
+
+
+
Implements a
NNOperation that multiplies its two inputs element-by-element.
+
+
+
+
+
+
Implements a
NNOperation that converts its first argument to a
ColumnRepetition matrix
+ with a number of columns equal to the second argument.
+
+
+
+
+
+
Implements a
NNOperation that lists the second element of the 2D matrix element iterator.
+
+
+
+
Implements a
NNOperation that performs matrix transposition.
+
+
+
+
+
+
+
+
mklab.JGNN.nn.operations Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/nn/operations/package-use.html b/docs/javadoc/mklab/JGNN/nn/operations/package-use.html
new file mode 100644
index 00000000..5f3e7acf
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/nn/operations/package-use.html
@@ -0,0 +1,87 @@
+
+
+
+
+Uses of Package mklab.JGNN.nn.operations
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/nn/optimizers/Adam.html b/docs/javadoc/mklab/JGNN/nn/optimizers/Adam.html
new file mode 100644
index 00000000..85f5cb99
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/nn/optimizers/Adam.html
@@ -0,0 +1,289 @@
+
+
+
+
+Adam
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Optimizer
+Thic class implements an Adam
Optimizer as explained in the paper:
+
Kingma, Diederik P., and Jimmy Ba. "Adam: A method for stochastic optimization." arXiv preprint arXiv:1412.6980 (2014).
+
+
+ It also supports the NDAdam improvement, which ports advantages of SGD to Adam, as introduced in the paper:
+
Zhang, Zijun. "Improved adam optimizer for deep neural networks." 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS). IEEE, 2018.
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
Initializes an NDAdam instance of an
Adam optimizer with the default parameters recommended by the papers.
+
+
Adam (boolean NDmode,
+ double learningRate)
+
+
Initializes an
Adam optimizer with the default parameters recommended by the papers
+ but allows for the specification of the learning rate and whether NDAdam or simple Adam
+ is used.
+
+
Adam (boolean NDmode,
+ double learningRate,
+ double b1,
+ double b2)
+
+
Initializes an NDAdam instance of an
Adam optimizer with the default parameters with custom parameters.
+
+
Adam (boolean NDmode,
+ double learningRate,
+ double b1,
+ double b2,
+ double epsilon)
+
+
Adam (double learningRate)
+
+
Initializes an NDAdam instance of an
Adam optimizer with the default parameters recommended by the papers
+ but allows for the specification of the learning rate.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
void
+
+
+
Resets (and lets the garbage collector free) optimizer memory.
+
+
void
+
+
+
In-place updates the value of a tensor given its gradient.
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Adam
+public Adam ()
+Initializes an NDAdam instance of an
Adam optimizer with the default parameters recommended by the papers.
+
+
+
+Adam
+public Adam (double learningRate)
+Initializes an NDAdam instance of an
Adam optimizer with the default parameters recommended by the papers
+ but allows for the specification of the learning rate.
+Parameters:
+learningRate - The learning rate.
+
+
+
+
+Adam
+public Adam (boolean NDmode,
+ double learningRate)
+Initializes an
Adam optimizer with the default parameters recommended by the papers
+ but allows for the specification of the learning rate and whether NDAdam or simple Adam
+ is used.
+Parameters:
+NDmode - Should be true to use NDAdam and false to use simple Adam optimization.learningRate - The learning rate.
+
+
+
+
+Adam
+public Adam (boolean NDmode,
+ double learningRate,
+ double b1,
+ double b2)
+Initializes an NDAdam instance of an
Adam optimizer with the default parameters with custom parameters.
+Parameters:
+learningRate - The learning rate.
+
+
+
+
+Adam
+public Adam (boolean NDmode,
+ double learningRate,
+ double b1,
+ double b2,
+ double epsilon)
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+update
+
+
+In-place updates the value of a tensor given its gradient.
+ Some optimizers (e.g. Adama) require the exact same tensor instance to be provided
+ so as to keep track of its optimization progress. The library makes sure to keep
+ this constraint.
+
+Specified by:
+update Optimizer Parameters:
+value - The tensor to update.gradient - The tensor's gradient.
+
+
+
+
+reset
+public void reset ()
+
+Resets (and lets the garbage collector free) optimizer memory.
+ Should be called at the beginning of training (not after each epoch).
+
+Specified by:
+reset Optimizer
+
+
+
+
+
+
+
+
+
+
+
BatchOptimizer
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Optimizer
+Wraps an
Optimizer by accumulating derivatives and calling
+
Optimizer.update(Tensor, Tensor) with the average derivative
+ after a fixed number of accumulations. Accumulation restarts after
+ update. Provides a method
updateAll() to update all accumulated
+ derivatives, for example in case the constructor
BatchOptimizer(Optimizer)
+ is used without inputting a fixed number of derivative updates.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+
+
Initializes a
BatchOptimizer that accumulates derivatives and updates them
+ with
updateAll() after every fixed number of updates.
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
void
+
+
+
Resets (and lets the garbage collector free) optimizer memory.
+
+
void
+
+
+
In-place updates the value of a tensor given its gradient.
+
+
void
+
+
+
Updates all tracked variables with pending batch calculations using the
+ wrapped optimizer.
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+BatchOptimizer
+public BatchOptimizer (Optimizer baseOptimizer)
+Parameters:
+baseOptimizer - The base optimizer with which to perform the derivative updates.
+
+
+
+
+BatchOptimizer
+public BatchOptimizer (Optimizer baseOptimizer,
+ long batchSize) Initializes a
BatchOptimizer that accumulates derivatives and updates them
+ with
updateAll() after every fixed number of updates.
+Parameters:
+baseOptimizer - The base optimizer with which to perform the derivative updates.batchSize - The number of updates at which to pass the average accumulation to the base optimizer.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+updateAll
+public void updateAll ()
+Updates all tracked variables with pending batch calculations using the
+ wrapped optimizer. This overrides any potential waiting over pre-specified batch sizes.
+
+
+
+
+update
+
+
+In-place updates the value of a tensor given its gradient.
+ Some optimizers (e.g. Adama) require the exact same tensor instance to be provided
+ so as to keep track of its optimization progress. The library makes sure to keep
+ this constraint.
+
+Specified by:
+update Optimizer Parameters:
+value - The tensor to update.gradient - The tensor's gradient.
+
+
+
+
+reset
+public void reset ()
+
+Resets (and lets the garbage collector free) optimizer memory.
+ Should be called at the beginning of training (not after each epoch).
+
+Specified by:
+reset Optimizer
+
+
+
+
+
+
+
+
+
+
+
GradientDescent
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Optimizer
+Implements a gradient descent
Optimizer. It supports degrading learning rates.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
void
+
+
+
Resets (and lets the garbage collector free) optimizer memory.
+
+
void
+
+
+
In-place updates the value of a tensor given its gradient.
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+GradientDescent
+public GradientDescent (double learningRate)
+
+
+Parameters:
+learningRate - The learning rate.
+
+
+
+
+GradientDescent
+public GradientDescent (double learningRate,
+ double degradation)
+
+
+Parameters:
+learningRate - The learning rate.degradation - The quantity to multiply each tensor's learning rate with after each iteration.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+update
+
+
+In-place updates the value of a tensor given its gradient.
+ Some optimizers (e.g. Adama) require the exact same tensor instance to be provided
+ so as to keep track of its optimization progress. The library makes sure to keep
+ this constraint.
+
+Specified by:
+update Optimizer Parameters:
+value - The tensor to update.gradient - The tensor's gradient.
+
+
+
+
+reset
+public void reset ()
+
+Resets (and lets the garbage collector free) optimizer memory.
+ Should be called at the beginning of training (not after each epoch).
+
+Specified by:
+reset Optimizer
+
+
+
+
+
+
+
+
+
+
+
Regularization
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+All Implemented Interfaces:
+Optimizer
+
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
+
+
+
void
+
+
+
Resets (and lets the garbage collector free) optimizer memory.
+
+
void
+
+
+
In-place updates the value of a tensor given its gradient.
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Regularization
+public Regularization (Optimizer baseOptimizer,
+ double regularization)
+Parameters:
+baseOptimizer - The base optimizer on which to apply regularization.regularization - The weight of the regularization.
+
+
+
+
+
+
+
+
+Method Details
+
+
+
+update
+
+
+In-place updates the value of a tensor given its gradient.
+ Some optimizers (e.g. Adama) require the exact same tensor instance to be provided
+ so as to keep track of its optimization progress. The library makes sure to keep
+ this constraint.
+
+Specified by:
+update Optimizer Parameters:
+value - The tensor to update.gradient - The tensor's gradient.
+
+
+
+
+reset
+public void reset ()
+
+Resets (and lets the garbage collector free) optimizer memory.
+ Should be called at the beginning of training (not after each epoch).
+
+Specified by:
+reset Optimizer
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.nn.optimizers.Adam
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.optimizers.Adam
+
+
Uses of Class mklab.JGNN.nn.optimizers.BatchOptimizer
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.optimizers.BatchOptimizer
+
+
Uses of Class mklab.JGNN.nn.optimizers.GradientDescent
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.optimizers.GradientDescent
+
+
Uses of Class mklab.JGNN.nn.optimizers.Regularization
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.optimizers.Regularization
+
+
mklab.JGNN.nn.optimizers
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+package mklab.JGNN.nn.optimizers
+
+
+
+
+
+
+
+
Classes
+
+
+
+
+
+
Thic class implements an Adam
Optimizer as explained in the paper:
+
Kingma, Diederik P., and Jimmy Ba.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
mklab.JGNN.nn.optimizers Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/nn/optimizers/package-use.html b/docs/javadoc/mklab/JGNN/nn/optimizers/package-use.html
new file mode 100644
index 00000000..75c5545e
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/nn/optimizers/package-use.html
@@ -0,0 +1,59 @@
+
+
+
+
+Uses of Package mklab.JGNN.nn.optimizers
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.optimizers
+
+
mklab.JGNN.nn
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+package mklab.JGNN.nn
+
+
+
+
+
+
+
+
All Classes and Interfaces Interfaces Classes
+
+
+
+
+
+
+
This class defines an abstract interface for applying initializers to models.
+
+
+
+
This class provides an abstract implementation of loss functions
+ to be used during
Model training.
+
+
+
+
This class is a way to organize
NNOperation trees into trainable machine
+ learning models.
+
+
+
+
This is a helper class that automates the definition of training processes of
Model instances
+ by defining the number of epochs, loss functions, number of batches and the ability to use
ThreadPool
+ for parallelized batch computations.
+
+
+
+
This class defines an abstract neural network operation with forward and
+ backpropagation capabilities.
+
+
+
+
Provides an interface for training tensors.
+
+
+
+
+
+
+
+
+
mklab.JGNN.nn Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/nn/package-use.html b/docs/javadoc/mklab/JGNN/nn/package-use.html
new file mode 100644
index 00000000..24a598c0
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/nn/package-use.html
@@ -0,0 +1,264 @@
+
+
+
+
+Uses of Package mklab.JGNN.nn
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
This class is a way to organize
NNOperation trees into trainable machine
+ learning models.
+
+
+
+
This class defines an abstract neural network operation with forward and
+ backpropagation capabilities.
+
+
+
+
+
+
+
+
+
+
+
+
This class defines an abstract interface for applying initializers to models.
+
+
+
+
This class provides an abstract implementation of loss functions
+ to be used during
Model training.
+
+
+
+
This class is a way to organize
NNOperation trees into trainable machine
+ learning models.
+
+
+
+
This is a helper class that automates the definition of training processes of
Model instances
+ by defining the number of epochs, loss functions, number of batches and the ability to use
ThreadPool
+ for parallelized batch computations.
+
+
+
+
This class defines an abstract neural network operation with forward and
+ backpropagation capabilities.
+
+
+
+
Provides an interface for training tensors.
+
+
+
+
+
+
+
+
+
+
+
+
This class defines an abstract neural network operation with forward and
+ backpropagation capabilities.
+
+
+
+
+
+
+
+
+
+
+
+
This class defines an abstract interface for applying initializers to models.
+
+
+
+
This class is a way to organize
NNOperation trees into trainable machine
+ learning models.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
This class provides an abstract implementation of loss functions
+ to be used during
Model training.
+
+
+
+
+
+
+
+
+
+
+
+
This class provides an abstract implementation of loss functions
+ to be used during
Model training.
+
+
+
+
+
+
+
+
+
+
+
+
This class defines an abstract neural network operation with forward and
+ backpropagation capabilities.
+
+
+
+
Provides an interface for training tensors.
+
+
+
+
+
+
+
+
+
+
+
+
Provides an interface for training tensors.
+
+
+
+
+
+
+
+
+
+
+
+
This class defines an abstract neural network operation with forward and
+ backpropagation capabilities.
+
+
+
+
+
+
+
+
Max
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that performs row-wise or column-wise
+ maximum reduction on vector tensors or matrices.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+
+
+
+Max
+public Max (boolean colMode)
+
+
+
+
+
+
+
+
+
+
+
Mean
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that performs row-wise or column-wise
+ mean reduction on vector tensors or matrices.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+
+
+
+Mean
+public Mean (boolean colMode)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
SoftMax
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that performs row-wise or column-wise
+ softmax on vector tensors or matrices.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+SoftMax
+public SoftMax ()
+
+
+
+
+SoftMax
+public SoftMax (boolean colMode)
+
+
+
+
+
+
+
+
+
+
+
Sort
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+Sort
+public Sort (int k)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Sum
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+
+
+Implements a
NNOperation that performs row-wise or column-wise
+ sum reduction on vector tensors or matrices.
+Author:
+Emmanouil Krasanakis
+
+
+
+
+
+
+
+
+
+
+
+Constructor Summary
+Constructors
+
+
+
+
+
+
+Method Summary
+
+
All Methods Instance Methods Concrete Methods
+
+
+
Methods inherited from class mklab.JGNN.nn.NNOperation
+
addInput , clearPrediction , describe , getDescription , getInputs , getLastTapeError , getNonLinearity , getOutputs , getPrediction , getSimpleDescription , isCachable , isConstant , run , run , runPrediction , runPredictionAndAutosize , setDescription , view
+
+
+
+
+
+
+
+
+Constructor Details
+
+
+
+
+
+
+Sum
+public Sum (boolean colMode)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Uses of Class mklab.JGNN.nn.pooling.Max
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.pooling.Max
+
+
Uses of Class mklab.JGNN.nn.pooling.Mean
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.pooling.Mean
+
+
Uses of Class mklab.JGNN.nn.pooling.SoftMax
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.pooling.SoftMax
+
+
Uses of Class mklab.JGNN.nn.pooling.Sort
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/mklab/JGNN/nn/pooling/class-use/Sum.html b/docs/javadoc/mklab/JGNN/nn/pooling/class-use/Sum.html
new file mode 100644
index 00000000..aa56129e
--- /dev/null
+++ b/docs/javadoc/mklab/JGNN/nn/pooling/class-use/Sum.html
@@ -0,0 +1,59 @@
+
+
+
+
+Uses of Class mklab.JGNN.nn.pooling.Sum
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+No usage of mklab.JGNN.nn.pooling.Sum
+
+
mklab.JGNN.nn.pooling
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+package mklab.JGNN.nn.pooling
+
+
+
+
+
+
+
+
Classes
+
+
+
+
+
+
Implements a
NNOperation that performs row-wise or column-wise
+ maximum reduction on vector tensors or matrices.
+
+
+
+
Implements a
NNOperation that performs row-wise or column-wise
+ mean reduction on vector tensors or matrices.
+
+
+
+
Implements a
NNOperation that performs row-wise or column-wise
+ softmax on vector tensors or matrices.
+
+
+
+
+
+
Implements a
NNOperation that performs row-wise or column-wise
+ sum reduction on vector tensors or matrices.
+
+
+
+
+
+
+
+
mklab.JGNN.nn.pooling Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
+
+
+
+Class Hierarchy
+
+java.lang.Object
+
+mklab.JGNN.nn.NNOperation
+
+mklab.JGNN.nn.pooling.Max
+mklab.JGNN.nn.pooling.Mean
+mklab.JGNN.nn.pooling.SoftMax
+mklab.JGNN.nn.pooling.Sort
+mklab.JGNN.nn.pooling.Sum
+
+
+
+
+
+
+
+
+
Uses of Package mklab.JGNN.nn.pooling
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/module-search-index.js b/docs/javadoc/module-search-index.js
new file mode 100644
index 00000000..0d59754f
--- /dev/null
+++ b/docs/javadoc/module-search-index.js
@@ -0,0 +1 @@
+moduleSearchIndex = [];updateSearchResults();
\ No newline at end of file
diff --git a/docs/javadoc/overview-summary.html b/docs/javadoc/overview-summary.html
new file mode 100644
index 00000000..724af739
--- /dev/null
+++ b/docs/javadoc/overview-summary.html
@@ -0,0 +1,26 @@
+
+
+
+
+Generated Documentation (Untitled)
+
+
+
+
+
+
+JavaScript is disabled on your browser.
+
+index.html
+
+
+
diff --git a/docs/javadoc/overview-tree.html b/docs/javadoc/overview-tree.html
new file mode 100644
index 00000000..efe46a9e
--- /dev/null
+++ b/docs/javadoc/overview-tree.html
@@ -0,0 +1,217 @@
+
+
+
+
+Class Hierarchy
+
+JavaScript is disabled on your browser.
+
+
+
+
diff --git a/docs/javadoc/package-search-index.js b/docs/javadoc/package-search-index.js
new file mode 100644
index 00000000..49d10b8e
--- /dev/null
+++ b/docs/javadoc/package-search-index.js
@@ -0,0 +1 @@
+packageSearchIndex = [{"l":"All Packages","u":"allpackages-index.html"},{"l":"mklab.JGNN.adhoc"},{"l":"mklab.JGNN.adhoc.datasets"},{"l":"mklab.JGNN.adhoc.parsers"},{"l":"mklab.JGNN.core.distribution"},{"l":"mklab.JGNN.core.empy"},{"l":"mklab.JGNN.core.matrix"},{"l":"mklab.JGNN.core.tensor"},{"l":"mklab.JGNN.nn"},{"l":"mklab.JGNN.nn.activations"},{"l":"mklab.JGNN.nn.initializers"},{"l":"mklab.JGNN.nn.inputs"},{"l":"mklab.JGNN.nn.loss"},{"l":"mklab.JGNN.nn.loss.report"},{"l":"mklab.JGNN.nn.operations"},{"l":"mklab.JGNN.nn.optimizers"},{"l":"mklab.JGNN.nn.pooling"}];updateSearchResults();
\ No newline at end of file
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s=i.item.data("ui-autocomplete-item"),n=this.previous;this.element[0]!==t.ui.safeActiveElement(this.document[0])&&(this.element.trigger("focus"),this.previous=n,this._delay(function(){this.previous=n,this.selectedItem=s})),!1!==this._trigger("select",e,{item:s})&&this._value(s.value),this.term=this._value(),this.close(e),this.selectedItem=s}}),this.liveRegion=t("
",{role:"status","aria-live":"assertive","aria-relevant":"additions"}).appendTo(this.document[0].body),this._addClass(this.liveRegion,null,"ui-helper-hidden-accessible"),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_destroy:function(){clearTimeout(this.searching),this.element.removeAttr("autocomplete"),this.menu.element.remove(),this.liveRegion.remove()},_setOption:function(t,e){this._super(t,e),"source"===t&&this._initSource(),"appendTo"===t&&this.menu.element.appendTo(this._appendTo()),"disabled"===t&&e&&this.xhr&&this.xhr.abort()},_isEventTargetInWidget:function(e){var i=this.menu.element[0];return e.target===this.element[0]||e.target===i||t.contains(i,e.target)},_closeOnClickOutside:function(t){this._isEventTargetInWidget(t)||this.close()},_appendTo:function(){var e=this.options.appendTo;return e&&(e=e.jquery||e.nodeType?t(e):this.document.find(e).eq(0)),e&&e[0]||(e=this.element.closest(".ui-front, dialog")),e.length||(e=this.document[0].body),e},_initSource:function(){var e,i,s=this;t.isArray(this.options.source)?(e=this.options.source,this.source=function(i,s){s(t.ui.autocomplete.filter(e,i.term))}):"string"==typeof this.options.source?(i=this.options.source,this.source=function(e,n){s.xhr&&s.xhr.abort(),s.xhr=t.ajax({url:i,data:e,dataType:"json",success:function(t){n(t)},error:function(){n([])}})}):this.source=this.options.source},_searchTimeout:function(t){clearTimeout(this.searching),this.searching=this._delay(function(){var e=this.term===this._value(),i=this.menu.element.is(":visible"),s=t.altKey||t.ctrlKey||t.metaKey||t.shiftKey;(!e||e&&!i&&!s)&&(this.selectedItem=null,this.search(null,t))},this.options.delay)},search:function(t,e){return t=null!=t?t:this._value(),this.term=this._value(),t.length
").append(t("").text(i.label)).appendTo(e)},_move:function(t,e){return this.menu.element.is(":visible")?this.menu.isFirstItem()&&/^previous/.test(t)||this.menu.isLastItem()&&/^next/.test(t)?(this.isMultiLine||this._value(this.term),this.menu.blur(),void 0):(this.menu[t](e),void 0):(this.search(null,e),void 0)},widget:function(){return this.menu.element},_value:function(){return this.valueMethod.apply(this.element,arguments)},_keyEvent:function(t,e){(!this.isMultiLine||this.menu.element.is(":visible"))&&(this._move(t,e),e.preventDefault())},_isContentEditable:function(t){if(!t.length)return!1;var e=t.prop("contentEditable");return"inherit"===e?this._isContentEditable(t.parent()):"true"===e}}),t.extend(t.ui.autocomplete,{escapeRegex:function(t){return t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&")},filter:function(e,i){var s=RegExp(t.ui.autocomplete.escapeRegex(i),"i");return t.grep(e,function(t){return s.test(t.label||t.value||t)})}}),t.widget("ui.autocomplete",t.ui.autocomplete,{options:{messages:{noResults:"No search results.",results:function(t){return t+(t>1?" results are":" result is")+" available, use up and down arrow keys to navigate."}}},__response:function(e){var i;this._superApply(arguments),this.options.disabled||this.cancelSearch||(i=e&&e.length?this.options.messages.results(e.length):this.options.messages.noResults,this.liveRegion.children().hide(),t("
").text(i).appendTo(this.liveRegion))}}),t.ui.autocomplete});
\ No newline at end of file
diff --git a/docs/javadoc/script-dir/jquery-ui.structure.min.css b/docs/javadoc/script-dir/jquery-ui.structure.min.css
new file mode 100644
index 00000000..e8808927
--- /dev/null
+++ b/docs/javadoc/script-dir/jquery-ui.structure.min.css
@@ -0,0 +1,5 @@
+/*! jQuery UI - v1.12.1 - 2018-12-06
+* http://jqueryui.com
+* Copyright jQuery Foundation and other contributors; Licensed MIT */
+
+.ui-helper-hidden{display:none}.ui-helper-hidden-accessible{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px}.ui-helper-reset{margin:0;padding:0;border:0;outline:0;line-height:1.3;text-decoration:none;font-size:100%;list-style:none}.ui-helper-clearfix:before,.ui-helper-clearfix:after{content:"";display:table;border-collapse:collapse}.ui-helper-clearfix:after{clear:both}.ui-helper-zfix{width:100%;height:100%;top:0;left:0;position:absolute;opacity:0;filter:Alpha(Opacity=0)}.ui-front{z-index:100}.ui-state-disabled{cursor:default!important;pointer-events:none}.ui-icon{display:inline-block;vertical-align:middle;margin-top:-.25em;position:relative;text-indent:-99999px;overflow:hidden;background-repeat:no-repeat}.ui-widget-icon-block{left:50%;margin-left:-8px;display:block}.ui-widget-overlay{position:fixed;top:0;left:0;width:100%;height:100%}.ui-autocomplete{position:absolute;top:0;left:0;cursor:default}.ui-menu{list-style:none;padding:0;margin:0;display:block;outline:0}.ui-menu .ui-menu{position:absolute}.ui-menu .ui-menu-item{margin:0;cursor:pointer;list-style-image:url("data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7")}.ui-menu .ui-menu-item-wrapper{position:relative;padding:3px 1em 3px .4em}.ui-menu .ui-menu-divider{margin:5px 0;height:0;font-size:0;line-height:0;border-width:1px 0 0 0}.ui-menu .ui-state-focus,.ui-menu .ui-state-active{margin:-1px}.ui-menu-icons{position:relative}.ui-menu-icons .ui-menu-item-wrapper{padding-left:2em}.ui-menu .ui-icon{position:absolute;top:0;bottom:0;left:.2em;margin:auto 0}.ui-menu .ui-menu-icon{left:auto;right:0}
\ No newline at end of file
diff --git a/docs/javadoc/script.js b/docs/javadoc/script.js
new file mode 100644
index 00000000..0765364e
--- /dev/null
+++ b/docs/javadoc/script.js
@@ -0,0 +1,132 @@
+/*
+ * Copyright (c) 2013, 2020, Oracle and/or its affiliates. All rights reserved.
+ * ORACLE PROPRIETARY/CONFIDENTIAL. Use is subject to license terms.
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ */
+
+var moduleSearchIndex;
+var packageSearchIndex;
+var typeSearchIndex;
+var memberSearchIndex;
+var tagSearchIndex;
+function loadScripts(doc, tag) {
+ createElem(doc, tag, 'search.js');
+
+ createElem(doc, tag, 'module-search-index.js');
+ createElem(doc, tag, 'package-search-index.js');
+ createElem(doc, tag, 'type-search-index.js');
+ createElem(doc, tag, 'member-search-index.js');
+ createElem(doc, tag, 'tag-search-index.js');
+}
+
+function createElem(doc, tag, path) {
+ var script = doc.createElement(tag);
+ var scriptElement = doc.getElementsByTagName(tag)[0];
+ script.src = pathtoroot + path;
+ scriptElement.parentNode.insertBefore(script, scriptElement);
+}
+
+function show(tableId, selected, columns) {
+ if (tableId !== selected) {
+ document.querySelectorAll('div.' + tableId + ':not(.' + selected + ')')
+ .forEach(function(elem) {
+ elem.style.display = 'none';
+ });
+ }
+ document.querySelectorAll('div.' + selected)
+ .forEach(function(elem, index) {
+ elem.style.display = '';
+ var isEvenRow = index % (columns * 2) < columns;
+ elem.classList.remove(isEvenRow ? oddRowColor : evenRowColor);
+ elem.classList.add(isEvenRow ? evenRowColor : oddRowColor);
+ });
+ updateTabs(tableId, selected);
+}
+
+function updateTabs(tableId, selected) {
+ document.querySelector('div#' + tableId +' .summary-table')
+ .setAttribute('aria-labelledby', selected);
+ document.querySelectorAll('button[id^="' + tableId + '"]')
+ .forEach(function(tab, index) {
+ if (selected === tab.id || (tableId === selected && index === 0)) {
+ tab.className = activeTableTab;
+ tab.setAttribute('aria-selected', true);
+ tab.setAttribute('tabindex',0);
+ } else {
+ tab.className = tableTab;
+ tab.setAttribute('aria-selected', false);
+ tab.setAttribute('tabindex',-1);
+ }
+ });
+}
+
+function switchTab(e) {
+ var selected = document.querySelector('[aria-selected=true]');
+ if (selected) {
+ if ((e.keyCode === 37 || e.keyCode === 38) && selected.previousSibling) {
+ // left or up arrow key pressed: move focus to previous tab
+ selected.previousSibling.click();
+ selected.previousSibling.focus();
+ e.preventDefault();
+ } else if ((e.keyCode === 39 || e.keyCode === 40) && selected.nextSibling) {
+ // right or down arrow key pressed: move focus to next tab
+ selected.nextSibling.click();
+ selected.nextSibling.focus();
+ e.preventDefault();
+ }
+ }
+}
+
+var updateSearchResults = function() {};
+
+function indexFilesLoaded() {
+ return moduleSearchIndex
+ && packageSearchIndex
+ && typeSearchIndex
+ && memberSearchIndex
+ && tagSearchIndex;
+}
+
+// Workaround for scroll position not being included in browser history (8249133)
+document.addEventListener("DOMContentLoaded", function(e) {
+ var contentDiv = document.querySelector("div.flex-content");
+ window.addEventListener("popstate", function(e) {
+ if (e.state !== null) {
+ contentDiv.scrollTop = e.state;
+ }
+ });
+ window.addEventListener("hashchange", function(e) {
+ history.replaceState(contentDiv.scrollTop, document.title);
+ });
+ contentDiv.addEventListener("scroll", function(e) {
+ var timeoutID;
+ if (!timeoutID) {
+ timeoutID = setTimeout(function() {
+ history.replaceState(contentDiv.scrollTop, document.title);
+ timeoutID = null;
+ }, 100);
+ }
+ });
+ if (!location.hash) {
+ history.replaceState(contentDiv.scrollTop, document.title);
+ }
+});
diff --git a/docs/javadoc/search.js b/docs/javadoc/search.js
new file mode 100644
index 00000000..13aba853
--- /dev/null
+++ b/docs/javadoc/search.js
@@ -0,0 +1,354 @@
+/*
+ * Copyright (c) 2015, 2020, Oracle and/or its affiliates. All rights reserved.
+ * ORACLE PROPRIETARY/CONFIDENTIAL. Use is subject to license terms.
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ *
+ */
+
+var noResult = {l: "No results found"};
+var loading = {l: "Loading search index..."};
+var catModules = "Modules";
+var catPackages = "Packages";
+var catTypes = "Classes and Interfaces";
+var catMembers = "Members";
+var catSearchTags = "Search Tags";
+var highlight = "
$& ";
+var searchPattern = "";
+var fallbackPattern = "";
+var RANKING_THRESHOLD = 2;
+var NO_MATCH = 0xffff;
+var MIN_RESULTS = 3;
+var MAX_RESULTS = 500;
+var UNNAMED = "
";
+function escapeHtml(str) {
+ return str.replace(//g, ">");
+}
+function getHighlightedText(item, matcher, fallbackMatcher) {
+ var escapedItem = escapeHtml(item);
+ var highlighted = escapedItem.replace(matcher, highlight);
+ if (highlighted === escapedItem) {
+ highlighted = escapedItem.replace(fallbackMatcher, highlight)
+ }
+ return highlighted;
+}
+function getURLPrefix(ui) {
+ var urlPrefix="";
+ var slash = "/";
+ if (ui.item.category === catModules) {
+ return ui.item.l + slash;
+ } else if (ui.item.category === catPackages && ui.item.m) {
+ return ui.item.m + slash;
+ } else if (ui.item.category === catTypes || ui.item.category === catMembers) {
+ if (ui.item.m) {
+ urlPrefix = ui.item.m + slash;
+ } else {
+ $.each(packageSearchIndex, function(index, item) {
+ if (item.m && ui.item.p === item.l) {
+ urlPrefix = item.m + slash;
+ }
+ });
+ }
+ }
+ return urlPrefix;
+}
+function createSearchPattern(term) {
+ var pattern = "";
+ var isWordToken = false;
+ term.replace(/,\s*/g, ", ").trim().split(/\s+/).forEach(function(w, index) {
+ if (index > 0) {
+ // whitespace between identifiers is significant
+ pattern += (isWordToken && /^\w/.test(w)) ? "\\s+" : "\\s*";
+ }
+ var tokens = w.split(/(?=[A-Z,.()<>[\/])/);
+ for (var i = 0; i < tokens.length; i++) {
+ var s = tokens[i];
+ if (s === "") {
+ continue;
+ }
+ pattern += $.ui.autocomplete.escapeRegex(s);
+ isWordToken = /\w$/.test(s);
+ if (isWordToken) {
+ pattern += "([a-z0-9_$<>\\[\\]]*?)";
+ }
+ }
+ });
+ return pattern;
+}
+function createMatcher(pattern, flags) {
+ var isCamelCase = /[A-Z]/.test(pattern);
+ return new RegExp(pattern, flags + (isCamelCase ? "" : "i"));
+}
+var watermark = 'Search';
+$(function() {
+ var search = $("#search-input");
+ var reset = $("#reset-button");
+ search.val('');
+ search.prop("disabled", false);
+ reset.prop("disabled", false);
+ search.val(watermark).addClass('watermark');
+ search.blur(function() {
+ if ($(this).val().length === 0) {
+ $(this).val(watermark).addClass('watermark');
+ }
+ });
+ search.on('click keydown paste', function() {
+ if ($(this).val() === watermark) {
+ $(this).val('').removeClass('watermark');
+ }
+ });
+ reset.click(function() {
+ search.val('').focus();
+ });
+ search.focus()[0].setSelectionRange(0, 0);
+});
+$.widget("custom.catcomplete", $.ui.autocomplete, {
+ _create: function() {
+ this._super();
+ this.widget().menu("option", "items", "> :not(.ui-autocomplete-category)");
+ },
+ _renderMenu: function(ul, items) {
+ var rMenu = this;
+ var currentCategory = "";
+ rMenu.menu.bindings = $();
+ $.each(items, function(index, item) {
+ var li;
+ if (item.category && item.category !== currentCategory) {
+ ul.append("" + item.category + " ");
+ currentCategory = item.category;
+ }
+ li = rMenu._renderItemData(ul, item);
+ if (item.category) {
+ li.attr("aria-label", item.category + " : " + item.l);
+ li.attr("class", "result-item");
+ } else {
+ li.attr("aria-label", item.l);
+ li.attr("class", "result-item");
+ }
+ });
+ },
+ _renderItem: function(ul, item) {
+ var label = "";
+ var matcher = createMatcher(escapeHtml(searchPattern), "g");
+ var fallbackMatcher = new RegExp(fallbackPattern, "gi")
+ if (item.category === catModules) {
+ label = getHighlightedText(item.l, matcher, fallbackMatcher);
+ } else if (item.category === catPackages) {
+ label = getHighlightedText(item.l, matcher, fallbackMatcher);
+ } else if (item.category === catTypes) {
+ label = (item.p && item.p !== UNNAMED)
+ ? getHighlightedText(item.p + "." + item.l, matcher, fallbackMatcher)
+ : getHighlightedText(item.l, matcher, fallbackMatcher);
+ } else if (item.category === catMembers) {
+ label = (item.p && item.p !== UNNAMED)
+ ? getHighlightedText(item.p + "." + item.c + "." + item.l, matcher, fallbackMatcher)
+ : getHighlightedText(item.c + "." + item.l, matcher, fallbackMatcher);
+ } else if (item.category === catSearchTags) {
+ label = getHighlightedText(item.l, matcher, fallbackMatcher);
+ } else {
+ label = item.l;
+ }
+ var li = $("
").appendTo(li);
+ if (item.category === catSearchTags && item.h) {
+ if (item.d) {
+ div.html(label + " (" + item.h + ") "
+ + item.d + " (" + item.h + ") ");
+ }
+ } else {
+ if (item.m) {
+ div.html(item.m + "/" + label);
+ } else {
+ div.html(label);
+ }
+ }
+ return li;
+ }
+});
+function rankMatch(match, category) {
+ if (!match) {
+ return NO_MATCH;
+ }
+ var index = match.index;
+ var input = match.input;
+ var leftBoundaryMatch = 2;
+ var periferalMatch = 0;
+ // make sure match is anchored on a left word boundary
+ if (index === 0 || /\W/.test(input[index - 1]) || "_" === input[index]) {
+ leftBoundaryMatch = 0;
+ } else if ("_" === input[index - 1] || (input[index] === input[index].toUpperCase() && !/^[A-Z0-9_$]+$/.test(input))) {
+ leftBoundaryMatch = 1;
+ }
+ var matchEnd = index + match[0].length;
+ var leftParen = input.indexOf("(");
+ var endOfName = leftParen > -1 ? leftParen : input.length;
+ // exclude peripheral matches
+ if (category !== catModules && category !== catSearchTags) {
+ var delim = category === catPackages ? "/" : ".";
+ if (leftParen > -1 && leftParen < index) {
+ periferalMatch += 2;
+ } else if (input.lastIndexOf(delim, endOfName) >= matchEnd) {
+ periferalMatch += 2;
+ }
+ }
+ var delta = match[0].length === endOfName ? 0 : 1; // rank full match higher than partial match
+ for (var i = 1; i < match.length; i++) {
+ // lower ranking if parts of the name are missing
+ if (match[i])
+ delta += match[i].length;
+ }
+ if (category === catTypes) {
+ // lower ranking if a type name contains unmatched camel-case parts
+ if (/[A-Z]/.test(input.substring(matchEnd)))
+ delta += 5;
+ if (/[A-Z]/.test(input.substring(0, index)))
+ delta += 5;
+ }
+ return leftBoundaryMatch + periferalMatch + (delta / 200);
+
+}
+function doSearch(request, response) {
+ var result = [];
+ searchPattern = createSearchPattern(request.term);
+ fallbackPattern = createSearchPattern(request.term.toLowerCase());
+ if (searchPattern === "") {
+ return this.close();
+ }
+ var camelCaseMatcher = createMatcher(searchPattern, "");
+ var fallbackMatcher = new RegExp(fallbackPattern, "i");
+
+ function searchIndexWithMatcher(indexArray, matcher, category, nameFunc) {
+ if (indexArray) {
+ var newResults = [];
+ $.each(indexArray, function (i, item) {
+ item.category = category;
+ var ranking = rankMatch(matcher.exec(nameFunc(item)), category);
+ if (ranking < RANKING_THRESHOLD) {
+ newResults.push({ranking: ranking, item: item});
+ }
+ return newResults.length <= MAX_RESULTS;
+ });
+ return newResults.sort(function(e1, e2) {
+ return e1.ranking - e2.ranking;
+ }).map(function(e) {
+ return e.item;
+ });
+ }
+ return [];
+ }
+ function searchIndex(indexArray, category, nameFunc) {
+ var primaryResults = searchIndexWithMatcher(indexArray, camelCaseMatcher, category, nameFunc);
+ result = result.concat(primaryResults);
+ if (primaryResults.length <= MIN_RESULTS && !camelCaseMatcher.ignoreCase) {
+ var secondaryResults = searchIndexWithMatcher(indexArray, fallbackMatcher, category, nameFunc);
+ result = result.concat(secondaryResults.filter(function (item) {
+ return primaryResults.indexOf(item) === -1;
+ }));
+ }
+ }
+
+ searchIndex(moduleSearchIndex, catModules, function(item) { return item.l; });
+ searchIndex(packageSearchIndex, catPackages, function(item) {
+ return (item.m && request.term.indexOf("/") > -1)
+ ? (item.m + "/" + item.l) : item.l;
+ });
+ searchIndex(typeSearchIndex, catTypes, function(item) {
+ return request.term.indexOf(".") > -1 ? item.p + "." + item.l : item.l;
+ });
+ searchIndex(memberSearchIndex, catMembers, function(item) {
+ return request.term.indexOf(".") > -1
+ ? item.p + "." + item.c + "." + item.l : item.l;
+ });
+ searchIndex(tagSearchIndex, catSearchTags, function(item) { return item.l; });
+
+ if (!indexFilesLoaded()) {
+ updateSearchResults = function() {
+ doSearch(request, response);
+ }
+ result.unshift(loading);
+ } else {
+ updateSearchResults = function() {};
+ }
+ response(result);
+}
+$(function() {
+ $("#search-input").catcomplete({
+ minLength: 1,
+ delay: 300,
+ source: doSearch,
+ response: function(event, ui) {
+ if (!ui.content.length) {
+ ui.content.push(noResult);
+ } else {
+ $("#search-input").empty();
+ }
+ },
+ autoFocus: true,
+ focus: function(event, ui) {
+ return false;
+ },
+ position: {
+ collision: "flip"
+ },
+ select: function(event, ui) {
+ if (ui.item.category) {
+ var url = getURLPrefix(ui);
+ if (ui.item.category === catModules) {
+ url += "module-summary.html";
+ } else if (ui.item.category === catPackages) {
+ if (ui.item.u) {
+ url = ui.item.u;
+ } else {
+ url += ui.item.l.replace(/\./g, '/') + "/package-summary.html";
+ }
+ } else if (ui.item.category === catTypes) {
+ if (ui.item.u) {
+ url = ui.item.u;
+ } else if (ui.item.p === UNNAMED) {
+ url += ui.item.l + ".html";
+ } else {
+ url += ui.item.p.replace(/\./g, '/') + "/" + ui.item.l + ".html";
+ }
+ } else if (ui.item.category === catMembers) {
+ if (ui.item.p === UNNAMED) {
+ url += ui.item.c + ".html" + "#";
+ } else {
+ url += ui.item.p.replace(/\./g, '/') + "/" + ui.item.c + ".html" + "#";
+ }
+ if (ui.item.u) {
+ url += ui.item.u;
+ } else {
+ url += ui.item.l;
+ }
+ } else if (ui.item.category === catSearchTags) {
+ url += ui.item.u;
+ }
+ if (top !== window) {
+ parent.classFrame.location = pathtoroot + url;
+ } else {
+ window.location.href = pathtoroot + url;
+ }
+ $("#search-input").focus();
+ }
+ }
+ });
+});
diff --git a/docs/javadoc/stylesheet.css b/docs/javadoc/stylesheet.css
new file mode 100644
index 00000000..836c62da
--- /dev/null
+++ b/docs/javadoc/stylesheet.css
@@ -0,0 +1,865 @@
+/*
+ * Javadoc style sheet
+ */
+
+@import url('resources/fonts/dejavu.css');
+
+/*
+ * Styles for individual HTML elements.
+ *
+ * These are styles that are specific to individual HTML elements. Changing them affects the style of a particular
+ * HTML element throughout the page.
+ */
+
+body {
+ background-color:#ffffff;
+ color:#353833;
+ font-family:'DejaVu Sans', Arial, Helvetica, sans-serif;
+ font-size:14px;
+ margin:0;
+ padding:0;
+ height:100%;
+ width:100%;
+}
+iframe {
+ margin:0;
+ padding:0;
+ height:100%;
+ width:100%;
+ overflow-y:scroll;
+ border:none;
+}
+a:link, a:visited {
+ text-decoration:none;
+ color:#4A6782;
+}
+a[href]:hover, a[href]:focus {
+ text-decoration:none;
+ color:#bb7a2a;
+}
+a[name] {
+ color:#353833;
+}
+pre {
+ font-family:'DejaVu Sans Mono', monospace;
+ font-size:14px;
+}
+h1 {
+ font-size:20px;
+}
+h2 {
+ font-size:18px;
+}
+h3 {
+ font-size:16px;
+}
+h4 {
+ font-size:15px;
+}
+h5 {
+ font-size:14px;
+}
+h6 {
+ font-size:13px;
+}
+ul {
+ list-style-type:disc;
+}
+code, tt {
+ font-family:'DejaVu Sans Mono', monospace;
+}
+:not(h1, h2, h3, h4, h5, h6) > code,
+:not(h1, h2, h3, h4, h5, h6) > tt {
+ font-size:14px;
+ padding-top:4px;
+ margin-top:8px;
+ line-height:1.4em;
+}
+dt code {
+ font-family:'DejaVu Sans Mono', monospace;
+ font-size:14px;
+ padding-top:4px;
+}
+.summary-table dt code {
+ font-family:'DejaVu Sans Mono', monospace;
+ font-size:14px;
+ vertical-align:top;
+ padding-top:4px;
+}
+sup {
+ font-size:8px;
+}
+button {
+ font-family: 'DejaVu Sans', Arial, Helvetica, sans-serif;
+ font-size: 14px;
+}
+/*
+ * Styles for HTML generated by javadoc.
+ *
+ * These are style classes that are used by the standard doclet to generate HTML documentation.
+ */
+
+/*
+ * Styles for document title and copyright.
+ */
+.clear {
+ clear:both;
+ height:0;
+ overflow:hidden;
+}
+.about-language {
+ float:right;
+ padding:0 21px 8px 8px;
+ font-size:11px;
+ margin-top:-9px;
+ height:2.9em;
+}
+.legal-copy {
+ margin-left:.5em;
+}
+.tab {
+ background-color:#0066FF;
+ color:#ffffff;
+ padding:8px;
+ width:5em;
+ font-weight:bold;
+}
+/*
+ * Styles for navigation bar.
+ */
+@media screen {
+ .flex-box {
+ position:fixed;
+ display:flex;
+ flex-direction:column;
+ height: 100%;
+ width: 100%;
+ }
+ .flex-header {
+ flex: 0 0 auto;
+ }
+ .flex-content {
+ flex: 1 1 auto;
+ overflow-y: auto;
+ }
+}
+.top-nav {
+ background-color:#4D7A97;
+ color:#FFFFFF;
+ float:left;
+ padding:0;
+ width:100%;
+ clear:right;
+ min-height:2.8em;
+ padding-top:10px;
+ overflow:hidden;
+ font-size:12px;
+}
+.sub-nav {
+ background-color:#dee3e9;
+ float:left;
+ width:100%;
+ overflow:hidden;
+ font-size:12px;
+}
+.sub-nav div {
+ clear:left;
+ float:left;
+ padding:0 0 5px 6px;
+ text-transform:uppercase;
+}
+.sub-nav .nav-list {
+ padding-top:5px;
+}
+ul.nav-list {
+ display:block;
+ margin:0 25px 0 0;
+ padding:0;
+}
+ul.sub-nav-list {
+ float:left;
+ margin:0 25px 0 0;
+ padding:0;
+}
+ul.nav-list li {
+ list-style:none;
+ float:left;
+ padding: 5px 6px;
+ text-transform:uppercase;
+}
+.sub-nav .nav-list-search {
+ float:right;
+ margin:0 0 0 0;
+ padding:5px 6px;
+ clear:none;
+}
+.nav-list-search label {
+ position:relative;
+ right:-16px;
+}
+ul.sub-nav-list li {
+ list-style:none;
+ float:left;
+ padding-top:10px;
+}
+.top-nav a:link, .top-nav a:active, .top-nav a:visited {
+ color:#FFFFFF;
+ text-decoration:none;
+ text-transform:uppercase;
+}
+.top-nav a:hover {
+ text-decoration:none;
+ color:#bb7a2a;
+ text-transform:uppercase;
+}
+.nav-bar-cell1-rev {
+ background-color:#F8981D;
+ color:#253441;
+ margin: auto 5px;
+}
+.skip-nav {
+ position:absolute;
+ top:auto;
+ left:-9999px;
+ overflow:hidden;
+}
+/*
+ * Hide navigation links and search box in print layout
+ */
+@media print {
+ ul.nav-list, div.sub-nav {
+ display:none;
+ }
+}
+/*
+ * Styles for page header and footer.
+ */
+.title {
+ color:#2c4557;
+ margin:10px 0;
+}
+.sub-title {
+ margin:5px 0 0 0;
+}
+.header ul {
+ margin:0 0 15px 0;
+ padding:0;
+}
+.header ul li, .footer ul li {
+ list-style:none;
+ font-size:13px;
+}
+/*
+ * Styles for headings.
+ */
+body.class-declaration-page .summary h2,
+body.class-declaration-page .details h2,
+body.class-use-page h2,
+body.module-declaration-page .block-list h2 {
+ font-style: italic;
+ padding:0;
+ margin:15px 0;
+}
+body.class-declaration-page .summary h3,
+body.class-declaration-page .details h3,
+body.class-declaration-page .summary .inherited-list h2 {
+ background-color:#dee3e9;
+ border:1px solid #d0d9e0;
+ margin:0 0 6px -8px;
+ padding:7px 5px;
+}
+/*
+ * Styles for page layout containers.
+ */
+main {
+ clear:both;
+ padding:10px 20px;
+ position:relative;
+}
+dl.notes > dt {
+ font-family: 'DejaVu Sans', Arial, Helvetica, sans-serif;
+ font-size:12px;
+ font-weight:bold;
+ margin:10px 0 0 0;
+ color:#4E4E4E;
+}
+dl.notes > dd {
+ margin:5px 10px 10px 0;
+ font-size:14px;
+ font-family:'DejaVu Serif', Georgia, "Times New Roman", Times, serif;
+}
+dl.name-value > dt {
+ margin-left:1px;
+ font-size:1.1em;
+ display:inline;
+ font-weight:bold;
+}
+dl.name-value > dd {
+ margin:0 0 0 1px;
+ font-size:1.1em;
+ display:inline;
+}
+/*
+ * Styles for lists.
+ */
+li.circle {
+ list-style:circle;
+}
+ul.horizontal li {
+ display:inline;
+ font-size:0.9em;
+}
+div.inheritance {
+ margin:0;
+ padding:0;
+}
+div.inheritance div.inheritance {
+ margin-left:2em;
+}
+ul.block-list,
+ul.details-list,
+ul.member-list,
+ul.summary-list {
+ margin:10px 0 10px 0;
+ padding:0;
+}
+ul.block-list > li,
+ul.details-list > li,
+ul.member-list > li,
+ul.summary-list > li {
+ list-style:none;
+ margin-bottom:15px;
+ line-height:1.4;
+}
+.summary-table dl, .summary-table dl dt, .summary-table dl dd {
+ margin-top:0;
+ margin-bottom:1px;
+}
+ul.see-list, ul.see-list-long {
+ padding-left: 0;
+ list-style: none;
+}
+ul.see-list li {
+ display: inline;
+}
+ul.see-list li:not(:last-child):after,
+ul.see-list-long li:not(:last-child):after {
+ content: ", ";
+ white-space: pre-wrap;
+}
+/*
+ * Styles for tables.
+ */
+.summary-table, .details-table {
+ width:100%;
+ border-spacing:0;
+ border-left:1px solid #EEE;
+ border-right:1px solid #EEE;
+ border-bottom:1px solid #EEE;
+ padding:0;
+}
+.caption {
+ position:relative;
+ text-align:left;
+ background-repeat:no-repeat;
+ color:#253441;
+ font-weight:bold;
+ clear:none;
+ overflow:hidden;
+ padding:0;
+ padding-top:10px;
+ padding-left:1px;
+ margin:0;
+ white-space:pre;
+}
+.caption a:link, .caption a:visited {
+ color:#1f389c;
+}
+.caption a:hover,
+.caption a:active {
+ color:#FFFFFF;
+}
+.caption span {
+ white-space:nowrap;
+ padding-top:5px;
+ padding-left:12px;
+ padding-right:12px;
+ padding-bottom:7px;
+ display:inline-block;
+ float:left;
+ background-color:#F8981D;
+ border: none;
+ height:16px;
+}
+div.table-tabs {
+ padding:10px 0 0 1px;
+ margin:0;
+}
+div.table-tabs > button {
+ border: none;
+ cursor: pointer;
+ padding: 5px 12px 7px 12px;
+ font-weight: bold;
+ margin-right: 3px;
+}
+div.table-tabs > button.active-table-tab {
+ background: #F8981D;
+ color: #253441;
+}
+div.table-tabs > button.table-tab {
+ background: #4D7A97;
+ color: #FFFFFF;
+}
+.two-column-summary {
+ display: grid;
+ grid-template-columns: minmax(15%, max-content) minmax(15%, auto);
+}
+.three-column-summary {
+ display: grid;
+ grid-template-columns: minmax(10%, max-content) minmax(15%, max-content) minmax(15%, auto);
+}
+.four-column-summary {
+ display: grid;
+ grid-template-columns: minmax(10%, max-content) minmax(10%, max-content) minmax(10%, max-content) minmax(10%, auto);
+}
+@media screen and (max-width: 600px) {
+ .two-column-summary {
+ display: grid;
+ grid-template-columns: 1fr;
+ }
+}
+@media screen and (max-width: 800px) {
+ .three-column-summary {
+ display: grid;
+ grid-template-columns: minmax(10%, max-content) minmax(25%, auto);
+ }
+ .three-column-summary .col-last {
+ grid-column-end: span 2;
+ }
+}
+@media screen and (max-width: 1000px) {
+ .four-column-summary {
+ display: grid;
+ grid-template-columns: minmax(15%, max-content) minmax(15%, auto);
+ }
+}
+.summary-table > div, .details-table > div {
+ text-align:left;
+ padding: 8px 3px 3px 7px;
+}
+.col-first, .col-second, .col-last, .col-constructor-name, .col-summary-item-name {
+ vertical-align:top;
+ padding-right:0;
+ padding-top:8px;
+ padding-bottom:3px;
+}
+.table-header {
+ background:#dee3e9;
+ font-weight: bold;
+}
+.col-first, .col-first {
+ font-size:13px;
+}
+.col-second, .col-second, .col-last, .col-constructor-name, .col-summary-item-name, .col-last {
+ font-size:13px;
+}
+.col-first, .col-second, .col-constructor-name {
+ vertical-align:top;
+ overflow: auto;
+}
+.col-last {
+ white-space:normal;
+}
+.col-first a:link, .col-first a:visited,
+.col-second a:link, .col-second a:visited,
+.col-first a:link, .col-first a:visited,
+.col-second a:link, .col-second a:visited,
+.col-constructor-name a:link, .col-constructor-name a:visited,
+.col-summary-item-name a:link, .col-summary-item-name a:visited,
+.constant-values-container a:link, .constant-values-container a:visited,
+.all-classes-container a:link, .all-classes-container a:visited,
+.all-packages-container a:link, .all-packages-container a:visited {
+ font-weight:bold;
+}
+.table-sub-heading-color {
+ background-color:#EEEEFF;
+}
+.even-row-color, .even-row-color .table-header {
+ background-color:#FFFFFF;
+}
+.odd-row-color, .odd-row-color .table-header {
+ background-color:#EEEEEF;
+}
+/*
+ * Styles for contents.
+ */
+.deprecated-content {
+ margin:0;
+ padding:10px 0;
+}
+div.block {
+ font-size:14px;
+ font-family:'DejaVu Serif', Georgia, "Times New Roman", Times, serif;
+}
+.col-last div {
+ padding-top:0;
+}
+.col-last a {
+ padding-bottom:3px;
+}
+.module-signature,
+.package-signature,
+.type-signature,
+.member-signature {
+ font-family:'DejaVu Sans Mono', monospace;
+ font-size:14px;
+ margin:14px 0;
+ white-space: pre-wrap;
+}
+.module-signature,
+.package-signature,
+.type-signature {
+ margin-top: 0;
+}
+.member-signature .type-parameters-long,
+.member-signature .parameters,
+.member-signature .exceptions {
+ display: inline-block;
+ vertical-align: top;
+ white-space: pre;
+}
+.member-signature .type-parameters {
+ white-space: normal;
+}
+/*
+ * Styles for formatting effect.
+ */
+.source-line-no {
+ color:green;
+ padding:0 30px 0 0;
+}
+h1.hidden {
+ visibility:hidden;
+ overflow:hidden;
+ font-size:10px;
+}
+.block {
+ display:block;
+ margin:0 10px 5px 0;
+ color:#474747;
+}
+.deprecated-label, .descfrm-type-label, .implementation-label, .member-name-label, .member-name-link,
+.module-label-in-package, .module-label-in-type, .override-specify-label, .package-label-in-type,
+.package-hierarchy-label, .type-name-label, .type-name-link, .search-tag-link, .preview-label {
+ font-weight:bold;
+}
+.deprecation-comment, .help-footnote, .preview-comment {
+ font-style:italic;
+}
+.deprecation-block {
+ font-size:14px;
+ font-family:'DejaVu Serif', Georgia, "Times New Roman", Times, serif;
+ border-style:solid;
+ border-width:thin;
+ border-radius:10px;
+ padding:10px;
+ margin-bottom:10px;
+ margin-right:10px;
+ display:inline-block;
+}
+.preview-block {
+ font-size:14px;
+ font-family:'DejaVu Serif', Georgia, "Times New Roman", Times, serif;
+ border-style:solid;
+ border-width:thin;
+ border-radius:10px;
+ padding:10px;
+ margin-bottom:10px;
+ margin-right:10px;
+ display:inline-block;
+}
+div.block div.deprecation-comment {
+ font-style:normal;
+}
+/*
+ * Styles specific to HTML5 elements.
+ */
+main, nav, header, footer, section {
+ display:block;
+}
+/*
+ * Styles for javadoc search.
+ */
+.ui-autocomplete-category {
+ font-weight:bold;
+ font-size:15px;
+ padding:7px 0 7px 3px;
+ background-color:#4D7A97;
+ color:#FFFFFF;
+}
+.result-item {
+ font-size:13px;
+}
+.ui-autocomplete {
+ max-height:85%;
+ max-width:65%;
+ overflow-y:scroll;
+ overflow-x:scroll;
+ white-space:nowrap;
+ box-shadow: 0 3px 6px rgba(0,0,0,0.16), 0 3px 6px rgba(0,0,0,0.23);
+}
+ul.ui-autocomplete {
+ position:fixed;
+ z-index:999999;
+}
+ul.ui-autocomplete li {
+ float:left;
+ clear:both;
+ width:100%;
+}
+.result-highlight {
+ font-weight:bold;
+}
+#search-input {
+ background-image:url('resources/glass.png');
+ background-size:13px;
+ background-repeat:no-repeat;
+ background-position:2px 3px;
+ padding-left:20px;
+ position:relative;
+ right:-18px;
+ width:400px;
+}
+#reset-button {
+ background-color: rgb(255,255,255);
+ background-image:url('resources/x.png');
+ background-position:center;
+ background-repeat:no-repeat;
+ background-size:12px;
+ border:0 none;
+ width:16px;
+ height:16px;
+ position:relative;
+ left:-4px;
+ top:-4px;
+ font-size:0px;
+}
+.watermark {
+ color:#545454;
+}
+.search-tag-desc-result {
+ font-style:italic;
+ font-size:11px;
+}
+.search-tag-holder-result {
+ font-style:italic;
+ font-size:12px;
+}
+.search-tag-result:target {
+ background-color:yellow;
+}
+.module-graph span {
+ display:none;
+ position:absolute;
+}
+.module-graph:hover span {
+ display:block;
+ margin: -100px 0 0 100px;
+ z-index: 1;
+}
+.inherited-list {
+ margin: 10px 0 10px 0;
+}
+section.class-description {
+ line-height: 1.4;
+}
+.summary section[class$="-summary"], .details section[class$="-details"],
+.class-uses .detail, .serialized-class-details {
+ padding: 0px 20px 5px 10px;
+ border: 1px solid #ededed;
+ background-color: #f8f8f8;
+}
+.inherited-list, section[class$="-details"] .detail {
+ padding:0 0 5px 8px;
+ background-color:#ffffff;
+ border:none;
+}
+.vertical-separator {
+ padding: 0 5px;
+}
+ul.help-section-list {
+ margin: 0;
+}
+ul.help-subtoc > li {
+ display: inline-block;
+ padding-right: 5px;
+ font-size: smaller;
+}
+ul.help-subtoc > li::before {
+ content: "\2022" ;
+ padding-right:2px;
+}
+span.help-note {
+ font-style: italic;
+}
+/*
+ * Indicator icon for external links.
+ */
+main a[href*="://"]::after {
+ content:"";
+ display:inline-block;
+ background-image:url('data:image/svg+xml; utf8, \
+ \
+ ');
+ background-size:100% 100%;
+ width:7px;
+ height:7px;
+ margin-left:2px;
+ margin-bottom:4px;
+}
+main a[href*="://"]:hover::after,
+main a[href*="://"]:focus::after {
+ background-image:url('data:image/svg+xml; utf8, \
+ \
+ ');
+}
+
+/*
+ * Styles for user-provided tables.
+ *
+ * borderless:
+ * No borders, vertical margins, styled caption.
+ * This style is provided for use with existing doc comments.
+ * In general, borderless tables should not be used for layout purposes.
+ *
+ * plain:
+ * Plain borders around table and cells, vertical margins, styled caption.
+ * Best for small tables or for complex tables for tables with cells that span
+ * rows and columns, when the "striped" style does not work well.
+ *
+ * striped:
+ * Borders around the table and vertical borders between cells, striped rows,
+ * vertical margins, styled caption.
+ * Best for tables that have a header row, and a body containing a series of simple rows.
+ */
+
+table.borderless,
+table.plain,
+table.striped {
+ margin-top: 10px;
+ margin-bottom: 10px;
+}
+table.borderless > caption,
+table.plain > caption,
+table.striped > caption {
+ font-weight: bold;
+ font-size: smaller;
+}
+table.borderless th, table.borderless td,
+table.plain th, table.plain td,
+table.striped th, table.striped td {
+ padding: 2px 5px;
+}
+table.borderless,
+table.borderless > thead > tr > th, table.borderless > tbody > tr > th, table.borderless > tr > th,
+table.borderless > thead > tr > td, table.borderless > tbody > tr > td, table.borderless > tr > td {
+ border: none;
+}
+table.borderless > thead > tr, table.borderless > tbody > tr, table.borderless > tr {
+ background-color: transparent;
+}
+table.plain {
+ border-collapse: collapse;
+ border: 1px solid black;
+}
+table.plain > thead > tr, table.plain > tbody tr, table.plain > tr {
+ background-color: transparent;
+}
+table.plain > thead > tr > th, table.plain > tbody > tr > th, table.plain > tr > th,
+table.plain > thead > tr > td, table.plain > tbody > tr > td, table.plain > tr > td {
+ border: 1px solid black;
+}
+table.striped {
+ border-collapse: collapse;
+ border: 1px solid black;
+}
+table.striped > thead {
+ background-color: #E3E3E3;
+}
+table.striped > thead > tr > th, table.striped > thead > tr > td {
+ border: 1px solid black;
+}
+table.striped > tbody > tr:nth-child(even) {
+ background-color: #EEE
+}
+table.striped > tbody > tr:nth-child(odd) {
+ background-color: #FFF
+}
+table.striped > tbody > tr > th, table.striped > tbody > tr > td {
+ border-left: 1px solid black;
+ border-right: 1px solid black;
+}
+table.striped > tbody > tr > th {
+ font-weight: normal;
+}
+/**
+ * Tweak font sizes and paddings for small screens.
+ */
+@media screen and (max-width: 1050px) {
+ #search-input {
+ width: 300px;
+ }
+}
+@media screen and (max-width: 800px) {
+ #search-input {
+ width: 200px;
+ }
+ .top-nav,
+ .bottom-nav {
+ font-size: 11px;
+ padding-top: 6px;
+ }
+ .sub-nav {
+ font-size: 11px;
+ }
+ .about-language {
+ padding-right: 16px;
+ }
+ ul.nav-list li,
+ .sub-nav .nav-list-search {
+ padding: 6px;
+ }
+ ul.sub-nav-list li {
+ padding-top: 5px;
+ }
+ main {
+ padding: 10px;
+ }
+ .summary section[class$="-summary"], .details section[class$="-details"],
+ .class-uses .detail, .serialized-class-details {
+ padding: 0 8px 5px 8px;
+ }
+ body {
+ -webkit-text-size-adjust: none;
+ }
+}
+@media screen and (max-width: 500px) {
+ #search-input {
+ width: 150px;
+ }
+ .top-nav,
+ .bottom-nav {
+ font-size: 10px;
+ }
+ .sub-nav {
+ font-size: 10px;
+ }
+ .about-language {
+ font-size: 10px;
+ padding-right: 12px;
+ }
+}
diff --git a/docs/javadoc/tag-search-index.js b/docs/javadoc/tag-search-index.js
new file mode 100644
index 00000000..0367dae6
--- /dev/null
+++ b/docs/javadoc/tag-search-index.js
@@ -0,0 +1 @@
+tagSearchIndex = [];updateSearchResults();
\ No newline at end of file
diff --git a/docs/javadoc/type-search-index.js b/docs/javadoc/type-search-index.js
new file mode 100644
index 00000000..85734293
--- /dev/null
+++ b/docs/javadoc/type-search-index.js
@@ -0,0 +1 @@
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\ No newline at end of file
