TSNE Class

Barnes-Hutt t-SNE.

Inheritance Hierarchy

SystemObject
  Accord.MachineLearningTransformBaseDouble, Double
    Accord.MachineLearningMultipleTransformBaseDouble, Double
      Accord.MachineLearning.ClusteringTSNE

Namespace: Accord.MachineLearning.Clustering
Assembly: Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0

Syntax

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public class TSNE : MultipleTransformBase<double[], double>

Public Class TSNE
	Inherits MultipleTransformBase(Of Double(), Double)

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The TSNE type exposes the following members.

Constructors

	Name	Description
	TSNE	Initializes a new instance of the TSNE class.

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Properties

	Name	Description
	NumberOfInputs	Gets the number of inputs accepted by the model. (Inherited from TransformBaseTInput, TOutput.)
	NumberOfOutputs	Gets the number of outputs generated by the model. (Inherited from TransformBaseTInput, TOutput.)
	Perplexity	Gets or sets t-SNE's perplexity value. Default is 50.
	Theta	Gets or sets t-SNE's Theta value. Default is 0.5

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Methods

	Name	Description
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)
	Transform(TInput)	Applies the transformation to an input, producing an associated output. (Inherited from MultipleTransformBaseTInput, TOutput.)
	Transform(TInput)	Applies the transformation to a set of input vectors, producing an associated set of output vectors. (Inherited from MultipleTransformBaseTInput, TOutput.)
	Transform(Double, Double)	Not supported. (Overrides MultipleTransformBaseTInput, TOutputTransform(TInput, TOutput).)
	Transform(Double, Double)	Applies the transformation to an input, producing an associated output. (Overrides MultipleTransformBaseTInput, TOutputTransform(TInput, TOutput).)

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Extension Methods

	Name	Description
	HasMethod	Checks whether an object implements a method with the given name. (Defined by ExtensionMethods.)
	IsEqual	Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)
	To(Type)	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)
	ToT	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)

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Remarks

The code contained in this class was adapted from Laurens van der Maaten excellent BH T-SNE code from https://github.com/lvdmaaten/bhtsne/. The original license is listed below:

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Copyright (c) 2014, Laurens van der Maaten (Delft University of Technology)
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright
   notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
   notice, this list of conditions and the following disclaimer in the
   documentation and/or other materials provided with the distribution.
3. All advertising materials mentioning features or use of this software
   must display the following acknowledgement:
   This product includes software developed by the Delft University of Technology.
4. Neither the name of the Delft University of Technology nor the names of 
   its contributors may be used to endorse or promote products derived from 
   this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY LAURENS VAN DER MAATEN ''AS IS'' AND ANY EXPRESS
OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES 
OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO 
EVENT SHALL LAURENS VAN DER MAATEN BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR 
BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN 
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING 
IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY 
OF SUCH DAMAGE.

Examples

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Accord.Math.Random.Generator.Seed = 0;

// Declare some observations
double[][] observations =
{
    new double[] { -5, -2, -1 },
    new double[] { -5, -5, -6 },
    new double[] {  2,  1,  1 },
    new double[] {  1,  1,  2 },
    new double[] {  1,  2,  2 },
    new double[] {  3,  1,  2 },
    new double[] { 11,  5,  4 },
    new double[] { 15,  5,  6 },
    new double[] { 10,  5,  6 },
};

// Create a new t-SNE algorithm 
TSNE tSNE = new TSNE()
{
    NumberOfOutputs = 1,
    Perplexity = 1.5
};

// Transform to a reduced dimensionality space
double[][] output = tSNE.Transform(observations);

// Make it 1-dimensional
double[] y = output.Reshape();

Reference

Accord.MachineLearning.Clustering Namespace