t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections
https://doi.org/10.1109/TVCG.2020.2986996
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64 lines
3.3 KiB
64 lines
3.3 KiB
5 years ago
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/*
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*
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* Copyright (c) 2014, Laurens van der Maaten (Delft University of Technology)
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* 2. Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* 3. All advertising materials mentioning features or use of this software
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* must display the following acknowledgement:
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* This product includes software developed by the Delft University of Technology.
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* 4. Neither the name of the Delft University of Technology nor the names of
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* its contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY LAURENS VAN DER MAATEN ''AS IS'' AND ANY EXPRESS
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* OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
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* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
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* EVENT SHALL LAURENS VAN DER MAATEN BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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* SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
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* BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING
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* IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY
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* OF SUCH DAMAGE.
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*
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*/
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#ifndef TSNE_H
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#define TSNE_H
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static inline double sign(double x) { return (x == .0 ? .0 : (x < .0 ? -1.0 : 1.0)); }
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class TSNE
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{
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public:
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void run(double* X, int N, int D, double* Y, int no_dims, double perplexity, double theta, int rand_seed,
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bool skip_random_init, int max_iter=1000, int stop_lying_iter=250, int mom_switch_iter=250);
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bool load_data(double** data, int* n, int* d, int* no_dims, double* theta, double* perplexity, int* rand_seed, int* max_iter);
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void save_data(double* data, int* landmarks, double* costs, int n, int d);
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void symmetrizeMatrix(unsigned int** row_P, unsigned int** col_P, double** val_P, int N); // should be static!
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private:
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void computeGradient(double* P, unsigned int* inp_row_P, unsigned int* inp_col_P, double* inp_val_P, double* Y, int N, int D, double* dC, double theta);
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void computeExactGradient(double* P, double* Y, int N, int D, double* dC);
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double evaluateError(double* P, double* Y, int N, int D);
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double evaluateError(unsigned int* row_P, unsigned int* col_P, double* val_P, double* Y, int N, int D, double theta);
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void zeroMean(double* X, int N, int D);
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void computeGaussianPerplexity(double* X, int N, int D, double* P, double perplexity);
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void computeGaussianPerplexity(double* X, int N, int D, unsigned int** _row_P, unsigned int** _col_P, double** _val_P, double perplexity, int K);
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void computeSquaredEuclideanDistance(double* X, int N, int D, double* DD);
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double randn();
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};
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#endif
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