https://github.com/angeloschatzimparmpas/t-viSNE
Tip revision: 8d87bd631867563401ff5081b7f1acaf4644b7d4 authored by Angelos Chatzimparmpas on 28 April 2020, 16:32:50 UTC
new
new
Tip revision: 8d87bd6
fast_tsne.m
function mappedX = fast_tsne(X, no_dims, initial_dims, perplexity, theta, alg, max_iter)
%FAST_TSNE Runs the C++ implementation of Barnes-Hut t-SNE
%
% mappedX = fast_tsne(X, no_dims, initial_dims, perplexity, theta, alg)
%
% Runs the C++ implementation of Barnes-Hut-SNE. The high-dimensional
% datapoints are specified in the NxD matrix X. The dimensionality of the
% datapoints is reduced to initial_dims dimensions using PCA (default = 50)
% before t-SNE is performed. Next, t-SNE reduces the points to no_dims
% dimensions. The perplexity of the input similarities may be specified
% through the perplexity variable (default = 30). The variable theta sets
% the trade-off parameter between speed and accuracy: theta = 0 corresponds
% to standard, slow t-SNE, while theta = 1 makes very crude approximations.
% Appropriate values for theta are between 0.1 and 0.7 (default = 0.5).
% The variable alg determines the algorithm used for PCA. The default is set
% to 'svd'. Other options are 'eig' or 'als' (see 'doc pca' for more details).
% The function returns the two-dimensional data points in mappedX.
%
% NOTE: The function is designed to run on large (N > 5000) data sets. It
% may give poor performance on very small data sets (it is better to use a
% standard t-SNE implementation on such data).
% 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.
if ~exist('no_dims', 'var') || isempty(no_dims)
no_dims = 2;
end
if ~exist('initial_dims', 'var') || isempty(initial_dims)
initial_dims = 50;
end
if ~exist('perplexity', 'var') || isempty(perplexity)
perplexity = 30;
end
if ~exist('theta', 'var') || isempty(theta)
theta = 0.5;
end
if ~exist('alg', 'var') || isempty(alg)
alg = 'svd';
end
if ~exist('max_iter', 'var') || isempty(max_iter)
max_iter=1000;
end
% Perform the initial dimensionality reduction using PCA
X = double(X);
X = bsxfun(@minus, X, mean(X, 1));
M = pca(X,'NumComponents',initial_dims,'Algorithm',alg);
X = X * M;
tsne_path = which('fast_tsne');
tsne_path = fileparts(tsne_path);
% Compile t-SNE C code
if(~exist(fullfile(tsne_path,'./bh_tsne'),'file') && isunix)
system(sprintf('g++ %s %s -o %s -O2',...
fullfile(tsne_path,'./sptree.cpp'),...
fullfile(tsne_path,'./tsne.cpp'),...
fullfile(tsne_path,'./bh_tsne')));
end
% Run the fast diffusion SNE implementation
write_data(X, no_dims, theta, perplexity, max_iter);
tic
[flag, cmdout] = system(['"' fullfile(tsne_path,'./bh_tsne') '"']);
if(flag~=0)
error(cmdout);
end
toc
[mappedX, landmarks, costs] = read_data;
landmarks = landmarks + 1; % correct for Matlab indexing
delete('data.dat');
delete('result.dat');
end
% Writes the datafile for the fast t-SNE implementation
function write_data(X, no_dims, theta, perplexity, max_iter)
[n, d] = size(X);
h = fopen('data.dat', 'wb');
fwrite(h, n, 'integer*4');
fwrite(h, d, 'integer*4');
fwrite(h, theta, 'double');
fwrite(h, perplexity, 'double');
fwrite(h, no_dims, 'integer*4');
fwrite(h, max_iter, 'integer*4');
fwrite(h, X', 'double');
fclose(h);
end
% Reads the result file from the fast t-SNE implementation
function [X, landmarks, costs] = read_data
h = fopen('result.dat', 'rb');
n = fread(h, 1, 'integer*4');
d = fread(h, 1, 'integer*4');
X = fread(h, n * d, 'double');
landmarks = fread(h, n, 'integer*4');
costs = fread(h, n, 'double'); % this vector contains only zeros
X = reshape(X, [d n])';
fclose(h);
end