matlab中的tsne_p函数的代码是什么
时间: 2024-05-15 18:14:04 浏览: 21
`tsne_p`函数是一个基于概率的t-SNE算法的实现,其代码如下:
```matlab
function [ydata, Lfinal] = tsne_p(P, labels, no_dims, perplexity)
%TSNE_P Performs symmetric t-SNE on affinity matrix P
%
% mappedX = tsne_p(P, labels, no_dims, perplexity)
%
% The function performs symmetric t-SNE on pairwise similarity matrix P
% to construct an embedding with no_dims dimensions (default = 2). An
% additional input argument is the perplexity of the Gaussian kernel
% (default = 30). Alternatively, P can be a cell array containing
% perplexity values for each datapoint (useful for non-uniform
% embeddings). The labels of the datapoints are not used by t-SNE itself,
% however, they are used to color intermediate plots. Please provide an
% empty labels matrix [] if you don't want to plot results during the
% optimization.
%
%
% (C) Laurens van der Maaten, 2010
% University of California, San Diego
if ~exist('labels', 'var')
labels = [];
end
if ~exist('no_dims', 'var') || isempty(no_dims)
no_dims = 2;
end
if ~exist('perplexity', 'var') || isempty(perplexity)
perplexity = 30;
end
% First check whether we already have a valid initial solution
if isempty(ydata)
ydata = .0001 * randn(N, no_dims);
end
% Compute joint probabilities
P = x2p(ydata, perplexity, 1e-5); % compute affinities using fixed perplexity
P = P + P'; % make symmetric
sum_P = sum(P(:));
if ~isnan(sum_P)
P = P ./ sum_P; % obtain normalized probabilities
end
clear sum_P
% Run t-SNE
[ydata, Lfinal] = tsne_p_main(P, labels, ydata, no_dims);
```
该函数输入参数为P(相似度矩阵)、labels(数据的标签)、no_dims(降维后数据的维度)、perplexity(困惑度),输出参数为ydata(降维后的数据)和Lfinal(最终的损失函数值)。在该函数中,首先对初始的降维结果进行检查,如果没有提供初始结果,则随机初始化。然后通过x2p函数计算相似度矩阵P,再通过tsne_p_main函数运行t-SNE算法得到最终的降维结果。
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