function ydata = tsne(X, labels, no_dims, initial_dims, perplexity)
%TSNE Performs symmetric t-SNE on dataset X
%
%   mappedX = tsne(X, labels, no_dims, initial_dims, perplexity)
%   mappedX = tsne(X, labels, initial_solution, perplexity)
%
% The function performs symmetric t-SNE on the NxD dataset X to reduce its 
% dimensionality to no_dims dimensions (default = 2). The data is 
% preprocessed using PCA, reducing the dimensionality to initial_dims 
% dimensions (default = 30). Alternatively, an initial solution obtained 
% from an other dimensionality reduction technique may be specified in 
% initial_solution. The perplexity of the Gaussian kernel that is employed 
% can be specified through perplexity (default = 30). The labels of the
% data 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.
% The low-dimensional data representation is returned in mappedX.
%
%

% This file is part of the Matlab Toolbox for Dimensionality Reduction.
% The toolbox can be obtained from http://homepage.tudelft.nl/19j49
% You are free to use, change, or redistribute this code in any way you
% want for non-commercial purposes. However, it is appreciated if you 
% maintain the name of the original author.
%
% (C) Laurens van der Maaten, Delft University of Technology


    if ~exist('labels', 'var')
        labels = [];
    end
    if ~exist('no_dims', 'var') || isempty(no_dims)
        no_dims = 2;
    end
    if ~exist('initial_dims', 'var') || isempty(initial_dims)
        initial_dims = min(50, size(X, 2));
    end
    if ~exist('perplexity', 'var') || isempty(perplexity)
        perplexity = 30;
    end
    
    % First check whether we already have an initial solution
    if numel(no_dims) > 1
        initial_solution = true;
        ydata = no_dims;
        no_dims = size(ydata, 2);
        perplexity = initial_dims;
    else
        initial_solution = false;
    end
    
    % Normalize input data
    X = X - min(X(:));
    X = X / max(X(:));
    X = bsxfun(@minus, X, mean(X, 1));
    
    % Perform preprocessing using PCA
    if ~initial_solution
        disp('Preprocessing data using PCA...');
        if size(X, 2) < size(X, 1)
            C = X' * X;
        else
            C = (1 / size(X, 1)) * (X * X');
        end
        [M, lambda] = eig(C);
        [lambda, ind] = sort(diag(lambda), 'descend');
        M = M(:,ind(1:initial_dims));
        lambda = lambda(1:initial_dims);
        if ~(size(X, 2) < size(X, 1))
            M = bsxfun(@times, X' * M, (1 ./ sqrt(size(X, 1) .* lambda))');
        end
        X = X * M;
        clear M lambda ind
    end
    
    % Compute pairwise distance matrix
    sum_X = sum(X .^ 2, 2);
    D = bsxfun(@plus, sum_X, bsxfun(@plus, sum_X', -2 * (X * X')));
    
    % Compute joint probabilities
    P = d2p(D, perplexity, 1e-5);                                           % compute affinities using fixed perplexity
    clear D
    
    % Run t-SNE
    if initial_solution
        ydata = tsne_p(P, labels, ydata);
    else
        ydata = tsne_p(P, labels, no_dims);
    end
    