SquaredDistanceMatrix computes the squared distance matrix for row vectors in X. Returns a matrix where the {i, j}-th element is the squared euclidean

EmbedData initializes the pairwise affinity matrix P with the similarity probabilities calculated based on the provided data matrix and runs t-SNE. It

scaleSprites scales all the sprites to match spriteScale.

Diagonal returns the diagonal elements of the specified matrix as a Vector.

InitDistances initializes the pairwise affinity matrix P with the similarity probabilities calculated based on the provided (squared) distance matrix

Utility functions RandNormal samples from a Gaussian distribution with the specified mean and standard deviation.

costGradient computes the Kullback-Leibler divergence between P and the Student-t based joint probability distribution Q. It also computes the gradien

createDigitSprites creates a sprite for each digit in the dataset.

d2p computes the P matrix based on a (squared) distance matrix D. It performs a binary search to obtain a similarity probability for each pairwise dis

initSolution initializes the t-SNE solution.

plotY2D plots the 2D embedding Y and saves an image of the plot with the specified filename.

run performs batch gradient descent to reduce the Kullback-Leibler divergence between P and Q, the high dimensional affinities and the low dimensional

updateSprites updates the position of the sprites based on the specified embedding matrix.

NewTSNE creates and returns a new t-SNE dimensionality reductor with the specified parameters.

TestSquaredDistanceMatrix verifies that SquaredDistanceMatrix is behaving properly.