R/tsne-internal.R

In tsne: T-Distributed Stochastic Neighbor Embedding for R (t-SNE)

.Hbeta <-
function(D, beta){
	P = exp(-D * beta)
	sumP = sum(P)
	if (sumP == 0){
		H = 0
		P = D * 0
	} else {
		H = log(sumP) + beta * sum(D %*% P) /sumP
		P = P/sumP
	}
	r = {}
	r$H = H
	r$P = P
	r
}

.x2p <-
function(X,perplexity = 15,tol = 1e-5){
	if (inherits(X, 'dist')) {
		D = X
		n = attr(D,'Size')
	} else{
		D = dist(X)
		n = attr(D,'Size')
	}

	D = as.matrix(D)
	P = matrix(0, n, n )		
	beta = rep(1, n)
	logU = log(perplexity)
	
	for (i in 1:n){
		betamin = -Inf
		betamax = Inf
		Di = D[i, -i]
		hbeta = .Hbeta(Di, beta[i])
		H = hbeta$H; 
		thisP = hbeta$P
		Hdiff = H - logU;
		tries = 0;

		while(abs(Hdiff) > tol && tries < 50){
			if (Hdiff > 0){
				betamin = beta[i]
				if (is.infinite(betamax)) beta[i] = beta[i] * 2
				else beta[i] = (beta[i] + betamax)/2
			} else{
				betamax = beta[i]
				if (is.infinite(betamin))  beta[i] = beta[i]/ 2
				else beta[i] = ( beta[i] + betamin) / 2
			}
			
			hbeta = .Hbeta(Di, beta[i])
			H = hbeta$H
			thisP = hbeta$P
			Hdiff = H - logU
			tries = tries + 1
		}	
			P[i,-i]  = thisP	
	}	
	
	r = {}
	r$P = P
	r$beta = beta
	sigma = sqrt(1/beta)
	
	message('sigma summary: ', paste(names(summary(sigma)),':',summary(sigma),'|',collapse=''))

	r 
}

.pca <-
function(X, n.comp=ncol(X))
{
	X = scale(X, scale = FALSE)
	e = eigen(t(X) %*% X)
	X %*% Re(e$vectors[, 1:n.comp])
}