public Array <float> x2p(Array <float> X, float tol = 1e-5f, float perplexity = 30f, bool sym = true, bool normalize = true)
{
//"""Performs a binary search to get P-values in such a way that each conditional Gaussian has the same perplexity."""
// Initialize some variables
Console.WriteLine("Computing pairwise distances...");
int n = X.Shape[0], d = X.Shape[1];
var sum_X = NN.Sum(X * X, axis: 1);
var D = (-2 * X.Dot(X.T) + sum_X).T + sum_X;
var P = NN.Zeros(n, n);
var beta = NN.Ones(n);
var logU = (float)Math.Log(perplexity);
var Di = NN.Zeros(n, n - 1);
// Loop over all datapoints
for (int i = 0; i < n; ++i)
{
// Print progress
if (i % 500 == 0)
{
Console.WriteLine("Computing P-values for point {0} of {1} ...", i, n);
}
// Compute the Gaussian kernel and entropy for the current precision
var betamin = float.NegativeInfinity;
var betamax = float.PositiveInfinity;
Di[i, Until(i)] = D[Until(i)];
if (i + 1 < n)
{
Di[i, From(i + 1)] = D[From(i)];
}
var H_thisP = Hbeta(Di, beta.Item[i]);
var H = H_thisP.Item1; var thisP = H_thisP.Item2;
// Evaluate whether the perplexity is within tolerance
var Hdiff = H - logU;
var tries = 0;
while (Math.Abs(Hdiff) > tol && tries < 50)
{
// If not, increase or decrease precision
if (Hdiff > 0)
{
betamin = beta.Item[i];
if (float.IsInfinity(betamax))
{
beta.Item[i] = beta.Item[i] * 2;
}
else
{
beta.Item[i] = (beta.Item[i] + betamax) / 2;
}
}
else
{
betamax = beta.Item[i];
if (float.IsInfinity(betamin))
{
beta.Item[i] = beta.Item[i] / 2;
}
else
{
beta.Item[i] = (beta.Item[i] + betamin) / 2;
}
}
// Recompute the values
H_thisP = Hbeta(Di, beta.Item[i]);
H = H_thisP.Item1; thisP = H_thisP.Item2;
Hdiff = H - logU;
tries = tries + 1;
}
// Set the final row of P
P[i, Until(i)] = thisP[Until(i)];
if (i + 1 < n)
{
P[i, From(i + 1)] = thisP[From(i)];
}
}
var sigma = NN.Mean(NN.Sqrt(1 / beta));
Console.WriteLine("Mean value of sigma: {0}", sigma);
// Return final P-matrix
if (sym)
{
P += P.T;
}
if (normalize)
{
P /= NN.Sum(P);
}
return(P);
}
