public void evaluateError_2()
{
int N = 6;
int D = 2;
double[,] P = Matrix.Random(6, 6, new NormalDistribution());
double[][] p = P.ToJagged();
double[,] Y = Matrix.Random(6, 2, new NormalDistribution());
double[][] y = Y.ToJagged();
double expected = TSNEWrapper.evaluateError(P, Y, N, D);
double actual = TSNE.evaluateError(p, y, N, D);
Assert.AreEqual(expected, actual);
}
public void evaluateError_1()
{
double perplexity = 0.5;
double theta = 0.5;
int N = 6;
int K = (int)(3 * perplexity);
int D = 2;
uint[] row_P = Vector.Create(N + 1, new uint[] { 0, 1, 2, 3, 4, 5, 6 });
uint[] col_P = Vector.Create(N * K, new uint[] { 5, 3, 1, 1, 2, 1 });
double[] val_P = Vector.Create(N * K, new double[]
{
0.99901046609114708,
0.99901047304189827,
0.99901046869768451,
0.99901047304189827,
0.99901046869768484,
0.99901046869768451,
});
double[,] Y = Matrix.Random(6, 2, new NormalDistribution());
double[][] y = Y.ToJagged();
uint[] expected_row = Vector.Create(row_P);
uint[] expected_col = Vector.Create(col_P);
double[] expected_val = Vector.Create(val_P);
double expected = TSNEWrapper.evaluateError(expected_row, expected_col, expected_val, Y, N, D, theta);
int[] actual_row = row_P.To <int[]>();
int[] actual_col = col_P.To <int[]>();
double[] actual_val = (double[])val_P.Clone();
double actual = TSNE.evaluateError(actual_row, actual_col, actual_val, y, N, D, theta);
Assert.AreEqual(expected, actual);
Assert.IsTrue(actual_col.IsEqual(expected_col));
Assert.IsTrue(actual_row.IsEqual(expected_row));
Assert.IsTrue(actual_val.IsEqual(expected_val));
}