protected virtual void _CalcGradient(double[] solution, double[] grad)
{
GradientEstimation.CalcGradient(solution, grad, (s, constraints) =>
{
return(Evaluate(s));
});
mEvaluationCount += mDimensionCount;
}
public static void Test_CollaborativeFilteringRSCostFunction_CalcGradient(double lambda = 0)
{
Matrix <double> X_t = CreateRandomMatrix(4, 3);
Matrix <double> Theta_t = CreateRandomMatrix(5, 3);
Matrix <double> Y = X_t.Multiply(Theta_t.Transpose());
int[,] R = new int[Y.RowCount, Y.ColumnCount];
for (int i = 0; i < Y.RowCount; ++i)
{
for (int j = 0; j < Y.ColumnCount; ++j)
{
if (mRand.NextDouble() > 0.5)
{
Y[i, j] = 0;
}
if (Y[i, j] == 0)
{
R[i, j] = 0;
}
else
{
R[i, j] = 1;
}
}
}
Matrix <double> X = CreateRandomMatrix(4, 3);
Matrix <double> Theta = CreateRandomMatrix(5, 3);
int num_users = Y.ColumnCount;
int num_movies = Y.RowCount;
int num_features = Theta_t.ColumnCount;
int dimension = num_movies * num_features + num_users * num_features; //total number of entries in X and Theta
double[] theta_x = new double[dimension];
CollaborativeFilteringRSCostFunction.UnrollMatrixIntoVector(Theta, X, theta_x);
CollaborativeFilteringRSCostFunction f = new CollaborativeFilteringRSCostFunction(Y, R, num_movies, num_features, dimension);
f.RegularizationLambda = lambda;
double[] numgrad = new double[dimension];
double[] grad = new double[dimension];
GradientEstimation.CalcGradient(theta_x, numgrad, (x_pi, constraints) =>
{
return(f.Evaluate(x_pi));
});
f.CalcGradient(theta_x, grad);
Console.WriteLine("The relative difference will be small:");
for (int i = 0; i < dimension; ++i)
{
Console.WriteLine("{0}\t{1}", numgrad[i], grad[i]);
}
}
