END_TEST
START_TEST(test_output_gaussian_vector)
{
gaussian* g1 = make_gaussian(2, 3);
gaussian* g2 = make_gaussian(4, 2);
gauss_vector* G = malloc(sizeof(gauss_vector));
gaussian** gs = malloc(2 * sizeof(gaussian*));
gs[0] = g1;
gs[1] = g2;
G->gaussians = gs;
G->len = 2;
G->w = malloc(2 * sizeof(double));
G->w[0] = 1.5;
G->w[1] = 3.5;
output_gaussian_vector("files/gauss_out", "w", G);
FILE *fp = fopen("files/gauss_out", "r");
double mu, st, wt;
char* line = malloc(100);
/* while ((line = fgets(line, 100, fp)) != NULL){ */
/* printf("line %s\n", line); */
/* } */
line = fgets(line, 100, fp);
int s = sscanf(line, "%lf %lf %lf", &mu, &st, &wt);
fail_unless(s == 3, NULL);
fail_unless(mu == 2, NULL);
fail_unless(st == 3, NULL);
fail_unless(wt == 1.5, NULL);
line = fgets(line, 100, fp);
int t = sscanf(line, "%lf %lf %lf", &mu, &st, &wt);
fail_unless(t == 3, NULL);
fail_unless(mu == 4, NULL);
fail_unless(st == 2, NULL);
fail_unless(wt == 3.5, NULL);
fclose(fp);
}
void RandomPCA::pca(MatrixXd &X, int method, bool transpose,
unsigned int ndim, unsigned int nextra, unsigned int maxiter, double tol,
long seed, int kernel, double sigma, bool rbf_center,
unsigned int rbf_sample, bool save_kernel, bool do_orth, bool do_loadings)
{
unsigned int N;
if(kernel != KERNEL_LINEAR)
{
transpose = false;
verbose && std::cout << timestamp()
<< " Kernel not linear, can't transpose" << std::endl;
}
verbose && std::cout << timestamp() << " Transpose: "
<< (transpose ? "yes" : "no") << std::endl;
if(transpose)
{
if(stand_method != STANDARDIZE_NONE)
X_meansd = standardize_transpose(X, stand_method, verbose);
N = X.cols();
}
else
{
if(stand_method != STANDARDIZE_NONE)
X_meansd = standardize(X, stand_method, verbose);
N = X.rows();
}
unsigned int total_dim = ndim + nextra;
MatrixXd R = make_gaussian(X.cols(), total_dim, seed);
MatrixXd Y = X * R;
verbose && std::cout << timestamp() << " dim(Y): " << dim(Y) << std::endl;
normalize(Y);
MatrixXd Yn;
verbose && std::cout << timestamp() << " dim(X): " << dim(X) << std::endl;
MatrixXd K;
if(kernel == KERNEL_RBF)
{
if(sigma == 0)
{
unsigned int med_samples = fminl(rbf_sample, N);
double med = median_dist(X, med_samples, seed, verbose);
sigma = sqrt(med);
}
verbose && std::cout << timestamp() << " Using RBF kernel with sigma="
<< sigma << std::endl;
K.noalias() = rbf_kernel(X, sigma, rbf_center, verbose);
}
else
{
verbose && std::cout << timestamp() << " Using linear kernel" << std::endl;
K.noalias() = X * X.transpose() / (N - 1);
}
//trace = K.diagonal().array().sum() / (N - 1);
trace = K.diagonal().array().sum();
verbose && std::cout << timestamp() << " Trace(K): " << trace
<< " (N: " << N << ")" << std::endl;
verbose && std::cout << timestamp() << " dim(K): " << dim(K) << std::endl;
if(save_kernel)
{
verbose && std::cout << timestamp() << " saving K" << std::endl;
save_text("kernel.txt", K);
}
for(unsigned int iter = 0 ; iter < maxiter ; iter++)
{
verbose && std::cout << timestamp() << " iter " << iter;
Yn.noalias() = K * Y;
if(do_orth)
{
verbose && std::cout << " (orthogonalising)";
ColPivHouseholderQR<MatrixXd> qr(Yn);
MatrixXd I = MatrixXd::Identity(Yn.rows(), Yn.cols());
Yn = qr.householderQ() * I;
Yn.conservativeResize(NoChange, Yn.cols());
}
else
normalize(Yn);
double diff = (Y - Yn).array().square().sum() / Y.size();
verbose && std::cout << " " << diff << std::endl;
Y.noalias() = Yn;
if(diff < tol)
break;
}
verbose && std::cout << timestamp() << " QR begin" << std::endl;
ColPivHouseholderQR<MatrixXd> qr(Y);
MatrixXd Q = MatrixXd::Identity(Y.rows(), Y.cols());
Q = qr.householderQ() * Q;
Q.conservativeResize(NoChange, Y.cols());
verbose && std::cout << timestamp() << " dim(Q): " << dim(Q) << std::endl;
verbose && std::cout << timestamp() << " QR done" << std::endl;
MatrixXd B = Q.transpose() * X;
verbose && std::cout << timestamp() << " dim(B): " << dim(B) << std::endl;
MatrixXd Et;
pca_small(B, method, Et, d, verbose);
verbose && std::cout << timestamp() << " dim(Et): " << dim(Et) << std::endl;
d = d.array() / (N - 1);
if(transpose)
{
V.noalias() = Q * Et;
// We divide P by sqrt(N - 1) since X has not been divided
// by it (but B has)
P.noalias() = X.transpose() * V;
VectorXd s = 1 / (d.array().sqrt() * sqrt(N - 1));
MatrixXd Dinv = s.asDiagonal();
U = P * Dinv;
}
else
{
// P = U D = X V
U.noalias() = Q * Et;
P.noalias() = U * d.asDiagonal();
if(do_loadings)
{
VectorXd s = 1 / (d.array().sqrt() * sqrt(N - 1));
MatrixXd Dinv = s.asDiagonal();
V = X.transpose() * U * Dinv;
}
}
P.conservativeResize(NoChange, ndim);
U.conservativeResize(NoChange, ndim);
V.conservativeResize(NoChange, ndim);
d.conservativeResize(ndim);
pve = d.array() / trace;
}
