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Print verbose to stdout instead of stderr #60

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Print verbose to stdout instead of stderr #60

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22 changes: 11 additions & 11 deletions multicore_tsne/tsne.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,7 @@ void TSNE<treeT, dist_fn>::run(double* X, int N, int D, double* Y,
if (N - 1 < 3 * perplexity) {
perplexity = (N - 1) / 3;
if (verbose)
fprintf(stderr, "Perplexity too large for the number of data points! Adjusting ...\n");
fprintf(stdout, "Perplexity too large for the number of data points! Adjusting ...\n");
}

#ifdef _OPENMP
Expand All @@ -68,7 +68,7 @@ void TSNE<treeT, dist_fn>::run(double* X, int N, int D, double* Y,
*/

if (verbose)
fprintf(stderr, "Using no_dims = %d, perplexity = %f, and theta = %f\n", no_dims, perplexity, theta);
fprintf(stdout, "Using no_dims = %d, perplexity = %f, and theta = %f\n", no_dims, perplexity, theta);

// Set learning parameters
float total_time = .0;
Expand All @@ -81,14 +81,14 @@ void TSNE<treeT, dist_fn>::run(double* X, int N, int D, double* Y,
double* dY = (double*) malloc(N * no_dims * sizeof(double));
double* uY = (double*) calloc(N * no_dims , sizeof(double));
double* gains = (double*) malloc(N * no_dims * sizeof(double));
if (dY == NULL || uY == NULL || gains == NULL) { fprintf(stderr, "Memory allocation failed!\n"); exit(1); }
if (dY == NULL || uY == NULL || gains == NULL) { fprintf(stdout, "Memory allocation failed!\n"); exit(1); }
for (int i = 0; i < N * no_dims; i++) {
gains[i] = 1.0;
}

// Normalize input data (to prevent numerical problems)
if (verbose)
fprintf(stderr, "Computing input similarities...\n");
fprintf(stdout, "Computing input similarities...\n");

start = time(0);
zeroMean(X, N, D);
Expand Down Expand Up @@ -118,7 +118,7 @@ void TSNE<treeT, dist_fn>::run(double* X, int N, int D, double* Y,

end = time(0);
if (verbose)
fprintf(stderr, "Done in %4.2f seconds (sparsity = %f)!\nLearning embedding...\n", (float)(end - start) , (double) row_P[N] / ((double) N * (double) N));
fprintf(stdout, "Done in %4.2f seconds (sparsity = %f)!\nLearning embedding...\n", (float)(end - start) , (double) row_P[N] / ((double) N * (double) N));

/*
======================
Expand Down Expand Up @@ -181,10 +181,10 @@ void TSNE<treeT, dist_fn>::run(double* X, int N, int D, double* Y,
end = time(0);

if (iter == 0)
fprintf(stderr, "Iteration %d: error is %f\n", iter + 1, error);
fprintf(stdout, "Iteration %d: error is %f\n", iter + 1, error);
else {
total_time += (float) (end - start);
fprintf(stderr, "Iteration %d: error is %f (50 iterations in %4.2f seconds)\n", iter + 1, error, (float) (end - start) );
fprintf(stdout, "Iteration %d: error is %f (50 iterations in %4.2f seconds)\n", iter + 1, error, (float) (end - start) );
}
start = time(0);
}
Expand All @@ -205,7 +205,7 @@ void TSNE<treeT, dist_fn>::run(double* X, int N, int D, double* Y,
free(val_P); val_P = NULL;

if (verbose)
fprintf(stderr, "Fitting performed in %4.2f seconds.\n", total_time);
fprintf(stdout, "Fitting performed in %4.2f seconds.\n", total_time);
}

// Compute gradient of the t-SNE cost function (using Barnes-Hut algorithm)
Expand Down Expand Up @@ -358,7 +358,7 @@ void TSNE<treeT, dist_fn>::computeGaussianPerplexity(double* X, int N, int D, in

// Loop over all points to find nearest neighbors
if (verbose)
fprintf(stderr, "Building tree...\n");
fprintf(stdout, "Building tree...\n");

int steps_completed = 0;
#ifdef _OPENMP
Expand Down Expand Up @@ -446,7 +446,7 @@ void TSNE<treeT, dist_fn>::computeGaussianPerplexity(double* X, int N, int D, in
#ifdef _OPENMP
#pragma omp critical
#endif
fprintf(stderr, " - point %d of %d\n", steps_completed, N);
fprintf(stdout, " - point %d of %d\n", steps_completed, N);
}
}

Expand Down Expand Up @@ -607,7 +607,7 @@ extern "C"
double *final_error = NULL, int distance = 1)
{
if (verbose)
fprintf(stderr, "Performing t-SNE using %d cores.\n", NUM_THREADS(num_threads));
fprintf(stdout, "Performing t-SNE using %d cores.\n", NUM_THREADS(num_threads));
if (distance == 0) {
TSNE<SplitTree, euclidean_distance> tsne;
tsne.run(X, N, D, Y, no_dims, perplexity, theta, num_threads, max_iter, n_iter_early_exag,
Expand Down