#feedback_data = reader.read_file_with_minval(filename, 25, 300);
feedback_data = reader.read_file_with_minval(filename, 35, 300);
print feedback_data;
print 'Maximum Genre.'
print np.max(feedback_data.meta['pggr_gr']) + 1;
print 'Normalizing data.'
feedback_data.normalize_row();
# build model with 3 latent factors.
r = 5;
# the L_2 norm regularizer
lamb = 0.001;
# the stopping delta value
delta = 0.01;
# the maximum iteration number
maxiter = 500;
HierLat_model = HierLat(r,lamb,delta,maxiter, verbose = True);
#HierLat_model.train(feedback_data, simplex_projection = False);
HierLat_model.train(feedback_data, simplex_projection = True);
'''
# test.
loc_row = [200, 4, 105];
loc_col = [ 10, 22, 4];
print 'Prediction:'
print HierLat_model.predict(loc_row, loc_col);
'''
