def main(): # convert_to_numpy() print 'Loading data...' data = load_data_set('Data/trainData.npy') obj = DimReductionMissingData(data, reduced_dim=100) obj.optimize(num_epochs=5, batch_size=1000)
def main(): data_set, _, _ = mnist_loader() data = np.asarray(data_set[0][0:20000]) np.random.shuffle(data) data_org = data.copy() set_random_missing_values(data, 0.70) dim_red_object = DimReductionMissingData(data, reduced_dim=60) B = dim_red_object.optimize(num_epochs=5, batch_size=1000) data_reduced = dim_red_object.get_reduced_dimensions(B=B, X=dim_red_object.X, mask=dim_red_object.X_mask) data_reconstructed = np.dot(data_reduced, B) + dim_red_object.X_mean show_mnist_image(data_org, data, data_reconstructed)