np.testing.assert_allclose(X_train_scaled.mean(axis=0), 0.0, atol=1e-12)
np.testing.assert_allclose(X_train_scaled.std(axis=0), 1.0, atol=1e-12)
np.testing.assert_array_equal(scaler.mean_, fitted_mean)


def format_values(values):
    return "[" + ", ".join(f"{value:.3f}" for value in values) + "]"


print(f"training rows: {scaler.n_samples_seen_}")
print(f"learned means: {format_values(scaler.mean_)}")
print(f"learned scales: {format_values(scaler.scale_)}")
print(f"scaled training means: {format_values(X_train_scaled.mean(axis=0))}")
print(f"scaled training standard deviations: {format_values(X_train_scaled.std(axis=0))}")
print(f"scaled holdout row: {format_values(X_holdout_scaled[0])}")
print("training statistics unchanged after holdout transform: yes")