def test_gradient():
    # Test gradient.

    # This makes sure that the activation functions and their derivatives
    # are correct. The numerical and analytical computation of the gradient
    # should be close.
    for n_labels in [2, 3]:
        n_samples = 5
        n_features = 10
        X = np.random.random((n_samples, n_features))
        y = 1 + np.mod(np.arange(n_samples) + 1, n_labels)
        Y = LabelBinarizer().fit_transform(y)

        for activation in ACTIVATION_TYPES:
            mlp = MLPClassifier(activation=activation, hidden_layer_sizes=10,
                                algorithm='l-bfgs', alpha=1e-5,
                                learning_rate_init=0.2, max_iter=1,
                                random_state=1)
            mlp.fit(X, y)

            theta = np.hstack([l.ravel() for l in mlp.coefs_ +
                               mlp.intercepts_])

            layer_units = ([X.shape[1]] + [mlp.hidden_layer_sizes] +
                           [mlp.n_outputs_])

            activations = []
            deltas = []
            coef_grads = []
            intercept_grads = []

            activations.append(X)
            for i in range(mlp.n_layers_ - 1):
                activations.append(np.empty((X.shape[0],
                                             layer_units[i + 1])))
                deltas.append(np.empty((X.shape[0],
                                        layer_units[i + 1])))

                fan_in = layer_units[i]
                fan_out = layer_units[i + 1]
                coef_grads.append(np.empty((fan_in, fan_out)))
                intercept_grads.append(np.empty(fan_out))

            # analytically compute the gradients
            def loss_grad_fun(t):
                return mlp._loss_grad_lbfgs(t, X, Y, activations, deltas,
                                            coef_grads, intercept_grads)

            [value, grad] = loss_grad_fun(theta)
            numgrad = np.zeros(np.size(theta))
            n = np.size(theta, 0)
            E = np.eye(n)
            epsilon = 1e-5
            # numerically compute the gradients
            for i in range(n):
                dtheta = E[:, i] * epsilon
                numgrad[i] = ((loss_grad_fun(theta + dtheta)[0] -
                              loss_grad_fun(theta - dtheta)[0]) /
                              (epsilon * 2.0))
            assert_almost_equal(numgrad, grad)
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/neural_network/tests/test_mlp.py/test_gradient/original