def test_fit():
    # Test that the algorithm solution is equal to a worked out example."""
    X = np.array([[0.6, 0.8, 0.7]])
    y = np.array([0])
    mlp = MLPClassifier(algorithm='sgd', learning_rate_init=0.1, alpha=0.1,
                        activation='logistic', random_state=1, max_iter=1,
                        hidden_layer_sizes=2, momentum=0)
    # set weights
    mlp.coefs_ = [0] * 2
    mlp.intercepts_ = [0] * 2
    mlp.classes_ = [0, 1]
    mlp.n_outputs_ = 1
    mlp.coefs_[0] = np.array([[0.1, 0.2], [0.3, 0.1], [0.5, 0]])
    mlp.coefs_[1] = np.array([[0.1], [0.2]])
    mlp.intercepts_[0] = np.array([0.1, 0.1])
    mlp.intercepts_[1] = np.array([1.0])
    mlp._coef_grads = [] * 2
    mlp._intercept_grads = [] * 2

    mlp.label_binarizer_.y_type_ = 'binary'

    # Initialize parameters
    mlp.n_iter_ = 0
    mlp.learning_rate_ = 0.1

    # Compute the number of layers
    mlp.n_layers_ = 3

    # Pre-allocate gradient matrices
    mlp._coef_grads = [0] * (mlp.n_layers_ - 1)
    mlp._intercept_grads = [0] * (mlp.n_layers_ - 1)

    mlp.out_activation_ = 'logistic'
    mlp.t_ = 0
    mlp.best_loss_ = np.inf
    mlp.loss_curve_ = []
    mlp._no_improvement_count = 0
    mlp._intercept_velocity = [np.zeros_like(intercepts) for
                               intercepts in
                               mlp.intercepts_]
    mlp._coef_velocity = [np.zeros_like(coefs) for coefs in
                          mlp.coefs_]

    mlp.partial_fit(X, y, classes=[0, 1])
    # Manually worked out example
    # h1 = g(X1 * W_i1 + b11) = g(0.6 * 0.1 + 0.8 * 0.3 + 0.7 * 0.5 + 0.1)
    #       =  0.679178699175393
    # h2 = g(X2 * W_i2 + b12) = g(0.6 * 0.2 + 0.8 * 0.1 + 0.7 * 0 + 0.1)
    #         = 0.574442516811659
    # o1 = g(h * W2 + b21) = g(0.679 * 0.1 + 0.574 * 0.2 + 1)
    #       = 0.7654329236196236
    # d21 = -(0 - 0.765) = 0.765
    # d11 = (1 - 0.679) * 0.679 * 0.765 * 0.1 = 0.01667
    # d12 = (1 - 0.574) * 0.574 * 0.765 * 0.2 = 0.0374
    # W1grad11 = X1 * d11 + alpha * W11 = 0.6 * 0.01667 + 0.1 * 0.1 = 0.0200
    # W1grad11 = X1 * d12 + alpha * W12 = 0.6 * 0.0374 + 0.1 * 0.2 = 0.04244
    # W1grad21 = X2 * d11 + alpha * W13 = 0.8 * 0.01667 + 0.1 * 0.3 = 0.043336
    # W1grad22 = X2 * d12 + alpha * W14 = 0.8 * 0.0374 + 0.1 * 0.1 = 0.03992
    # W1grad31 = X3 * d11 + alpha * W15 = 0.6 * 0.01667 + 0.1 * 0.5 = 0.060002
    # W1grad32 = X3 * d12 + alpha * W16 = 0.6 * 0.0374 + 0.1 * 0 = 0.02244
    # W2grad1 = h1 * d21 + alpha * W21 = 0.679 * 0.765 + 0.1 * 0.1 = 0.5294
    # W2grad2 = h2 * d21 + alpha * W22 = 0.574 * 0.765 + 0.1 * 0.2 = 0.45911
    # b1grad1 = d11 = 0.01667
    # b1grad2 = d12 = 0.0374
    # b2grad = d21 = 0.765
    # W1 = W1 - eta * [W1grad11, .., W1grad32] = [[0.1, 0.2], [0.3, 0.1],
    #          [0.5, 0]] - 0.1 * [[0.0200, 0.04244], [0.043336, 0.03992],
    #          [0.060002, 0.02244]] = [[0.098, 0.195756], [0.2956664,
    #          0.096008], [0.4939998, -0.002244]]
    # W2 = W2 - eta * [W2grad1, W2grad2] = [[0.1], [0.2]] - 0.1 *
    #        [[0.5294], [0.45911]] = [[0.04706], [0.154089]]
    # b1 = b1 - eta * [b1grad1, b1grad2] = 0.1 - 0.1 * [0.01667, 0.0374]
    #         = [0.098333, 0.09626]
    # b2 = b2 - eta * b2grad = 1.0 - 0.1 * 0.765 = 0.9235
    assert_almost_equal(mlp.coefs_[0], np.array([[0.098, 0.195756],
                                                 [0.2956664, 0.096008],
                                                 [0.4939998, -0.002244]]),
                        decimal=3)
    assert_almost_equal(mlp.coefs_[1], np.array([[0.04706], [0.154089]]),
                        decimal=3)
    assert_almost_equal(mlp.intercepts_[0],
                        np.array([0.098333, 0.09626]), decimal=3)
    assert_almost_equal(mlp.intercepts_[1], np.array(0.9235), decimal=3)
    # Testing output
    #  h1 = g(X1 * W_i1 + b11) = g(0.6 * 0.098 + 0.8 * 0.2956664 +
    #               0.7 * 0.4939998 + 0.098333) = 0.677
    #  h2 = g(X2 * W_i2 + b12) = g(0.6 * 0.195756 + 0.8 * 0.096008 +
    #            0.7 * -0.002244 + 0.09626) = 0.572
    #  o1 = h * W2 + b21 = 0.677 * 0.04706 +
    #             0.572 * 0.154089 + 0.9235 = 1.043
    assert_almost_equal(mlp.decision_function(X), 1.043, decimal=3)
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/neural_network/tests/test_mlp.py/test_fit/original