python瀹炵幇绾挎€у洖褰掍箣寮规€х綉鍥炲綊，,鏍囩锛?a hre

python瀹炵幇绾挎€у洖褰掍箣寮规€х綉鍥炲綊，,鏍囩锛?a hre

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涓婅堪瑙ｉ噴鎽樿嚜锛?strong>https://blog.csdn.net/weixin_41500849/article/details/80447501

鎺ヤ笅鏉ユ槸瀹炵幇浠ｇ爜锛屼唬鐮佹潵婧愶細https://github.com/eriklindernoren/ML-From-Scratch

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class Regression(object):    """ Base regression model. Models the relationship between a scalar dependent variable y and the independent     variables X.     Parameters:    -----------    n_iterations: float        The number of training iterations the algorithm will tune the weights for.    learning_rate: float        The step length that will be used when updating the weights.    """    def __init__(self, n_iterations, learning_rate):        self.n_iterations = n_iterations        self.learning_rate = learning_rate    def initialize_weights(self, n_features):        """ Initialize weights randomly [-1/N, 1/N] """        limit = 1 / math.sqrt(n_features)        self.w = np.random.uniform(-limit, limit, (n_features, ))    def fit(self, X, y):        # Insert constant ones for bias weights        X = np.insert(X, 0, 1, axis=1)        self.training_errors = []        self.initialize_weights(n_features=X.shape[1])        # Do gradient descent for n_iterations        for i in range(self.n_iterations):            y_pred = X.dot(self.w)            # Calculate l2 loss            mse = np.mean(0.5 * (y - y_pred)**2 + self.regularization(self.w))            self.training_errors.append(mse)            # Gradient of l2 loss w.r.t w            grad_w = -(y - y_pred).dot(X) + self.regularization.grad(self.w)            # Update the weights            self.w -= self.learning_rate * grad_w    def predict(self, X):        # Insert constant ones for bias weights        X = np.insert(X, 0, 1, axis=1)        y_pred = X.dot(self.w)        return y_pred

鐒跺悗鏄脊鎬х綉鍥炲綊鐨勬牳蹇冿細

class l1_l2_regularization():    """ Regularization for Elastic Net Regression """    def __init__(self, alpha, l1_ratio=0.5):        self.alpha = alpha        self.l1_ratio = l1_ratio    def __call__(self, w):        l1_contr = self.l1_ratio * np.linalg.norm(w)        l2_contr = (1 - self.l1_ratio) * 0.5 * w.T.dot(w)         return self.alpha * (l1_contr + l2_contr)    def grad(self, w):        l1_contr = self.l1_ratio * np.sign(w)        l2_contr = (1 - self.l1_ratio) * w        return self.alpha * (l1_contr + l2_contr) 

鎺ョ潃鏄脊鎬х綉鍥炲綊鐨勪唬鐮侊細

class ElasticNet(Regression):    """ Regression where a combination of l1 and l2 regularization are used. The    ratio of their contributions are set with the 鈥榣1_ratio鈥?parameter.    Parameters:    -----------    degree: int        The degree of the polynomial that the independent variable X will be transformed to.    reg_factor: float        The factor that will determine the amount of regularization and feature        shrinkage.     l1_ration: float        Weighs the contribution of l1 and l2 regularization.    n_iterations: float        The number of training iterations the algorithm will tune the weights for.    learning_rate: float        The step length that will be used when updating the weights.    """    def __init__(self, degree=1, reg_factor=0.05, l1_ratio=0.5, n_iterations=3000,                 learning_rate=0.01):        self.degree = degree        self.regularization = l1_l2_regularization(alpha=reg_factor, l1_ratio=l1_ratio)        super(ElasticNet, self).__init__(n_iterations,                                         learning_rate)    def fit(self, X, y):        X = normalize(polynomial_features(X, degree=self.degree))        super(ElasticNet, self).fit(X, y)    def predict(self, X):        X = normalize(polynomial_features(X, degree=self.degree))        return super(ElasticNet, self).predict(X)

鍏朵腑娑夊強鍒扮殑涓€浜涘嚱鏁板彲鍙傝€冿細https://www.cnblogs.com/xiximayou/p/12802868.html

鏈€鍚庢槸杩愯涓诲嚱鏁帮細

from __future__ import print_functionimport matplotlib.pyplot as pltimport syssys.path.append("/content/drive/My Drive/learn/ML-From-Scratch/")import numpy as npimport pandas as pd# Import helper functionsfrom mlfromscratch.supervised_learning import ElasticNetfrom mlfromscratch.utils import k_fold_cross_validation_sets, normalize, mean_squared_errorfrom mlfromscratch.utils import train_test_split, polynomial_features, Plotdef main():    # Load temperature data    data = pd.read_csv(鈥?/span>mlfromscratch/data/TempLinkoping2016.txt鈥?/span>, sep="\t")    time = np.atleast_2d(data["time"].values).T    temp = data["temp"].values    X = time # fraction of the year [0, 1]    y = temp    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)    poly_degree = 13    model = ElasticNet(degree=15,                         reg_factor=0.01,                        l1_ratio=0.7,                        learning_rate=0.001,                        n_iterations=4000)    model.fit(X_train, y_train)    # Training error plot    n = len(model.training_errors)    training, = plt.plot(range(n), model.training_errors, label="Training Error")    plt.legend(handles=[training])    plt.title("Error Plot")    plt.ylabel(鈥?/span>Mean Squared Error鈥?/span>)    plt.xlabel(鈥?/span>Iterations鈥?/span>)    plt.savefig("test1.png")    plt.show()    y_pred = model.predict(X_test)    mse = mean_squared_error(y_test, y_pred)    print ("Mean squared error: %s (given by reg. factor: %s)" % (mse, 0.05))    y_pred_line = model.predict(X)    # Color map    cmap = plt.get_cmap(鈥?/span>viridis鈥?/span>)    # Plot the results    m1 = plt.scatter(366 * X_train, y_train, color=cmap(0.9), s=10)    m2 = plt.scatter(366 * X_test, y_test, color=cmap(0.5), s=10)    plt.plot(366 * X, y_pred_line, color=鈥?/span>black鈥?/span>, linewidth=2, label="Prediction")    plt.suptitle("Elastic Net")    plt.title("MSE: %.2f" % mse, fontsize=10)    plt.xlabel(鈥?/span>Day鈥?/span>)    plt.ylabel(鈥?/span>Temperature in Celcius鈥?/span>)    plt.legend((m1, m2), ("Training data", "Test data"), loc=鈥?/span>lower right鈥?/span>)    plt.savefig("test2.png")    plt.show()if __name__ == "__main__":    main()

缁撴灉锛?/p>

Mean squared error: 11.232800207362782 (given by reg. factor: 0.05)

python瀹炵幇绾挎€у洖褰掍箣寮规€х綉鍥炲綊

鏍囩锛?a href='http://www.byrx.net/so/1/determine' title='determine'>determine

鍘熸枃鍦板潃锛歨ttps://www.cnblogs.com/xiximayou/p/12808904.html