Ridge Regression 岭回归

# coding:utf-8import sklearn.datasetsimport sklearn.linear_modelimport numpy.randomimport numpy.linalgimport matplotlib.pyplotif __name__ == "__main__":    # Load boston dataset    boston = sklearn.datasets.load_boston()    # Split the dataset with sampleRatio    sampleRatio = 0.5    n_samples = len(boston.target)    sampleBoundary = int(n_samples * sampleRatio)    # Shuffle the whole data    shuffleIdx = range(n_samples)    numpy.random.shuffle(shuffleIdx)    # Make the training data    train_features = boston.data[shuffleIdx[:sampleBoundary]]    train_targets = boston.target[shuffleIdx[:sampleBoundary]]    # Make the testing data    test_features = boston.data[shuffleIdx[sampleBoundary:]]    test_targets = boston.target[shuffleIdx[sampleBoundary:]]    # Train with Cross Validation    ridgeRegression = sklearn.linear_model.RidgeCV(alphas=[0.01, 0.05, 0.1, 0.5, 1.0, 10.0])    # 这个地方使用RidgeCV 直接交叉验证出我需要试验的几个惩罚因子，它会帮我选择这些里面在集内测试表现最优的一个参数。后面的输出选择了0.1.    ridgeRegression.fit(train_features, train_targets)    print("Alpha = ", ridgeRegression.alpha_)    # Predict    predict_targets = ridgeRegression.predict(test_features)    # Evaluation    n_test_samples = len(test_targets)    X = range(n_test_samples)    error = numpy.linalg.norm(predict_targets - test_targets, ord = 1) / n_test_samples    print("Ridge Regression (Boston) Error: %.2f" %(error))    # Draw    matplotlib.pyplot.plot(X, predict_targets, 'r--', label = 'Predict Price')    matplotlib.pyplot.plot(X, test_targets, 'g:', label = 'True Price')    legend = matplotlib.pyplot.legend()    matplotlib.pyplot.title("Ridge Regression (Boston)")    matplotlib.pyplot.ylabel("Price (1000 U.S.D)")    matplotlib.pyplot.savefig("Ridge Regression (Boston).png", format= 'png')    matplotlib.pyplot.show()