LogisticRegression逻辑回归

# coding:utf-8import sklearn.datasetsimport sklearn.linear_modelimport numpy.randomimport matplotlib.pyplotif __name__ == "__main__":    # Load iris dataset    iris = sklearn.datasets.load_iris()    # Split the dataset with sampleRatio    sampleRatio = 0.5    n_samples = len(iris.target)    sampleBoundary = int(n_samples * sampleRatio)    # Shuffle the whole data    shuffleIdx = range(n_samples)    numpy.random.shuffle(shuffleIdx)    # Make the training data    train_features = iris.data[shuffleIdx[:sampleBoundary]]    train_targets = iris.target[shuffleIdx[:sampleBoundary]]    # Make the testing data    test_features = iris.data[shuffleIdx[sampleBoundary:]]    test_targets = iris.target[shuffleIdx[sampleBoundary:]]    # Train    logisticRegression = sklearn.linear_model.LogisticRegression()    logisticRegression.fit(train_features, train_targets)    # Predict    predict_targets = logisticRegression.predict(test_features)    # Evaluation    n_test_samples = len(test_targets)    X = range(n_test_samples)    correctNum = 0    for i in X:        if(predict_targets[i] == test_targets[i]):            correctNum += 1    accuracy = correctNum * 1.0 / n_test_samples    print("Logistic Regression (Iris) Accuracy: %.2f" %(accuracy))    # Draw    matplotlib.pyplot.subplot(2, 1, 1)    matplotlib.pyplot.title("Logistic Regression (Iris)")    matplotlib.pyplot.plot(X, predict_targets, 'ro-', label = 'Predict Labels')    matplotlib.pyplot.ylabel("Predict Class")    legend = matplotlib.pyplot.legend()    matplotlib.pyplot.subplot(2, 1, 2)    matplotlib.pyplot.plot(X, test_targets, 'g+-', label='True Labels')    legend = matplotlib.pyplot.legend()    matplotlib.pyplot.ylabel("True Class")    matplotlib.pyplot.savefig("Logistic Regression (Iris).png", format='png')    matplotlib.pyplot.show()