《机器学习》课后题6.3

iris线性可分所以肯定正确率会分的很高
pybrain一直安装报错不清楚为什么，C4.5之前的还没写完，先继续往下写，欠着吧

python代码

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn import svm


def create_data():
    iris = load_iris()
    df = pd.DataFrame(iris.data, columns=iris.feature_names)
    df['label'] = iris.target
    df.columns = ['sepal length', 'sepal width', 'petal length', 'petal width', 'label']
    data = np.array(df.iloc[:100, [0, 1, -1]])
    for i in range(len(data)):
        if data[i, -1] == 0:
            data[i, -1] = -1
    # print(data)
    return data[:, :2], data[:, -1]


X, y = create_data()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
plt.scatter(X[:50, 0], X[:50, 1], label='0')
plt.scatter(X[50:, 0], X[50:, 1], label='1')
plt.xlabel(load_iris().feature_names[0])
plt.ylabel(load_iris().feature_names[1])
plt.legend()
plt.show()


for kernel in ['linear', 'rbf']:
    clf = svm.SVC(C=1000, kernel=kernel)
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    accuracy = metrics.accuracy_score(y_test, y_pred)

    print('accuracy of %s kernel SVM: %.3f' % (kernel, accuracy))

运行结果

accuracy of linear kernel SVM: 1.000
accuracy of rbf kernel SVM: 1.000

最后

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