支持向量机中SVM算法+sklearn库的实现

直接上代码了：

import numpy as np
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split


def load_data(filename):
    data = np.genfromtxt(filename, delimiter='\t')
    x = data[:, 1:]  # 数据特征
    y = data[:, 0].astype(int)  # 标签
    scaler = StandardScaler()  # 采用标准化形式
    x_std = scaler.fit_transform(x)  # 标准化
    # 将数据划分为训练集和测试集，test_size=.5表示50%的测试集
    x_train, x_test, y_train, y_test = train_test_split(x_std, y, test_size=.5)
    print(len(x_train), len(x_test), len(y_train), len(y_test))
    return x_train, x_test, y_train, y_test


def svm_c(x_train, x_test, y_train, y_test):
    predictor = SVC(gamma='scale', C=1.0, decision_function_shape='ovr', kernel='rbf')
    predictor.fit(x_train, y_train)
    # answer = predictor.predict(x_test) 预测
    print(predictor.score(x_test, y_test))

    # print(predictor.support_vectors_)  # 获取支持向量
    # print(predictor.support_)  # 获取支持向量的索引
    # print(predictor.n_support_)  # 获取每个类的支持向量数


if __name__ == '__main__':
    svm_c(*load_data('txt/10/frame505/all.txt'))

我的txt数据集中，第一列是标签，也就是最后的分类结果，后面几列是特征，所以load_data（）函数中的数据提取具体列大家应该可以看懂了