Scikit-Learn 机器学习笔记 – SVM
import numpy as np # 加载鸢尾花数据集 def load_dataset(): from sklearn import datasets iris = datasets.load_iris() # print(iris) # 使用第3和第4个特征 X = iris['data'][:, (2, 3)] # bool类型转为数值型 y = (iris['target'] == 2).astype(np.float64) return X, y # 线性SVM二分类器 def linear_svm_classify(X, y): from sklearn.preprocessing import StandardScaler from sklearn.svm import LinearSVC # 特征缩放 scale = StandardScaler() X_scaled = scale.fit_transform(X.astype(np.float64)) # C是损失函数项的系数，称为惩罚系数 svm_clf = LinearSVC(C=1, loss='hinge') svm_clf.fit(X_scaled, y) # 预测 predict = svm_clf.predict([[5.5, 1.7]]) print('线性SVM二分类器预测为：', predict) # 非线性SVM二分类器、 def nonlinear_svm_classify(X, y): from sklearn.preprocessing import PolynomialFeatures, StandardScaler from sklearn.svm import LinearSVC # 增加多项式特征 poly_features = PolynomialFeatures(degree=3) X_poly = poly_features.fit_transform(X.astype(np.float64)) # 特征缩放 scale = StandardScaler() X_scaled = scale.fit_transform(X_poly) # 需要指定损失函数 poly_svm_clf = LinearSVC(C=10, loss='hinge') poly_svm_clf.fit(X_scaled, y) # 预测，先把样本特征向量转为包含多项式特征得向量 sample_poly = poly_features.fit_transform([[5.5, 1.7]]) predict = poly_svm_clf.predict(sample_poly) print('非线性SVM二分类器预测为：', predict) # 多项式核SVM二分类器 def kernel_poly_svm_classify(X, y): from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC # 特征缩放 scale = StandardScaler() X_scaled = scale.fit_transform(X) # 使用多项式核， kernel_poly_svm_clf = SVC(kernel="poly", degree=3, coef0=1, C=5) kernel_poly_svm_clf.fit(X_scaled, y) # 预测，特征向量不用转换 predict = kernel_poly_svm_clf.predict([[5.5, 1.7]]) print('核SVM二分类器预测为：', predict) # 高斯RBF核SVM二分类器 def kernel_rbf_svm_classify(X, y): from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC # 特征缩放 scale = StandardScaler() X_scaled = scale.fit_transform(X) # 增大gamma和减小C都会加大拟合 kernel_rbf_svm_clf = SVC(kernel="rbf", gamma=5, C=1) kernel_rbf_svm_clf.fit(X_scaled, y) # 预测，特征向量不用缩放 predict = kernel_rbf_svm_clf.predict([[5.5, 1.7]]) print('高斯RBF核SVM二分类器预测为：', predict) if __name__ == '__main__': # 加载数据集 X, y = load_dataset() # 线性SVM二分类器 # linear_svm_classify(X, y) # 非线性SVM二分类器 # nonlinear_svm_classify(X, y) # 多项式核SVM二分类器 kernel_poly_svm_classify(X, y) # 高斯rbf核SVM二分类器 kernel_rbf_svm_classify(X, y) import numpy as np # 加
import numpy as np # 加载鸢尾花数据集 def load_dataset(): from sklearn import datasets iris = datasets.load_iris() # print(iris) # 使用第3和第4个特征 X = iris['data'][:, (2, 3)] # bool类型转为数值型 y = (iris['target'] == 2).astype(np.float64) return X, y # 线性SVM二分类器 def linear_svm_classify(X, y): from sklearn.preprocessing import StandardScaler from sklearn.svm import LinearSVC # 特征缩放 scale = StandardScaler() X_scaled = scale.fit_transform(X.astype(np.float64)) # C是损失函数项的系数，称为惩罚系数 svm_clf = LinearSVC(C=1, loss='hinge') svm_clf.fit(X_scaled, y) # 预测 predict = svm_clf.predict([[5.5, 1.7]]) print('线性SVM二分类器预测为：', predict) # 非线性SVM二分类器、 def nonlinear_svm_classify(X, y): from sklearn.preprocessing import PolynomialFeatures, StandardScaler from sklearn.svm import LinearSVC # 增加多项式特征 poly_features = PolynomialFeatures(degree=3) X_poly = poly_features.fit_transform(X.astype(np.float64)) # 特征缩放 scale = StandardScaler() X_scaled = scale.fit_transform(X_poly) # 需要指定损失函数 poly_svm_clf = LinearSVC(C=10, loss='hinge') poly_svm_clf.fit(X_scaled, y) # 预测，先把样本特征向量转为包含多项式特征得向量 sample_poly = poly_features.fit_transform([[5.5, 1.7]]) predict = poly_svm_clf.predict(sample_poly) print('非线性SVM二分类器预测为：', predict) # 多项式核SVM二分类器 def kernel_poly_svm_classify(X, y): from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC # 特征缩放 scale = StandardScaler() X_scaled = scale.fit_transform(X) # 使用多项式核， kernel_poly_svm_clf = SVC(kernel="poly", degree=3, coef0=1, C=5) kernel_poly_svm_clf.fit(X_scaled, y) # 预测，特征向量不用转换 predict = kernel_poly_svm_clf.predict([[5.5, 1.7]]) print('核SVM二分类器预测为：', predict) # 高斯RBF核SVM二分类器 def kernel_rbf_svm_classify(X, y): from sklearn.preprocessing import StandardScaler from sklearn.svm import SVC # 特征缩放 scale = StandardScaler() X_scaled = scale.fit_transform(X) # 增大gamma和减小C都会加大拟合 kernel_rbf_svm_clf = SVC(kernel="rbf", gamma=5, C=1) kernel_rbf_svm_clf.fit(X_scaled, y) # 预测，特征向量不用缩放 predict = kernel_rbf_svm_clf.predict([[5.5, 1.7]]) print('高斯RBF核SVM二分类器预测为：', predict) if __name__ == '__main__': # 加载数据集 X, y = load_dataset() # 线性SVM二分类器 # linear_svm_classify(X, y) # 非线性SVM二分类器 # nonlinear_svm_classify(X, y) # 多项式核SVM二分类器 kernel_poly_svm_classify(X, y) # 高斯rbf核SVM二分类器 kernel_rbf_svm_classify(X, y) import numpy as np # 加
