??????機器學(xué)習(xí) 實戰(zhàn)系列 總目錄

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SVM分類實戰(zhàn)1之簡單SVM分類
SVM分類實戰(zhàn)2線性SVM
SVM分類實戰(zhàn)3非線性SVM文章來源地址http://www.zghlxwxcb.cn/news/detail-733679.html

4、非線性SVM

4.1 創(chuàng)建非線性數(shù)據(jù)

from sklearn.datasets import make_moons 
X, y = make_moons(n_samples=100, noise=0.15, random_state=42)

def plot_dataset(X, y, axes):
    plt.plot(X[:, 0][y==0], X[:, 1][y==0], "bs")
    plt.plot(X[:, 0][y==1], X[:, 1][y==1], "g^")
    plt.axis(axes)
    plt.grid(True, which='both')
    plt.xlabel(r"$x_1$", fontsize=20)
    plt.ylabel(r"$x_2$", fontsize=20, rotation=0)

plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])
plt.show()

4.2 分類預(yù)測

from sklearn.datasets import make_moons
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
polynomial_svm_clf=Pipeline((("poly_features",PolynomialFeatures(degree=3)),
                            ("scaler",StandardScaler()),
                            ("svm_clf",LinearSVC(C=10,loss="hinge"))
                            ))
polynomial_svm_clf.fit(X,y)

1. 使用PolynomialFeatures模塊進(jìn)行預(yù)處理，使用這個可以增加數(shù)據(jù)維度
2. polynomial_svm_clf.fit(X,y)對當(dāng)前進(jìn)行訓(xùn)練傳進(jìn)去X和y數(shù)據(jù)

def plot_predictions(clf,axes):
    x0s = np.linspace(axes[0],axes[1],100)
    x1s = np.linspace(axes[2],axes[3],100)
    x0,x1 = np.meshgrid(x0s,x1s)
    X = np.c_[x0.ravel(),x1.ravel()]
    y_pred = clf.predict(X).reshape(x0.shape)
    plt.contourf(x0,x1,y_pred,cmap=plt.cm.brg,alpha=0.2)

plot_predictions(polynomial_svm_clf,[-1.5,2.5,-1,1.5])
plot_dataset(X,y,[-1.5,2.5,-1,1.5])

5、核函數(shù)

5.1 核函數(shù)

from sklearn.svm import SVC

poly_kernel_svm_clf = Pipeline([
        ("scaler", StandardScaler()),
        ("svm_clf", SVC(kernel="poly", degree=3, coef0=1, C=5))
    ])

poly_kernel_svm_clf.fit(X, y)

poly100_kernel_svm_clf = Pipeline([
        ("scaler", StandardScaler()),
        ("svm_clf", SVC(kernel="poly", degree=10, coef0=100, C=5))
    ])

poly100_kernel_svm_clf.fit(X, y)

plt.figure(figsize=(11, 4))

plt.subplot(121)
plot_predictions(poly_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])
plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])
plt.title(r"$d=3, r=1, C=5$", fontsize=18)

plt.subplot(122)
plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])
plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])
plt.title(r"$d=10, r=100, C=5$", fontsize=18)

plt.show()

5.2 高斯核函數(shù)

SVM分類實戰(zhàn)1之簡單SVM分類
SVM分類實戰(zhàn)2線性SVM
SVM分類實戰(zhàn)3非線性SVM

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