In [19]:

    

np.random.seed(0)
X_xor = np.random.randn(200, 2)
y_xor = np.logical_xor(X_xor[:, 0] > 0, X_xor[:, 1] > 0)
y_xor = np.where(y_xor, 1, -1)
plt.scatter(X_xor[y_xor==1, 0], X_xor[y_xor==1, 1], c='b', marker='o', label='1', s=100)
plt.scatter(X_xor[y_xor==-1, 0], X_xor[y_xor==-1, 1], c='r', marker='s', label='-1', s=100)
plt.ylim(-3.0)
plt.legend()
plt.title("XOR problem")
plt.show()


    

In [20]:

    

def plot_xor(X, y, model, title, xmin=-3, xmax=3, ymin=-3, ymax=3):
    XX, YY = np.meshgrid(np.arange(xmin, xmax, (xmax-xmin)/1000), np.arange(ymin, ymax, (ymax-ymin)/1000))
    ZZ = np.reshape(model.predict(np.array([XX.ravel(), YY.ravel()]).T), XX.shape)
    plt.contourf(XX, YY, ZZ, cmap=mpl.cm.Paired_r, alpha=0.5)
    plt.scatter(X[y== 1, 0], X[y== 1, 1], c='b', marker='o', label='+1', s=100)
    plt.scatter(X[y==-1, 0], X[y==-1, 1], c='r', marker='s', label='-1', s=100)
    plt.xlim(xmin, xmax)
    plt.ylim(ymin, ymax)
    plt.title(title)
    plt.show()


    

In [9]:

    

import numpy as np
import matplotlib.pyplot as plt
from sklearn.svm import SVC


    

In [11]:

    

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
iris = load_iris()
X = iris.data[:, [2, 3]]
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))

def plot_iris(X, y, model, title, xmin=-2.5, xmax=2.5, ymin=-2.5, ymax=2.5):
    XX, YY = np.meshgrid(np.arange(xmin, xmax, (xmax-xmin)/1000), np.arange(ymin, ymax, (ymax-ymin)/1000))
    ZZ = np.reshape(model.predict(np.array([XX.ravel(), YY.ravel()]).T), XX.shape)
    plt.contourf(XX, YY, ZZ, alpha=0.5)
    plt.scatter(X[y==0, 0], X[y==0, 1], c='r', marker='^', label='0', s=100)
    plt.scatter(X[y==1, 0], X[y==1, 1], c='g', marker='o', label='1', s=100)
    plt.scatter(X[y==2, 0], X[y==2, 1], c='b', marker='s', label='2', s=100)
    plt.xlim(xmin, xmax)
    plt.ylim(ymin, ymax)
    plt.title(title)
    plt.show()


    

In [12]:

    

model = SVC(kernel='linear').fit(X_test_std, y_test)
plot_iris(X_test_std, y_test, model, "Linear SVC")


    

In [13]:

    

model = SVC(kernel='poly', random_state=0, gamma=10, C=1.0).fit(X_test_std, y_test)
plot_iris(X_test_std, y_test, model, "RBF SVC (gamma=10, C=1)")


    

In [14]:

    

model = SVC(kernel='rbf', random_state=0, gamma=1, C=1.0).fit(X_test_std, y_test)
plot_iris(X_test_std, y_test, model, "RBF SVC (gamma=1, C=1)")