50 lines
1.4 KiB
Python
50 lines
1.4 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.metrics import classification_report
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from sklearn import svm
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# 载入数据
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data = np.genfromtxt("LR-testSet2.txt", delimiter=",")
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x_data = data[:,:-1]
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y_data = data[:,-1]
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def plot():
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x0 = []
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x1 = []
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y0 = []
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y1 = []
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# 切分不同类别的数据
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for i in range(len(x_data)):
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if y_data[i]==0:
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x0.append(x_data[i,0])
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y0.append(x_data[i,1])
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else:
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x1.append(x_data[i,0])
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y1.append(x_data[i,1])
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# 画图
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scatter0 = plt.scatter(x0, y0, c='b', marker='o')
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scatter1 = plt.scatter(x1, y1, c='r', marker='x')
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#画图例
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plt.legend(handles=[scatter0,scatter1],labels=['label0','label1'],loc='best')
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plot()
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plt.show()
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# fit the model
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# C和gamma
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# 'linear', 'poly', 'rbf', 'sigmoid'
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model = svm.SVC(kernel='rbf', C=2, gamma=1)
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model.fit(x_data, y_data)
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model.score(x_data,y_data)
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# 获取数据值所在的范围
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x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1
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y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1
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# 生成网格矩阵
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xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
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np.arange(y_min, y_max, 0.02))
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z = model.predict(np.c_[xx.ravel(), yy.ravel()])# ravel与flatten类似，多维数据转一维。flatten不会改变原始数据，ravel会改变原始数据
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z = z.reshape(xx.shape)
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# 等高线图
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cs = plt.contourf(xx, yy, z)
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plot()
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plt.show()
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