SVC算法——手写识别

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from sklearn import datasets
from sklearn import svm
import matplotlib.pyplot as plt
digits = datasets.load_digits()
clf = svm.SVC(gamma=0.001, C=100.)
clf.fit(digits.data[:-100], digits.target[:-100])  
predict_y = clf.predict(digits.data[-100:])
fig, axes = plt.subplots(10, 10, figsize=(8, 8), subplot_kw={'xticks':[], 'yticks':[]}, gridspec_kw=dict(hspace=0.1, wspace=0.1))
for i, ax in enumerate(axes.flat):
    ax.imshow(digits.images[i-100], cmap='binary', interpolation='nearest')
    ax.text(0.05, 0.05, str(digits.target[i-100]), transform=ax.transAxes, color='green')
    color = 'green'
    if digits.target[i-100] != predict_y[i]:
        color = 'red'
    ax.text(0.80, 0.05, str(predict_y[i]), transform=ax.transAxes, color=color)
plt.show()


    

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In [41]:

    

import numpy as np 
import matplotlib.pyplot as plt 
x=np.arange(-np.pi,np.pi,0.01) 
y=np.sin(x) 
plt.plot(x,y,'g') 
plt.show()


    

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