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%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
digits = datasets.load_digits() # 加载数据
# 把数据所代表的图片显示出来
images_and_labels = list(zip(digits.images, digits.target))
plt.figure(figsize=(8, 6), dpi=200)
for index, (image, label) in enumerate(images_and_labels[:8]):
plt.subplot(2, 4, index + 1)
plt.axis('off')
plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
plt.title('Digit: %i' % label, fontsize=20)
print("shape of raw image data: {0}".format(digits.images.shape))
print("shape of data: {0}".format(digits.data.shape))
'''
shape of raw image data: (1797, 8, 8)
shape of data: (1797, 64)
'''
# 把数据分成训练数据集和测试数据集
from sklearn.cross_validation import train_test_split
Xtrain, Xtest, Ytrain, Ytest = train_test_split(digits.data, digits.target, test_size=0.20, random_state=2);
# 使用支持向量机来训练模型
from sklearn import svm
clf = svm.SVC(gamma=0.001, C=100., probability=True)
clf.fit(Xtrain, Ytrain);
# 评估模型的准确度
from sklearn.metrics import accuracy_score
Ypred = clf.predict(Xtest);
accuracy_score(Ytest, Ypred)
'''
0.97777777777777775
'''
clf.score(Xtest, Ytest)
'''
0.97777777777777775
'''
# 查看预测的情况
fig, axes = plt.subplots(4, 4, figsize=(8, 8)) # axes 是 各子图对象
fig.subplots_adjust(hspace=0.1, wspace=0.1)
for i, ax in enumerate(axes.flat):
ax.imshow(Xtest[i].reshape(8, 8), cmap=plt.cm.gray_r, interpolation='nearest')
ax.text(0.05, 0.05, str(Ypred[i]), fontsize=32,
transform=ax.transAxes,
color='green' if Ypred[i] == Ytest[i] else 'red')
ax.text(0.8, 0.05, str(Ytest[i]), fontsize=32,
transform=ax.transAxes,
color='black')
ax.set_xticks([])
ax.set_yticks([])
# Xtest[4] 的各种可能性
clf.predict_proba(Xtest[4].reshape(1, -1))
'''
array([[ 0.00448546, 0.02391122, 0.01574738, 0.01128528, 0.03207432,
0.03084909, 0.00492786, 0.16602092, 0.56699315, 0.14370532]])
'''
# 保存模型参数
from sklearn.externals import joblib
joblib.dump(clf, 'digits_svm.pkl');
# 导入模型参数,直接进行预测
clf = joblib.load('digits_svm.pkl')
Ypred = clf.predict(Xtest);
clf.score(Xtest, Ytest)
'''
0.97777777777777775
'''