机器学习支持向量机python例子实现

python用SVM实现手写数字识别

import matplotlib.pyplot as plt
from sklearn import datasets, svm, metrics
#载入数据集
digits = datasets.load_digits()

# The data that we are interested in is made of 8x8 images of digits, let's
# have a look at the first 4 images, stored in the `images` attribute of the
# dataset.  If we were working from image files, we could load them using
# matplotlib.pyplot.imread.  Note that each image must have the same size. For these
# images, we know which digit they represent: it is given in the 'target' of
# the dataset.
images_and_labels = list(zip(digits.images, digits.target))
#画图
for index, (image, label) in enumerate(images_and_labels[:4]):
    plt.subplot(2, 4, index + 1)
    plt.axis('off')
    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
    plt.title('Training: %i' % label)

#取图片样本数
n_samples = len(digits.images)
#降维
data = digits.images.reshape((n_samples, -1))
#调用svm算法，其中gamma=0.001
classifier = svm.SVC(gamma=0.001)
#拟合
classifier.fit(data[:n_samples // 2], digits.target[:n_samples // 2])

#测试集数据
expected = digits.target[n_samples // 2:]
#预测
predicted = classifier.predict(data[n_samples // 2:])

print("Classification report for classifier %s:\n%s\n"
      % (classifier, metrics.classification_report(expected, predicted)))
print("Confusion matrix:\n%s" % metrics.confusion_matrix(expected, predicted))

images_and_predictions = list(zip(digits.images[n_samples // 2:], predicted))
#画图
for index, (image, prediction) in enumerate(images_and_predictions[:4]):
    plt.subplot(2, 4, index + 5)
    plt.axis('off')
    plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
    plt.title('Prediction: %i' % prediction)
plt.show()

运行结果