model_now += 1
print("SVM 1训练中...")
svm = Svm(svm_label, svm_images, svm_test_label, svm_test_images)
results[model_now] = svm.train(numToClassfy, numToTrain, "-q -m 1000")
model_now += 1
print("SVM 2训练中...")
svm = Svm(svm_label, svm_images, svm_test_label, svm_test_images)
results[model_now] = svm.train(numToClassfy, numToTrain, "-q -m 1000 -t 3")
model_now += 1
knn = knn(10, images, label)
results[model_now] = knn.start(test_images, test_label, numToClassfy)
model_now += 1
bayes = Bayes(10, 784, images, label)
results[model_now] = bayes.start(test_images, test_label, numToClassfy)
model_now += 1
results = results.T
count = 0
for i in range(numToClassfy):
progress = Progress(numToClassfy, "正在投票")
k = np.argmax(np.bincount(results[i]))
if (k == test_label[i]):
count += 1
progress.updata(i + 1)
print("集成识别完成，样本个数: " + str(numToClassfy) + " 识别数: " + str(count) +
" 正确率: %.2f %%" % ((count / numToClassfy) * 100))
