def post(self):
try:
jsonByte = self.request.body
jsonStr = jsonByte.decode("utf-8")
svmVo = json.loads(jsonStr, object_hook=svm.dictToSvmVo)
file_name = svmVo.trainDataPath
model = svmVo.modelPath
# 1、导入训练数据
dataSet, labels = load_data_libsvm(file_name)
# 2、训练SVM模型
C = 0.6
toler = 0.001
maxIter = 500
svm_model = svm.SVM_training(dataSet, labels, C, toler, maxIter)
# 3、计算训练的准确性
svm.cal_accuracy(svm_model, dataSet, labels)
# 4、保存最终的SVM模型
svm.save_svm_model(svm_model, model)
commonResponse = com.CommonResponse(com.successCode,
com.successMsg,
dict([("obj", 1)]))
except BaseException as e:
print("执行支持向量机-生成模型失败：{}".format(e))
commonResponse = com.CommonResponse(com.errorCode, "{}".format(e),
dict([("obj", 1)]))
finally:
# 返回数据
self.write(commonResponse.__dict__)
while (int(li[0]) - 1 > index):
tmp.append(0)
index += 1
# 前面填充为0，之后，现在的这个索引所指向的位置，继续写上它对应的value
tmp.append(float(li[1]))
index += 1
while len(tmp) < 13:
tmp.append(0)
data.append(tmp)
f.close()
return np.mat(data), np.mat(label).T
if __name__ == "__main__":
# 1、导入训练数据
print("------------ 1、load data --------------")
dataSet, labels = load_data_libsvm("heart_scale")
# 2、训练SVM模型
print("------------ 2、training ---------------")
C = 0.6
toler = 0.001
maxIter = 500
svm_model = svm.SVM_training(dataSet, labels, C, toler, maxIter)
# 3、计算训练的准确性
print("------------ 3、cal accuracy --------------")
accuracy = svm.cal_accuracy(svm_model, dataSet, labels)
print("The training accuracy is: %.3f%%" % (accuracy * 100))
# 4、保存最终的SVM模型
print("------------ 4、save model ----------------")
svm.save_svm_model(svm_model, "model_file")
tmp.append(float(li[1]))
else:
while(int(li[0]) - 1 > index):
tmp.append(0)
index += 1
tmp.append(float(li[1]))
index += 1
while len(tmp) < 13:
tmp.append(0)
data.append(tmp)
f.close()
return np.mat(data), np.mat(label).T
if __name__ == "__main__":
# 1、导入训练数据
print "------------ 1、load data --------------"
dataSet, labels = load_data_libsvm("heart_scale")
# 2、训练SVM模型
print "------------ 2、training ---------------"
C = 0.6
toler = 0.001
maxIter = 500
svm_model = svm.SVM_training(dataSet, labels, C, toler, maxIter)
# 3、计算训练的准确性
print "------------ 3、cal accuracy --------------"
accuracy = svm.cal_accuracy(svm_model, dataSet, labels)
print "The training accuracy is: %.3f%%" % (accuracy * 100)
# 4、保存最终的SVM模型
print "------------ 4、save model ----------------"
svm.save_svm_model(svm_model, "model_file")