import pandas as pd
from sklearn import svm,preprocessing
from data_out import *

ZGPA = pd.read_csv('zgpn.csv')
ZGPA.index = ZGPA['date']
df_CB = ZGPA.sort_index(ascending=True, axis=0)

df_CB = df_CB.set_index('date')
df_CB = df_CB.sort_index()
#print df_CB.head()
#value表示涨跌, =1为涨，=0为跌
value = pd.Series(df_CB['close']-df_CB['close'].shift(1),\
                  index=df_CB.index)
value = value.bfill()
value[value>=0]=1
value[value<0]=0
df_CB['Value']=value
#后向填充空缺值
df_CB=df_CB.fillna(method='bfill')
df_CB=df_CB.astype('float64')
print(df_CB.head())

L=len(df_CB)
train=int(L*0.8)
total_predict_data=L-train

#对样本特征进行归一化处理
df_CB_X=df_CB.drop(['Value'],axis=1)
df_CB_X=preprocessing.scale(df_CB_X)

#开始循环预测，每次向前预测一个值
correct = 0
train_original=train
while train<L:
    Data_train=df_CB_X[train-train_original:train]
    value_train = value[train-train_original:train]
    Data_predict=df_CB_X[train:train+1]
    value_real = value[train:train+1]
    #核函数分别选取'ploy','linear','rbf'
    #classifier = svm.SVC(C=1.0, kernel='poly')
    #classifier = svm.SVC(kernel='linear')
    classifier = svm.SVC(C=1.0,kernel='rbf')
    classifier.fit(Data_train,value_train)
    value_predict=classifier.predict(Data_predict)
    print("value_real=%d value_predict=%d"%(value_real[0],value_predict))

    #计算测试集中的正确率
    if(value_real[0]==int(value_predict)):
        correct=correct+1
    train = train+1

print(correct)
print(total_predict_data)
correct=correct*100/total_predict_data
print("Correct=%.2f%%"%correct)