predict = [] #predict market from day 8 for index in range(start_pre_date,len(alldata)): data = alldata[0:index,:] data_x = data[:,0:-1] data_y = data[:,-1] test = alldata[index,:] # ---------------------------------------- # linear regression # ---------------------------------------- #using three classification method classifier = Classifier(data_x,data_y,test[0:-1]) #1. logisticReg pre1 = classifier.logisticReg()[0] #2. svm pre2 = classifier.svm()[0] #3. knn pre3 = classifier.Gaussian_NaiveBayes()[0] predict.append([pre1,pre2,pre3]) test_data = alldata[start_pre_date:len(alldata),-1] predict = np.array(predict) #accuracy miss_pre = [] accuracy = [] for index in range(len(predict[0])): temp = (predict[:,index] != test_data).sum()
