#NewtopdocveccategoryMat_train,NewtopdocveccategoryMat_test,
#target_train, target_test,NewpreW,NewpreWdict,NewDimentionN,DimentionN,n_epoch = 10,batchsize = 50)
#pararelで計算
#k = IIalgorithm_model.caluculatemodel_without_kf(IIalgorithm_simple_pararell(NewpreW,NewpreWdict,NewDimentionN,DimentionN),
#NewtopdocveccategoryMat_train,NewtopdocveccategoryMat_test,
#target_train, target_test,NewpreW,NewpreWdict,NewDimentionN,DimentionN,n_epoch = 10,batchsize = 50)
result_dic = defaultdict(list)
for dic_key in newl_dic.keys():
print dic_key
target_train,target_test,NewtopdocveccategoryMat_train, NewtopdocveccategoryMat_test,NewpreWdict, NewpreW, NewpreW_namelist_dic,NewDimentionN = preprocess_NewCategoryVec(
newl_dic, toptarget_dic, dic_key,"09302015")
for index in range(5):
k = IIalgorithm_model.caluculatemodel_without_kf(
IIalgorithm_model.IIalgorithm(NewpreW,NewpreWdict,NewDimentionN,DimentionN),
NewtopdocveccategoryMat_train,NewtopdocveccategoryMat_test,
target_train, target_test,NewpreW,NewpreWdict,NewDimentionN,DimentionN,n_epoch = 10,batchsize = 50)
result_dic[dic_key].append(k)
for dic_key in newl_dic.keys():
all_result_pred = []
all_result_true = []
for k in result_dic[dic_key]:
all_result_pred += k[-1]
all_result_true += k[-2]
print dic_key
print classification_report(all_result_true, all_result_pred, digits = 4)
print confusion_matrix(all_result_true, all_result_pred)
print accuracy_score(all_result_true, all_result_pred)
