update

main.py

@@ -4,7 +4,6 @@ import os
 from sklearn.model_selection import train_test_split
 from model_tensorflow import train, predict
 
-
 frame = "tensorflow"
 
 
@@ -31,7 +30,7 @@ class Config:
 	shuffle_train_data = True
 
 	# train_data_rate = 0.95 #comment yqy
-	train_data_rate = 1 #add yqy
+	train_data_rate = 1  # add yqy
 	valid_data_rate = 0.15
 
 	batch_size = 64
@@ -47,11 +46,11 @@ class Config:
 		batch_size = 1
 		continue_flag = "continue_"
 
-	#comment yqy
+	# comment yqy
 	# train_data_path = "./data/stock_data.csv"
 	model_save_path = "./checkpoint/"
 	figure_save_path = "./figure/"
-	#comment end
+	# comment end
 	# add yqy
 	train_data_path = "./data/stock_data_30.csv"
 	# model_save_path = "./checkpoint/30/"
@@ -129,10 +128,10 @@ class Data:
 		if test_data_yqy is None:
 			test_data_yqy = []
 		# test_data_yqy=test_data_yqy[1:21]
-		feature_data=(test_data_yqy - self.mean) / self.std
-		test_x=[feature_data]
+		feature_data = (test_data_yqy - self.mean) / self.std
+		test_x = [feature_data]
 		return np.array(test_x)
-	# add end
+# add end
 
 
 def draw(config, origin_data, predict_norm_data):
@@ -161,38 +160,42 @@ def draw(config, origin_data, predict_norm_data):
 	print(predict_data)
 
 
-def draw_yqy(config, origin_data, predict_norm_data,mean_yqy,std_yqy):# 这里origin_data等同于test_data_values_yqy
+def draw_yqy(config, origin_data, predict_norm_data, mean_yqy, std_yqy):  # 这里origin_data等同于test_data_values_yqy
 	label_norm_data = (origin_data - mean_yqy) / std_yqy
-	assert label_norm_data.shape[0] == predict_norm_data.shape[0], "The element number in origin and predicted data is different"
+	assert label_norm_data.shape[0] == predict_norm_data.shape[
+		0], "The element number in origin and predicted data is different"
 
-	#label_norm_data=label_norm_data[:,1]
+	# label_norm_data=label_norm_data[:,1]
 	label_name = 'high'
 	label_column_num = 1
 
-	loss = np.mean((label_norm_data[config.predict_day:][:,1] - predict_norm_data[:-config.predict_day][0:]) ** 2, axis=0)[1]
+	loss = \
+	np.mean((label_norm_data[config.predict_day:][:, 1] - predict_norm_data[:-config.predict_day][0:]) ** 2, axis=0)[1]
 	print("The mean squared error of stock {} is ".format(label_name), loss)
 
 	# label_X = range(origin_data.data_num - origin_data.train_num - origin_data.start_num_in_test)
 	# predict_X = [x + config.predict_day for x in label_X]
 
-	label_data = label_norm_data[:,1] * std_yqy[1]+ mean_yqy[1]
+	label_data = label_norm_data[:, 1] * std_yqy[1] + mean_yqy[1]
 
-	predict_data = predict_norm_data * std_yqy[1]+ mean_yqy[1]
+	predict_data = predict_norm_data * std_yqy[1] + mean_yqy[1]
 
 	print(label_data)
 	print(predict_data)
-	# print(label_data[-1])
-	# print(predict_data[-1][0])
+
+
+# print(label_data[-1])
+# print(predict_data[-1][0])
+
 
 def main(config):
 	np.random.seed(config.random_seed)
 	data_gainer = Data(config)
 
 	# add yqy
-	mean_yqy=Data(config).mean
-	std_yqy=Data(config).std
-	#add end
-
+	mean_yqy = Data(config).mean
+	std_yqy = Data(config).std
+	# add end
 
 	if config.do_train:
 		train_X, valid_X, train_Y, valid_Y = data_gainer.get_train_and_valid_data()
@@ -200,15 +203,15 @@ def main(config):
 
 	if config.do_predict:
 		# add yqy
-		test_data_yqy = pd.read_csv("./data/test_data.csv",usecols=list([2, 5]))
-		test_data_values_yqy=test_data_yqy.values[:]
+		test_data_yqy = pd.read_csv("./data/test_data.csv", usecols=list([2, 5]))
+		test_data_values_yqy = test_data_yqy.values[:]
 		# test_data_yqy=[104.3,104.39]
-		test_X =data_gainer.get_test_data_yqy(test_data_values_yqy)
+		test_X = data_gainer.get_test_data_yqy(test_data_values_yqy)
 		# add end
 		# test_X, test_Y = data_gainer.get_test_data(return_label_data=True)# comment yqy
 		pred_result = predict(config, test_X)
 		# draw(config, data_gainer, pred_result)# comment yqy
-		draw_yqy(config, test_data_values_yqy, pred_result,mean_yqy,std_yqy)
+		draw_yqy(config, test_data_values_yqy, pred_result, mean_yqy, std_yqy)
 
 
 if __name__ == "__main__":