diff --git a/.idea/workspace.xml b/.idea/workspace.xml index e693b86..42c1a53 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -3,7 +3,7 @@ - + @@ -224,7 +224,7 @@ - + @@ -265,12 +265,12 @@ - @@ -339,16 +339,6 @@ - - - - - - - - - - @@ -366,10 +356,37 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + - - + + @@ -378,32 +395,15 @@ - - + + - + - - - - - - - - - - - - - - - - - diff --git a/serve.py b/serve.py index 315a241..3a50801 100644 --- a/serve.py +++ b/serve.py @@ -21,7 +21,7 @@ class Config: predict_day = 1 - input_size = len(feature_columns) - 1 + input_size = len(feature_columns) output_size = len(label_columns) hidden_size = 128 @@ -35,7 +35,7 @@ class Config: shuffle_train_data = True # train_data_rate = 0.95 #comment yqy - train_data_rate = 0.95 # add yqy + train_data_rate = 1 # add yqy valid_data_rate = 0.15 batch_size = 64 @@ -115,8 +115,8 @@ class Data: time_step_size = feature_data.shape[0] // self.config.time_step test_x = [feature_data[self.start_num_in_test + i * self.config.time_step: self.start_num_in_test + ( - i + 1) * self.config.time_step] for i in range(time_step_size)] - + i + 1) * self.config.time_step] + for i in range(time_step_size)] if return_label_data: label_data = self.norm_data[self.train_num + self.start_num_in_test:, self.config.label_in_feature_columns] return np.array(test_x), label_data @@ -128,9 +128,7 @@ class Data: test_data_yqy = [] # test_data_yqy=test_data_yqy[1:21] feature_data = (test_data_yqy - self.mean) / self.std - print(feature_data[:, :1]) - test_x = [feature_data[:, :1]] - print(feature_data) + test_x = [feature_data] return np.array(test_x) @@ -142,6 +140,7 @@ def draw_yqy(config2, origin_data, predict_norm_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" + print("dsa") # label_norm_data=label_norm_data[:,1] label_name = 'high' label_column_num = 3 @@ -153,14 +152,16 @@ def draw_yqy(config2, origin_data, predict_norm_data, mean_yqy, std_yqy): # 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] - # print(label_norm_data[:, 1:2]) - label_data = label_norm_data[:, 1:2] * std_yqy[1:2] + mean_yqy[1:2] - # print(label_data) + print("2") - # print(predict_norm_data) + print(label_norm_data[:, 1:2]) + label_data = label_norm_data[:, 1:2] * std_yqy[1:2] + mean_yqy[1:2] + print(label_data) + + print(predict_norm_data) predict_data = predict_norm_data * std_yqy[config.label_in_feature_columns] + mean_yqy[ config.label_in_feature_columns] - # print(predict_data) + print(predict_data) print(label_data[:, -1]) print(predict_data[:, -1]) @@ -178,13 +179,14 @@ def train_models(): train_X, valid_X, train_Y, valid_Y = data_gainer.get_train_and_valid_data() + print(train_X, valid_X, train_Y, valid_Y) + print(train_X.shape[0]) if train_X.shape[0] < 500: config.batch_size = 32 if train_X.shape[0] < 200: config.batch_size = 16 - print(train_X[:, :, :1], valid_X[:, :, :1], train_Y, valid_Y) - train(config, train_X[:, :, :1], train_Y, valid_X[:, :, :1], valid_Y) + train(config, train_X, train_Y, valid_X, valid_Y) lock.release() @@ -328,7 +330,7 @@ if __name__ == '__main__': csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL ) - # spamwriter.writerow(["job", "model", "time", "utilGPU", "utilCPU", "pre", "main", "post"]) + #spamwriter.writerow(["job", "model", "time", "utilGPU", "utilCPU", "pre", "main", "post"]) spamwriter.writerow(["seq", "value"]) # Wait forever for incoming http requests