


'''simple docstring'''





import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
					UpperCAmelCase__  			=				pd.read_csv('''sample_data.csv''', header=None)
					UpperCAmelCase__  			=				df.shape[:1][0]
					# If you're using some other dataset input the target column
					UpperCAmelCase__  			=				df.iloc[:, 1:2]
					UpperCAmelCase__  			=				actual_data.values.reshape(len_data, 1)
					UpperCAmelCase__  			=				MinMaxScaler().fit_transform(actual_data)
					UpperCAmelCase__  			=				1_0
					UpperCAmelCase__  			=				5
					UpperCAmelCase__  			=				2_0
					UpperCAmelCase__  			=				len_data - periods * look_back
					UpperCAmelCase__  			=				actual_data[:division]
					UpperCAmelCase__  			=				actual_data[division - look_back :]
					UpperCAmelCase__			,  UpperCAmelCase__  			=				[], []
					UpperCAmelCase__			,  UpperCAmelCase__  			=				[], []

					for i in range(0, len(train_data) - forward_days - look_back + 1):
										train_x.append(train_data[i : i + look_back])
										train_y.append(train_data[i + look_back : i + look_back + forward_days])
					for i in range(0, len(test_data) - forward_days - look_back + 1):
										test_x.append(test_data[i : i + look_back])
										test_y.append(test_data[i + look_back : i + look_back + forward_days])
					UpperCAmelCase__  			=				np.array(train_x)
					UpperCAmelCase__  			=				np.array(test_x)
					UpperCAmelCase__  			=				np.array([list(i.ravel()) for i in train_y])
					UpperCAmelCase__  			=				np.array([list(i.ravel()) for i in test_y])

					UpperCAmelCase__  			=				Sequential()
					model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
					model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
					model.add(Dense(forward_days))
					model.compile(loss='''mean_squared_error''', optimizer='''adam''')
					UpperCAmelCase__  			=				model.fit(
					    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
					)
					UpperCAmelCase__  			=				model.predict(x_test)







'''simple docstring'''





# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


UpperCAmelCase__  			=				subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
UpperCAmelCase__  			=				subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()

UpperCAmelCase__  			=				'''|'''.join(sys.argv[1:])
UpperCAmelCase__  			=				re.compile(rF"""^({joined_dirs}).*?\.py$""")

UpperCAmelCase__  			=				[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')



