
"""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__":
    _a		       =							pd.read_csv('sample_data.csv', header=None)
    _a		       =							df.shape[:1][0]
    # If you're using some other dataset input the target column
    _a		       =							df.iloc[:, 1:2]
    _a		       =							actual_data.values.reshape(len_data, 1)
    _a		       =							MinMaxScaler().fit_transform(actual_data)
    _a		       =							10
    _a		       =							5
    _a		       =							20
    _a		       =							len_data - periods * look_back
    _a		       =							actual_data[:division]
    _a		       =							actual_data[division - look_back :]
    _a       ,       _a		       =							[], []
    _a       ,       _a		       =							[], []

    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])
    _a		       =							np.array(train_x)
    _a		       =							np.array(test_x)
    _a		       =							np.array([list(i.ravel()) for i in train_y])
    _a		       =							np.array([list(i.ravel()) for i in test_y])

    _a		       =							Sequential()
    model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
    model.add(LSTM(64, input_shape=(128, 1)))
    model.add(Dense(forward_days))
    model.compile(loss='mean_squared_error', optimizer='adam')
    _a		       =							model.fit(
        x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
    )
    _a		       =							model.predict(x_test)










"""simple docstring"""
def 				snake_case      (   A__	,A__	,A__	,A__	,A__						):
			if index == number_of_items:
						return 0
			UpperCAmelCase_     : str			     =					0
			UpperCAmelCase_     : Dict			     =					0
			UpperCAmelCase_     : Tuple			     =					knapsack(A__	,A__	,A__	,A__	,index + 1						)
			if weights[index] <= max_weight:
						UpperCAmelCase_     : Union[str, Any]			     =					values[index] + knapsack(
						    A__	,A__	,A__	,max_weight - weights[index]	,index + 1						)
			return max(A__	,A__						)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



