





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

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

	_lowerCamelCase   				=			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')
	_lowerCamelCase   				=			model.fit(
	    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
	)
	_lowerCamelCase   				=			model.predict(x_test)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]    ):
		SCREAMING_SNAKE_CASE__			=      []
		for data in source_data:
				for i, el in enumerate(UpperCamelCase__    ):
						if len(UpperCamelCase__    ) < i + 1:
								data_lists.append([]    )
						data_lists[i].append(float(UpperCamelCase__    )    )
		return data_lists






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]					,   UpperCamelCase__:							list[int]    ):
		SCREAMING_SNAKE_CASE__			=      []
		for dlist, weight in zip(UpperCamelCase__					,   UpperCamelCase__    ):
				SCREAMING_SNAKE_CASE__			=      min(UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      max(UpperCamelCase__    )

				SCREAMING_SNAKE_CASE__			=      []
				# for weight 0 score is 1 - actual score
				if weight == 0:
						for item in dlist:
								try:
										score.append(1 - ((item - mind) / (maxd - mind))    )
								except ZeroDivisionError:
										score.append(1    )

				elif weight == 1:
						for item in dlist:
								try:
										score.append((item - mind) / (maxd - mind)    )
								except ZeroDivisionError:
										score.append(0    )

        # weight not 0 or 1
				else:
						SCREAMING_SNAKE_CASE__			=      f'''Invalid weight of {weight:f} provided'''
						raise ValueError(UpperCamelCase__    )

				score_lists.append(UpperCamelCase__    )

		return score_lists






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]    ):
		SCREAMING_SNAKE_CASE__			=      [0 for i in range(len(score_lists[0]    )    )]

		for slist in score_lists:
				for j, ele in enumerate(UpperCamelCase__    ):
						SCREAMING_SNAKE_CASE__			=      final_scores[j] + ele

		return final_scores






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]					,   UpperCamelCase__:							list[int]    ):
		SCREAMING_SNAKE_CASE__			=      get_data(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      calculate_each_score(UpperCamelCase__					,   UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      generate_final_scores(UpperCamelCase__    )

		# append scores to source data
		for i, ele in enumerate(UpperCamelCase__    ):
				source_data[i].append(UpperCamelCase__    )

		return source_data