



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

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

				lowercase_	       =			Sequential()
				model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
				model.add(LSTM(64, input_shape=(1_28, 1)))
				model.add(Dense(forward_days))
				model.compile(loss='''mean_squared_error''', optimizer='''adam''')
				lowercase_	       =			model.fit(
				    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
				)
				lowercase_	       =			model.predict(x_test)









import math

from numpy import inf
from scipy.integrate import quad






def 					__lowerCAmelCase		(  __lowerCamelCase	:    float	)  ->						float:
					if num <= 0:
										raise ValueError("""math domain error"""	)

					return quad(__lowerCamelCase				,   0				,   __lowerCamelCase				,   args=(__lowerCamelCase)	)[0]






def 					__lowerCAmelCase		(  __lowerCamelCase	:    float				,   __lowerCamelCase	:    float	)  ->						float:
					return math.pow(__lowerCamelCase				,   z - 1	) * math.exp(-x	)


if __name__ == "__main__":
				from doctest import testmod

				testmod()




