



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

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

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










"""simple docstring"""


from math import factorial, radians

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_ = 18		,     lowercase_ = 10						):
						UpperCAmelCase					=			angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0)

						# Converting from degrees to radians
						UpperCAmelCase					=			radians(lowercase_						)

						UpperCAmelCase					=			angle_in_radians
						UpperCAmelCase					=			3
						UpperCAmelCase					=			-1

						for _ in range(lowercase_						):
												result += (b * (angle_in_radians**a)) / factorial(lowercase_						)

												UpperCAmelCase					=			-b  # One positive term and the next will be negative and so on...
												a += 2  # Increased by 2 for every term.

						return round(lowercase_		,     lowercase_						)


if __name__ == "__main__":
				__import__("""doctest""").testmod()





