






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

			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			:    str					  =np.array(train_x)
			_A			:    Optional[int]					  =np.array(test_x)
			_A			:    Tuple					  =np.array([list(i.ravel()) for i in train_y])
			_A			:    str					  =np.array([list(i.ravel()) for i in test_y])

			_A			:    List[str]					  =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			:    Union[str, Any]					  =model.fit(
			    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
			)
			_A			:    Any					  =model.predict(x_test)









'''simple docstring'''







import sys
import turtle





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> tuple[float, float]:
							return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    )   -> None:
							my_pen.up()
							my_pen.goto(vertexa[0]       ,    vertexa[1]   )
							my_pen.down()
							my_pen.goto(vertexa[0]       ,    vertexa[1]   )
							my_pen.goto(vertexa[0]       ,    vertexa[1]   )
							my_pen.goto(vertexa[0]       ,    vertexa[1]   )

							if depth == 0:
														return

							triangle(UpperCamelCase       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    depth - 1   )
							triangle(UpperCamelCase       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    depth - 1   )
							triangle(UpperCamelCase       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    get_mid(UpperCamelCase       ,    UpperCamelCase   )       ,    depth - 1   )


if __name__ == "__main__":
			if len(sys.argv) != 2:
						raise ValueError(
						    '''Correct format for using this script: '''
						    '''python fractals.py <int:depth_for_fractal>'''
						)
			_A			:    Any					  =turtle.Turtle()
			my_pen.ht()
			my_pen.speed(5)
			my_pen.pencolor('''red''')

			_A			:    Dict					  =[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
			triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))

