


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__":
 SCREAMING_SNAKE_CASE       :      Dict												= pd.read_csv("sample_data.csv", header=None)
 SCREAMING_SNAKE_CASE       :      Dict												= df.shape[:1][0]
 # If you're using some other dataset input the target column
 SCREAMING_SNAKE_CASE       :      Optional[int]												= df.iloc[:, 1:2]
 SCREAMING_SNAKE_CASE       :      List[Any]												= actual_data.values.reshape(len_data, 1)
 SCREAMING_SNAKE_CASE       :      List[str]												= MinMaxScaler().fit_transform(actual_data)
 SCREAMING_SNAKE_CASE       :      int												= 10
 SCREAMING_SNAKE_CASE       :      Optional[int]												= 5
 SCREAMING_SNAKE_CASE       :      Optional[int]												= 20
 SCREAMING_SNAKE_CASE       :      List[Any]												= len_data - periods * look_back
 SCREAMING_SNAKE_CASE       :      Tuple												= actual_data[:division]
 SCREAMING_SNAKE_CASE       :      Union[str, Any]												= actual_data[division - look_back :]
 SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE       :      Optional[Any]												= [], []
 SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE       :      List[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])
 SCREAMING_SNAKE_CASE       :      Union[str, Any]												= np.array(train_x)
 SCREAMING_SNAKE_CASE       :      Tuple												= np.array(test_x)
 SCREAMING_SNAKE_CASE       :      Dict												= np.array([list(i.ravel()) for i in train_y])
 SCREAMING_SNAKE_CASE       :      Union[str, Any]												= np.array([list(i.ravel()) for i in test_y])

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






from __future__ import annotations



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						bool:
    if len(lowerCamelCase_ ) < 2:
        raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
    if any(i <= 0 for i in nums ):
        raise ValueError('All values must be greater than 0' )
    _lowercase       :							Tuple      		=		nums.copy()
    copy_nums.sort()
    return copy_nums[-1] < sum(copy_nums[:-1] )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


