





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

   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    : Optional[Any]    	=  np.array(train_x)
   lowercase    : Tuple    	=  np.array(test_x)
   lowercase    : List[Any]    	=  np.array([list(i.ravel()) for i in train_y])
   lowercase    : Optional[Any]    	=  np.array([list(i.ravel()) for i in test_y])

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













from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       return math.sqrt(sum(pow(a - b    ,       2					) for a, b in zip(lowerCAmelCase    ,       lowerCAmelCase					)					)					)


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):

       if dataset.ndim != value_array.ndim:
              _UpperCamelCase              =						(
                  '''Wrong input data\'s dimensions... '''
                  f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
              )
              raise ValueError(lowerCAmelCase					)

       try:
              if dataset.shape[1] != value_array.shape[1]:
                     _UpperCamelCase              =						(
                         '''Wrong input data\'s shape... '''
                         f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
                     )
                     raise ValueError(lowerCAmelCase					)
       except IndexError:
              if dataset.ndim != value_array.ndim:
                     raise TypeError('''Wrong shape'''					)

       if dataset.dtype != value_array.dtype:
              _UpperCamelCase              =						(
                  '''Input data have different datatype... '''
                  f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
              )
              raise TypeError(lowerCAmelCase					)

       _UpperCamelCase              =						[]

       for value in value_array:
              _UpperCamelCase              =						euclidean(lowerCAmelCase    ,       dataset[0]					)
              _UpperCamelCase              =						dataset[0].tolist()

              for dataset_value in dataset[1:]:
                     _UpperCamelCase              =						euclidean(lowerCAmelCase    ,       lowerCAmelCase					)

                     if dist > temp_dist:
                            _UpperCamelCase              =						temp_dist
                            _UpperCamelCase              =						dataset_value.tolist()

              answer.append([vector, dist]					)

       return answer


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       return np.dot(lowerCAmelCase    ,       lowerCAmelCase					) / (norm(lowerCAmelCase					) * norm(lowerCAmelCase					))


if __name__ == "__main__":
       import doctest

       doctest.testmod()






