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

					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    :							Any      =					np.array(train_x)
					lowercase    :							List[str]      =					np.array(test_x)
					lowercase    :							Optional[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    :							Optional[int]      =					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    :							int      =					model.fit(
					    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
					)
					lowercase    :							List[Any]      =					model.predict(x_test)


"""simple docstring"""


UpperCAmelCase__     						=  {
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
UpperCAmelCase__     						=  {
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 1_2,
    """Pm""": 1_5,
    """Em""": 1_8,
    """Zm""": 2_1,
    """Ym""": 2_4,
}






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					from_type.lower().strip("""s"""						)
  _UpperCAmelCase         =					to_type.lower().strip("""s"""						)

  _UpperCAmelCase         =					UNIT_SYMBOL.get(lowercase      ,lowercase						)
  _UpperCAmelCase         =					UNIT_SYMBOL.get(lowercase      ,lowercase						)

  if from_sanitized not in METRIC_CONVERSION:
    _UpperCAmelCase         =					(
        f'''Invalid \'from_type\' value: {from_type!r}.\n'''
        f'''Conversion abbreviations are: {", ".join(lowercase						)}'''
    )
    raise ValueError(lowercase						)
  if to_sanitized not in METRIC_CONVERSION:
    _UpperCAmelCase         =					(
        f'''Invalid \'to_type\' value: {to_type!r}.\n'''
        f'''Conversion abbreviations are: {", ".join(lowercase						)}'''
    )
    raise ValueError(lowercase						)
  _UpperCAmelCase         =					METRIC_CONVERSION[from_sanitized]
  _UpperCAmelCase         =					METRIC_CONVERSION[to_sanitized]
  _UpperCAmelCase         =					1

  if from_exponent > to_exponent:
    _UpperCAmelCase         =					from_exponent - to_exponent
  else:
    _UpperCAmelCase         =					-(to_exponent - from_exponent)

  return value * pow(10      ,lowercase						)


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

    testmod()
