


'''simple docstring'''

from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray





def __snake_case					(      UpperCAmelCase_						:						float					,				UpperCAmelCase_						:						float					,				UpperCAmelCase_						:						bool = False		):
 if radian_mode:
  return [magnitude * cos(__snake_case		), magnitude * sin(__snake_case		)]
 return [magnitude * cos(radians(__snake_case		)		), magnitude * sin(radians(__snake_case		)		)]





def __snake_case					(      UpperCAmelCase_						:						NDArray[floataa]					,				UpperCAmelCase_						:						NDArray[floataa]					,				UpperCAmelCase_						:						float = 10**-1		):
 lowerCamelCase_      =  cross(__snake_case					,				__snake_case		)
 lowerCamelCase_      =  sum(__snake_case		)
 return abs(__snake_case		) < eps


if __name__ == "__main__":
     # Test to check if it works
     a_  :				List[Any]						= array(
         [
             polar_force(7_1_8.4, 180 - 30),
             polar_force(8_7_9.5_4, 45),
             polar_force(100, -90),
         ]
     )

     a_  :				NDArray[floataa]						= array([[0, 0], [0, 0], [0, 0]])

     assert in_static_equilibrium(forces, location)

     # Problem 1 in image_data/2D_problems.jpg
     a_  :				Dict						= array(
         [
             polar_force(30 * 9.8_1, 15),
             polar_force(215, 180 - 45),
             polar_force(264, 90 - 30),
         ]
     )

     a_  :				Any						= array([[0, 0], [0, 0], [0, 0]])

     assert in_static_equilibrium(forces, location)

     # Problem in image_data/2D_problems_1.jpg
     a_  :				Optional[int]						= array([[0, -2000], [0, -1200], [0, 15600], [0, -12400]])

     a_  :				Optional[int]						= array([[0, 0], [6, 0], [10, 0], [12, 0]])

     assert in_static_equilibrium(forces, location)

     import doctest

     doctest.testmod()










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

  lowercase__    :      Optional[Any]						=    Sequential()
  model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
  model.add(LSTM(64, input_shape=(1_28, 1)))
  model.add(Dense(forward_days))
  model.compile(loss='''mean_squared_error''', optimizer='''adam''')
  lowercase__    :      Tuple						=    model.fit(
      x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
  )
  lowercase__    :      int						=    model.predict(x_test)