






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

  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 							=						np.array(train_x)
  SCREAMING_SNAKE_CASE 							=						np.array(test_x)
  SCREAMING_SNAKE_CASE 							=						np.array([list(i.ravel()) for i in train_y])
  SCREAMING_SNAKE_CASE 							=						np.array([list(i.ravel()) for i in test_y])

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












import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


SCREAMING_SNAKE_CASE 							=						'\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n'


SCREAMING_SNAKE_CASE 							=						'\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'


SCREAMING_SNAKE_CASE 							=						R'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n    >>> metric = datasets.load_metric("competition_math")\n    >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION ,   _KWARGS_DESCRIPTION			)
class 							A_    (						datasets.Metric			):






 '''simple docstring'''




 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     return datasets.MetricInfo(
         description=_DESCRIPTION							,  citation=_CITATION							,  inputs_description=_KWARGS_DESCRIPTION							,  features=datasets.Features(
             {
                 '''predictions''': datasets.Value('''string'''),
                 '''references''': datasets.Value('''string'''),
             })							,  homepage='''https://github.com/hendrycks/math'''							,  codebase_urls=['''https://github.com/hendrycks/math''']							,  )


 def  snake_case__				(	self							,  _A							,  _A) ->		Tuple:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				0.0
     for i, j in zip(_A							,  _A):
         n_correct += 1.0 if math_equivalence.is_equiv(_A							,  _A) else 0.0
     _UpperCAmelCase				:      Tuple			  =				n_correct / len(_A)
     return {
         "accuracy": accuracy,
     }




