






import argparse
from collections import defaultdict

def 			_lowerCamelCase	(						__A    :							str		,		__A    :							List[str]		,		__A    :							Any		,		__A    :							str		,		__A    :							int )      ->					Tuple:
    _UpperCAmelCase				:      int			  =				f'''{file}_{class_name}_{test_name}'''
    done_test[_id] += 1

    with open(__lowerCAmelCase		,		'''r''' ) as f:
        _UpperCAmelCase				:      Optional[Any]			  =				f.readlines()

    _UpperCAmelCase				:      Any			  =				f'''class {class_name}('''
    _UpperCAmelCase				:      Any			  =				f'''{4 * ' '}def {test_name}('''
    _UpperCAmelCase				:      str			  =				f'''{8 * ' '}{correct_line.split()[0]}'''
    _UpperCAmelCase				:      Union[str, Any]			  =				f'''{16 * ' '}{correct_line.split()[0]}'''
    _UpperCAmelCase				:      Tuple			  =				False
    _UpperCAmelCase				:      Tuple			  =				False
    _UpperCAmelCase				:      Any			  =				False
    _UpperCAmelCase				:      Tuple			  =				False
    _UpperCAmelCase				:      str			  =				0
    _UpperCAmelCase				:      List[str]			  =				0

    _UpperCAmelCase				:      int			  =				[]
    for line in lines:
        if line.startswith(__lowerCAmelCase ):
            _UpperCAmelCase				:      Optional[Any]			  =				True
        elif in_class and line.startswith(__lowerCAmelCase ):
            _UpperCAmelCase				:      int			  =				True
        elif in_class and in_func and (line.startswith(__lowerCAmelCase ) or line.startswith(__lowerCAmelCase )):
            _UpperCAmelCase				:      Union[str, Any]			  =				len(line.split(correct_line.split()[0] )[0] )
            count += 1

            if count == done_test[_id]:
                _UpperCAmelCase				:      Optional[Any]			  =				True

        if in_class and in_func and in_line:
            if ")" not in line:
                continue
            else:
                _UpperCAmelCase				:      Optional[int]			  =				True

        if in_class and in_func and in_line and insert_line:
            new_lines.append(f'''{spaces * ' '}{correct_line}''' )
            _UpperCAmelCase				:      str			  =				False
        else:
            new_lines.append(__lowerCAmelCase )

    with open(__lowerCAmelCase		,		'''w''' ) as f:
        for line in new_lines:
            f.write(__lowerCAmelCase )

def 			_lowerCamelCase	(						__A    :							List[Any]		,		__A    :							Union[str, Any]=None )      ->					Optional[int]:


    if fail is not None:
        with open(__lowerCAmelCase		,		'''r''' ) as f:
            _UpperCAmelCase				:      Tuple			  =				{l.strip() for l in f.readlines()}
    else:
        _UpperCAmelCase				:      Any			  =				None

    with open(__lowerCAmelCase		,		'''r''' ) as f:
        _UpperCAmelCase				:      str			  =				f.readlines()

    _UpperCAmelCase				:      Dict			  =				defaultdict(__lowerCAmelCase )
    for line in correct_lines:
        _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      str			  =				line.split(''';''' )
        if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
            overwrite_file(__lowerCAmelCase		,		__lowerCAmelCase		,		__lowerCAmelCase		,		__lowerCAmelCase		,		__lowerCAmelCase )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()
  parser.add_argument('--correct_filename', help='filename of tests with expected result')
  parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()

  main(args.correct_filename, args.fail_filename)












# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets

def      _lowerCamelCase	(						__A    :							Optional[int] )      ->					str:
    return 1 / (1 + np.exp(-z ))

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							List[Any] )      ->					Optional[int]:
    return (-y * np.log(__A ) - (1 - y) * np.log(1 - h )).mean()

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							List[Any]		,		__A    :							int )      ->					Any:
    _UpperCAmelCase				:      int			  =				np.dot(__A		,		__A )
    return np.sum(y * scores - np.log(1 + np.exp(__A ) ) )

def      _lowerCamelCase	(						__A    :							List[Any]		,		__A    :							Union[str, Any]		,		__A    :							str		,		__A    :							Dict=70_000 )      ->					Optional[Any]:
    _UpperCAmelCase				:      Union[str, Any]			  =				np.zeros(x.shape[1] )

    for iterations in range(__A ):
        _UpperCAmelCase				:      List[Any]			  =				np.dot(__A		,		__A )
        _UpperCAmelCase				:      Union[str, Any]			  =				sigmoid_function(__A )
        _UpperCAmelCase				:      Optional[Any]			  =				np.dot(x.T		,		h - y ) / y.size
        _UpperCAmelCase				:      Optional[Any]			  =				theta - alpha * gradient  # updating the weights
        _UpperCAmelCase				:      List[Any]			  =				np.dot(__A		,		__A )
        _UpperCAmelCase				:      Any			  =				sigmoid_function(__A )
        _UpperCAmelCase				:      Union[str, Any]			  =				cost_function(__A		,		__A )
        if iterations % 100 == 0:
            print(f'''loss: {j} \t''' )  # printing the loss after every 100 iterations
    return theta


# In[68]:

if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						datasets.load_iris()
  SCREAMING_SNAKE_CASE 							=						iris.data[:, :2]
  SCREAMING_SNAKE_CASE 							=						(iris.target != 0) * 1

  SCREAMING_SNAKE_CASE 							=						0.1
  SCREAMING_SNAKE_CASE 							=						logistic_reg(alpha, x, y, max_iterations=70000)
  print('theta: ', theta)  # printing the theta i.e our weights vector

  def      _lowerCamelCase	(						__A    :							Any )      ->					int:
      return sigmoid_function(
          np.dot(__A		,		__A ) )  # predicting the value of probability from the logistic regression algorithm

  plt.figure(figsize=(10, 6))
  plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='b', label='0')
  plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='r', label='1')
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						(x[:, 0].min(), x[:, 0].max())
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						(x[:, 1].min(), x[:, 1].max())
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  SCREAMING_SNAKE_CASE 							=						np.c_[xxa.ravel(), xxa.ravel()]
  SCREAMING_SNAKE_CASE 							=						predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

  plt.legend()


  plt.show()




