




import math
import os
import sys







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					''''''
       try:
              with open(lowercase_				,       '''rb'''		) as binary_file:
                     A__   				=					binary_file.read()
              for dat in data:
                     A__   				=					f"""{dat:08b}"""
                     result += curr_byte
              return result
       except OSError:
              print('''File not accessible'''		)
              sys.exit()







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)     -> None:



       """simple docstring"""

       lexicon.pop(lowercase_		)
       A__   				=					last_match_id

       if math.loga(lowercase_		).is_integer():
              for curr_key in lexicon:
                     A__   				=					'''0''' + lexicon[curr_key]

       A__   				=					bin(lowercase_		)[2:]







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					{'''0''': '''0''', '''1''': '''1'''}
       A__				,							A__   				=					'''''', ''''''
       A__   				=					len(lowercase_		)

       for i in range(len(lowercase_		)		):
              curr_string += data_bits[i]
              if curr_string not in lexicon:
                     continue

              A__   				=					lexicon[curr_string]
              result += last_match_id
              add_key_to_lexicon(lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)
              index += 1
              A__   				=					''''''

       while curr_string != "" and curr_string not in lexicon:
              curr_string += "0"

       if curr_string != "":
              A__   				=					lexicon[curr_string]
              result += last_match_id

       return result







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					os.path.getsize(lowercase_		)
       A__   				=					bin(lowercase_		)[2:]
       A__   				=					len(lowercase_		)

       return "0" * (length_length - 1) + file_length_binary + compressed







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> None:



       """simple docstring"""

       A__   				=					8
       try:
              with open(lowercase_				,       '''wb'''		) as opened_file:
                     A__   				=					[
                         to_write[i : i + byte_length]
                         for i in range(0				,       len(lowercase_		)				,       lowercase_		)
                     ]

                     if len(result_byte_array[-1]		) % byte_length == 0:
                            result_byte_array.append('''10000000'''		)
                     else:
                            result_byte_array[-1] += "1" + "0" * (
                                byte_length - len(result_byte_array[-1]		) - 1
                            )

                     for elem in result_byte_array:
                            opened_file.write(int(lowercase_				,       2		).to_bytes(1				,       byteorder='''big'''		)		)
       except OSError:
              print('''File not accessible'''		)
              sys.exit()







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> None:



       """simple docstring"""

       A__   				=					read_file_binary(lowercase_		)
       A__   				=					compress_data(lowercase_		)
       A__   				=					add_file_length(lowercase_				,       lowercase_		)
       write_file_binary(lowercase_				,       lowercase_		)


if __name__ == "__main__":
  compress(sys.argv[1], sys.argv[2])








# 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 SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Dict:



       """simple docstring"""

       return 1 / (1 + np.exp(-z		))







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Optional[int]:



       """simple docstring"""

       return (-y * np.log(lowercase_		) - (1 - y) * np.log(1 - h		)).mean()







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					np.dot(lowercase_				,       lowercase_		)
       return np.sum(y * scores - np.log(1 + np.exp(lowercase_		)		)		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_=70_000		)     -> Dict:



       """simple docstring"""

       A__   				=					np.zeros(x.shape[1]		)

       for iterations in range(lowercase_		):
              A__   				=					np.dot(lowercase_				,       lowercase_		)
              A__   				=					sigmoid_function(lowercase_		)
              A__   				=					np.dot(x.T				,       h - y		) / y.size
              A__   				=					theta - alpha * gradient  # updating the weights
              A__   				=					np.dot(lowercase_				,       lowercase_		)
              A__   				=					sigmoid_function(lowercase_		)
              A__   				=					cost_function(lowercase_				,       lowercase_		)
              if iterations % 100 == 0:
                     print(f"""loss: {j} \t"""		)  # printing the loss after every 100 iterations
       return theta


# In[68]:

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

  _lowerCamelCase			:	Union[str, Any]							 =     0.1
  _lowerCamelCase			:	Tuple							 =     logistic_reg(alpha, x, y, max_iterations=70000)
  print("""theta: """, theta)  # printing the theta i.e our weights vector







  def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[Any]:



         """simple docstring"""

         return sigmoid_function(
             np.dot(lowercase_				,       lowercase_		)		)  # 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""")
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	int							 =     (x[:, 0].min(), x[:, 0].max())
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	Union[str, Any]							 =     (x[:, 1].min(), x[:, 1].max())
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	List[Any]							 =     np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  _lowerCamelCase			:	str							 =     np.c_[xxa.ravel(), xxa.ravel()]
  _lowerCamelCase			:	Tuple							 =     predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

  plt.legend()
  plt.show()


