
from __future__ import annotations

import collections
import pprint
from pathlib import Path
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		str:
			return "".join(sorted(__lowerCamelCase     )     )
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		list[str]:
			return word_by_signature[signature(__lowerCamelCase     )]


lowerCamelCase__          =		Path(__file__).parent.joinpath("""words.txt""").read_text(encoding="""utf-8""")
lowerCamelCase__          =		sorted({word.strip().lower() for word in data.splitlines()})

lowerCamelCase__          =		collections.defaultdict(list)
for word in word_list:
					word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
					lowerCamelCase__          =		{word: anagram(word) for word in word_list if len(anagram(word)) > 1}

					with open("""anagrams.txt""", """w""") as file:
										file.write("""all_anagrams = \n """)
										file.write(pprint.pformat(all_anagrams))

"""simple docstring"""



# 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 					_UpperCAmelCase      ( __lowerCamelCase   :       str		)     ->					List[Any]:
  return 1 / (1 + np.exp(-z		))
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       List[str]		)     ->					Optional[Any]:
  return (-y * np.log(__lowerCamelCase		) - (1 - y) * np.log(1 - h		)).mean()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Dict		)     ->					List[str]:
  _snake_case					     =       np.dot(__lowerCamelCase ,					__lowerCamelCase		)
  return np.sum(y * scores - np.log(1 + np.exp(__lowerCamelCase		)		)		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       str=7_00_00		)     ->					Optional[Any]:
  _snake_case					     =       np.zeros(x.shape[1]		)

  for iterations in range(__lowerCamelCase		):
    _snake_case					     =       np.dot(__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       sigmoid_function(__lowerCamelCase		)
    _snake_case					     =       np.dot(x.T ,					h - y		) / y.size
    _snake_case					     =       theta - alpha * gradient  # updating the weights
    _snake_case					     =       np.dot(__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       sigmoid_function(__lowerCamelCase		)
    _snake_case					     =       cost_function(__lowerCamelCase ,					__lowerCamelCase		)
    if iterations % 1_00 == 0:
      print(f'''loss: {j} \t'''		)  # printing the loss after every 100 iterations
  return theta


# In[68]:

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

 UpperCAmelCase__          = 0.1
 UpperCAmelCase__          = logistic_reg(alpha, x, y, max_iterations=70000)
 print('theta: ', theta)  # printing the theta i.e our weights vector
 def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Union[str, Any]:
   return sigmoid_function(
       np.dot(__lowerCamelCase ,					__lowerCamelCase		)		)  # 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')
 ((UpperCAmelCase__)      ,	(UpperCAmelCase__))          = (x[:, 0].min(), x[:, 0].max())
 ((UpperCAmelCase__)      ,	(UpperCAmelCase__))          = (x[:, 1].min(), x[:, 1].max())
 ((UpperCAmelCase__)      ,	(UpperCAmelCase__))          = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
 UpperCAmelCase__          = np.c_[xxa.ravel(), xxa.ravel()]
 UpperCAmelCase__          = predict_prob(grid).reshape(xxa.shape)
 plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

 plt.legend()

 plt.show()



