





"""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 						__lowercase	(  snake_case_    :    Optional[int]		)  ->str:



    '''simple docstring'''







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







def 						__lowercase	(  snake_case_    :    int							,snake_case_    :    Union[str, Any]		)  ->Tuple:



    '''simple docstring'''







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







def 						__lowercase	(  snake_case_    :    Union[str, Any]							,snake_case_    :    Any							,snake_case_    :    int		)  ->List[str]:



    '''simple docstring'''







    __A       :		Optional[Any]							=	np.dot(snake_case_							,snake_case_		)
    return np.sum(y * scores - np.log(1 + np.exp(snake_case_		)		)		)







def 						__lowercase	(  snake_case_    :    List[Any]							,snake_case_    :    int							,snake_case_    :    Optional[int]							,snake_case_    :    Dict=70000		)  ->List[str]:



    '''simple docstring'''







    __A       :		List[Any]							=	np.zeros(x.shape[1]		)

    for iterations in range(snake_case_		):
        __A       :		Optional[Any]							=	np.dot(snake_case_							,snake_case_		)
        __A       :		str							=	sigmoid_function(snake_case_		)
        __A       :		Optional[int]							=	np.dot(x.T							,h - y		) / y.size
        __A       :		List[Any]							=	theta - alpha * gradient  # updating the weights
        __A       :		Dict							=	np.dot(snake_case_							,snake_case_		)
        __A       :		Dict							=	sigmoid_function(snake_case_		)
        __A       :		int							=	cost_function(snake_case_							,snake_case_		)
        if iterations % 100 == 0:
            print(F"""loss: {j} \t"""		)  # printing the loss after every 100 iterations
    return theta


# In[68]:

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

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







       def 						__lowercase	(  snake_case_    :    List[str]		)  ->Dict:



           '''simple docstring'''







           return sigmoid_function(
               np.dot(snake_case_							,snake_case_		)		)  # 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""")
       ((a_)					,     (a_))					=     (x[:, 0].min(), x[:, 0].max())
       ((a_)					,     (a_))					=     (x[:, 1].min(), x[:, 1].max())
       ((a_)					,     (a_))					=     np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
       a_					=     np.c_[xxa.ravel(), xxa.ravel()]
       a_					=     predict_prob(grid).reshape(xxa.shape)
       plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

       plt.legend()




       plt.show()











"""simple docstring"""



import requests

a_					=     """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""







def 						__lowercase	(  snake_case_    :    str		)  ->None:



    '''simple docstring'''







    __A       :		str							=	requests.get(_NEWS_API + bbc_news_api_key		).json()
    # each article in the list is a dict
    for i, article in enumerate(bbc_news_page['''articles''']							,1		):
        print(F"""{i}.) {article['title']}"""		)


if __name__ == "__main__":
       fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")




