"""simple docstring"""



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





def    SCREAMING_SNAKE_CASE       (     snake_case):
     return 1 / (1 + np.exp(-z))





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     return (-y * np.log(a__) - (1 - y) * np.log(1 - h)).mean()





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case):
     __snake_case				=							np.dot(a__,      a__)
     return np.sum(y * scores - np.log(1 + np.exp(a__)))





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case=7_00_00):
     __snake_case				=							np.zeros(x.shape[1])

     for iterations in range(a__):
          __snake_case				=							np.dot(a__,      a__)
          __snake_case				=							sigmoid_function(a__)
          __snake_case				=							np.dot(x.T,      h - y) / y.size
          __snake_case				=							theta - alpha * gradient  # updating the weights
          __snake_case				=							np.dot(a__,      a__)
          __snake_case				=							sigmoid_function(a__)
          __snake_case				=							cost_function(a__,      a__)
          if iterations % 1_00 == 0:
               print(f"loss: {j} \t")  # printing the loss after every 100 iterations
     return theta


# In[68]:

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

       __lowercase		:		Union[str, Any]														=							0.1
       __lowercase		:		int														=							logistic_reg(alpha, x, y, max_iterations=7_0000)
       print("theta: ", theta)  # printing the theta i.e our weights vector





       def    SCREAMING_SNAKE_CASE       (     snake_case):
            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")
       ((__lowercase)							,(__lowercase))		:		int														=							(x[:, 0].min(), x[:, 0].max())
       ((__lowercase)							,(__lowercase))		:		int														=							(x[:, 1].min(), x[:, 1].max())
       ((__lowercase)							,(__lowercase))		:		Any														=							np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
       __lowercase		:		Optional[Any]														=							np.c_[xxa.ravel(), xxa.ravel()]
       __lowercase		:		Dict														=							predict_prob(grid).reshape(xxa.shape)
       plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="black")

       plt.legend()




       plt.show()
"""simple docstring"""



from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate



@dataclass(frozen=_UpperCAmelCase					)
class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					field(default='''summarization'''							, metadata={'''include_in_asdict_even_if_is_default''': True}					)
    UpperCamelCase_	:					ClassVar[Features]							=					Features({'''text''': Value('''string'''					)}					)
    UpperCamelCase_	:					ClassVar[Features]							=					Features({'''summary''': Value('''string'''					)}					)
    UpperCamelCase_	:					str							=					"text"
    UpperCamelCase_	:					str							=					"summary"
    @property
    def  lowercase					(		self							:     List[Any]    )    ->       Dict[str, str]:
         return {self.text_column: "text", self.summary_column: "summary"}