






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



def       _A    (__a							)				-> List[str]:
    """simple docstring"""






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



def       _A    (__a					,       __a							)				-> Tuple:
    """simple docstring"""






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



def       _A    (__a					,       __a					,       __a							)				-> List[Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[str]							=	np.dot(a__					,       a__							)
    return np.sum(y * scores - np.log(1 + np.exp(a__							)							)							)



def       _A    (__a					,       __a					,       __a					,       __a=7_00_00							)				-> Tuple:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						str							=	np.zeros(x.shape[1]							)

    for iterations in range(a__							):
        SCREAMING_SNAKE_CASE_   :						Any							=	np.dot(a__					,       a__							)
        SCREAMING_SNAKE_CASE_   :						Tuple							=	sigmoid_function(a__							)
        SCREAMING_SNAKE_CASE_   :						Dict							=	np.dot(x.T					,       h - y							) / y.size
        SCREAMING_SNAKE_CASE_   :						int							=	theta - alpha * gradient  # updating the weights
        SCREAMING_SNAKE_CASE_   :						Any							=	np.dot(a__					,       a__							)
        SCREAMING_SNAKE_CASE_   :						str							=	sigmoid_function(a__							)
        SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	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__":
  UpperCAmelCase_     :				Tuple         =				datasets.load_iris()
  UpperCAmelCase_     :				List[str]         =				iris.data[:, :2]
  UpperCAmelCase_     :				Optional[Any]         =				(iris.target != 0) * 1

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



  def       _A    (__a							)				-> Optional[int]:
      """simple docstring"""






      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""")
  ((UpperCAmelCase_)    ,						(UpperCAmelCase_))     :				Any         =				(x[:, 0].min(), x[:, 0].max())
  ((UpperCAmelCase_)    ,						(UpperCAmelCase_))     :				Optional[Any]         =				(x[:, 1].min(), x[:, 1].max())
  ((UpperCAmelCase_)    ,						(UpperCAmelCase_))     :				str         =				np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  UpperCAmelCase_     :				Any         =				np.c_[xxa.ravel(), xxa.ravel()]
  UpperCAmelCase_     :				Tuple         =				predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

  plt.legend()





  plt.show()










"""simple docstring"""






# Algorithm for the pigeonhole sorting





def       _A    (__a							)				-> Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Dict							=	min(__a							)  # min() finds the minimum value
    SCREAMING_SNAKE_CASE_   :						int							=	max(__a							)  # max() finds the maximum value

    SCREAMING_SNAKE_CASE_   :						Dict							=	max_val - min_val + 1  # size is difference of max and min values plus one

    # list of pigeonholes of size equal to the variable size
    SCREAMING_SNAKE_CASE_   :						Any							=	[0] * size

    # Populate the pigeonholes.
    for x in a:
        assert isinstance(__a					,       __a							), "integers only please"
        holes[x - min_val] += 1

    # Putting the elements back into the array in an order.
    SCREAMING_SNAKE_CASE_   :						Any							=	0
    for count in range(__a							):
        while holes[count] > 0:
            holes[count] -= 1
            SCREAMING_SNAKE_CASE_   :						Dict							=	count + min_val
            i += 1



def       _A    ()				-> Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						List[str]							=	[8, 3, 2, 7, 4, 6, 8]
    pigeonhole_sort(__a							)
    print('''Sorted order is:'''					,       ''' '''.join(__a							)							)


if __name__ == "__main__":
  main()


