




from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass




@dataclass
class       _A				:
		__a					    =       42
		__a					    =       None
		__a					    =       None



a													=			namedtuple("CoinsDistribResult", "moves excess")




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				int:

						if root is None:
												return 0

						# Validation
						def count_nodes(snake_case       ) -> int:
												if node is None:
																		return 0

												return count_nodes(node.left       ) + count_nodes(node.right       ) + 1

						def count_coins(snake_case       ) -> int:
												if node is None:
																		return 0

												return count_coins(node.left       ) + count_coins(node.right       ) + node.data

						if count_nodes(snake_case       ) != count_coins(snake_case       ):
												raise ValueError("""The nodes number should be same as the number of coins"""       )

						# Main calculation
						def get_distrib(snake_case       ) -> CoinsDistribResult:

												if node is None:
																		return CoinsDistribResult(0			,     1       )

												_UpperCAmelCase    ,					_UpperCAmelCase      =			get_distrib(node.left       )
												_UpperCAmelCase    ,					_UpperCAmelCase      =			get_distrib(node.right       )

												_UpperCAmelCase      =			1 - left_distrib_excess
												_UpperCAmelCase      =			1 - right_distrib_excess

												_UpperCAmelCase      =			(
												    left_distrib_moves
												    + right_distrib_moves
												    + abs(snake_case       )
												    + abs(snake_case       )
												)
												_UpperCAmelCase      =			node.data - coins_to_left - coins_to_right

												return CoinsDistribResult(snake_case			,     snake_case       )

						return get_distrib(snake_case       )[0]


if __name__ == "__main__":
				import doctest

				doctest.testmod()





# 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      (		snake_case       )						->				List[str]:
						return 1 / (1 + np.exp(-z       ))




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Tuple:
						return (-y * np.log(snake_case       ) - (1 - y) * np.log(1 - h       )).mean()




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
						return np.sum(y * scores - np.log(1 + np.exp(snake_case       )       )       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case=7_0_0_0_0       )						->				Tuple:
						_UpperCAmelCase      =			np.zeros(x.shape[1]       )

						for iterations in range(snake_case       ):
												_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
												_UpperCAmelCase      =			sigmoid_function(snake_case       )
												_UpperCAmelCase      =			np.dot(x.T			,     h - y       ) / y.size
												_UpperCAmelCase      =			theta - alpha * gradient  # updating the weights
												_UpperCAmelCase      =			np.dot(snake_case			,     snake_case       )
												_UpperCAmelCase      =			sigmoid_function(snake_case       )
												_UpperCAmelCase      =			cost_function(snake_case			,     snake_case       )
												if iterations % 1_0_0 == 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        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[Any]:
										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()