"""simple docstring"""


from __future__ import annotations

import time

import numpy as np

__UpperCamelCase									=			[8, 5, 9, 7]
__UpperCamelCase									=			[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__UpperCamelCase									=			[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]




class  UpperCamelCase					:







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							)      ->      None:
            snake_case_					       =					claim_vector
            snake_case_					       =					allocated_resources_table
            snake_case_					       =					maximum_claim_table







       def 		a_						( self)      ->      list[int]:
            return [
                sum(p_item[i] for p_item in self.__allocated_resources_table)
                for i in range(len(self.__allocated_resources_table[0]))
            ]







       def 		a_						( self)      ->      list[int]:
            return np.array(self.__claim_vector) - np.array(
                self.__processes_resource_summation())







       def 		a_						( self)      ->      list[list[int]]:
            return [
                list(np.array(self.__maximum_claim_table[i]) - np.array(lowerCAmelCase__))
                for i, allocated_resource in enumerate(self.__allocated_resources_table)
            ]







       def 		a_						( self)      ->      dict[int, list[int]]:
            return {self.__need().index(lowerCAmelCase__): i for i in self.__need()}







       def 		a_						( self,							**lowerCAmelCase__)      ->      None:
            snake_case_					       =					self.__need()
            snake_case_					       =					self.__allocated_resources_table
            snake_case_					       =					self.__available_resources()
            snake_case_					       =					self.__need_index_manager()
            for kw, val in kwargs.items():
                 if kw and val is True:
                      self.__pretty_data()
            print('_' * 50 + '\n')
            while need_list:
                 snake_case_					       =					False
                 for each_need in need_list:
                      snake_case_					       =					True
                      for index, need in enumerate(lowerCAmelCase__):
                           if need > available_resources[index]:
                                snake_case_					       =					False
                                break
                      if execution:
                           snake_case_					       =					True
                           # get the original index of the process from ind_ctrl db
                           for original_need_index, need_clone in need_index_manager.items():
                                if each_need == need_clone:
                                     snake_case_					       =					original_need_index
                           print(f'Process {process_number + 1} is executing.')
                           # remove the process run from stack
                           need_list.remove(lowerCAmelCase__)
                           # update available/freed resources stack
                           snake_case_					       =					np.array(lowerCAmelCase__) + np.array(
                               alloc_resources_table[process_number])
                           print(
                               'Updated available resource stack for processes: '
                               + ' '.join([str(lowerCAmelCase__) for x in available_resources]))
                           break
                 if safe:
                      print('The process is in a safe state.\n')
                 else:
                      print('System in unsafe state. Aborting...\n')
                      break







       def 		a_						( self)      ->      List[Any]:
            print(' ' * 9 + 'Allocated Resource Table')
            for item in self.__allocated_resources_table:
                 print(
                     f'P{self.__allocated_resources_table.index(lowerCAmelCase__) + 1}'
                     + ' '.join(f'{it:>8}' for it in item)
                     + '\n')
            print(' ' * 9 + 'System Resource Table')
            for item in self.__maximum_claim_table:
                 print(
                     f'P{self.__maximum_claim_table.index(lowerCAmelCase__) + 1}'
                     + ' '.join(f'{it:>8}' for it in item)
                     + '\n')
            print(
                'Current Usage by Active Processes: '
                + ' '.join(str(lowerCAmelCase__) for x in self.__claim_vector))
            print(
                'Initial Available Resources:       '
                + ' '.join(str(lowerCAmelCase__) for x in self.__available_resources()))
            time.sleep(1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



"""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       lowerCamelCase__		(			__snake_case	)							->		Tuple:


					"""simple docstring"""





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



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		List[str]:


					"""simple docstring"""





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



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		int:


					"""simple docstring"""





					_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
					return np.sum(y * scores - np.log(1 + np.exp(__snake_case	)	)	)



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case=7_00_00	)							->		Union[str, Any]:


					"""simple docstring"""





					_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_00 == 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=7_0000)
				print("""theta: """, theta)  # printing the theta i.e our weights vector



				def       lowerCamelCase__		(			__snake_case	)							->		Tuple:


									"""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()






