




from statistics import mean

import numpy as np







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)     -> list:



       """simple docstring"""

       A__   				=					0
       # Number of processes finished
       A__   				=					0
       # Displays the finished process.
       # If it is 0, the performance is completed if it is 1, before the performance.
       A__   				=					[0] * no_of_process
       # List to include calculation results
       A__   				=					[0] * no_of_process

       # Sort by arrival time.
       A__   				=					[burst_time[i] for i in np.argsort(lowercase_		)]
       A__   				=					[process_name[i] for i in np.argsort(lowercase_		)]
       arrival_time.sort()

       while no_of_process > finished_process_count:
              A__   				=					0
              while finished_process[i] == 1:
                     i += 1
              if current_time < arrival_time[i]:
                     A__   				=					arrival_time[i]

              A__   				=					0
              # Index showing the location of the process being performed
              A__   				=					0
              # Saves the current response ratio.
              A__   				=					0
              for i in range(0				,       lowercase_		):
                     if finished_process[i] == 0 and arrival_time[i] <= current_time:
                            A__   				=					(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
                                i
                            ]
                     if response_ratio < temp:
                            A__   				=					temp
                            A__   				=					i

        # Calculate the turn around time
              A__   				=					current_time + burst_time[loc] - arrival_time[loc]
              current_time += burst_time[loc]
              # Indicates that the process has been performed.
              A__   				=					1
              # Increase finished_process_count by 1
              finished_process_count += 1

       return turn_around_time







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)     -> list:



       """simple docstring"""

       A__   				=					[0] * no_of_process
       for i in range(0				,       lowercase_		):
              A__   				=					turn_around_time[i] - burst_time[i]
       return waiting_time


if __name__ == "__main__":
  _lowerCamelCase			:	Optional[int]							 =     5
  _lowerCamelCase			:	str							 =     ["""A""", """B""", """C""", """D""", """E"""]
  _lowerCamelCase			:	Optional[int]							 =     [1, 2, 3, 4, 5]
  _lowerCamelCase			:	Tuple							 =     [1, 2, 3, 4, 5]

  _lowerCamelCase			:	Union[str, Any]							 =     calculate_turn_around_time(
      process_name, arrival_time, burst_time, no_of_process
  )
  _lowerCamelCase			:	Dict							 =     calculate_waiting_time(
      process_name, turn_around_time, burst_time, no_of_process
  )

  print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
  for i in range(0, no_of_process):
    print(
        F'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
        F'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
    )

  print(F'''average waiting time : {mean(waiting_time):.5f}''')
  print(F'''average turn around time : {mean(turn_around_time):.5f}''')








# 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       ( lowercase_		)     -> Dict:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Optional[int]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_		)     -> int:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_=70_000		)     -> Dict:



       """simple docstring"""

       A__   				=					np.zeros(x.shape[1]		)

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


# In[68]:

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

  _lowerCamelCase			:	Union[str, Any]							 =     0.1
  _lowerCamelCase			:	Tuple							 =     logistic_reg(alpha, x, y, max_iterations=70000)
  print("""theta: """, theta)  # printing the theta i.e our weights vector







  def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> List[Any]:



         """simple docstring"""

         return sigmoid_function(
             np.dot(lowercase_				,       lowercase_		)		)  # 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""")
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	int							 =     (x[:, 0].min(), x[:, 0].max())
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	Union[str, Any]							 =     (x[:, 1].min(), x[:, 1].max())
  ((_lowerCamelCase) ,    (_lowerCamelCase))			:	List[Any]							 =     np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  _lowerCamelCase			:	str							 =     np.c_[xxa.ravel(), xxa.ravel()]
  _lowerCamelCase			:	Tuple							 =     predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

  plt.legend()
  plt.show()


