
'''simple docstring'''







def 			_a		(	_SCREAMING_SNAKE_CASE		:   list	,			_SCREAMING_SNAKE_CASE		:   list	,			_SCREAMING_SNAKE_CASE		:   int  ):
			if len(UpperCAmelCase__  ) != len(UpperCAmelCase__  ):
						raise ValueError("The length of profit and weight must be same."  )
			if max_weight <= 0:
						raise ValueError("max_weight must greater than zero."  )
			if any(p < 0 for p in profit  ):
						raise ValueError("Profit can not be negative."  )
			if any(w < 0 for w in weight  ):
						raise ValueError("Weight can not be negative."  )

			# List created to store profit gained for the 1kg in case of each weight
			# respectively.  Calculate and append profit/weight for each element.
			_SCREAMING_SNAKE_CASE				= [p / w for p, w in zip(UpperCAmelCase__	,			UpperCAmelCase__  )]

			# Creating a copy of the list and sorting profit/weight in ascending order
			_SCREAMING_SNAKE_CASE				= sorted(UpperCAmelCase__  )

			# declaring useful variables
			_SCREAMING_SNAKE_CASE				= len(UpperCAmelCase__  )
			_SCREAMING_SNAKE_CASE				= 0
			_SCREAMING_SNAKE_CASE				= 0
			_SCREAMING_SNAKE_CASE				= 0

			# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
			while limit <= max_weight and i < length:
						# flag value for encountered greatest element in sorted_profit_by_weight
						_SCREAMING_SNAKE_CASE				= sorted_profit_by_weight[length - i - 1]
						_SCREAMING_SNAKE_CASE				= profit_by_weight.index(UpperCAmelCase__  )
						_SCREAMING_SNAKE_CASE				= -1

						# check if the weight encountered is less than the total weight
						# encountered before.
						if max_weight - limit >= weight[index]:
									limit += weight[index]
									# Adding profit gained for the given weight 1 ===
									# weight[index]/weight[index]
									gain += 1 * profit[index]
						else:
									# Since the weight encountered is greater than limit, therefore take the
									# required number of remaining kgs and calculate profit for it.
									# weight remaining / weight[index]
									gain += (max_weight - limit) / weight[index] * profit[index]
									break
						i += 1
			return gain


if __name__ == "__main__":
						print(
						    """Input profits, weights, and then max_weight (all positive ints) separated by """
						    """spaces."""
						)

						_snake_case				:   Tuple     =				[int(x) for x in input("""Input profits separated by spaces: """).split()]
						_snake_case				:   int     =				[int(x) for x in input("""Input weights separated by spaces: """).split()]
						_snake_case				:   Tuple     =				int(input("""Max weight allowed: """))

						# Function Call
						calc_profit(profit, weight, max_weight)

'''simple docstring'''







import numpy

# List of input, output pairs
_snake_case				:   Optional[Any]     =				(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_snake_case				:   Any     =				(((515, 22, 13), 555), ((61, 35, 49), 150))
_snake_case				:   Any     =				[2, 4, 1, 5]
_snake_case				:   str     =				len(train_data)
_snake_case				:   int     =				0.009



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   List[Any]="train"  ):
						return calculate_hypothesis_value(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ) - output(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						_SCREAMING_SNAKE_CASE				= 0
						for i in range(len(_SCREAMING_SNAKE_CASE  ) - 1  ):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]  ):
						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Any	,			_SCREAMING_SNAKE_CASE		:   str  ):
						if data_set == "train":
												return _hypothesis_value(train_data[example_no][0]  )
						elif data_set == "test":
												return _hypothesis_value(test_data[example_no][0]  )
						return None



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   Optional[int]=m  ):
						_SCREAMING_SNAKE_CASE				= 0
						for i in range(_SCREAMING_SNAKE_CASE  ):
												if index == -1:
																		summation_value += _error(_SCREAMING_SNAKE_CASE  )
												else:
																		summation_value += _error(_SCREAMING_SNAKE_CASE  ) * train_data[i][0][index]
						return summation_value



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]  ):
						_SCREAMING_SNAKE_CASE				= summation_of_cost_derivative(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ) / m
						return cost_derivative_value



def 			_a		(	):
						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						_SCREAMING_SNAKE_CASE				= 0.000_002
						_SCREAMING_SNAKE_CASE				= 0
						_SCREAMING_SNAKE_CASE				= 0
						while True:
												j += 1
												_SCREAMING_SNAKE_CASE				= [0, 0, 0, 0]
												for i in range(0	,			len(_SCREAMING_SNAKE_CASE  )  ):
																		_SCREAMING_SNAKE_CASE				= get_cost_derivative(i - 1  )
																		_SCREAMING_SNAKE_CASE				= (
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			atol=_SCREAMING_SNAKE_CASE	,			rtol=_SCREAMING_SNAKE_CASE	,			):
																		break
												_SCREAMING_SNAKE_CASE				= temp_parameter_vector
						print(("Number of iterations:", j)  )



def 			_a		(	):
						for i in range(len(_SCREAMING_SNAKE_CASE  )  ):
												print(("Actual output value:", output(_SCREAMING_SNAKE_CASE	,			"test"  ))  )
												print(("Hypothesis output:", calculate_hypothesis_value(_SCREAMING_SNAKE_CASE	,			"test"  ))  )


if __name__ == "__main__":
						run_gradient_descent()
						print("""\nTesting gradient descent for a linear hypothesis function.\n""")
						test_gradient_descent()