本文整理汇总了Python中tester.Tester.is_valid_solution方法的典型用法代码示例。如果您正苦于以下问题：Python Tester.is_valid_solution方法的具体用法？Python Tester.is_valid_solution怎么用？Python Tester.is_valid_solution使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tester.Tester的用法示例。
在下文中一共展示了Tester.is_valid_solution方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from tester import Tester [as 别名]
# 或者: from tester.Tester import is_valid_solution [as 别名]
class Optimizer:
"""
Object that implements different
optimization strategies for a solution.
Attrs:
solution: A solution being optimized.
case: A list of customers with their requirements.
"""
def __init__(self, solution, case):
self.solution = solution
self.current_mattes = sum([int(x) for x in solution])
self.case = case
self.tester = Tester()
self.valid_solution = False
self.steps = 0
self.METHODS = {
'random_optimizer': self.random_optimizer,
'matte_minimizer': self.matte_minimizer
}
'''
Generates the optimal naive solution.
The optimal solution is to produce a batch
of each color glossy.
[0, 0, ..., 0]
Args:
n: The number of colors.
Returns:
A list of n 0s.
'''
def generate_naive_solution(self, n):
return ['0' for i in range(n)]
'''
Modifies a solution.
Args:
solution: An array with 0s and 1s.
i: Position to switch value.
Returns
The same solution with the i-th
position switched.
'''
def change_solution(self, solution, i):
solution[i] = '1' if solution[i] == '0' else '0'
return solution
'''
Checks if the solution has improved
Args:
solution: An array with 0s and 1s.
Returns:
True if the solution is still valid
and the number of 1s has decreased.
False otherwise.
'''
def solution_improved(self, s):
candidate_mattes = sum([int(x) for x in s])
if self.valid_solution and candidate_mattes < self.current_mattes:
self.current_mattes = candidate_mattes
return True
return False
'''
Uses inspection to optimize randomly a solution.
The idea is to switch the value of random positions in the
solution array and check if the solution satisfies. Does
nothing if the solution already satisfies.
Args:
solution: A solution candidate.
Returns
The first solution that satisfies
'''
def random_optimizer(self, i=1):
tester = self.tester
case = self.case
s = self.solution
for k in range(i):
self.valid_solution = self.tester.is_valid_solution(self.solution, case)
pos = randint(0, len(solution)-1)
s = self.change_solution(s, pos)
if self.solution_improved(s):
self.solution = s
else:
s = self.change_solution(s, pos)
self.steps += i
'''
Tries to improve a solution incrementally.
This method generates a solution that satisfies, and
then tries to turn 1s into 0s until it no longer satisfies.
#.........这里部分代码省略.........