def __init__(self):
# self.graph = [(2, 4), (4, 7), (3, 1), (5, 1), (2, 7), (6, 1), (0, 6), (1, 4), (7, 6)]
# self.nodes = 8
# self.maxVal = 2
# self.graph = [(12, 13), (12, 14), (7, 11), (8, 4), (3, 5), (0, 10), (8, 12), (5, 4),
# (1, 9), (0, 8), (6, 14), (7, 6), (6, 5), (13, 4), (2, 8), (1, 14), (9, 10),
# (4, 11), (11, 6), (11, 1), (5, 11), (4, 7), (2, 13), (12, 10), (10, 5), (4, 10),
# (11, 7), (13, 12), (9, 11), (11, 0), (0, 4), (8, 6), (12, 8), (5, 10), (8, 2),
# (0, 1), (6, 11), (0, 3), (9, 0), (14, 10)]
# self.nodes = 15
# self.maxVal = 2
self.graph = [(1, 2), (1, 9), (3, 6), (5, 7), (8, 2), (0, 7), (3, 7),
(9, 7), (4, 3), (3, 8), (2, 6), (8, 7), (6, 3), (1, 7),
(7, 2), (0, 9), (8, 3), (6, 5), (8, 6), (5, 1), (2, 4),
(5, 3), (1, 4), (9, 5), (1, 3), (0, 1), (4, 6), (5, 6),
(9, 6), (9, 2), (6, 0), (3, 0)]
self.nodes = 10
self.maxVal = 3
self.edges = len(self.graph)
self.length = self.edges
self.initState = np.random.randint(self.maxVal, size=self.length)
self.fitness = mlrose.MaxKColor(edges=self.graph)
self.max = False
self.problem = mlrose.DiscreteOpt(length=self.length,
fitness_fn=self.fitness,
maximize=self.max,
max_val=self.maxVal)
def __init__(self, length):
self.length = length
self.maxVal = 2
self.fitness = mlrose.FlipFlop()
self.initState = np.random.randint(self.maxVal, size=self.length)
self.max = True
self.problem = mlrose.DiscreteOpt(length = self.length, fitness_fn = self.fitness, maximize = self.max, max_val = self.maxVal)
def __init__(self, length):
self.length = length
self.maxVal = length - 1
self.fitness = mlrose.Queens()
self.initState = np.random.randint(self.maxVal, size=self.length)
self.max = False
self.problem = mlrose.DiscreteOpt(length=self.length,
fitness_fn=self.fitness,
maximize=self.max,
max_val=self.maxVal)
def __init__(self):
self.weights = [10, 5, 2, 8, 15]
self.values = [1, 2, 3, 4, 5]
self.maxWeightPct = 0.5
self.initState = np.array([0, 0, 0, 0, 0])
self.length = len(self.weights)
self.maxVal = 12
self.fitness = mlrose.Knapsack(self.weights, self.values,
self.maxWeightPct)
self.max = True
self.problem = mlrose.DiscreteOpt(length=self.length,
fitness_fn=self.fitness,
maximize=self.max,
max_val=self.maxVal)