Functional non-threading "genetic algorithm" solution
This commit is contained in:
Caesar2011
226
__main__.py
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226
__main__.py
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import numpy as np
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import threading
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INPUT_FILE = "data/medium.in"
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POPULATION = 1000
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MUTATION_AMOUNT = 250
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ITERATIONS = 30
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data = [line for line in open(INPUT_FILE)]
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params = list(map(int, data[0].split(" ")))
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data = [[0 if x=="T" else 1 for x in line] for line in data[1:]]
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data = np.array(data)[:, :-1]
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clusters = np.arange(params[0]*params[1]).reshape((1, params[0], params[1]))
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clusters = np.repeat(clusters, POPULATION, axis=0)+1
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print(params)
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print(data)
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print(clusters[0])
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values = {}
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first = True
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class myThread (threading.Thread):
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def __init__(self, cluster, clean, id):
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threading.Thread.__init__(self)
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self.cluster = cluster
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self.clean = clean
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self.id = id
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self.result = None
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self.first = False
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self.values = {}
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self.vfunc = np.vectorize(self.myfunc)
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def run(self):
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# calc fitness
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self.values = {}
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self.first = True
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self.vfunc(self.cluster, data)
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self.result = get_fitness(self.values, clean=self.clean)
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print("Exit thread", self.id)
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def myfunc(self, a, b):
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if self.first:
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self.first = False
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return
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if a not in values:
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self.values[a] = [0, 0]
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self.values[a][b] += 1
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def get_fitnesses(clusts, clean=False):
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threads = []
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for i, cluster in enumerate(clusters):
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if i % 20 == 0:
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print("fitness", i, iteration)
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# Create new threads
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thread = myThread(cluster, clean, i)
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# Start new Threads
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thread.start()
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# Add threads to thread list
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threads.append(thread)
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# Wait for all threads to complete
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for t in threads:
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t.join()
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print("Exiting Main Thread")
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return np.array([thread.result for thread in threads])
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def get_fitness(vals, clean=False):
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fit = 0
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for key, val in vals.items():
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if key == 0:
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continue
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size = sum(val)
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if size <= params[3] and min(val) >= params[2]:
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fit += size
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if clean:
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continue
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size_diff = params[3]-size
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if size_diff < 0:
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fit += 1-size_diff**2
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elif size_diff > 0:
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fit += np.exp(-abs(size-params[3]))
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return fit
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def get_left_bound(clust, y, x):
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val = clust[y, x]
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while x > 0 and val == clust[y, x-1]:
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x -= 1
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return (y, x)
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def get_right_bound(clust, y, x):
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val = clust[y, x]
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while x+1 < clust.shape[1] and val == clust[y, x+1]:
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x += 1
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return (y, x)
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def get_top_bound(clust, y, x):
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val = clust[y, x]
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while y > 0 and val == clust[y-1, x]:
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y -= 1
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return (y, x)
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def get_bottom_bound(clust, y, x):
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val = clust[y, x]
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while y+1 < clust.shape[0] and val == clust[y+1, x]:
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y += 1
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return (y, x)
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def set_area(clust, y1, x1, y2, x2, value):
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for y in range(y1, y2+1):
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for x in range(x1, x2+1):
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clust[y, x] = value
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return clust
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def mutation(clust):
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for _ in range(np.random.random_integers(MUTATION_AMOUNT)):
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y = np.random.random_integers(params[0])-1
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x = np.random.random_integers(params[1])-1
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z = np.random.random()
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if z < 0.2:
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if y > 0: # expand to top
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yn = y-1
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_, inner_left = get_left_bound(clust, y, x)
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_, outer_left = get_left_bound(clust, yn, inner_left)
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_, inner_right = get_right_bound(clust, y, x)
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_, outer_right = get_right_bound(clust, yn, inner_right)
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clust = set_area(clust, yn, outer_left, yn, outer_right, 0)
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clust = set_area(clust, yn, inner_left, yn, inner_right, clust[y, x])
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elif z < 0.4:
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if x > 0: # expand to left
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xn = x-1
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inner_top, _ = get_top_bound(clust, y, x)
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outer_top, _ = get_top_bound(clust, inner_top, xn)
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inner_bot, _ = get_bottom_bound(clust, y, x)
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outer_bot, _ = get_bottom_bound(clust, inner_bot, xn)
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clust = set_area(clust, outer_top, xn, outer_bot, xn, 0)
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clust = set_area(clust, inner_top, xn, inner_bot, xn, clust[y, x])
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elif z < 0.6:
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if y < params[0]-1: # expand to bottom
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yn = y+1
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_, inner_left = get_left_bound(clust, y, x)
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_, outer_left = get_left_bound(clust, yn, inner_left)
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_, inner_right = get_right_bound(clust, y, x)
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_, outer_right = get_right_bound(clust, yn, inner_right)
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clust = set_area(clust, yn, outer_left, yn, outer_right, 0)
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clust = set_area(clust, yn, inner_left, yn, inner_right, clust[y, x])
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elif z < 0.8:
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if x < params[1]-1: # expand to right
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xn = x+1
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inner_top, _ = get_top_bound(clust, y, x)
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outer_top, _ = get_top_bound(clust, inner_top, xn)
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inner_bot, _ = get_bottom_bound(clust, y, x)
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outer_bot, _ = get_bottom_bound(clust, inner_bot, xn)
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clust = set_area(clust, outer_top, xn, outer_bot, xn, 0)
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clust = set_area(clust, inner_top, xn, inner_bot, xn, clust[y, x])
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else:
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pass#clust[y, x] = np.amax(clust)+1
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return clust
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def myfunc(a, b):
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global first, values
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if first:
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first = False
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return
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if a not in values:
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values[a] = [0, 0]
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values[a][b] += 1
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vfunc = np.vectorize(myfunc)
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# mutation
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xx = MUTATION_AMOUNT
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for i in range(POPULATION):
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if i % 20 == 0:
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print("mutation", i)
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MUTATION_AMOUNT = 500
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clusters[i] = mutation(clusters[i])
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MUTATION_AMOUNT = xx
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for iteration in range(ITERATIONS):
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fitnesses = get_fitnesses(clusters, clean=False)
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# calc fitness
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fitnesses = np.zeros((POPULATION, ))
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for i, cluster in enumerate(clusters):
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if i % 20 == 0:
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print("fitness", i, iteration)
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values = {}
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first = True
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vfunc(cluster, data)
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fitnesses[i] = get_fitness(values)
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# select
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z_exp = [np.exp(i) for i in fitnesses]
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sum_z_exp = sum(z_exp)
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softmax = [i / sum_z_exp for i in z_exp]
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idx = np.random.choice(POPULATION, POPULATION, p=softmax)
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clusters = clusters[idx, :, :]
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# print best
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max_idx = np.argmax(fitnesses)
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print(clusters[max_idx])
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print(iteration, max(fitnesses))
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# mutation
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for i in range(POPULATION):
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clusters[i] = mutation(clusters[i])
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fitnesses = np.zeros((POPULATION, ))
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for i, cluster in enumerate(clusters):
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values = {}
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first = True
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vfunc(cluster, data)
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fitnesses[i] = get_fitness(values, clean=True)
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max_idx = np.argmax(fitnesses)
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print(clusters[max_idx])
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print(max(fitnesses))
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