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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2020-01-29 14:26:37 -08:00
parent 8fac566a87
commit 3ee6eb438a
1 changed files with 8 additions and 8 deletions
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train.py
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@ -449,7 +449,7 @@ if __name__ == '__main__':
# Select parent(s) # Select parent(s)
parent = 'single' # parent selection method: 'single' or 'weighted' parent = 'single' # parent selection method: 'single' or 'weighted'
x = np.loadtxt('evolve.txt', ndmin=2) x = np.loadtxt('evolve.txt', ndmin=2)
n = min(8, len(x)) # number of previous results to consider n = min(5, len(x)) # number of previous results to consider
x = x[np.argsort(-fitness(x))][:n] # top n mutations x = x[np.argsort(-fitness(x))][:n] # top n mutations
w = fitness(x) - fitness(x).min() # weights w = fitness(x) - fitness(x).min() # weights
if parent == 'single' or len(x) == 1: if parent == 'single' or len(x) == 1:
@ -459,20 +459,20 @@ if __name__ == '__main__':
x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
# Mutate # Mutate
method = 3 method, mp, s = 3, 0.9, 0.1 # method, mutation probability, sigma
s = 0.3 # 30% sigma npr = np.random
np.random.seed(int(time.time())) npr.seed(int(time.time()))
g = np.array([1, 1, 1, 1, 1, 1, 1, 0, .1, 1, 0, 1, 1, 1, 1, 1, 1, 1]) # gains g = np.array([1, 1, 1, 1, 1, 1, 1, 0, .1, 1, 0, 1, 1, 1, 1, 1, 1, 1]) # gains
ng = len(g) ng = len(g)
if method == 1: if method == 1:
v = (np.random.randn(ng) * np.random.random() * g * s + 1) ** 2.0 v = (npr.randn(ng) * npr.random() * g * s + 1) ** 2.0
elif method == 2: elif method == 2:
v = (np.random.randn(ng) * np.random.random(ng) * g * s + 1) ** 2.0 v = (npr.randn(ng) * npr.random(ng) * g * s + 1) ** 2.0
elif method == 3: elif method == 3:
v = np.ones(ng) v = np.ones(ng)
while all(v == 1): # mutate until a change occurs (prevent duplicates) while all(v == 1): # mutate until a change occurs (prevent duplicates)
r = (np.random.random(ng) < 0.1) * np.random.randn(ng) # 10% mutation probability # v = (g * (npr.random(ng) < mp) * npr.randn(ng) * s + 1) ** 2.0
v = (g * s * r + 1) ** 2.0 v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
hyp[k] = x[i + 7] * v[i] # mutate hyp[k] = x[i + 7] * v[i] # mutate