51 lines
1.0 KiB
Python
51 lines
1.0 KiB
Python
import numpy as np
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from numpy.random import uniform
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def softmax(values: np.array, eps=1.0E-6) -> np.array:
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result = (values + eps) / (sum(values + eps))
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return result
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def sample(values: np.array, with_debug=False):
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# Normalize and sort
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probs = softmax(values)
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sorted_indices = np.argsort(probs)
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sorted_probs = probs[sorted_indices]
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z = uniform()
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if with_debug:
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print(f"Probs={probs}")
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print(f"Z={z:.3f}")
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index = None
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p_sum = 0
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for idx, p in enumerate(sorted_probs):
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p_sum += p
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if z <= p_sum:
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index = sorted_indices[idx]
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break
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return index
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def test_sample(data):
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p = np.array(data)
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c = np.array([0]*len(data))
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for i in range(0, 1000):
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index = sample(p, with_debug=False)
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c[index] += 1
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print(c)
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if __name__ == '__main__':
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print("Testing sample()")
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test_sample([0.7, 0.1, 0.1, 0.1])
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test_sample([0.1, 0.1, 0.2, 0.1])
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test_sample([0.2, 0.8])
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test_sample([0.1, 0.1])
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test_sample([0.3, 0.0, 0.2, 0.0])
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test_sample([0.0, 0.0, 0.0, 0.0])
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test_sample([0.5, 0.0, 0.2, 0.3])
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test_sample([0.3, 0.1, 0.4, 0.2])
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