56 lines
1.4 KiB
Python
56 lines
1.4 KiB
Python
# imports
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import numpy as np
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rng = np.random.default_rng(123)
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DEFAULT_N = 1000000
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# three simple functions
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def normal(mean, std, n=DEFAULT_N):
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return np.array(rng.normal(mean, std, n))
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def lognormal(mean, std, n=DEFAULT_N):
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return np.array(rng.lognormal(mean, std, n))
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def to(low, high, n=DEFAULT_N):
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normal95confidencePoint = 1.6448536269514722
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logLow = np.log(low)
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logHigh = np.log(high)
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meanlog = (logLow + logHigh)/2
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sdlog = (logHigh - logLow) / (2.0 * normal95confidencePoint)
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return lognormal(meanlog, sdlog, n)
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def mixture(samples_list, weights_array, n=DEFAULT_N):
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normalized_weights = weights_array/sum(weights_array)
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cummulative_sums = np.cumsum(normalized_weights)
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helper_probs = rng.random(n)
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results = np.empty(n)
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for i in range(n):
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helper_list = [j for j in range(
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len(cummulative_sums)) if cummulative_sums[j] > helper_probs[i]]
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if len(helper_list) == 0:
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helper_loc = 0
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else:
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helper_loc = helper_list[0]
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target_samples = samples_list[helper_loc]
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result = rng.choice(target_samples, 1)[0]
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results[i] = result
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return (results)
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# Example
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p_a = 0.8
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p_b = 0.5
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p_c = p_a * p_b
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dists = [[0], [1], to(1, 3), to(2, 10)]
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# print(dists)
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weights = np.array([1 - p_c, p_c/2, p_c/4, p_c/4])
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# print(weights)
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result = mixture(dists, weights)
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mean_result = np.mean(result)
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print(mean_result)
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