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Fix bugs in Discrete, Tabular, and Mixture sample methods

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This commit is contained in:
Paul Romano 2022-07-28 17:06:13 -05:00
parent 3e5afd21eb
commit b2fed3c3ce
1 changed files with 10 additions and 7 deletions
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17
openmc/stats/univariate.py
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@ -139,7 +139,8 @@ class Discrete(Univariate):
def sample(self, n_samples=1, seed=None):
np.random.seed(seed)
return np.random.choice(self.x, n_samples, p=self.p)
p = self.p / self.p.sum()
return np.random.choice(self.x, n_samples, p=p)
def normalize(self):
"""Normalize the probabilities stored on the distribution"""
@ -944,10 +945,11 @@ class Tabular(Univariate):
'linear-linear interpolation')
np.random.seed(seed)
xi = np.random.rand(n_samples)
cdf = self.cdf()
cdf /= cdf.max()
# always use normalized probabilities when sampling
cdf = self.cdf()
p = self.p / cdf.max()
cdf /= cdf.max()
# get CDF bins that are above the
# sampled values
@ -962,7 +964,7 @@ class Tabular(Univariate):
# the random number is less than the next cdf
# entry
x_i = self.x[cdf_idx]
p_i = self.p[cdf_idx]
p_i = p[cdf_idx]
if self.interpolation == 'histogram':
# mask where probability is greater than zero
@ -980,7 +982,7 @@ class Tabular(Univariate):
# get variable and probability values for the
# next entry
x_i1 = self.x[cdf_idx + 1]
p_i1 = self.p[cdf_idx + 1]
p_i1 = p[cdf_idx + 1]
# compute slope between entries
m = (p_i1 - p_i) / (x_i1 - x_i)
# set values for zero slope
@ -1166,9 +1168,10 @@ class Mixture(Univariate):
def sample(self, n_samples=1, seed=None):
np.random.seed(seed)
idx = np.random.choice(self.distribution, n_samples, p=self.probability)
idx = np.random.choice(range(len(self.distribution)),
n_samples, p=self.probability)
out = np.zeros_like(idx)
out = np.empty_like(idx, dtype=float)
for i in np.unique(idx):
n_dist_samples = np.count_nonzero(idx == i)
samples = self.distribution[i].sample(n_dist_samples)