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Raise exception when predicting probabilities given an impossible set of observations #1129

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Raise exception when predicting probabilities given an impossible set of observations #1129

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13 changes: 11 additions & 2 deletions pomegranate/factor_graph.py
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Original file line number Diff line number Diff line change
Expand Up @@ -413,8 +413,10 @@ def predict_proba(self, X):


dims = tuple(range(1, len(current_marginals[i].shape)))
current_marginals[i] /= current_marginals[i].sum(dim=dims,
keepdims=True)
sum = current_marginals[i].sum(dim=dims, keepdims=True)
if sum == 0:
raise ImpossibleObservationsError
current_marginals[i] /= sum

loss += torch.nn.KLDivLoss(reduction="batchmean")(torch.log(
current_marginals[i] + 1e-8), prior_marginals[i])
Expand Down Expand Up @@ -574,3 +576,10 @@ def from_summaries(self):

for distribution in self.factors:
distribution.from_summaries()


class ImpossibleObservationsError(Exception):
"""Exception raised when predicting probabilities given an impossible set of
observations.
"""
pass