From 7fdfc57ad07981b1a22a7f091ca6c4a64123a7d0 Mon Sep 17 00:00:00 2001
From: SebastianBruijns <>
Date: Fri, 23 Sep 2022 20:19:13 +0200
Subject: [PATCH] real fix
---
pybasicbayes/distributions/multinomial.py | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/pybasicbayes/distributions/multinomial.py b/pybasicbayes/distributions/multinomial.py
index f779c84..e13624f 100644
--- a/pybasicbayes/distributions/multinomial.py
+++ b/pybasicbayes/distributions/multinomial.py
@@ -111,13 +111,13 @@ class Categorical(GibbsSampling, MeanField, MeanFieldSVI, MaxLikelihood, MAP):
self.weights = np.random.dirichlet(self.alphav_0 + counts)
except ZeroDivisionError as e:
# print("ZeroDivisionError {}".format(e))
- self.weights = np.random.dirichlet(self.alphav_0 + 0.03 + counts)
+ self.weights = np.random.dirichlet(self.alphav_0 + 0.01 + counts)
except ValueError as e:
# print("ValueError {}".format(e))
- self.weights = np.random.dirichlet(self.alphav_0 + 0.03 + counts)
+ self.weights = np.random.dirichlet(self.alphav_0 + 0.01 + counts)
if np.isnan(self.weights).any():
- self.weights = np.random.dirichlet(self.alphav_0 + 0.03 + counts)
- np.clip(self.weights, np.spacing(1.), np.inf, out=self.weights)
+ self.weights = np.random.dirichlet(self.alphav_0 + 0.01 + counts)
+ np.clip(self.weights, np.spacing(1.), 1-np.spacing(1.), out=self.weights)
# NOTE: next line is so we can use Gibbs sampling to initialize mean field
self._alpha_mf = self.weights * self.alphav_0.sum()
assert (self._alpha_mf >= 0.).all()
--
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