From 50c864ac058f73bd723da32fc5255d2c18771779 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?M=C3=A9d=C3=A9ric=20Boquien?=
Date: Mon, 10 Apr 2017 14:05:48 -0300
Subject: [PATCH] Use a much more readable formula to compute the
uncertainties. We use the fact that Var(X) = E(Var(X)) + Var(E(X)). At the
same time, broadcast the weights array to the same shape as the other arrays
in order to simplify the syntax for the computation.
---
pcigale/managers/results.py | 18 +++++++++---------
1 file changed, 9 insertions(+), 9 deletions(-)
diff --git a/pcigale/managers/results.py b/pcigale/managers/results.py
index 894b48de5..b863b2e44 100644
--- a/pcigale/managers/results.py
+++ b/pcigale/managers/results.py
@@ -72,7 +72,7 @@ class BayesResultsManager(object):
means = np.array([result.means for result in results])
errors = np.array([result.errors for result in results])
- weights = np.array([result.weights for result in results])
+ weights = np.array([result.weights for result in results])[..., None]
merged = results[0]
merged._means = shared_array((merged.nobs, merged.nproperties))
@@ -80,16 +80,16 @@ class BayesResultsManager(object):
merged._weights = None
sumweights = np.sum(weights, axis=0)
- merged.means[:] = np.sum(means*weights[:, :, None], axis=0) / \
- sumweights[:, None]
+
+ merged.means[:] = np.sum(means * weights, axis=0) / sumweights
+
# We compute the merged standard deviation by combining the standard
- # deviations for each block. This is done using a parallel algorithm.
- # See https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
+ # deviations for each block. See http://stats.stackexchange.com/a/10445
# where the number of datapoints has been substituted with the weights.
- merged.errors[:] = np.sqrt((np.sum(errors**2.*weights[:, :, None], axis=0) +
- np.sum((means[1:, :, :]-means[:1, :, :])**2 *
- (weights[1:, :]*weights[:1, :] / sumweights)[:, :, None],
- axis=0)) / sumweights[:, None])
+ # In short we exploit the fact that Var(X) = E(Var(X)) + Var(E(X)).
+ merged.errors[:] = np.sqrt(np.sum(weights * (errors**2 +
+ (means-merged.means)**2), axis=0) /
+ sumweights)
return merged
--
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