From 2bdecbeab2b18522c026a8835ee3ac4d106da064 Mon Sep 17 00:00:00 2001
From: Scott Linderman <scott.linderman@gmail.com>
Date: Wed, 18 Oct 2017 11:38:32 -0400
Subject: [PATCH] make robustregression _robust_ to nans. har har.
---
pybasicbayes/distributions/regression.py | 16 ++++++++++------
1 file changed, 10 insertions(+), 6 deletions(-)
diff --git a/pybasicbayes/distributions/regression.py b/pybasicbayes/distributions/regression.py
index 3b5488a..f09b5f2 100644
--- a/pybasicbayes/distributions/regression.py
+++ b/pybasicbayes/distributions/regression.py
@@ -942,9 +942,9 @@ class RobustRegression(Regression):
sigma_inv, L = inv_psd(sigma, return_chol=True)
r = y - self.predict(x)
- z = solve_triangular(L, r.T, lower=True).T
+ z = sigma_inv.dot(r.T).T
- out = -0.5 * (nu + D) * np.log(1.0 + (z * z).sum(1) / nu)
+ out = -0.5 * (nu + D) * np.log(1.0 + (r * z).sum(1) / nu)
out += gammaln((nu + D) / 2.0) - gammaln(nu / 2.0) - D / 2.0 * np.log(nu) \
- D / 2.0 * np.log(np.pi) - np.log(np.diag(L)).sum()
@@ -1054,16 +1054,20 @@ class RobustRegression(Regression):
assert y.shape[1] == self.D_out
N = x.shape[0]
+ # Weed out the nan's
+ bad = np.any(np.isnan(x), axis=1) | np.any(np.isnan(y), axis=1)
+
# Compute posterior params of gamma distribution
a_post = self.nu / 2.0 + self.D_out / 2.0
r = y - self.predict(x)
- L = np.linalg.cholesky(self.sigma)
- z = solve_triangular(L, r.T, lower=True).T
- b_post = self.nu / 2.0 + (z * z).sum(1) / 2.0
+ sigma_inv = inv_psd(self.sigma)
+ z = sigma_inv.dot(r.T).T
+ b_post = self.nu / 2.0 + (r * z).sum(1) / 2.0
assert np.isscalar(a_post) and b_post.shape == (N,)
- tau = np.random.gamma(a_post, 1./b_post)
+ tau = np.zeros(N)
+ tau[~bad] = np.random.gamma(a_post, 1./b_post[~bad])
return tau
--
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