From 61f134aa45668d19f70b05bdffa0f73824971a3a Mon Sep 17 00:00:00 2001
From: hanna julienne <hanna.julienne@pasteur.fr>
Date: Tue, 19 Nov 2019 11:36:59 +0100
Subject: [PATCH] add adaptation of rcond to data if needed
---
raiss/stat_models.py | 13 ++++++++++---
1 file changed, 10 insertions(+), 3 deletions(-)
diff --git a/raiss/stat_models.py b/raiss/stat_models.py
index 1cec678..ea864f8 100644
--- a/raiss/stat_models.py
+++ b/raiss/stat_models.py
@@ -68,6 +68,14 @@ def var_in_boundaries(var,lamb):
return var
+def invert_sig_t(sig_t, lamb, rcond):
+ try:
+ np.fill_diagonal(sig_t.values, (1+lamb))
+ sig_t_inv = scipy.linalg.pinv(sig_t, rcond=rcond)
+ return(sig_t_inv)
+ except np.linalg.LinAlgError:
+ invert_sig_t(sig_t, lamb*1.1, rcond*1.1)
+
def raiss_model(zt, sig_t, sig_i_t, lamb=0.01, rcond=0.01, batch=True):
"""
Compute the variance
@@ -80,9 +88,8 @@ def raiss_model(zt, sig_t, sig_i_t, lamb=0.01, rcond=0.01, batch=True):
rcond (float): threshold to filter eigenvector with a eigenvalue under rcond
make inversion biased but much more numerically robust
"""
- sig_t = sig_t.values
- np.fill_diagonal(sig_t, (1+lamb))
- sig_t_inv = scipy.linalg.pinv(sig_t, rcond=rcond)
+ sig_t_inv = invert_sig_t(sig_t, lamb, rcond)
+
if batch:
condition_number = np.array([np.linalg.cond(sig_t)]*sig_i_t.shape[0])
--
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