From 3875dec5f95bd06ade6dcbc5ea500e0537408864 Mon Sep 17 00:00:00 2001
From: SebastianBruijns <>
Date: Thu, 29 Jun 2023 15:32:34 +0200
Subject: [PATCH] Revert "comments and variable renaming"
This reverts commit a080f3ca77d3dd25ee0352e29e5c313f2f7a7dd0.
---
pybasicbayes/distributions/dynamic_glm.py | 23 ++++++-----------------
1 file changed, 6 insertions(+), 17 deletions(-)
diff --git a/pybasicbayes/distributions/dynamic_glm.py b/pybasicbayes/distributions/dynamic_glm.py
index c18202c..117a210 100644
--- a/pybasicbayes/distributions/dynamic_glm.py
+++ b/pybasicbayes/distributions/dynamic_glm.py
@@ -84,11 +84,8 @@ class Dynamic_GLM(GibbsSampling):
return outputs
def log_likelihood(self, input, timepoint):
- """
- Given input from a single session (a matrix containing regressors and responses) and the timepoint information from that session, return log-likelihoods for observations.
- """
predictors, responses = input[:, :-1], input[:, -1]
- nans = np.isnan(responses) # can come from cross-validation
+ nans = np.isnan(responses)
probs = np.zeros((input.shape[0], 2))
out = np.zeros(input.shape[0])
# I could possibly save the 1 / ..., since it's logged it's just - log (but the other half of the probs is an issue)
@@ -102,11 +99,6 @@ class Dynamic_GLM(GibbsSampling):
# Gibbs sampling
def resample(self, data=[]):
- """
- Resampling of dynamic logistic random variables.
- We follow the resampling scheme of Windle: "Efficient Data Augmentation in Dynamic Models for Binary and Count Data".
- This makes use of the forward filter backwards sample algorithm. Which uses Kalman filtering, for which we use Anderson & Moore 1979
- """
# TODO: Clean up this mess, I always have to call delete_obs_data because of all the saved shit!
self.psi_diff_saves = []
summary_statistics, all_times = self._get_statistics(data)
@@ -144,19 +136,17 @@ class Dynamic_GLM(GibbsSampling):
if k in timepoint_map:
self.pseudo_Q[timepoint_map[k]] = self.Q[k] # for every timepoint, map it's variance onto the pseudo_Q
- """Prepare for pseudo-observation sampling"""
+ """sample pseudo obs"""
temp = np.empty(actual_obs_count)
psis = np.empty(actual_obs_count)
- psi_counter = 0
+ psi_count = 0
predictors = []
for type, time in zip(types, all_times):
for t in type:
- psis[psi_counter] = np.sum(self.weights[time] * t)
+ psis[psi_count] = np.sum(self.weights[time] * t)
predictors.append(t)
- psi_counter += 1
+ psi_count += 1
- # Here we use the pg package to draw augmenting variable from a polya-gamma distribution
- # Windle calls the counts b_t = a_t + d_t, just the total number of times a psi has been seen. temp is the array in which our draws will be saved
ppgs.pgdrawv(np.concatenate(counts).astype(float), psis, temp)
self.R = np.zeros(total_types)
mask = np.ones(total_types, dtype=np.bool)
@@ -249,9 +239,8 @@ class Dynamic_GLM(GibbsSampling):
"""Sigmas can be precomputed (without z), we do this here."""
# We rely on the fact that H.T.dot(sigma).dot(H) is just a number, no matrix inversion needed
# furthermore we use the fact that many matrices are identities, namely F and G
- # We follow Anderson & Moore 1979, equations are listed where applicable
self.sigma_k = [] # we have to reset this for repeating this calculation later for the resampling (R changes)
- self.sigma_k_k_minus = [self.P_0] # (1.10)
+ self.sigma_k_k_minus = [self.P_0]
self.gain_save = []
for k in range(T):
if self.R[k] == 0:
--
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