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Eventseg bugfix #339

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Merged
mihaic merged 4 commits into from
Feb 20, 2018
Merged

Eventseg bugfix #339

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181d30e
Fixing bug in event segmentation code
cbaldassano Feb 20, 2018
36c03d6
Adding news fragment
cbaldassano Feb 20, 2018
5157fc1
Updating news fragment title
cbaldassano Feb 20, 2018
4c2c85b
Updating news fragment title
cbaldassano Feb 20, 2018
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20 changes: 10 additions & 10 deletions brainiak/eventseg/event.py
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Original file line number Diff line number Diff line change
Expand Up @@ -268,23 +268,23 @@ def _forward_backward(self, logprob):
p_end[0, -2] = 1

# Forward pass
for t in range(t):
if t == 0:
for i in range(t):
if i == 0:
log_alpha[0, :] = self._log(p_start) + logprob[0, :]
else:
log_alpha[t, :] = self._log(np.exp(log_alpha[t - 1, :])
.dot(P)) + logprob[t, :]
log_alpha[i, :] = self._log(np.exp(log_alpha[i - 1, :])
.dot(P)) + logprob[i, :]

log_scale[t] = np.logaddexp.reduce(log_alpha[t, :])
log_alpha[t] -= log_scale[t]
log_scale[i] = np.logaddexp.reduce(log_alpha[i, :])
log_alpha[i] -= log_scale[i]

# Backward pass
log_beta[-1, :] = self._log(p_end) - log_scale[-1]
for t in reversed(range(t - 1)):
obs_weighted = log_beta[t + 1, :] + logprob[t + 1, :]
for i in reversed(range(t - 1)):
obs_weighted = log_beta[i + 1, :] + logprob[i + 1, :]
offset = np.max(obs_weighted)
log_beta[t, :] = offset + self._log(
np.exp(obs_weighted - offset).dot(P.T)) - log_scale[t]
log_beta[i, :] = offset + self._log(
np.exp(obs_weighted - offset).dot(P.T)) - log_scale[i]

# Combine and normalize
log_gamma = log_alpha + log_beta
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1 change: 1 addition & 0 deletions docs/newsfragments/339.bugfix
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@@ -0,0 +1 @@
Fixed bug in eventseg that was causing fits to be asymmetric
2 changes: 1 addition & 1 deletion tests/.flake8
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Expand Up @@ -12,5 +12,5 @@ ignore =
W503,
W504,
# Print restriction only applies to libraries
T001
T001,
T003
14 changes: 14 additions & 0 deletions tests/eventseg/test_event.py
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Expand Up @@ -74,3 +74,17 @@ def test_weighted_var():
assert np.allclose(
es.calc_weighted_event_var(D, weights, mean_pat), true_var),\
"Failed to compute variance with fractional weights"


def test_sym():
es = brainiak.eventseg.event.EventSegment(4)

evpat = np.repeat(np.arange(10).reshape(-1, 1), 4, axis=1)
es.set_event_patterns(evpat)

D = np.repeat(np.arange(10).reshape(1, -1), 20, axis=0)
ev = es.find_events(D, var=1)[0]

# Check that events 1-4 and 2-3 are symmetric
assert np.all(np.isclose(ev[:, :2], np.fliplr(np.flipud(ev[:, 2:])))),\
"Fit with constant data is not symmetric"