Prevent intensity from being exactly zero

If the intensity is zero, then an inf value will occur in the likelihood, so need to bias a small amount .
Eden-Kramer-Lab · Jul 16, 2018 · 6320671 · 6320671
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commit 6320671
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Showing 5 changed files with 348 additions and 348 deletions.
diff --git a/examples/Simulate_Ripple_Decoding_Data_Clusterless.ipynb b/examples/Simulate_Ripple_Decoding_Data_Clusterless.ipynb
diff --git a/examples/Simulate_Ripple_Decoding_Data_Sorted_Spikes.ipynb b/examples/Simulate_Ripple_Decoding_Data_Sorted_Spikes.ipynb
diff --git a/replay_classification/clusterless.py b/replay_classification/clusterless.py
@@ -70,6 +70,7 @@ def poisson_mark_log_likelihood(marks, joint_mark_intensity_functions=None,
         [[jmi(signal_marks) for jmi in jmi_by_state]
          for signal_marks, jmi_by_state
          in zip(marks, joint_mark_intensity_functions)])
+    joint_mark_intensity += np.spacing(1)
     return np.log(joint_mark_intensity) + probability_no_spike
 
 

diff --git a/replay_classification/sorted_spikes.py b/replay_classification/sorted_spikes.py
@@ -81,10 +81,9 @@ def poisson_log_likelihood(is_spike, conditional_intensity=None,
                                                 n_place_bins)
 
     '''
+    conditional_intensity += np.spacing(1)
     probability_no_spike = -conditional_intensity * time_bin_size
     is_spike = atleast_kd(is_spike, conditional_intensity.ndim)
-    conditional_intensity[
-        np.isclose(conditional_intensity, 0.0)] = np.spacing(1)
     return (np.log(conditional_intensity) * is_spike +
             probability_no_spike)
 

diff --git a/setup.py b/setup.py
@@ -10,7 +10,7 @@
 
 setup(
         name='replay_classification',
-        version='0.4.1',
+        version='0.4.2',
         license='GPL-3.0',
         description=('Non-parametric categorization of replay content from'
                      ' multiunit spiking activity'),