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authorThéo de la Hogue2023-05-24 10:53:38 +0200
committerThéo de la Hogue2023-05-24 10:53:38 +0200
commit8c7924b288d277823676b706d3a53fed4af8acc3 (patch)
tree54b24e60e7ef882c9ff2483c69da47eff7900dab /src
parent2fc1686f2d5587128730f564495794386bc57581 (diff)
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Improving stationary and transition entropy calculation.
Diffstat (limited to 'src')
-rw-r--r--src/argaze/GazeAnalysis/Entropy.py12
1 files changed, 6 insertions, 6 deletions
diff --git a/src/argaze/GazeAnalysis/Entropy.py b/src/argaze/GazeAnalysis/Entropy.py
index a185852..e6a60f1 100644
--- a/src/argaze/GazeAnalysis/Entropy.py
+++ b/src/argaze/GazeAnalysis/Entropy.py
@@ -24,32 +24,32 @@ class AOIScanPathAnalyzer(GazeFeatures.AOIScanPathAnalyzer):
pass
- def analyze(self, aoi_scan_path: GazeFeatures.AOIScanPathType, transition_matrix_probability: pandas.DataFrame) -> Any:
+ def analyze(self, aoi_scan_path: GazeFeatures.AOIScanPathType, transition_matrix_probabilities: pandas.DataFrame) -> Any:
"""Analyze aoi scan."""
assert(len(aoi_scan_path) > 1)
# Count total number of fixations and how many fixations are there per aoi
- scan_fixation_count, aoi_fixation_count = aoi_scan_path.fixation_count()
+ scan_fixations_count, aoi_fixations_count = aoi_scan_path.fixations_count()
# Probability to have a fixation onto each aoi
- stationary_probalities = {aoi: count/scan_fixation_count for aoi, count in aoi_fixation_count.items()}
+ stationary_probalities = {aoi: count/scan_fixations_count for aoi, count in aoi_fixations_count.items()}
# Stationary entropy
stationary_entropy = 0
for aoi, p in stationary_probalities.items():
- stationary_entropy += p * numpy.log(p + 0.00000001)
+ stationary_entropy += p * numpy.log(p + 1e-9)
stationary_entropy *= -1
# Transition entropy
transition_entropy = 0
- aoi_sum = transition_matrix_probability.apply(lambda row: row.apply(lambda p: p * numpy.log(p + 0.00000001)).sum(), axis=1)
+ destination_p_log_sum = transition_matrix_probabilities.apply(lambda row: row.apply(lambda p: p * numpy.log(p + 1e-9)).sum(), axis=1)
- for aoi, s in aoi_sum.items():
+ for aoi, s in destination_p_log_sum.items():
transition_entropy += s * stationary_probalities[aoi]