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fix shebang PATH problem on macos
1 parent 91a0602 commit 9ab9cec

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src/PCA.py

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# reduce dimensionality of internal activation states with PCA
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src/UMAP.py

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# reduce dimensionality of internal activation states with UMAP. optionally
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# reducing with PCA first too

src/accuracy

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# generate learning curves, confusion matrices, precision-recall curves, thresholds, etc.
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src/activations

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#This file, originally from the TensorFlow speech recognition tutorial,
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#has been heavily modified for use by SongExplorer.

src/classify

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#This file, originally from the TensorFlow speech recognition tutorial,
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#has been heavily modified for use by SongExplorer.

src/cluster-plugin.py

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# e.g. cluster-plugin.py \
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# --data_dir=`pwd`/groundtruth-data \

src/compare

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# plot accuracy across hyperparameter values
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src/congruence

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# generate figures of false positives and negatives
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src/delete-ckpts

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# delete unused checkpoints, to compact the size of models and conserve disk space.
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# `ckpts-tokeep` is a comma-separated list of checkpoints to keep, one for each model

src/detect-plugin.py

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# e.g. detect-plugin.py \
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# --filename=`pwd`/groundtruth-data/round2/20161207T102314_ch1_p1.wav \

src/ensemble

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# freeze an ensemble of trained models. they must have the same architecture, and
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# the outputs are simply averaged.

src/ethogram

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# apply per-class thresholds to discretize probabilities
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src/freeze

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#This file, originally from the TensorFlow speech recognition tutorial,
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#has been heavily modified for use by SongExplorer.

src/generalize

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# train several networks, withholding different subsets of the recordings to test upon
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src/loop

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#This file, originally from the TensorFlow speech recognition tutorial,
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#has been heavily modified for use by SongExplorer.

src/misses

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# find events that are detected but not predicted, a.k.a. false negatives
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src/mistakes

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# record whether annotations where correctly or mistakenly classified
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src/songexplorer

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# launch the graphical user interface
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src/tSNE.py

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# reduce dimensionality of internal activation states with tSNE. optionally
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# reducing with PCA first too

src/time-freq-threshold.py

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# threshold an audio recording in both the time and frequency spaces.
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# specifically, in the time domain, subtract the median, take the absolute value,

src/train

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# train a neural network with the annotations
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src/xvalidate

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# train several networks on different subsets of the annotations
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test/annotating.py

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# test doing, undoing, and redoing annotations
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test/congruence.py

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# test congruence
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test/freeze-classify.py

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# test that freeze and classify work with different representations,
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# downsamplings via strided convolutions, and numbers of outputs tics

test/runtests

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# run all of the tests
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test/seeds.py

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# test that weights_seed works
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test/shiftby.py

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# test that shift_by works
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test/tutorial.py

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# recapitulate the tutorial via the python interface
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test/visualization.py

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# test that the cluster, snippets, and context windows work
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