This is a pre-production deployment of Warehouse, however changes made here WILL affect the production instance of PyPI.
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Project Description
MD-ELM

Detection of originally mislabelled samples in a dataset, with Optimally Pruned Extreme Learning Machine (OP-ELM).

The MDELM function is the core of MD-ELM method, which returns 'likelihood of being a mislabel' score for each sample.

Additional methods are given for running the whole methodology. They generate multiple models, store them in files,
process the models and combine results. Here is an example code to use them:

X,Y = cPickle.load(open("data.pkl","rb"))
mfiles = build_models(X,Y, X.shape[0]/10, k=4, path="./try")

# run all experiments
for data in mfiles:
for elm in data:
run_model(elm)

scores = np.zeros((X.shape[0],))
for data in mfiles:
found = analyze_models(data)
scores[found] += 1
print scores
print "done"

Model files from path="./try" folder can be processed independently with run_model() function on different machines.
Release History

Release History

0.61

This version

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0.42

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0.41

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0.6

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0.5

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0.4

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0.3

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0.2

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0.1

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Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
MD-ELM-0.61.tar.gz (9.6 kB) Copy SHA256 Checksum SHA256 Source Mar 21, 2014

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