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%!s(int64=6) %!d(string=hai) anos | |
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| .. | ||
| README.md | %!s(int64=6) %!d(string=hai) anos | |
| Vector.py | %!s(int64=6) %!d(string=hai) anos | |
| classifier.py | %!s(int64=6) %!d(string=hai) anos | |
| logs.py | %!s(int64=6) %!d(string=hai) anos | |
| nearestneighbors.py | %!s(int64=6) %!d(string=hai) anos | |
| pcap.py | %!s(int64=6) %!d(string=hai) anos | |
The train(classifications: int, data: list, results: list, testdata: list, testresults: list) function should be used.
Data and Testdata should be arrays of arrays of features, ex:
Suppose there are 3 features, each a float from 0 to 1. Data could be: [[.3, .2, .3], [.3, .3, .3], [.3, .4, .5]...]
Results should be ints in an array, each result accoring to the list of features it should represent the classification of.
Usage: nearestneighbors.py datafile.bin classificationsfile.bin testdatafile.bin
A command line utility that reads in FeatureVectors and runs a KNN classification on them.
Plan for classifier.py: Discuss data formatting at meeting, expand utility to include choice of classification and make more robust in general