simple_knn 0.0.1

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Calculate k-nearest neighbours(knn) with a small subset of lodash with chaining capability

simple_knn #

Calculate k-nearest neighbours(knn) with a small subset of lodash with chaining capability

Features #

  • Calculate knn from List<List<num>> data
  • Provide several helper methods to handle List<List<num>> data
    • normalize
    • map
    • sortByColumn
    • zip
    • sum
    • and more
  • Simple Unit Testing

Usage #

num result = LodashChain.knn(trainingSet, LodashChain.initial(testPoint), k: k)

See example for a accuracy test loop.

import "package:simple_knn/simple_knn.dart";
import 'data.dart';

void main() {
  final testSetSize = 100;
  final List<List<num>> testSet = alldata.sublist(0, testSetSize);
  final List<List<num>> trainingSet = alldata.sublist(0, testSetSize);

  for (int k = 1; k < 7; k++) {
    var successes = testSet.where((List<num> testPoint) =>
        LodashChain.knn(trainingSet, LodashChain.initial(testPoint), k: k) ==
        testPoint.last);
    var accuracy = (successes.length / testSetSize) * 100;
    print("result for k=$k: $accuracy%");
  }
}

Unit Testing #

  • ✓ constructor and shape two dimensions filled
  • ✓ constructor and shape only one dimension filled
  • ✓ constructor and shape only scalar
  • ✓ slice two dimensions
  • ✓ slice only one dimension filled
  • ✓ slice only scalar
  • ✓ lastInChain two dimensions
  • ✓ lastInChain only one dimension filled
  • ✓ lastInChain only scalar
  • ✓ first two dimensions
  • ✓ first only one dimension filled
  • ✓ first only scalar
  • ✓ sortByColumn two dimensions
  • ✓ sortByColumn only one dimension filled
  • ✓ sortByColumn only scalar
  • ✓ size two dimensions
  • ✓ size only one dimension filled
  • ✓ size only scalar
  • ✓ map two dimensions
  • ✓ map only one dimension filled
  • ✓ map only scalar
  • ✓ filter two dimensions
  • ✓ filter only one dimension filled
  • ✓ filter only scalar
  • ✓ countByToPairs two dimensions
  • ✓ countByToPairs only one dimension filled
  • ✓ countByToPairs only scalar
  • ✓ zip two dimensions
  • ✓ zip only one dimension filled
  • ✓ zip only scalar
  • ✓ sum two dimensions
  • ✓ sum only one dimension filled
  • ✓ sum only scalar
  • ✓ distance chain distance chain
  • ✓ cloning two dimensions filled
  • ✓ cloning only one dimension filled
  • ✓ cloning only scalar
  • ✓ normalize two dimensions filled
  • ✓ normalize only one dimension filled
  • ✓ normalize only scalar
  • ✓ static min and max min
  • ✓ static min and max max
  • ✓ static distance simple v1 and v2
  • ✓ static distance simple v1 and v2
  • ✓ static initial of array length 4
  • ✓ static initial of array length 1
  • ✓ knn knn

The implementation is heavily influenced by the Udemy Course "Machine learning with javascript" by Stephen Grider. Thanks a lot to Stephen for his great explanations about knn using the Javascript lodash library.

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Calculate k-nearest neighbours(knn) with a small subset of lodash with chaining capability

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Uploader

aschilken@googlemail.com

License

MIT (LICENSE.md)

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