Description:

simple knn

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)
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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%");
}
}
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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.

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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