simple_knn

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.