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gyrdymgyrdym
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'ml_linalg' version fixed, benchmark info added (#247)
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-31
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‎CHANGELOG.md‎

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# Changelog
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## 16.17.6
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- ml_linalg version fixed (13.11.11)
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- benchmark info added
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## 16.17.5
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- ml_linalg 13.11.1 used
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‎benchmark/cross_validator.dart‎

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// 8.5 sec
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// Approx. 8.5 sec (Macbook Pro 2019, Dart 2.16.0)
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import 'package:benchmark_harness/benchmark_harness.dart';
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import 'package:ml_algo/ml_algo.dart';
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import 'package:ml_dataframe/ml_dataframe.dart';
@@ -26,8 +27,8 @@ class CrossValidatorBenchmark extends BenchmarkBase {
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@override
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void setup() {
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final samples = Matrix.fromRows(
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List.generate(observationsNum, (i) => Vector.randomFilled(columnsNum)));
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final samples = Matrix.fromRows(List.generate(
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observationsNum, (i) => Vector.randomFilled(columnsNum, seed: 12)));
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final dataFrame = DataFrame.fromMatrix(samples);
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crossValidator = CrossValidator.kFold(dataFrame, numberOfFolds: 5);

‎benchmark/decision_tree_classifier.dart‎

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// MacBook Air 13.3 mid 2017: ~ 6.5 sec
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// Approx. 7.0 sec (Macbook Pro 2019, Dart 2.16.0)
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import 'dart:math';
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‎benchmark/inverse_logit_link_function.dart‎

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// 0.3 sec
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// Approx. 2.5 sec (Macbook Pro 2019, Dart 2.16.0)
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import 'package:benchmark_harness/benchmark_harness.dart';
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import 'package:ml_algo/src/link_function/inverse_logit_link_function.dart';
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import 'package:ml_linalg/matrix.dart';
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import 'package:ml_linalg/vector.dart';
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const scoresCount = 100000;
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const scoresCount = 2000000;
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class InverseLogitLinkFunctionBenchmark extends BenchmarkBase {
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InverseLogitLinkFunctionBenchmark()
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@override
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void setup() {
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final scores = Vector.randomFilled(scoresCount, min: -20, max: 20);
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final scores =
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Vector.randomFilled(scoresCount, min: -20, max: 20, seed: 12);
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samples = Matrix.fromColumns([
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scores,

‎benchmark/knn_regressor.dart‎

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// 6.0 sec (MacBook Air mid 2017)
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// Approx. 5.2 sec (Macbook Pro 2019, Dart 2.16.0)
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import 'package:benchmark_harness/benchmark_harness.dart';
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import 'package:ml_algo/ml_algo.dart';
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import 'package:ml_dataframe/ml_dataframe.dart';
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@override
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void setup() {
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final featureMatrix = Matrix.fromRows(List.generate(
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observationsNum * 2, (i) => Vector.randomFilled(featuresNum)));
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observationsNum * 2, (i) => Vector.randomFilled(featuresNum, seed: 1)));
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final labelMatrix =
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Matrix.fromColumns([Vector.randomFilled(observationsNum * 2)]);
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Matrix.fromColumns([Vector.randomFilled(observationsNum * 2, seed: 2)]);
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final observations = DataFrame.fromMatrix(Matrix.fromColumns([
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...featureMatrix.columns,
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Matrix.fromRows(
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List.generate(
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observationsNum,
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(i) => Vector.randomFilled(featuresNum),
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(i) => Vector.randomFilled(featuresNum, seed: 3),
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),
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),
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);

‎benchmark/knn_solver.dart‎

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// MacBook Air 13.3 mid 2017: ~ 5 sec
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// Approx. 5.1 sec (Macbook Pro 2019, Dart 2.16.0)
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import 'package:benchmark_harness/benchmark_harness.dart';
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import 'package:ml_algo/src/knn_solver/knn_solver.dart';
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import 'package:ml_algo/src/knn_solver/knn_solver_impl.dart';
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@override
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void setup() {
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final trainFeatures = Matrix.fromRows(List.generate(
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trainObservationsNum, (i) => Vector.randomFilled(featuresNum)));
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final trainLabels =
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Matrix.fromColumns([Vector.randomFilled(trainObservationsNum)]);
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final trainFeatures = Matrix.fromRows(List.generate(trainObservationsNum,
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(i) => Vector.randomFilled(featuresNum, seed: 1)));
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final trainLabels = Matrix.fromColumns(
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[Vector.randomFilled(trainObservationsNum, seed: 2)]);
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solver =
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KnnSolverImpl(trainFeatures, trainLabels, k, Distance.euclidean, false);
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features = Matrix.fromRows(List.generate(
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observationsNum, (i) => Vector.randomFilled(featuresNum)));
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observationsNum, (i) => Vector.randomFilled(featuresNum, seed: 3)));
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}
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}
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‎benchmark/lasso_regressor.dart‎

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// Approx. 4.5 sec (Macbook Pro 2019, Dart 2.16.0)
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import 'dart:convert';
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import 'dart:io';
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‎benchmark/linear_regressor.dart‎

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// Approx. 5.5 second (MacBook Pro 2019), Dart version: 2.16.0
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// Approx. 1.7 second (MacBook Pro 2019, Dart version: 2.16.0)
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import 'package:benchmark_harness/benchmark_harness.dart';
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import 'package:ml_algo/ml_algo.dart';
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@override
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void setup() {
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final features = Matrix.fromRows(List.generate(
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observationsNum, (i) => Vector.randomFilled(featuresNum)));
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observationsNum, (i) => Vector.randomFilled(featuresNum, seed: 12)));
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final labels = Matrix.fromColumns([Vector.randomFilled(observationsNum)]);
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final labels =
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Matrix.fromColumns([Vector.randomFilled(observationsNum, seed: 13)]);
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fittingData = DataFrame.fromMatrix(
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Matrix.fromColumns([

‎benchmark/logistic_regressor.dart‎ renamed to ‎benchmark/logistic_regressor_gradient.dart‎

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// Approx. 3.3 second (MacBook Pro 2019, Dart version: 2.16.0)
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import 'package:benchmark_harness/benchmark_harness.dart';
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@override
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void run() {
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LogisticRegressor(
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_data,
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'col_20',
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minCoefficientsUpdate: 1e-100000,
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iterationsLimit: 200,
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);
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LogisticRegressor(_data, 'col_20',
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minCoefficientsUpdate: 1e-100000,
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iterationsLimit: 200,
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optimizerType: LinearOptimizerType.gradient);
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}
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@override

‎benchmark/main.dart‎

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import 'linear_regressor.dart' as gradient_descent_regression_benchmark;
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import 'logistic_regressor.dart' as logistic_regression_benchmark;
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import 'logistic_regressor_gradient.dart' as logistic_regression_benchmark;
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import 'knn_solver.dart' as knn_regressor_benchmark;
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Future<void> main() async {

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