mirror of
https://github.com/zjs81/meshcore-open.git
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zjs81
2ee2358ecc
Train a linear regression model on actual message delivery times to predict tighter timeouts, replacing worst-case physics estimates. Features: path length, message bytes, seconds since last RX, flood mode. Global model with per-contact blending after 10+ observations per contact. Falls back to existing physics formula when model has insufficient data.
123 lines
3.9 KiB
Dart
123 lines
3.9 KiB
Dart
import 'package:flutter/foundation.dart';
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import 'package:flutter_test/flutter_test.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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void main() {
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test('LinearRegressor basic sanity check', () {
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// Simple: y = 2x + 100
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final data = DataFrame([
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[1.0, 102.0],
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[2.0, 104.0],
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[3.0, 106.0],
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[4.0, 108.0],
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[5.0, 110.0],
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[10.0, 120.0],
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[20.0, 140.0],
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[50.0, 200.0],
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[0.0, 100.0],
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[100.0, 300.0],
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], headerExists: false, header: ['x', 'y']);
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debugPrint('Training data columns: ${data.header}');
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debugPrint('Training data rows: ${data.rows.length}');
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final model = LinearRegressor(data, 'y');
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final testDf = DataFrame(
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[[25.0]],
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headerExists: false,
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header: ['x'],
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);
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final prediction = model.predict(testDf);
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final value = prediction.rows.first.first;
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debugPrint('Predict x=25 → y=$value (expected ~150)');
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expect((value as num).toDouble(), closeTo(150, 5));
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});
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test('LinearRegressor multi-feature with constant column produces zeros', () {
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// isFlood=0 for all rows → zero-variance column → singular matrix
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final data = DataFrame([
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[0.0, 50.0, 14.0, 0.0, 1900.0],
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[0.0, 80.0, 14.0, 0.0, 2200.0],
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[2.0, 50.0, 14.0, 0.0, 5000.0],
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[4.0, 50.0, 14.0, 0.0, 9500.0],
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], headerExists: false, header: [
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'pathLength', 'messageBytes', 'hourOfDay', 'isFlood', 'deliveryMs',
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]);
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final model = LinearRegressor(data, 'deliveryMs');
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final testDf = DataFrame(
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[[2.0, 50.0, 14.0, 0.0]],
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headerExists: false,
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header: ['pathLength', 'messageBytes', 'hourOfDay', 'isFlood'],
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);
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final pred = model.predict(testDf).rows.first.first;
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debugPrint('With constant isFlood column: hops=2 → ${(pred as num).round()}ms (likely 0)');
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});
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test('LinearRegressor 2-feature works correctly', () {
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// Just pathLength + messageBytes → deliveryMs
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final data = DataFrame([
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[0.0, 50.0, 1900.0],
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[0.0, 80.0, 2200.0],
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[2.0, 50.0, 5000.0],
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[2.0, 80.0, 5500.0],
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[4.0, 50.0, 9500.0],
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[4.0, 80.0, 10000.0],
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[0.0, 30.0, 1800.0],
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[2.0, 30.0, 4800.0],
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[4.0, 30.0, 9000.0],
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[0.0, 60.0, 2000.0],
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], headerExists: false, header: ['pathLength', 'messageBytes', 'deliveryMs']);
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final model = LinearRegressor(data, 'deliveryMs');
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for (final hops in [0.0, 2.0, 4.0]) {
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final testDf = DataFrame(
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[[hops, 50.0]],
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headerExists: false,
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header: ['pathLength', 'messageBytes'],
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);
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final pred = model.predict(testDf).rows.first.first;
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debugPrint('2-feature: hops=$hops → ${(pred as num).round()}ms');
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}
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});
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test('LinearRegressor multi-feature with variance in all columns', () {
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// Mix flood and direct so isFlood has variance
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final data = DataFrame([
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[0.0, 50.0, 14.0, 0.0, 1900.0],
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[0.0, 80.0, 10.0, 0.0, 2200.0],
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[2.0, 50.0, 16.0, 0.0, 5000.0],
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[2.0, 80.0, 20.0, 0.0, 5500.0],
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[4.0, 50.0, 8.0, 0.0, 9500.0],
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[4.0, 80.0, 12.0, 0.0, 10000.0],
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[-1.0, 40.0, 14.0, 1.0, 5000.0],
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[-1.0, 60.0, 18.0, 1.0, 6500.0],
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[-1.0, 30.0, 10.0, 1.0, 4000.0],
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[-1.0, 80.0, 22.0, 1.0, 7000.0],
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], headerExists: false, header: [
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'pathLength', 'messageBytes', 'hourOfDay', 'isFlood', 'deliveryMs',
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]);
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final model = LinearRegressor(data, 'deliveryMs');
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for (final tc in [
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[0.0, 50.0, 14.0, 0.0],
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[2.0, 50.0, 14.0, 0.0],
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[4.0, 50.0, 14.0, 0.0],
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[-1.0, 50.0, 14.0, 1.0],
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]) {
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final testDf = DataFrame(
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[tc],
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headerExists: false,
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header: ['pathLength', 'messageBytes', 'hourOfDay', 'isFlood'],
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);
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final pred = model.predict(testDf).rows.first.first;
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debugPrint('4-feature: hops=${tc[0]} flood=${tc[3]} → ${(pred as num).round()}ms');
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}
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});
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}
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