mirror of
https://github.com/zjs81/meshcore-open.git
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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.
43 lines
1.2 KiB
Dart
43 lines
1.2 KiB
Dart
class DeliveryObservation {
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final String contactKey;
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final int pathLength;
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final int messageBytes;
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final int secondsSinceLastRx;
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final bool isFlood;
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final int deliveryMs;
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final DateTime timestamp;
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DeliveryObservation({
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required this.contactKey,
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required this.pathLength,
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required this.messageBytes,
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required this.secondsSinceLastRx,
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required this.isFlood,
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required this.deliveryMs,
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required this.timestamp,
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});
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Map<String, dynamic> toJson() {
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return {
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'contact_key': contactKey,
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'path_length': pathLength,
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'message_bytes': messageBytes,
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'seconds_since_last_rx': secondsSinceLastRx,
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'is_flood': isFlood,
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'delivery_ms': deliveryMs,
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'timestamp': timestamp.toIso8601String(),
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};
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}
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factory DeliveryObservation.fromJson(Map<String, dynamic> json) {
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return DeliveryObservation(
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contactKey: json['contact_key'] as String,
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pathLength: json['path_length'] as int,
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messageBytes: json['message_bytes'] as int,
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secondsSinceLastRx: json['seconds_since_last_rx'] as int? ?? 0,
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isFlood: json['is_flood'] as bool,
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deliveryMs: json['delivery_ms'] as int,
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timestamp: DateTime.parse(json['timestamp'] as String),
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);
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}
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}
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