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Tidy up normalising code

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This commit is contained in:
Starbeamrainbowlabs 2019-07-30 15:42:37 +01:00
parent 25bb302033
commit 4493fbb0fa
3 changed files with 92 additions and 76 deletions
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69
common/Normalisers.mjs Normal file
View file

@ -0,0 +1,69 @@
import { normalise, clamp } from './Math.mjs';
function normalise_lat(lat) {
return normalise(lat,
{ min: -90, max: +90 },
{ min: 0, max: 1 }
);
}
function normalise_lng(lng) {
return normalise(lng,
{ min: -180, max: +180 },
{ min: 0, max: 1 }
);
}
function unnormalise_lat(nlat) {
return normalise(nlat,
{ min: 0, max: 1 },
{ min: -90, max: +90 }
);
}
function unnormalise_lng(nlng) {
return normalise(nlng,
{ min: 0, max: 1 },
{ min: -180, max: +180 }
);
}
function normalise_gateway_distance(distance) {
return clamp(normalise(distance,
{ min: 0, max: 20000 },
{ min: 0, max: 1 }
), 0, 20000);
}
function unnormalise_gateway_distance(ndistance) {
return clamp(normalise(ndistance,
{ min: 0, max: 1 },
{ min: 0, max: 20000 }
), 0, 20000);
}
function normalise_rssi(rssi) {
return clamp(normalise(rssi,
{ min: -150, max: 0 },
{ min: 0, max: 1 }
), 0, 1)
}
function unnormalise_rssi(rssi) {
return clamp(normalise(rssi,
{ min: 0, max: 1 },
{ min: -150, max: 0 }
), 0, 1)
}
export {
normalise_lat,
normalise_lng,
unnormalise_lat,
unnormalise_lng,
normalise_gateway_distance,
unnormalise_gateway_distance,
normalise_rssi,
unnormalise_rssi
};

5
server/settings.default.toml
View file

@ -54,11 +54,6 @@ devices = [
[ai]
# Settings relating to the training of the AI. Note that a number of these settings can also be specified by environment variables, to aid with fiddling with the parameters to find the right settings.
# Min / max dataset values when training the AI, since neural networks only take values between 0 and 1.
# Note that changing these means that you've got to retrain the AIs all over again!
rssi_min = -150
rssi_max = 0
# The architecture of the neural network, as an arary of integers.
# Each integer represents the number of nodes in a layer of the neural network.
network_arch = [ 64 ]

94
server/train-ai/DatasetFetcher.mjs
View file

@ -2,7 +2,12 @@
import haversine from 'haversine-distance';
import { normalise, clamp } from '../../common/Math.mjs';
import {
normalise_lat,
normalise_lng,
normalise_rssi,
normalise_gateway_distance,
} from '../../common/Normalisers.mjs';
class DatasetFetcher {
constructor({ settings, GatewayRepo, RSSIRepo, ReadingRepo }) {
@ -12,92 +17,39 @@ class DatasetFetcher {
this.repo_reading = ReadingRepo;
}
normalise_latlng(lat, lng) {
return {
latitude: normalise(lat,
{ min: -90, max: +90 },
{ min: 0, max: 1 }
),
longitude: normalise(lng,
{ min: -180, max: +180 },
{ min: 0, max: 1 }
)
};
}
fetch_all(gateway_id) {
let gateway_location = this.repo_gateway.get_by_id(gateway_id);
let result = [];
for(let rssi of this.repo_rssi.iterate_gateway(gateway_id)) {
let next_input = this.normalise_latlng(rssi.latitude, rssi.longitude);
let distance_from_gateway = haversine(gateway_location, rssi);
next_input.distance = clamp(
normalise(distance_from_gateway,
{ min: 0, max: 20000 },
{ min: 0, max: 1 }
),
0, 1);
// console.log(`Distance from gateway: ${haversine(gateway_location, rssi)}m`);
let next_output = [
clamp(normalise(rssi.rssi,
{ min: this.settings.ai.rssi_min, max: this.settings.ai.rssi_max },
{ min: 0, max: 1 }
), 0, 1)
];
result.push({
input: next_input,
output: next_output
input: {
latitude: normalise_lat(rssi.latitude),
longitude: normalise_lng(rssi.longitude),
distance: normalise_gateway_distance(
haversine(gateway_location, rssi)
)
},
output: [
normalise_rssi(rssi.rssi)
]
});
}
for(let reading of this.repo_reading.iterate_unreceived()) {
let next_input = this.normalise_latlng(
reading.latitude,
reading.longitude
);
next_input.distance = clamp(
normalise(haversine(gateway_location, reading),
{ min: 0, max: 20000 },
{ min: 0, max: 1 }
),
0, 1);
result.push({
input: next_input,
input: {
latitude: normalise_lat(reading.latitude),
longitude: normalise_lng(reading.longitude),
distance: normalise_gateway_distance(
haversine(gateway_location, reading)
)
},
output: [ 0 ]
});
}
return result;
}
*fetch_input(gateway_id) {
for(let rssi of this.repo_rssi.iterate_gateway(gateway_id))
yield this.normalise_latlng(rssi.latitude, rssi.longitude);
for(let rssi of this.repo_reading.iterate_unreceived())
yield this.normalise_latlng(rssi.latitude, rssi.longitude);
}
*fetch_output(gateway_id) {
for(let rssi of this.repo_rssi.iterate_gateway(gateway_id)) {
yield [
clamp(normalise(rssi.rssi,
{ min: this.settings.ai.rssi_min, max: this.settings.ai.rssi_max },
{ min: 0, max: 1 }
), 0, 1)
];
}
// Yield 0 for every unreceived message, since we want to train it to predict a *terrible* signal where the gateway is not
for(let rssi of this.repo_reading.iterate_unreceived()) {
yield [ 0 ];
}
}
}
export default DatasetFetcher;