npm install svm-toolsSVM Tools provide some simple node wrappers around libsvm to train and test models.
* Install libsvm
* Ensure that svm-train and svm-predict are in the executable pathnpm install
* Install node and npm
* Install node dependencies:
Generate a model:
`
svm-gen [train.csv] [model.json]
Test a generated model:
`
svm-test [test.csv] [model.json] [report.csv]
Train/Test File Format (CSV):
* First element is the zone label
* Other elements are beacon measurements of the form BEACONID:RSSI
Example:
`
1,1-2:-71,1-1:-69,1-3:-86,1-4:-88
Model File Format
`json`
{
"simple": {
"mapping": {
BEACON_ID: ZONE_ID
}
},
"svm": {
"mapping": {
BEACON_ID: SVM_FEATURE_ID
},
"zones": [ZONE_ID]
"model": SVM_MODEL_DEF
}
}
`
var svmTools = require('svm-tools');
// generate a model
svmTools.train(data, progressCallback).then(function (model) { ... });
// test a model
svmTools.test(data, model).then(function (report) { ... });
`
Testdata Object:
`json`
[{
"zone": ZONE_ID,
"data": [{
"date": TIMESTAMP (optional),
"gps": {
"east": GPS_EAST (optional),
"north": GPS_NORTH (optional),
"accuracy": GPS_ACCURACY (optional)
},
"beacons": [{
"uuid": BEACON_UUID,
"major": BEACON_MAJOR,
"minor": BEACON_MINOR,
"rssi": BEACON_RSSI
}]
}]
}]
Model Object (see description in section CLI Usage).
* convert.js converts a legacy .csv file (from the BeaconScanner, see that repository) into one that can be used by svm-genconvertCSV.js` converts the result of convert.js into a json that can be imported into an inform-location-server (see that repository)
*
Copyright (c) 2015 Jonathan Diehl
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