fix: pass input through the extended filter functions

psantos9 · Oct 7, 2020 · 6a4b19a · 6a4b19a
1 parent 9cc0dc1
commit 6a4b19a
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Showing 3 changed files with 24 additions and 16 deletions.
diff --git a/index.js b/index.js
@@ -4,6 +4,8 @@ module.exports = {
 	registerDynamic: modelCollection.registerDynamic,
 	KalmanFilter: require('./lib/kalman-filter'),
 	registerObservation: modelCollection.registerObservation,
+	buildObservation:modelCollection.buildObservation,
+	buildDynamic: modelCollection.buildDynamic,
 	getCovariance: require('./lib/utils/get-covariance'),
 	State: require('./lib/state')
 };
diff --git a/lib/core-kalman-filter.js b/lib/core-kalman-filter.js
@@ -95,6 +95,7 @@ class CoreKalmanFilter {
 			dynCov,
 			covariancePrevious
 		);
+
 		return covariance;
 	}
 
@@ -128,6 +129,7 @@ class CoreKalmanFilter {
 
 		const predicted = new State({mean, covariance, index});
 		this.logger.debug('Prediction done', predicted);
+
 		return predicted;
 	}
 	/**
@@ -167,13 +169,17 @@ class CoreKalmanFilter {
 	*/
 
 	getCorrectedCovariance(options) {
-		const {predicted} = options;
-		const getValueOptions = Object.assign({}, {index: predicted.index}, options);
-		const identity = getIdentity(predicted.covariance.length);
-		const stateProj = this.getValue(this.observation.stateProjection, getValueOptions);
-		const optimalKalmanGain = this.getGain({predicted, stateProjection: stateProj});
+		const {predicted, optimalKalmanGain, stateProjection} = options;
+		const identity = getIdentity(predicted.covariance.length);		
+		if(!stateProjection){
+			const getValueOptions = Object.assign({}, {index: predicted.index}, options);
+			stateProjection = this.getValue(this.observation.stateProjection, getValueOptions);
+		}
+		if(!optimalKalmanGain){
+			optimalKalmanGain = this.getGain(Object.assign({stateProjection}, options));
+		}
 		return matMul(
-			sub(identity, matMul(optimalKalmanGain, stateProj)),
+			sub(identity, matMul(optimalKalmanGain, stateProjection)),
 			predicted.covariance
 		);
 	}
@@ -194,13 +200,13 @@ class CoreKalmanFilter {
 		}
 
 		const getValueOptions = Object.assign({}, {observation, predicted, index: predicted.index}, options);
-		const stateProj = this.getValue(this.observation.stateProjection, getValueOptions);
+		const stateProjection = this.getValue(this.observation.stateProjection, getValueOptions);
 
-		const optimalKalmanGain = this.getGain({predicted, stateProjection: stateProj});
+		const optimalKalmanGain = this.getGain(Object.assign({predicted, stateProjection}, options));
 
 		const innovation = sub(
 			observation,
-			matMul(stateProj, predicted.mean)
+			matMul(stateProjection, predicted.mean)
 		);
 		const mean = add(
 			predicted.mean,
@@ -211,7 +217,7 @@ class CoreKalmanFilter {
 			throw (new TypeError('Mean is NaN after correction'));
 		}
 
-		const covariance = this.getCorrectedCovariance({predicted});
+		const covariance = this.getCorrectedCovariance(Object.assign({predicted, optimalKalmanGain, stateProjection}, options));
 		const corrected = new State({mean, covariance, index: predicted.index});
 		this.logger.debug('Correction done', corrected);
 		return corrected;

diff --git a/lib/kalman-filter.js b/lib/kalman-filter.js
@@ -94,9 +94,9 @@ class KalmanFilter extends CoreKalmanFilter {
 		super(Object.assign({}, options, coreOptions));
 	}
 
-	correct({predicted, observation}) {
-		const coreObservation = arrayToMatrix({observation, dimension: this.observation.dimension});
-		return super.correct({predicted, observation: coreObservation});
+	correct(options) {
+		const coreObservation = arrayToMatrix({observation: options.observation, dimension: this.observation.dimension});
+		return super.correct(Object.assign({}, options, {observation: coreObservation}));
 	}
 
 	/**
@@ -106,9 +106,9 @@ class KalmanFilter extends CoreKalmanFilter {
 	*@returns {Array.<Number>} the mean of the corrections
 	*/
 
-	filter({previousCorrected, observation}) {
-		const predicted = super.predict({previousCorrected});
-		return this.correct({predicted, observation});
+	filter(options) {
+		const predicted = super.predict(options);
+		return this.correct(Object.assign({}, options, {predicted}));
 	}
 
 	/**