public void testAnalyze() {
BasicNetwork network = EncogUtility.simpleFeedForward(2, 2, 0, 1, false);
double[] weights = new double[network.encodedArrayLength()];
EngineArray.fill(weights, 1.0);
network.decodeFromArray(weights);
AnalyzeNetwork analyze = new AnalyzeNetwork(network);
Assert.assertEquals(weights.length, analyze.getWeightsAndBias().getSamples());
Assert.assertEquals(3, analyze.getBias().getSamples());
Assert.assertEquals(6, analyze.getWeights().getSamples());
}
/** Finalize the structure of this Bayesian network. */
public void finalizeStructure() {
for (BayesianEvent e : this.eventMap.values()) {
e.finalizeStructure();
}
if (this.query != null) {
this.query.finalizeStructure();
}
this.inputPresent = new boolean[this.events.size()];
EngineArray.fill(this.inputPresent, true);
this.classificationTarget = -1;
}
/** Clear the weight matrix. */
public void clearMatrix() {
EngineArray.fill(this.hopfield.getWeights(), 0);
}