/**
* Feeds the {@link Network with the final phrase consisting of the first
* two words of the final question "fox, eats, ...".
*
* @param network the current {@link Network} object
* @param it an {@link Iterator} over the input source file lines
* @return
*/
Term feedQuestion(Network network, String[] phrase) {
for (int i = 0; i < 2; i++) {
int[] sdr = getFingerprintSDR(phrase[i]);
network.compute(sdr);
}
int[] sdr = new int[128 * 128];
Arrays.fill(sdr, 1);
Layer<?> layer = network.lookup("Region 1").lookup("Layer 2/3");
Set<Cell> predictiveCells = layer.getConnections().getPredictiveCells();
System.out.println("predictiveCells " + predictiveCells.toString());
int[] prediction =
SDR.cellsAsColumnIndices(predictiveCells, layer.getConnections().getCellsPerColumn());
network.compute(sdr);
Set<Cell> ffActive = layer.getActiveCells();
System.out.println("activeCells " + ffActive.toString());
int[] paPrediction =
SDR.cellsAsColumnIndices(ffActive, layer.getConnections().getCellsPerColumn());
Term term = getClosestTerm(prediction);
Term paterm = getClosestTerm(paPrediction);
System.out.println(paterm.toString());
cache.put(term.getTerm(), term);
return term;
}
///////////////////////////////////////////
// HELPER METHODS //
///////////////////////////////////////////
private Network getLoadedDayOfWeekNetwork() {
Parameters p = NetworkTestHarness.getParameters().copy();
p = p.union(NetworkTestHarness.getDayDemoTestEncoderParams());
p.set(KEY.RANDOM, new FastRandom(42));
Sensor<ObservableSensor<String[]>> sensor =
Sensor.create(
ObservableSensor::create,
SensorParams.create(
Keys::obs,
new Object[] {
"name",
PublisherSupplier.builder()
.addHeader("dayOfWeek")
.addHeader("number")
.addHeader("B")
.build()
}));
Network network =
Network.create("test network", p)
.add(
Network.createRegion("r1")
.add(
Network.createLayer("1", p)
.alterParameter(KEY.AUTO_CLASSIFY, true)
.add(Anomaly.create())
.add(new TemporalMemory())
.add(new SpatialPooler())
.add(sensor)));
return network;
}
/**
* Feeds a single array of phrase words into the {@link Network}.
*
* @param network
* @param phrase
*/
void feedLine(Network network, String[] phrase) {
for (String term : phrase) {
int[] sdr = getFingerprintSDR(term);
// System.out.println(String.format("SDR for '%s'\t%d",term,sdr.length));
network.compute(sdr);
}
network.reset();
}
/**
* Creates and returns a {@link Network} for demo processing
*
* @return
*/
Network createNetwork() {
org.numenta.nupic.Parameters temporalParams = createParameters();
PALayer<?> l = new PALayer("Layer 2/3", null, temporalParams);
l.setPADepolarize(0.0); // 0.25
l.setVerbosity(1);
PASpatialPooler sp = new PASpatialPooler();
network =
Network.create("Cortical.io API Demo", temporalParams)
.add(Network.createRegion("Region 1").add(l.add(new TemporalMemory()).add(sp)));
return network;
}
/**
* Feeds the {@link Network with the final phrase consisting of the first
* two words of the final question "fox, eats, ...".
*
* @param network the current {@link Network} object
* @param it an {@link Iterator} over the input source file lines
* @return
*/
Term feedQuestion(Network network, String[] phrase) {
for (int i = 0; i < 2; i++) {
int[] sdr = getFingerprintSDR(phrase[i]);
network.compute(sdr);
}
Layer<?> layer = network.lookup("Region 1").lookup("Layer 2/3");
Set<Cell> predictiveCells = layer.getPredictiveCells();
int[] prediction =
SDR.cellsAsColumnIndices(predictiveCells, layer.getConnections().getCellsPerColumn());
Term term = getClosestTerm(prediction);
cache.put(term.getTerm(), term);
return term;
}
@Ignore
public void testPlayground() {
final int NUM_CYCLES = 600;
final int INPUT_GROUP_COUNT = 7; // Days of Week
///////////////////////////////////////
// Load a Network //
///////////////////////////////////////
Network network = getLoadedDayOfWeekNetwork();
int cellsPerCol = (int) network.getParameters().get(KEY.CELLS_PER_COLUMN);
network
.observe()
.subscribe(
new Observer<Inference>() {
@Override
public void onCompleted() {}
@Override
public void onError(Throwable e) {
e.printStackTrace();
}
@SuppressWarnings("unused")
@Override
public void onNext(Inference inf) {
/** see {@link #createDayOfWeekInferencePrintout()} */
int cycle = dayOfWeekPrintout.apply(inf, cellsPerCol);
}
});
Publisher pub = network.getPublisher();
network.start();
int cycleCount = 0;
for (; cycleCount < NUM_CYCLES; cycleCount++) {
for (double j = 0; j < INPUT_GROUP_COUNT; j++) {
pub.onNext("" + j);
}
network.reset();
}
// Test network output
try {
Region r1 = network.lookup("r1");
r1.lookup("1").getLayerThread().join(2000);
} catch (Exception e) {
e.printStackTrace();
}
}
/**
* Creates a {@link Layer} to hold algorithmic components and returns it.
*
* @param name the String identifier for the specified {@link Layer}
* @param p the {@link Parameters} to use for the specified {@link Layer}
* @return
*/
@SuppressWarnings("rawtypes")
public static Layer<?> createLayer(String name, Parameters p) {
Network.checkName(name);
return new Layer(name, null, p);
}
/**
* Creates and returns a child {@link Region} of this {@code Network}
*
* @param name The String identifier for the specified {@link Region}
* @return
*/
public static Region createRegion(String name) {
Network.checkName(name);
Region r = new Region(name, null);
return r;
}