HTMTest.java

import org.numenta.nupic.Parameters;
import org.numenta.nupic.algorithms.*;
import org.numenta.nupic.network.Network;
import org.numenta.nupic.network.PublisherSupplier;
import org.numenta.nupic.network.sensor.ObservableSensor;
import org.numenta.nupic.network.sensor.Sensor;
import org.numenta.nupic.network.sensor.SensorParams;
import org.numenta.nupic.util.FastRandom;
import org.numenta.nupic.util.Tuple;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.HashMap;
import java.util.Map;

/**
 * Created on 5/11/17.
 */
public class HTMLocal {
    private static final Logger LOGGER = LoggerFactory.getLogger(HTMLocal.class);

    // https://github.com/numenta/htm.java-examples/blob/master/src/main/java/org/numenta/nupic/examples/napi/hotgym/NetworkDemoHarness.java
    public Network createBasicNetwork() {
        Parameters p = getParameters().copy();
        p = p.union(getDayDemoTestEncoderParams());
        p.set(Parameters.KEY.RANDOM, new FastRandom(42));
  //      p.set(Parameters.KEY.INFERRED_FIELDS, getInferredFieldsMap("stamp", SDRClassifier.class));

        // The "headers" are the titles of your comma separated fields; (could be "timestamp,consumption,location" for 3 fields)
        // The "Data Type" of the field (see FieldMetaTypes) (could be "datetime,float,geo" for 3 field types corresponding to above)
        Sensor<ObservableSensor<String[]>> sensor = Sensor.create(
                ObservableSensor::create, SensorParams.create(SensorParams.Keys::obs, new Object[] {"name",
                        PublisherSupplier.builder()
                                .addHeader("stamp, asn, probeType, responseCode, result")
                                .addHeader("datetime, int, int, bool, int")
                                .addHeader("T, B")  // Special flag. "B" means Blank (see Tests for other examples)
                                .build() }));

        Network network = Network.create("test network", p).add(Network.createRegion("r1")
                .add(Network.createLayer("1", p)
                        //       .alterParameter(KEY.AUTO_CLASSIFY, true)    // <--- Remove this line if doing anomalies and not predictions
                        .add(Anomaly.create())                      // <--- Remove this line if doing predictions and not anomalies
                        .add(new TemporalMemory())
                        .add(new SpatialPooler())
                        .add(sensor)));


        return network;
    }

    public Parameters getDayDemoTestEncoderParams() {
        Map<String, Map<String, Object>> fieldEncodings = getDayDemoFieldEncodingMap();

        Parameters p = Parameters.getEncoderDefaultParameters();
        p.set(Parameters.KEY.FIELD_ENCODING_MAP, fieldEncodings);

        return p;
    }


    public Map<String, Map<String, Object>> getDayDemoFieldEncodingMap() {
        Map<String, Map<String, Object>> fieldEncodings = setupMap(
                null,
                0, // n
                0, // w
                0, 0, 0, 0, null, null, null,
                "stamp", "datetime", "DateEncoder");

        fieldEncodings.get("stamp").put(Parameters.KEY.DATEFIELD_PATTERN.getFieldName(), "YYYY-MM-dd HH:mm:ss");
        fieldEncodings.get("stamp").put(Parameters.KEY.DATEFIELD_DOFW.getFieldName(), new Tuple(1, 1.0)); // Day of week
        fieldEncodings.get("stamp").put(Parameters.KEY.DATEFIELD_TOFD.getFieldName(), new Tuple(21, 9.5)); // Time of day

        fieldEncodings = setupMap(
                fieldEncodings,
                500, // n
                21, // w
                0.0, 99999.0, -1, 1, null, null, null,
                "asn", "number", "RandomDistributedScalarEncoder");

        fieldEncodings = setupMap(
                fieldEncodings,
                500, // n
                21, // w
                0.0, 20, -1, 1, null, null, null,
                "probeType", "number", "ScalarEncoder");

        fieldEncodings = setupMap(
                fieldEncodings,
                500, // n
                21, // w
                0.0, 5.0, -1, 1, null, null, null,
                "responseCode", "number", "ScalarEncoder");

        fieldEncodings = setupMap(
                fieldEncodings,
                500, // n
                21, // w
                0.0, 99999.0, -1, 1, null, null, null,
                "results", "number", "RandomDistributedScalarEncoder");

        return fieldEncodings;
    }



    public Map<String, Map<String, Object>> setupMap(
            Map<String, Map<String, Object>> map,
            int n, int w, double min, double max, double radius, double resolution, Boolean periodic,
            Boolean clip, Boolean forced, String fieldName, String fieldType, String encoderType) {

        if(map == null) {
            map = new HashMap<>();
        }
        Map<String, Object> inner;
        if((inner = map.get(fieldName)) == null) {
            map.put(fieldName, inner = new HashMap<>());
        }

        inner.put("n", n);
        inner.put("w", w);
        inner.put("minVal", min);
        inner.put("maxVal", max);
        if(radius >= 0 ) {
            inner.put("radius", radius);
        }

        if(resolution >= 0 ) {
            inner.put("resolution", resolution);
        }

        if(periodic != null) inner.put("periodic", periodic);
        if(clip != null) inner.put("clipInput", clip);
        if(forced != null) inner.put("forced", forced);
        if(fieldName != null) inner.put("fieldName", fieldName);
        if(fieldType != null) inner.put("fieldType", fieldType);
        if(encoderType != null) inner.put("encoderType", encoderType);

        return map;
    }


    public Parameters getParameters() {
        Parameters parameters = Parameters.getAllDefaultParameters();
        parameters.set(Parameters.KEY.INPUT_DIMENSIONS, new int[] { 8 });
        parameters.set(Parameters.KEY.COLUMN_DIMENSIONS, new int[] { 20 });
        parameters.set(Parameters.KEY.CELLS_PER_COLUMN, 6);

        //SpatialPooler specific
        parameters.set(Parameters.KEY.POTENTIAL_RADIUS, 12);//3
        parameters.set(Parameters.KEY.POTENTIAL_PCT, 0.5);//0.5
        parameters.set(Parameters.KEY.GLOBAL_INHIBITION, false);
        parameters.set(Parameters.KEY.LOCAL_AREA_DENSITY, -1.0);
        parameters.set(Parameters.KEY.NUM_ACTIVE_COLUMNS_PER_INH_AREA, 5.0);
        parameters.set(Parameters.KEY.STIMULUS_THRESHOLD, 1.0);
        parameters.set(Parameters.KEY.SYN_PERM_INACTIVE_DEC, 0.01);
        parameters.set(Parameters.KEY.SYN_PERM_ACTIVE_INC, 0.1);
        parameters.set(Parameters.KEY.SYN_PERM_TRIM_THRESHOLD, 0.05);
        parameters.set(Parameters.KEY.SYN_PERM_CONNECTED, 0.1);
        parameters.set(Parameters.KEY.MIN_PCT_OVERLAP_DUTY_CYCLES, 0.1);
        parameters.set(Parameters.KEY.MIN_PCT_ACTIVE_DUTY_CYCLES, 0.1);
        parameters.set(Parameters.KEY.DUTY_CYCLE_PERIOD, 10);
        parameters.set(Parameters.KEY.MAX_BOOST, 10.0);
        parameters.set(Parameters.KEY.SEED, 42);

        //Temporal Memory specific
        parameters.set(Parameters.KEY.INITIAL_PERMANENCE, 0.2);
        parameters.set(Parameters.KEY.CONNECTED_PERMANENCE, 0.8);
        parameters.set(Parameters.KEY.MIN_THRESHOLD, 5);
        parameters.set(Parameters.KEY.MAX_NEW_SYNAPSE_COUNT, 6);
        parameters.set(Parameters.KEY.PERMANENCE_INCREMENT, 0.05);
        parameters.set(Parameters.KEY.PERMANENCE_DECREMENT, 0.05);
        parameters.set(Parameters.KEY.ACTIVATION_THRESHOLD, 4);

        return parameters;
    }

    public Map<String, Class<? extends Classifier>> getInferredFieldsMap(
            String field, Class<? extends Classifier> classifier) {
        Map<String, Class<? extends Classifier>> inferredFieldsMap = new HashMap<>();
        inferredFieldsMap.put(field, classifier);
        return inferredFieldsMap;
    }
}