/* ---------------------------------------------------------------------
* Numenta Platform for Intelligent Computing (NuPIC)
* Copyright (C) 2014, Numenta, Inc. Unless you have an agreement
* with Numenta, Inc., for a separate license for this software code, the
* following terms and conditions apply:
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero Public License version 3 as
* published by the Free Software Foundation.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
* See the GNU Affero Public License for more details.
*
* You should have received a copy of the GNU Affero Public License
* along with this program. If not, see http://www.gnu.org/licenses.
*
* http://numenta.org/licenses/
* ---------------------------------------------------------------------
*/
package org.numenta.nupic.network;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertFalse;
import static org.junit.Assert.assertNotNull;
import static org.junit.Assert.assertNull;
import static org.junit.Assert.assertTrue;
import static org.junit.Assert.fail;
import static org.numenta.nupic.algorithms.Anomaly.KEY_MODE;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.function.BiFunction;
import java.util.stream.Stream;
import org.junit.Test;
import org.numenta.nupic.Parameters;
import org.numenta.nupic.Parameters.KEY;
import org.numenta.nupic.algorithms.Anomaly;
import org.numenta.nupic.algorithms.Anomaly.Mode;
import org.numenta.nupic.algorithms.CLAClassifier;
import org.numenta.nupic.algorithms.SpatialPooler;
import org.numenta.nupic.algorithms.TemporalMemory;
import org.numenta.nupic.datagen.ResourceLocator;
import org.numenta.nupic.encoders.MultiEncoder;
import org.numenta.nupic.model.Connections;
import org.numenta.nupic.network.sensor.FileSensor;
import org.numenta.nupic.network.sensor.HTMSensor;
import org.numenta.nupic.network.sensor.ObservableSensor;
import org.numenta.nupic.network.sensor.Publisher;
import org.numenta.nupic.network.sensor.Sensor;
import org.numenta.nupic.network.sensor.SensorParams;
import org.numenta.nupic.network.sensor.SensorParams.Keys;
import org.numenta.nupic.util.FastRandom;
import org.numenta.nupic.util.MersenneTwister;
import static org.numenta.nupic.network.NetworkTestHarness.*;
import rx.Observer;
import rx.Subscriber;
import rx.observers.TestObserver;
public class NetworkTest extends ObservableTestBase {
private int[][] dayMap = new int[][] {
new int[] { 1, 1, 0, 0, 0, 0, 0, 1 }, // Sunday
new int[] { 1, 1, 1, 0, 0, 0, 0, 0 }, // Monday
new int[] { 0, 1, 1, 1, 0, 0, 0, 0 }, // Tuesday
new int[] { 0, 0, 1, 1, 1, 0, 0, 0 }, // Wednesday
new int[] { 0, 0, 0, 1, 1, 1, 0, 0 }, // Thursday
new int[] { 0, 0, 0, 0, 1, 1, 1, 0 }, // Friday
new int[] { 0, 0, 0, 0, 0, 1, 1, 1 }, // Saturday
};
private BiFunction<Inference, Integer, Integer> dayOfWeekPrintout = createDayOfWeekInferencePrintout();
@Test
public void testResetMethod() {
Parameters p = NetworkTestHarness.getParameters();
Network network = new Network("ResetTestNetwork", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("l1", p).add(new TemporalMemory())));
try {
network.reset();
assertTrue(network.lookup("r1").lookup("l1").hasTemporalMemory());
}catch(Exception e) {
fail();
}
network = new Network("ResetMethodTestNetwork", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("l1", p).add(new SpatialPooler())));
try {
network.reset();
assertFalse(network.lookup("r1").lookup("l1").hasTemporalMemory());
}catch(Exception e) {
fail();
}
}
@Test
public void testResetRecordNum() {
Parameters p = NetworkTestHarness.getParameters();
Network network = new Network("ResetRecordNumNetwork", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("l1", p).add(new TemporalMemory())));
network.observe().subscribe(new Observer<Inference>() {
@Override public void onCompleted() {}
@Override public void onError(Throwable e) { e.printStackTrace(); }
@Override public void onNext(Inference output) {
// System.out.println("output = " + Arrays.toString(output.getSDR()));
}
});
network.compute(new int[] { 2,3,4 });
network.compute(new int[] { 2,3,4 });
assertEquals(1, network.lookup("r1").lookup("l1").getRecordNum());
network.resetRecordNum();
assertEquals(0, network.lookup("r1").lookup("l1").getRecordNum());
}
@Test
public void testAdd() {
Parameters p = NetworkTestHarness.getParameters();
Network network = Network.create("test", NetworkTestHarness.getParameters());
// Add Layers to regions but regions not yet added to Network
Region r1 = Network.createRegion("r1").add(Network.createLayer("l", p).add(new SpatialPooler()));
Region r2 = Network.createRegion("r2").add(Network.createLayer("l", p).add(new SpatialPooler()));
Region r3 = Network.createRegion("r3").add(Network.createLayer("l", p).add(new SpatialPooler()));
Region r4 = Network.createRegion("r4").add(Network.createLayer("l", p).add(new SpatialPooler()));
Region r5 = Network.createRegion("r5").add(Network.createLayer("l", p).add(new SpatialPooler()));
Region[] regions = new Region[] { r1, r2, r3, r4, r5 };
for(Region r : regions) {
assertNull(network.lookup(r.getName()));
}
// Add the regions to the network
for(Region r : regions) {
network.add(r);
}
String[] names = new String[] { "r1","r2","r3","r4","r5" };
int i = 0;
for(Region r : regions) {
assertNotNull(network.lookup(r.getName()));
assertEquals(names[i++], r.getName());
}
}
@Test
public void testConnect() {
Parameters p = NetworkTestHarness.getParameters();
Network network = Network.create("test", NetworkTestHarness.getParameters());
Region r1 = Network.createRegion("r1").add(Network.createLayer("l", p).add(new SpatialPooler()));
Region r2 = Network.createRegion("r2").add(Network.createLayer("l", p).add(new SpatialPooler()));
Region r3 = Network.createRegion("r3").add(Network.createLayer("l", p).add(new SpatialPooler()));
Region r4 = Network.createRegion("r4").add(Network.createLayer("l", p).add(new SpatialPooler()));
Region r5 = Network.createRegion("r5").add(Network.createLayer("l", p).add(new SpatialPooler()));
try {
network.connect("r1", "r2");
fail();
}catch(Exception e) {
assertEquals("Region with name: r2 not added to Network.", e.getMessage());
}
Region[] regions = new Region[] { r1, r2, r3, r4, r5 };
for(Region r : regions) {
network.add(r);
}
for(int i = 1;i < regions.length;i++) {
try {
network.connect(regions[i - 1].getName(), regions[i].getName());
}catch(Exception e) {
fail();
}
}
Region upstream = r1;
Region tail = r1;
while((tail = tail.getUpstreamRegion()) != null) {
upstream = tail;
}
// Assert that the connect method sets the upstream region on all regions
assertEquals(regions[4], upstream);
Region downstream = r5;
Region head = r5;
while((head = head.getDownstreamRegion()) != null) {
downstream = head;
}
// Assert that the connect method sets the upstream region on all regions
assertEquals(regions[0], downstream);
assertEquals(network.getHead(), downstream);
}
String onCompleteStr = null;
@Test
public void testBasicNetworkHaltGetsOnComplete() {
Parameters p = NetworkTestHarness.getParameters();
p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
p.set(KEY.RANDOM, new MersenneTwister(42));
p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("consumption", CLAClassifier.class));
// Create a Network
Network network = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("1", p)
.alterParameter(KEY.AUTO_CLASSIFY, Boolean.TRUE)
.add(Anomaly.create())
.add(new TemporalMemory())
.add(new SpatialPooler())
.add(Sensor.create(FileSensor::create, SensorParams.create(
Keys::path, "", ResourceLocator.path("rec-center-hourly.csv"))))));
final List<String> lines = new ArrayList<>();
// Listen to network emissions
network.observe().subscribe(new Subscriber<Inference>() {
@Override public void onCompleted() {
onCompleteStr = "On completed reached!";
}
@Override public void onError(Throwable e) { e.printStackTrace(); }
@Override public void onNext(Inference i) {
// System.out.println(Arrays.toString(i.getSDR()));
// System.out.println(i.getRecordNum() + "," +
// i.getClassifierInput().get("consumption").get("inputValue") + "," + i.getAnomalyScore());
lines.add(i.getRecordNum() + "," +
i.getClassifierInput().get("consumption").get("inputValue") + "," + i.getAnomalyScore());
if(i.getRecordNum() == 9) {
network.halt();
}
}
});
// Start the network
network.start();
// Test network output
try {
Region r1 = network.lookup("r1");
r1.lookup("1").getLayerThread().join();
}catch(Exception e) {
e.printStackTrace();
}
assertEquals(10, lines.size());
int i = 0;
for(String l : lines) {
String[] sa = l.split("[\\s]*\\,[\\s]*");
assertEquals(3, sa.length);
assertEquals(i++, Integer.parseInt(sa[0]));
}
assertEquals("On completed reached!", onCompleteStr);
}
String onCompleteStr2 = null;
@Test
public void testBasicNetworkHalt_ThenRestart() {
Parameters p = NetworkTestHarness.getParameters();
p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
p.set(KEY.RANDOM, new MersenneTwister(42));
p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("consumption", CLAClassifier.class));
// Create a Network
Network network = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("1", p)
.alterParameter(KEY.AUTO_CLASSIFY, Boolean.TRUE)
.add(Anomaly.create())
.add(new TemporalMemory())
.add(new SpatialPooler())
.add(Sensor.create(FileSensor::create, SensorParams.create(
Keys::path, "", ResourceLocator.path("rec-center-hourly.csv"))))));
final List<String> lines = new ArrayList<>();
// Listen to network emissions
network.observe().subscribe(new Subscriber<Inference>() {
@Override public void onCompleted() {
onCompleteStr2 = "On completed reached!";
}
@Override public void onError(Throwable e) { e.printStackTrace(); }
@Override public void onNext(Inference i) {
lines.add(i.getRecordNum() + "," +
i.getClassifierInput().get("consumption").get("inputValue") + "," + i.getAnomalyScore());
if(i.getRecordNum() == 9) {
network.halt();
}
}
});
// Start the network
network.start();
// Test network output
try {
Region r1 = network.lookup("r1");
r1.lookup("1").getLayerThread().join();
}catch(Exception e) {
e.printStackTrace();
}
assertEquals(10, lines.size());
int i = 0;
for(String l : lines) {
String[] sa = l.split("[\\s]*\\,[\\s]*");
assertEquals(3, sa.length);
assertEquals(i++, Integer.parseInt(sa[0]));
System.out.println(l);
}
assertEquals("On completed reached!", onCompleteStr2);
///////////////////////
// Now Restart //
///////////////////////
onCompleteStr2 = null;
// Listen to network emissions
network.observe().subscribe(new Subscriber<Inference>() {
@Override public void onCompleted() {
onCompleteStr2 = "On completed reached!";
}
@Override public void onError(Throwable e) { e.printStackTrace(); }
@Override public void onNext(Inference i) {
// System.out.println("second listener: " + i.getRecordNum());
lines.add(i.getRecordNum() + "," +
i.getClassifierInput().get("consumption").get("inputValue") + "," + i.getAnomalyScore());
if(i.getRecordNum() == 19) {
network.halt();
}
}
});
network.restart();
// Test network output
try {
Region r1 = network.lookup("r1");
r1.lookup("1").getLayerThread().join();
}catch(Exception e) {
e.printStackTrace();
}
assertEquals(20, lines.size());
i = 0;
for(String l : lines) {
String[] sa = l.split("[\\s]*\\,[\\s]*");
assertEquals(3, sa.length);
assertEquals(i++, Integer.parseInt(sa[0]));
System.out.println(l);
}
assertEquals("On completed reached!", onCompleteStr2);
}
boolean expectedDataFlag = true;
String failMessage;
@Test
public void testBasicNetworkHalt_ThenRestart_TighterExpectation() {
final int NUM_CYCLES = 600;
final int INPUT_GROUP_COUNT = 7; // Days of Week
///////////////////////////////////////
// Run until CYCLE 284, then halt //
///////////////////////////////////////
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(); }
@Override
public void onNext(Inference inf) {
/** see {@link #createDayOfWeekInferencePrintout()} */
int cycle = dayOfWeekPrintout.apply(inf, cellsPerCol);
if(cycle == 284) {
System.out.println("halting publisher = " + network.getPublisher());
network.halt();
}
}
});
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();
if(cycleCount == 284) {
break;
}
}
// Test network output
try {
Region r1 = network.lookup("r1");
r1.lookup("1").getLayerThread().join(2000);
}catch(Exception e) {
e.printStackTrace();
}
// Announce new start
System.out.println("\n\n\n Network Restart \n\n\n");
///////////////////////
// Now Restart //
///////////////////////
// 1. Re-Attach Observer
// 2. Restart Network
network.observe().subscribe(new Observer<Inference>() {
@Override public void onCompleted() {}
@Override public void onError(Throwable e) { e.printStackTrace(); }
@Override
public void onNext(Inference inf) {
/** see {@link #createDayOfWeekInferencePrintout()} */
dayOfWeekPrintout.apply(inf, cellsPerCol);
////////////////////////////////////////////////////////////
// Ensure the records pick up precisely where we left off //
////////////////////////////////////////////////////////////
if(inf.getRecordNum() == 1975) {
expectedDataFlag = true;
}
}
});
network.halt();
try {
network.lookup("r1").lookup("1").getLayerThread().join(3000);
// Add a little more wait time
Thread.sleep(3000);
}catch(Exception e) { e.printStackTrace(); }
network.restart();
Publisher newPub = network.getPublisher();
// Assert that we have a new Publisher being created in the background upon restart()
assertFalse(pub == newPub);
for(;cycleCount < NUM_CYCLES;cycleCount++) {
for(double j = 0;j < INPUT_GROUP_COUNT;j++) {
newPub.onNext("" + j);
}
network.reset();
}
newPub.onComplete();
try {
Region r1 = network.lookup("r1");
r1.lookup("1").getLayerThread().join();
}catch(Exception e) {
e.printStackTrace();
}
if(!expectedDataFlag) {
fail(failMessage);
}
}
@Test
public void testBasicNetworkRunAWhileThenHalt() {
onCompleteStr = null;
Parameters p = NetworkTestHarness.getParameters();
p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
p.set(KEY.RANDOM, new MersenneTwister(42));
p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("consumption", CLAClassifier.class));
// Create a Network
Network network = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("1", p)
.alterParameter(KEY.AUTO_CLASSIFY, Boolean.TRUE)
.add(Anomaly.create())
.add(new TemporalMemory())
.add(new SpatialPooler())
.add(Sensor.create(FileSensor::create, SensorParams.create(
Keys::path, "", ResourceLocator.path("rec-center-hourly.csv"))))));
final List<String> lines = new ArrayList<>();
// Listen to network emissions
network.observe().subscribe(new Subscriber<Inference>() {
@Override public void onCompleted() {
onCompleteStr = "On completed reached!";
}
@Override public void onError(Throwable e) { e.printStackTrace(); }
@Override public void onNext(Inference i) {
// System.out.println(Arrays.toString(i.getSDR()));
// System.out.println(i.getRecordNum() + "," +
// i.getClassifierInput().get("consumption").get("inputValue") + "," + i.getAnomalyScore());
lines.add(i.getRecordNum() + "," +
i.getClassifierInput().get("consumption").get("inputValue") + "," + i.getAnomalyScore());
if(i.getRecordNum() == 1000) {
network.halt();
}
}
});
// Start the network
network.start();
// Test network output
try {
Region r1 = network.lookup("r1");
r1.lookup("1").getLayerThread().join();
}catch(Exception e) {
e.printStackTrace();
}
assertEquals(1001, lines.size());
int i = 0;
for(String l : lines) {
String[] sa = l.split("[\\s]*\\,[\\s]*");
assertEquals(3, sa.length);
assertEquals(i++, Integer.parseInt(sa[0]));
}
assertEquals("On completed reached!", onCompleteStr);
}
ManualInput netInference = null;
ManualInput topInference = null;
ManualInput bottomInference = null;
@Test
public void testRegionHierarchies() {
Parameters p = NetworkTestHarness.getParameters();
p.setPotentialRadius(16);
p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
p.set(KEY.RANDOM, new MersenneTwister(42));
p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("consumption", CLAClassifier.class));
Network network = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("2", p)
.add(Anomaly.create())
.add(new TemporalMemory())
.add(new SpatialPooler())))
.add(Network.createRegion("r2")
.add(Network.createLayer("1", p)
.alterParameter(KEY.AUTO_CLASSIFY, Boolean.TRUE)
.add(new TemporalMemory())
.add(new SpatialPooler())
.add(Sensor.create(FileSensor::create, SensorParams.create(
Keys::path, "", ResourceLocator.path("rec-center-hourly.csv"))))))
.connect("r1", "r2");
Region r1 = network.lookup("r1");
Region r2 = network.lookup("r2");
network.observe().subscribe(new Subscriber<Inference>() {
@Override public void onCompleted() {}
@Override public void onError(Throwable e) { e.printStackTrace(); }
@Override public void onNext(Inference i) {
netInference = (ManualInput)i;
if(r1.getHead().getInference().getPredictiveCells().size() > 0 &&
r2.getHead().getInference().getPredictiveCells().size() > 0) {
network.halt();
}
}
});
r1.observe().subscribe(new Subscriber<Inference>() {
@Override public void onCompleted() {}
@Override public void onError(Throwable e) { e.printStackTrace(); }
@Override public void onNext(Inference i) {
topInference = (ManualInput)i;
}
});
r2.observe().subscribe(new Subscriber<Inference>() {
@Override public void onCompleted() {}
@Override public void onError(Throwable e) { e.printStackTrace(); }
@Override public void onNext(Inference i) {
bottomInference = (ManualInput)i;
}
});
network.start();
// Let run for 5 secs.
try {
r2.lookup("1").getLayerThread().join();//5000);
// System.out.println("top ff = " + Arrays.toString(topInference.getFeedForwardSparseActives()));
// System.out.println("bot ff = " + Arrays.toString(bottomInference.getFeedForwardSparseActives()));
// System.out.println("top pred = " + topInference.getPredictiveCells());
// System.out.println("bot pred = " + bottomInference.getPredictiveCells());
// System.out.println("top active = " + topInference.getActiveCells());
// System.out.println("bot active = " + bottomInference.getActiveCells());
assertTrue(!topInference.getPredictiveCells().equals(bottomInference.getPredictiveCells()));
assertTrue(topInference.getPredictiveCells().size() > 0);
assertTrue(bottomInference.getPredictiveCells().size() > 0);
}catch(Exception e) {
e.printStackTrace();
}
}
/**
* Test that a null {@link Assembly.Mode} results in exception
*/
@Test
public void testFluentBuildSemantics() {
Parameters p = NetworkTestHarness.getParameters();
p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
p.set(KEY.RANDOM, new MersenneTwister(42));
Map<String, Object> anomalyParams = new HashMap<>();
anomalyParams.put(KEY_MODE, Mode.LIKELIHOOD);
try {
// Idea: Build up ResourceLocator paths in fluent style such as:
// Layer.using(
// ResourceLocator.addPath("...") // Adds a search path for later mentioning terminal resources (i.e. files)
// .addPath("...")
// .addPath("..."))
// .add(new SpatialPooler())
// ...
Network.create("test network", p) // Add Network.add() method for chaining region adds
.add(Network.createRegion("r1") // Add version of createRegion(String name) for later connecting by name
.add(Network.createLayer("2/3", p) // so that regions can be added and connecting in one long chain.
.using(new Connections()) // Test adding connections before elements which use them
.add(Sensor.create(FileSensor::create, SensorParams.create(
Keys::path, "", ResourceLocator.path("rec-center-hourly.csv"))))
.add(new SpatialPooler())
.add(new TemporalMemory())
.add(Anomaly.create(anomalyParams))
)
.add(Network.createLayer("1", p) // Add another Layer, and the Region internally connects it to the
.add(new SpatialPooler()) // previously added Layer
.using(new Connections()) // Test adding connections after one element and before another
.add(new TemporalMemory())
.add(Anomaly.create(anomalyParams))
))
.add(Network.createRegion("r2")
.add(Network.createLayer("2/3", p)
.add(new SpatialPooler())
.using(new Connections()) // Test adding connections after one element and before another
.add(new TemporalMemory())
.add(Anomaly.create(anomalyParams))
))
.add(Network.createRegion("r3")
.add(Network.createLayer("1", p)
.add(new SpatialPooler())
.add(new TemporalMemory())
.add(Anomaly.create(anomalyParams))
.using(new Connections()) // Test adding connections after elements which use them.
))
.connect("r1", "r2")
.connect("r2", "r3");
}catch(Exception e) {
e.printStackTrace();
}
}
@Test
public void testNetworkComputeWithNoSensor() {
Parameters p = NetworkTestHarness.getParameters();
p = p.union(NetworkTestHarness.getDayDemoTestEncoderParams());
p.set(KEY.GLOBAL_INHIBITION, true);
p.set(KEY.COLUMN_DIMENSIONS, new int[] { 30 });
p.set(KEY.SYN_PERM_INACTIVE_DEC, 0.008);
p.set(KEY.SYN_PERM_ACTIVE_INC, 0.1);
p.set(KEY.SYN_PERM_TRIM_THRESHOLD, 0.05);
p.set(KEY.SYN_PERM_CONNECTED, 0.1);
p.set(KEY.PERMANENCE_INCREMENT, 0.10);
p.set(KEY.PERMANENCE_DECREMENT, 0.10);
p.set(KEY.MAX_BOOST, 1.0);
p.set(KEY.DUTY_CYCLE_PERIOD, 7);
p.set(KEY.RANDOM, new MersenneTwister(42));
p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("dayOfWeek", CLAClassifier.class));
Map<String, Object> params = new HashMap<>();
params.put(KEY_MODE, Mode.PURE);
Network n = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("1", p)
.alterParameter(KEY.AUTO_CLASSIFY, Boolean.TRUE))
.add(Network.createLayer("2", p)
.add(Anomaly.create(params)))
.add(Network.createLayer("3", p)
.add(new TemporalMemory()))
.add(Network.createLayer("4", p)
.add(new SpatialPooler())
.add(MultiEncoder.builder().name("").build()))
.connect("1", "2")
.connect("2", "3")
.connect("3", "4"));
Region r1 = n.lookup("r1");
r1.lookup("3").using(r1.lookup("4").getConnections()); // How to share Connections object between Layers
r1.observe().subscribe(new Subscriber<Inference>() {
@Override public void onCompleted() {}
@Override public void onError(Throwable e) { e.printStackTrace(); }
@Override public void onNext(Inference i) {
// UNCOMMENT TO VIEW STABILIZATION OF PREDICTED FIELDS
// Set<Cell> prevPred = i.getPreviousPredictiveCells();
// if(prevPred == null) {
// prevPred = Collections.emptySet();
// }
// System.out.println("Day: " + r1.getInput() + " - predictions: " + Arrays.toString(SDR.cellsAsColumnIndices(prevPred, 6)) +
// " - " + Arrays.toString(i.getFeedForwardSparseActives()) + " - " +
// ((int)Math.rint(((Number)i.getClassification("dayOfWeek").getMostProbableValue(1)).doubleValue())));
}
});
final int NUM_CYCLES = 400;
final int INPUT_GROUP_COUNT = 7; // Days of Week
Map<String, Object> multiInput = new HashMap<>();
for(int i = 0;i < NUM_CYCLES;i++) {
for(double j = 0;j < INPUT_GROUP_COUNT;j++) {
multiInput.put("dayOfWeek", j);
r1.compute(multiInput);
}
n.reset();
}
// Test that we get proper output after prediction stabilization
TestObserver<Inference> tester;
r1.observe().subscribe(tester = new TestObserver<Inference>() {
@Override public void onCompleted() {}
@Override public void onNext(Inference i) {
int nextDay = ((int)Math.rint(((Number)i.getClassification("dayOfWeek").getMostProbableValue(1)).doubleValue()));
assertEquals(6, nextDay);
}
});
multiInput.put("dayOfWeek", 5.0);
n.compute(multiInput);
// Check for exception during the TestObserver's onNext() execution.
checkObserver(tester);
}
@Test
public void testSynchronousBlockingComputeCall() {
Parameters p = NetworkTestHarness.getParameters();
p = p.union(NetworkTestHarness.getDayDemoTestEncoderParams());
p.set(KEY.COLUMN_DIMENSIONS, new int[] { 30 });
p.set(KEY.SYN_PERM_INACTIVE_DEC, 0.1);
p.set(KEY.SYN_PERM_ACTIVE_INC, 0.1);
p.set(KEY.SYN_PERM_TRIM_THRESHOLD, 0.05);
p.set(KEY.SYN_PERM_CONNECTED, 0.4);
p.set(KEY.MAX_BOOST, 10.0);
p.set(KEY.DUTY_CYCLE_PERIOD, 7);
p.set(KEY.RANDOM, new MersenneTwister(42));
p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("dayOfWeek", CLAClassifier.class));
Map<String, Object> params = new HashMap<>();
params.put(KEY_MODE, Mode.PURE);
Network n = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("1", p)
.alterParameter(KEY.AUTO_CLASSIFY, Boolean.TRUE)
.add(new TemporalMemory())
.add(new SpatialPooler())
.add(MultiEncoder.builder().name("").build())));
boolean gotResult = false;
final int NUM_CYCLES = 400;
final int INPUT_GROUP_COUNT = 7; // Days of Week
Map<String, Object> multiInput = new HashMap<>();
for(int i = 0;i < NUM_CYCLES;i++) {
for(double j = 0;j < INPUT_GROUP_COUNT;j++) {
multiInput.put("dayOfWeek", j);
Inference inf = n.computeImmediate(multiInput);
if(inf.getPredictiveCells().size() > 6) {
assertTrue(inf.getPredictiveCells() != null);
// Make sure we've gotten all the responses
assertEquals((i * 7) + (int)j, inf.getRecordNum());
gotResult = true;
break;
}
}
if(gotResult) {
break;
}
}
assertTrue(gotResult);
}
@Test
public void testThreadedStartFlagging() {
Parameters p = NetworkTestHarness.getParameters();
p = p.union(NetworkTestHarness.getDayDemoTestEncoderParams());
p.set(KEY.COLUMN_DIMENSIONS, new int[] { 30 });
p.set(KEY.SYN_PERM_INACTIVE_DEC, 0.1);
p.set(KEY.SYN_PERM_ACTIVE_INC, 0.1);
p.set(KEY.SYN_PERM_TRIM_THRESHOLD, 0.05);
p.set(KEY.SYN_PERM_CONNECTED, 0.4);
p.set(KEY.MAX_BOOST, 10.0);
p.set(KEY.DUTY_CYCLE_PERIOD, 7);
p.set(KEY.RANDOM, new MersenneTwister(42));
p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("consumption", CLAClassifier.class));
Map<String, Object> params = new HashMap<>();
params.put(KEY_MODE, Mode.PURE);
Network n = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("1", p)
.alterParameter(KEY.AUTO_CLASSIFY, Boolean.TRUE))
.add(Network.createLayer("2", p)
.add(Anomaly.create(params)))
.add(Network.createLayer("3", p)
.add(new TemporalMemory()))
.add(Network.createLayer("4", p)
.add(new SpatialPooler())
.add(MultiEncoder.builder().name("").build()))
.connect("1", "2")
.connect("2", "3")
.connect("3", "4"));
assertFalse(n.isThreadedOperation());
n.start();
assertFalse(n.isThreadedOperation());
//////////////////////////////////////////////////////
// Add a Sensor which should allow Network to start //
//////////////////////////////////////////////////////
p = NetworkTestHarness.getParameters();
p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("consumption", CLAClassifier.class));
n = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("1", p)
.alterParameter(KEY.AUTO_CLASSIFY, Boolean.TRUE))
.add(Network.createLayer("2", p)
.add(Anomaly.create(params)))
.add(Network.createLayer("3", p)
.add(new TemporalMemory()))
.add(Network.createLayer("4", p)
.add(new SpatialPooler())
.add(Sensor.create(FileSensor::create, SensorParams.create(
Keys::path, "", ResourceLocator.path("rec-center-hourly.csv")))))
.connect("1", "2")
.connect("2", "3")
.connect("3", "4"));
assertFalse(n.isThreadedOperation());
n.start();
assertTrue(n.isThreadedOperation());
try {
p = NetworkTestHarness.getParameters();
p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("consumption", CLAClassifier.class));
n = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("1", p)
.alterParameter(KEY.AUTO_CLASSIFY, Boolean.TRUE)
.add(new TemporalMemory())
.add(new SpatialPooler())
.add(Sensor.create(FileSensor::create, SensorParams.create(
Keys::path, "", ResourceLocator.path("rec-center-hourly.csv"))))));
n.start();
n.computeImmediate(new HashMap<String, Object>());
// SHOULD FAIL HERE WITH EXPECTED EXCEPTION
fail();
}catch(Exception e) {
assertEquals("Cannot call computeImmediate() when Network has been started.", e.getMessage());
}
}
double anomaly = 1;
boolean completed = false;
@Test
public void testObservableWithCoordinateEncoder() {
Publisher manual = Publisher.builder()
.addHeader("timestamp,consumption,location")
.addHeader("datetime,float,geo")
.addHeader("T,,").build();
Sensor<ObservableSensor<String[]>> sensor = Sensor.create(
ObservableSensor::create, SensorParams.create(Keys::obs, "", manual));
Parameters p = NetworkTestHarness.getParameters().copy();
p = p.union(NetworkTestHarness.getGeospatialTestEncoderParams());
p.set(KEY.RANDOM, new MersenneTwister(42));
HTMSensor<ObservableSensor<String[]>> htmSensor = (HTMSensor<ObservableSensor<String[]>>)sensor;
Network network = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("1", p)
.add(Anomaly.create())
.add(new TemporalMemory())
.add(new SpatialPooler())
.add(htmSensor)));
network.start();
network.observe().subscribe(new Observer<Inference>() {
@Override public void onCompleted() {
assertEquals(0, anomaly, 0);
completed = true;
}
@Override public void onError(Throwable e) { e.printStackTrace(); }
@Override public void onNext(Inference output) {
//System.out.println(output.getRecordNum() + ": input = " + Arrays.toString(output.getEncoding()));//output = " + Arrays.toString(output.getSDR()) + ", " + output.getAnomalyScore());
if(output.getAnomalyScore() < anomaly) {
anomaly = output.getAnomalyScore();
// System.out.println("anomaly = " + anomaly);
}
}
});
int x = 0;
for(int i = 0;i < 100;i++) {
x = i % 10;
manual.onNext("7/12/10 13:10,35.3,40.6457;-73.7" + x + "692;" + x); //5 = meters per second
}
manual.onComplete();
Layer<?> l = network.lookup("r1").lookup("1");
try {
l.getLayerThread().join();
}catch(Exception e) {
e.printStackTrace();
}
assertTrue(completed);
}
String errorMessage = null;
@Test
public void testObservableWithCoordinateEncoder_NEGATIVE() {
Publisher manual = Publisher.builder()
.addHeader("timestamp,consumption,location")
.addHeader("datetime,float,geo")
.addHeader("T,,").build();
Sensor<ObservableSensor<String[]>> sensor = Sensor.create(
ObservableSensor::create, SensorParams.create(Keys::obs, "", manual));
Parameters p = NetworkTestHarness.getParameters().copy();
p = p.union(NetworkTestHarness.getGeospatialTestEncoderParams());
p.set(KEY.RANDOM, new MersenneTwister(42));
p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("", CLAClassifier.class));
HTMSensor<ObservableSensor<String[]>> htmSensor = (HTMSensor<ObservableSensor<String[]>>)sensor;
Network network = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("1", p)
.alterParameter(KEY.AUTO_CLASSIFY, Boolean.TRUE)
.add(Anomaly.create())
.add(new TemporalMemory())
.add(new SpatialPooler())
.add(htmSensor)));
TestObserver<Inference> tester;
network.observe().subscribe(tester = new TestObserver<Inference>() {
@Override public void onCompleted() {
//Should never happen here.
assertEquals(0, anomaly, 0);
completed = true;
super.onCompleted();
}
@Override public void onError(Throwable e) {
super.onError(e);
errorMessage = e.getMessage();
network.halt();
}
@Override public void onNext(Inference output) {}
});
network.start();
int x = 0;
for(int i = 0;i < 100;i++) {
x = i % 10;
manual.onNext("7/12/10 13:10,35.3,40.6457;-73.7" + x + "692;" + x); //1st "x" is attempt to vary coords, 2nd "x" = meters per second
}
manual.onComplete();
Layer<?> l = network.lookup("r1").lookup("1");
try {
l.getLayerThread().join();
}catch(Exception e) {
assertEquals(InterruptedException.class, e.getClass());
}
// Assert onNext condition never gets set
assertFalse(completed);
assertEquals("Cannot autoclassify with raw array input or " +
"Coordinate based encoders... Remove auto classify setting.", errorMessage);
assertTrue(hasErrors(tester));
}
@Test
public void testPotentialRadiusFollowsInputWidth() {
Parameters p = NetworkTestHarness.getParameters();
p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
p.set(KEY.INPUT_DIMENSIONS, new int[] { 200 });
p.set(KEY.RANDOM, new MersenneTwister(42));
Network network = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("2", p)
.add(Anomaly.create())
.add(new TemporalMemory())
.add(new SpatialPooler())
.close()));
Region r1 = network.lookup("r1");
Layer<?> layer2 = r1.lookup("2");
int width = layer2.calculateInputWidth();
assertEquals(200, width);
assertEquals(200, layer2.getConnections().getPotentialRadius());
}
///////////////////////////////////////////////////////////////////////////////////
// Tests of Calculate Input Width for inter-regional and inter-layer calcs //
///////////////////////////////////////////////////////////////////////////////////
@Test
public void testCalculateInputWidth_NoPrevLayer_UpstreamRegion_with_TM() {
Parameters p = NetworkTestHarness.getParameters();
p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
p.set(KEY.RANDOM, new MersenneTwister(42));
p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("consumption", CLAClassifier.class));
Network network = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("2", p)
.add(Anomaly.create())
.add(new TemporalMemory())
.add(new SpatialPooler())))
.add(Network.createRegion("r2")
.add(Network.createLayer("1", p)
.alterParameter(KEY.AUTO_CLASSIFY, Boolean.TRUE)
.add(new TemporalMemory())
.add(new SpatialPooler())
.add(Sensor.create(FileSensor::create, SensorParams.create(
Keys::path, "", ResourceLocator.path("rec-center-hourly.csv"))))))
.connect("r1", "r2");
Region r1 = network.lookup("r1");
Layer<?> layer2 = r1.lookup("2");
int width = layer2.calculateInputWidth();
assertEquals(65536, width);
}
@Test
public void testCalculateInputWidth_NoPrevLayer_UpstreamRegion_without_TM() {
Parameters p = NetworkTestHarness.getParameters();
p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
p.set(KEY.RANDOM, new MersenneTwister(42));
p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("consumption", CLAClassifier.class));
Network network = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("2", p)
.add(Anomaly.create())
.add(new TemporalMemory())
.add(new SpatialPooler())))
.add(Network.createRegion("r2")
.add(Network.createLayer("1", p)
.alterParameter(KEY.AUTO_CLASSIFY, Boolean.TRUE)
.add(new SpatialPooler())
.add(Sensor.create(FileSensor::create, SensorParams.create(
Keys::path, "", ResourceLocator.path("rec-center-hourly.csv"))))))
.connect("r1", "r2");
Region r1 = network.lookup("r1");
Layer<?> layer2 = r1.lookup("2");
int width = layer2.calculateInputWidth();
assertEquals(2048, width);
}
@Test
public void testCalculateInputWidth_NoPrevLayer_NoPrevRegion_andTM() {
Parameters p = NetworkTestHarness.getParameters();
p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
p.set(KEY.RANDOM, new MersenneTwister(42));
Network network = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("2", p)
.add(Anomaly.create())
.add(new TemporalMemory())
.close()));
Region r1 = network.lookup("r1");
Layer<?> layer2 = r1.lookup("2");
int width = layer2.calculateInputWidth();
assertEquals(65536, width);
}
@Test
public void testCalculateInputWidth_NoPrevLayer_NoPrevRegion_andSPTM() {
Parameters p = NetworkTestHarness.getParameters();
p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
p.set(KEY.RANDOM, new MersenneTwister(42));
Network network = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("2", p)
.add(Anomaly.create())
.add(new TemporalMemory())
.add(new SpatialPooler())
.close()));
Region r1 = network.lookup("r1");
Layer<?> layer2 = r1.lookup("2");
int width = layer2.calculateInputWidth();
assertEquals(8, width);
assertEquals(8, layer2.getConnections().getPotentialRadius());
}
@Test
public void testCalculateInputWidth_NoPrevLayer_NoPrevRegion_andNoTM() {
Parameters p = NetworkTestHarness.getParameters();
p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
p.set(KEY.RANDOM, new MersenneTwister(42));
Network network = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("2", p)
.add(Anomaly.create())
.add(new SpatialPooler())
.close()));
Region r1 = network.lookup("r1");
Layer<?> layer2 = r1.lookup("2");
int width = layer2.calculateInputWidth();
assertEquals(8, width);
assertEquals(8, layer2.getConnections().getPotentialRadius());
}
@Test
public void testCalculateInputWidth_WithPrevLayer_WithTM() {
Parameters p = NetworkTestHarness.getParameters();
p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
p.set(KEY.RANDOM, new MersenneTwister(42));
Network network = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("1", p)
.add(Anomaly.create())
.add(new SpatialPooler()))
.add(Network.createLayer("2", p)
.add(Anomaly.create())
.add(new TemporalMemory())
.add(new SpatialPooler()))
.connect("1", "2"));
Region r1 = network.lookup("r1");
Layer<?> layer1 = r1.lookup("1");
int width = layer1.calculateInputWidth();
assertEquals(65536, width);
}
@Test
public void testCalculateInputWidth_WithPrevLayer_NoTM() {
Parameters p = NetworkTestHarness.getParameters();
p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
p.set(KEY.RANDOM, new MersenneTwister(42));
Network network = Network.create("test network", p)
.add(Network.createRegion("r1")
.add(Network.createLayer("1", p)
.add(Anomaly.create())
.add(new SpatialPooler()))
.add(Network.createLayer("2", p)
.add(new SpatialPooler()))
.connect("1", "2"));
Region r1 = network.lookup("r1");
Layer<?> layer1 = r1.lookup("1");
int width = layer1.calculateInputWidth();
assertEquals(2048, width);
}
@Test
public void closeTest() {
Parameters p = NetworkTestHarness.getParameters();
p = p.union(NetworkTestHarness.getNetworkDemoTestEncoderParams());
p.set(KEY.RANDOM, new MersenneTwister(42));
Region region1 = Network.createRegion("region1");
Layer<?> layer1 = Network.createLayer("layer1", p);
region1.add(layer1);
Region region2 = Network.createRegion("region2");
Layer<?> layer2 = Network.createLayer("layer2", p);
region2.add(layer2);
Network network = Network.create("test network", p);
// Calling close on an empty Network should not throw any Exceptions
network.close();
// Calling close on a Network with a single unclosed Region
network.add(region1);
network.close();
assertTrue("Region 1 did not close, after closing Network", region1.isClosed());
assertTrue("Layer 1 did not close, after closing Network", layer1.isClosed());
// Calling close on a Network with two regions, one of which is closed
network.add(region2);
network.close();
assertTrue("Region 1 did not close, after closing Network with 2 Regions", region1.isClosed());
assertTrue("Layer 1 did not close, after closing Network with 2 Regions", layer1.isClosed());
assertTrue("Region 2 did not close, after closing Network with 2 Regions", region2.isClosed());
assertTrue("Layer 2 did not close, after closing Network with 2 Regions", layer2.isClosed());
}
@Test
public void testLearn() {
Region region = Network.createRegion("Region 1");
Layer<?> layer = Network.createLayer("Layer 2/3", Parameters.getAllDefaultParameters());
Network network = Network.create("Network 1", Parameters.getAllDefaultParameters());
region.add(layer);
network.add(region);
// Close must be called to have this work
region.close();
network.setLearn(true);
assertTrue(network.isLearn()); //true
assertTrue(region.isLearn()); //true
assertTrue(layer.isLearn()); //true
network.setLearn(false);
assertFalse(network.isLearn()); //false
assertFalse(region.isLearn()); //false
assertFalse(layer.isLearn());
}
////////////////////////////////////////
// Utility Methods //
////////////////////////////////////////
Publisher pub = null;
private Network getLoadedDayOfWeekNetwork() {
Parameters p = NetworkTestHarness.getParameters().copy();
p = p.union(NetworkTestHarness.getDayDemoTestEncoderParams());
p.set(KEY.RANDOM, new FastRandom(42));
p.set(KEY.INFERRED_FIELDS, getInferredFieldsMap("dayOfWeek", CLAClassifier.class));
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;
}
private BiFunction<Inference, Integer, Integer> createDayOfWeekInferencePrintout() {
return new BiFunction<Inference, Integer, Integer>() {
private int cycles = 1;
public Integer apply(Inference inf, Integer cellsPerColumn) {
// Classification<Object> result = inf.getClassification("dayOfWeek");
double day = mapToInputData((int[])inf.getLayerInput());
if(day == 1.0) {
// System.out.println("\n=========================");
// System.out.println("CYCLE: " + cycles);
cycles++;
}
// System.out.println("RECORD_NUM: " + inf.getRecordNum());
// System.out.println("ScalarEncoder Input = " + day);
// System.out.println("ScalarEncoder Output = " + Arrays.toString(inf.getEncoding()));
// System.out.println("SpatialPooler Output = " + Arrays.toString(inf.getFeedForwardActiveColumns()));
//
// if(inf.getPreviousPredictiveCells() != null)
// System.out.println("TemporalMemory Previous Prediction = " +
// Arrays.toString(SDR.cellsAsColumnIndices(inf.getPreviousPredictiveCells(), cellsPerColumn)));
//
// System.out.println("TemporalMemory Actives = " + Arrays.toString(SDR.asColumnIndices(inf.getSDR(), cellsPerColumn)));
//
// System.out.print("CLAClassifier prediction = " +
// stringValue((Double)result.getMostProbableValue(1)) + " --> " + ((Double)result.getMostProbableValue(1)));
//
// System.out.println(" | CLAClassifier 1 step prob = " + Arrays.toString(result.getStats(1)) + "\n");
return cycles;
}
};
}
private double mapToInputData(int[] encoding) {
for(int i = 0;i < dayMap.length;i++) {
if(Arrays.equals(encoding, dayMap[i])) {
return i + 1;
}
}
return -1;
}
public Stream<String> makeStream() {
return Stream.of(
"7/2/10 0:00,21.2",
"7/2/10 1:00,16.4",
"7/2/10 2:00,4.7",
"7/2/10 3:00,4.7",
"7/2/10 4:00,4.6",
"7/2/10 5:00,23.5",
"7/2/10 6:00,47.5",
"7/2/10 7:00,45.4",
"7/2/10 8:00,46.1",
"7/2/10 9:00,41.5",
"7/2/10 10:00,43.4",
"7/2/10 11:00,43.8",
"7/2/10 12:00,37.8",
"7/2/10 13:00,36.6",
"7/2/10 14:00,35.7",
"7/2/10 15:00,38.9",
"7/2/10 16:00,36.2",
"7/2/10 17:00,36.6",
"7/2/10 18:00,37.2",
"7/2/10 19:00,38.2",
"7/2/10 20:00,14.1");
}
}
