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/* ---------------------------------------------------------------------
* 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 java.math.BigDecimal;
import java.math.MathContext;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.concurrent.ConcurrentLinkedQueue;
import org.joda.time.DateTime;
import org.numenta.nupic.FieldMetaType;
import org.numenta.nupic.Parameters;
import org.numenta.nupic.Parameters.KEY;
import org.numenta.nupic.algorithms.Anomaly;
import org.numenta.nupic.algorithms.CLAClassifier;
import org.numenta.nupic.algorithms.Classification;
import org.numenta.nupic.algorithms.SpatialPooler;
import org.numenta.nupic.algorithms.TemporalMemory;
import org.numenta.nupic.encoders.DateEncoder;
import org.numenta.nupic.encoders.Encoder;
import org.numenta.nupic.encoders.EncoderTuple;
import org.numenta.nupic.encoders.MultiEncoder;
import org.numenta.nupic.model.Cell;
import org.numenta.nupic.model.ComputeCycle;
import org.numenta.nupic.model.Connections;
import org.numenta.nupic.model.Persistable;
import org.numenta.nupic.model.SDR;
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.Sensor;
import org.numenta.nupic.network.sensor.SensorParams;
import org.numenta.nupic.network.sensor.SensorParams.Keys;
import org.numenta.nupic.network.sensor.URISensor;
import org.numenta.nupic.util.ArrayUtils;
import org.numenta.nupic.util.NamedTuple;
import org.numenta.nupic.util.SparseBinaryMatrix;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import rx.Observable;
import rx.Observable.Transformer;
import rx.Observer;
import rx.Subscriber;
import rx.Subscription;
import rx.functions.Func1;
import rx.subjects.PublishSubject;
/**
*
* Implementation of the biological layer of a region in the neocortex. Here, a
* {@code Layer} contains the physical structure (columns, cells, dendrites etc)
* shared by a sequence of algorithms which serve to implement the predictive
* inferencing present in this, the allegory to its biological equivalent.
*
*
* COMPOSITION: A Layer is constructed with {@link Parameters} which
* configure the behavior of most of the key algorithms a Layer may contain. It
* is also optionally constructed with each of the algorithms in turn.
* A Layer that includes an {@link Encoder} is always initially configured with
* a {@link MultiEncoder}. The child encoders contained within the MultiEncoder
* are configured from the Map included with the specified Parameters, keyed by
* {@link Parameters.KEY#FIELD_ENCODING_MAP}.
*
*
* A field encoding map consists of one map for each of the fields to be
* encoded. Each individual map in the field encoding map contains the typical
* {@link Encoder} parameters, plus a few "meta" parameters needed to describe
* the field and its data type as follows:
*
*
*
* Map<String, Map<String, Object>> fieldEncodings = new HashMap<>();
*
* Map<String, Object> inner = new HashMap<>();
* inner.put("n", n);
* inner.put("w", w);
* inner.put("minVal", min);
* inner.put("maxVal", max);
* inner.put("radius", radius);
* inner.put("resolution", resolution);
* inner.put("periodic", periodic);
* inner.put("clip", clip);
* inner.put("forced", forced);
* // These are meta info to aid in Encoder construction
* inner.put("fieldName", fieldName);
* inner.put("fieldType", fieldType); (see {@link FieldMetaType} for type examples)
* inner.put("encoderType", encoderType); (i.e. ScalarEncoder, SDRCategoryEncoder, DateEncoder...etc.)
*
* Map<String, Object> inner2 = new HashMap<>();
* inner.put("n", n);
* inner.put("w", w);
* inner.put("minVal", min);
* inner.put("maxVal", max);
* inner.put("radius", radius);
* inner.put("resolution", resolution);
* inner.put("periodic", periodic);
* inner.put("clip", clip);
* inner.put("forced", forced);
* // These are meta info to aid in Encoder construction
* inner.put("fieldName", fieldName);
* inner.put("fieldType", fieldType); (see {@link FieldMetaType} for type examples)
* inner.put("encoderType", encoderType); (i.e. ScalarEncoder, SDRCategoryEncoder, DateEncoder...etc.)
*
* fieldEncodings.put("consumption", inner); // Where "consumption" is an example field name (field name is "generic" in above code)
* fieldEncodings.put("temperature", inner2);
*
* Parameters p = Parameters.getDefaultParameters();
* p.setParameterByKey(KEY.FIELD_ENCODING_MAP, fieldEncodings);
*
*
* For an example of how to create the field encodings map in a reusable way,
* see NetworkTestHarness and its usage within the LayerTest class.
*
*
* The following is an example of Layer construction with everything included
* (i.e. Sensor, SpatialPooler, TemporalMemory, CLAClassifier, Anomaly
* (computer))
*
*
* // See the test harness for more information
* Parameters p = NetworkTestHarness.getParameters();
*
* // How to merge (union) two {@link Parameters} objects. This one merges
* // the Encoder parameters into default parameters.
* p = p.union(NetworkTestHarness.getHotGymTestEncoderParams());
*
* // You can overwrite parameters as needed like this
* p.setParameterByKey(KEY.RANDOM, new MersenneTwister(42));
* p.setParameterByKey(KEY.COLUMN_DIMENSIONS, new int[] { 2048 });
* p.setParameterByKey(KEY.POTENTIAL_RADIUS, 200);
* p.setParameterByKey(KEY.INHIBITION_RADIUS, 50);
* p.setParameterByKey(KEY.GLOBAL_INHIBITIONS, true);
*
* Map<String, Object> params = new HashMap<>();
* params.put(KEY_MODE, Mode.PURE);
* params.put(KEY_WINDOW_SIZE, 3);
* params.put(KEY_USE_MOVING_AVG, true);
* Anomaly anomalyComputer = Anomaly.create(params);
*
* Layer<?> l = Network.createLayer("TestLayer", p).alterParameter(KEY.AUTO_CLASSIFY, true).add(anomalyComputer).add(new TemporalMemory()).add(new SpatialPooler())
* .add(Sensor.create(FileSensor::create, SensorParams.create(Keys::path, "", ResourceLocator.path("rec-center-hourly-small.csv"))));
*
*
*
*
*
* @author David Ray
*/
public class Layer implements Persistable {
private static final long serialVersionUID = 1L;
protected static final Logger LOGGER = LoggerFactory.getLogger(Layer.class);
protected int numColumns;
protected Network parentNetwork;
protected Region parentRegion;
protected Parameters params;
protected SensorParams sensorParams;
protected Connections connections;
protected HTMSensor sensor;
protected MultiEncoder encoder;
protected SpatialPooler spatialPooler;
protected TemporalMemory temporalMemory;
private Boolean autoCreateClassifiers;
private Anomaly anomalyComputer;
private transient ConcurrentLinkedQueue> subscribers = new ConcurrentLinkedQueue>();
private transient PublishSubject publisher = null;
private transient Observable userObservable;
private transient Subscription subscription;
private volatile Inference currentInference;
FunctionFactory factory;
/** Used to track and document the # of records processed */
private int recordNum = -1;
/** Keeps track of number of records to skip on restart */
private int skip = -1;
private String name;
private volatile boolean isClosed;
private volatile boolean isHalted;
private volatile boolean isPostSerialized;
protected volatile boolean isLearn = true;
private Layer next;
private Layer previous;
private transient List> observers = new ArrayList>();
private transient CheckPointOperator checkPointOp;
private transient List> checkPointOpObservers = new ArrayList<>();
/**
* Retains the order of added items - for use with interposed
* {@link Observable}
*/
private List addedItems = new ArrayList<>();
/** Indicates whether there is a generic processing node entered */
private boolean hasGenericProcess;
/**
* List of {@link Encoders} used when storing bucket information see
* {@link #doEncoderBucketMapping(Inference, Map)}
*/
private List encoderTuples;
private transient Map, Observable> observableDispatch = Collections.synchronizedMap(new HashMap, Observable>());
/** This layer's thread */
private transient Thread LAYER_THREAD;
static final byte SPATIAL_POOLER = 1;
static final byte TEMPORAL_MEMORY = 2;
static final byte CLA_CLASSIFIER = 4;
static final byte ANOMALY_COMPUTER = 8;
private byte algo_content_mask = 0;
/**
* Creates a new {@code Layer} using the {@link Network} level
* {@link Parameters}
*
* @param n
* the parent {@link Network}
*/
public Layer(Network n) {
this(n, n.getParameters());
}
/**
* Creates a new {@code Layer} using the specified {@link Parameters}
*
* @param n
* the parent {@link Network}
* @param p
* the {@link Parameters} to use with this {@code Layer}
*/
public Layer(Network n, Parameters p) {
this("[Layer " + System.currentTimeMillis() + "]", n, p);
}
/**
* Creates a new {@code Layer} using the specified {@link Parameters}
*
* @param name the name identifier of this {@code Layer}
* @param n the parent {@link Network}
* @param p the {@link Parameters} to use with this {@code Layer}
*/
public Layer(String name, Network n, Parameters p) {
this.name = name;
this.parentNetwork = n;
this.params = p;
connections = new Connections();
this.autoCreateClassifiers = (Boolean)p.get(KEY.AUTO_CLASSIFY);
factory = new FunctionFactory();
observableDispatch = createDispatchMap();
}
/**
* {@inheritDoc}
*/
@SuppressWarnings("unchecked")
@Override
public Layer preSerialize() {
isPostSerialized = false;
return this;
}
/**
* {@inheritDoc}
*/
@SuppressWarnings("unchecked")
@Override
public Layer postDeSerialize() {
recreateSensors();
FunctionFactory old = factory;
factory = new FunctionFactory();
factory.inference = old.inference.postDeSerialize(old.inference);
checkPointOpObservers = new ArrayList<>();
if(sensor != null) {
sensor.setLocalParameters(params);
// Initialize encoders and recreate encoding index mapping.
sensor.postDeSerialize();
}else{
// Dispatch functions (Observables) are transient & non-serializable so they must be rebuilt.
observableDispatch = createDispatchMap();
// Dispatch chain will not propagate unless it has subscribers.
parentNetwork.addDummySubscriber();
}
// Flag which lets us know to skip or do certain setups during initialization.
isPostSerialized = true;
observers = new ArrayList>();
return this;
}
/**
* Sets the parent {@link Network} on this {@code Layer}
*
* @param network
*/
public void setNetwork(Network network) {
this.parentNetwork = network;
}
/**
* Returns the parent {@link Network}
* @return the parent Network;
*/
public Network getNetwork() {
return this.parentNetwork;
}
/**
* Creates a new {@code Layer} initialized with the specified algorithmic
* components.
*
* @param params A {@link Parameters} object containing configurations for a
* SpatialPooler, TemporalMemory, and Encoder (all or none may be used).
* @param e (optional) The Network API only uses a {@link MultiEncoder} at
* the top level because of its ability to delegate to child encoders.
* @param sp (optional) {@link SpatialPooler}
* @param tm (optional) {@link TemporalMemory}
* @param autoCreateClassifiers (optional) Indicates that the {@link Parameters} object
* contains the configurations necessary to create the required encoders.
* @param a (optional) An {@link Anomaly} computer.
*/
public Layer(Parameters params, MultiEncoder e, SpatialPooler sp, TemporalMemory tm, Boolean autoCreateClassifiers, Anomaly a) {
// Make sure we have a valid parameters object
if(params == null) {
throw new IllegalArgumentException("No parameters specified.");
}
// Check to see if the Parameters include the encoder configuration.
if(params.get(KEY.FIELD_ENCODING_MAP) == null && e != null) {
throw new IllegalArgumentException("The passed in Parameters must contain a field encoding map " +
"specified by org.numenta.nupic.Parameters.KEY.FIELD_ENCODING_MAP");
}
this.params = params;
this.encoder = e;
this.spatialPooler = sp;
this.temporalMemory = tm;
this.autoCreateClassifiers = autoCreateClassifiers;
this.anomalyComputer = a;
connections = new Connections();
factory = new FunctionFactory();
observableDispatch = createDispatchMap();
initializeMask();
if(LOGGER.isDebugEnabled()) {
LOGGER.debug("Layer successfully created containing: {}{}{}{}{}",
(encoder == null ? "" : "MultiEncoder,"),
(spatialPooler == null ? "" : "SpatialPooler,"),
(temporalMemory == null ? "" : "TemporalMemory,"),
(autoCreateClassifiers == null ? "" : "Auto creating CLAClassifiers for each input field."),
(anomalyComputer == null ? "" : "Anomaly"));
}
}
/**
* USED INTERNALLY, DO NOT CALL.
* @return
*/
public CheckPointOp delegateCheckPointCall() {
if(parentNetwork != null) {
return parentNetwork.getCheckPointOperator();
}
return null;
}
/**
* Sets the parent region which contains this {@code Layer}
*
* @param r
*/
public void setRegion(Region r) {
this.parentRegion = r;
}
/**
* Returns the parent {@link Region}
*
* @return the parent Region
*/
public Region getRegion() {
return this.parentRegion;
}
/**
* Finalizes the initialization in one method call so that side effect
* operations to share objects and other special initialization tasks can
* happen all at once in a central place for maintenance ease.
*/
@SuppressWarnings("unchecked")
public Layer close() {
if(isClosed) {
LOGGER.warn("Close called on Layer " + getName() + " which is already closed.");
return this;
}
params.apply(connections);
if(sensor != null) {
encoder = encoder == null ? sensor.getEncoder() : encoder;
sensor.initEncoder(params);
connections.setNumInputs(encoder.getWidth());
if(parentNetwork != null && parentRegion != null) {
parentNetwork.setSensorRegion(parentRegion);
Object supplier;
if((supplier = sensor.getSensorParams().get("ONSUB")) != null) {
if(supplier instanceof PublisherSupplier) {
((PublisherSupplier)supplier).setNetwork(parentNetwork);
parentNetwork.setPublisher(((PublisherSupplier)supplier).get());
}
}
}
}
// Create Encoder hierarchy from definitions & auto create classifiers
// if specified
if(encoder != null) {
if(encoder.getEncoders(encoder) == null || encoder.getEncoders(encoder).size() < 1) {
if(params.get(KEY.FIELD_ENCODING_MAP) == null || ((Map>)params.get(KEY.FIELD_ENCODING_MAP)).size() < 1) {
LOGGER.error("No field encoding map found for specified MultiEncoder");
throw new IllegalStateException("No field encoding map found for specified MultiEncoder");
}
encoder.addMultipleEncoders((Map>)params.get(KEY.FIELD_ENCODING_MAP));
}
// Make the declared column dimensions match the actual input
// dimensions retrieved from the encoder
int product = 0, inputLength = 0, columnLength = 0;
if(((inputLength = ((int[])params.get(KEY.INPUT_DIMENSIONS)).length) != (columnLength = ((int[])params.get(KEY.COLUMN_DIMENSIONS)).length))
|| encoder.getWidth() != (product = ArrayUtils.product((int[])params.get(KEY.INPUT_DIMENSIONS)))) {
LOGGER.warn("The number of Input Dimensions (" + inputLength + ") != number of Column Dimensions " + "(" + columnLength + ") --OR-- Encoder width (" + encoder.getWidth()
+ ") != product of dimensions (" + product + ") -- now attempting to fix it.");
int[] inferredDims = inferInputDimensions(encoder.getWidth(), columnLength);
if(inferredDims != null && inferredDims.length > 0 && encoder.getWidth() == ArrayUtils.product(inferredDims)) {
LOGGER.info("Input dimension fix successful!");
LOGGER.info("Using calculated input dimensions: " + Arrays.toString(inferredDims));
}
params.setInputDimensions(inferredDims);
connections.setInputDimensions(inferredDims);
}
}
autoCreateClassifiers = autoCreateClassifiers != null && (autoCreateClassifiers | (Boolean)params.get(KEY.AUTO_CLASSIFY));
if(autoCreateClassifiers != null && autoCreateClassifiers.booleanValue() && (factory.inference.getClassifiers() == null || factory.inference.getClassifiers().size() < 1)) {
factory.inference.classifiers(makeClassifiers(encoder == null ? parentNetwork.getEncoder() : encoder));
// Note classifier addition by setting content mask
algo_content_mask |= CLA_CLASSIFIER;
}
// We must adjust this Layer's inputDimensions to the size of the input
// received from the previous Region's output vector.
if(parentRegion != null && parentRegion.getUpstreamRegion() != null) {
int[] upstreamDims = new int[] { calculateInputWidth() };
params.setInputDimensions(upstreamDims);
connections.setInputDimensions(upstreamDims);
} else if(parentRegion != null && parentNetwork != null
&& parentRegion.equals(parentNetwork.getSensorRegion()) && encoder == null && spatialPooler != null) {
Layer curr = this;
while((curr = curr.getPrevious()) != null) {
if(curr.getEncoder() != null) {
int[] dims = (int[])curr.getParameters().get(KEY.INPUT_DIMENSIONS);
params.setInputDimensions(dims);
connections.setInputDimensions(dims);
}
}
}
// Let the SpatialPooler initialize the matrix with its requirements
if(spatialPooler != null) {
// The exact dimensions don't have to be the same but the number of
// dimensions do!
int inputLength, columnLength = 0;
if((inputLength = ((int[])params.get(KEY.INPUT_DIMENSIONS)).length) !=
(columnLength = ((int[])params.get(KEY.COLUMN_DIMENSIONS)).length)) {
LOGGER.error("The number of Input Dimensions (" + inputLength + ") is not same as the number of Column Dimensions " +
"(" + columnLength + ") in Parameters! - SpatialPooler not initialized!");
return this;
}
spatialPooler.init(connections);
}
// Let the TemporalMemory initialize the matrix with its requirements
if(temporalMemory != null) {
TemporalMemory.init(connections);
}
this.numColumns = connections.getNumColumns();
this.isClosed = true;
LOGGER.debug("Layer " + name + " content initialize mask = " + Integer.toBinaryString(algo_content_mask));
return this;
}
/**
* Called from {@link FunctionFactory#createSpatialFunc(SpatialPooler)} and from {@link #close()}
* to calculate the size of the input vector given the output source either being a {@link TemporalMemory}
* or a {@link SpatialPooler} - from this {@link Region} or a previous {@link Region}.
*
* @return the length of the input vector
*/
int calculateInputWidth() {
// If no previous Layer, check upstream region for its output layer's output.
if(previous == null) {
if(parentRegion.getUpstreamRegion() != null) {
// Upstream region with TM
if((parentRegion.getUpstreamRegion().getHead().algo_content_mask & Layer.TEMPORAL_MEMORY) == Layer.TEMPORAL_MEMORY) {
int out = -1;
out = (parentRegion.getUpstreamRegion().getHead().getConnections().getCellsPerColumn() *
(parentRegion.getUpstreamRegion().getHead().getConnections().getMemory().getMaxIndex() + 1));
return out;
}
// Upstream region but no TM, so input is the upstream region's SP
return new SparseBinaryMatrix(parentRegion.getUpstreamRegion().getHead().getConnections().getColumnDimensions()).getMaxIndex() + 1;
}
// No previous Layer, and no upstream region
// layer contains a TM so compute by cells;
if(hasTM() && !hasSP()) {
return getConnections().getCellsPerColumn() * (getConnections().getMemory().getMaxIndex() + 1);
}
// layer only contains a SP
return connections.getNumInputs();
}else{
// There is a previous Layer and that layer contains a TM so compute by cells;
if((previous.algo_content_mask & Layer.TEMPORAL_MEMORY) == Layer.TEMPORAL_MEMORY) {
SparseBinaryMatrix matrix = new SparseBinaryMatrix(previous.getConnections().getColumnDimensions());
return previous.getConnections().getCellsPerColumn() * (matrix.getMaxIndex() + 1);
}
// Previous Layer but it has no TM so use the previous' column output (from SP)
return new SparseBinaryMatrix(previous.getConnections().getColumnDimensions()).getMaxIndex() + 1;
}
}
/**
* For internal use only. Returns a flag indicating whether this {@link Layer}
* contains a {@link TemporalMemory}
* @return
*/
boolean hasTM() {
return (algo_content_mask & Layer.TEMPORAL_MEMORY) == Layer.TEMPORAL_MEMORY;
}
/**
* For internal use only. Returns a flag indicating whether this {@link Layer}
* contains a {@link SpatialPooler}
* @return
*/
boolean hasSP() {
return (algo_content_mask & Layer.SPATIAL_POOLER) == Layer.SPATIAL_POOLER;
}
/**
* Given an input field width and Spatial Pooler dimensionality; this method
* will return an array of dimension sizes whose number is equal to the
* number of column dimensions. The sum of the returned dimensions will be
* equal to the flat input field width specified.
*
* This method should be called when a disparity in dimensionality between
* the input field and the number of column dimensions is detected.
* Otherwise if the input field dimensionality is correctly specified, this
* method should not be used.
*
* @param inputWidth the flat input width of an {@link Encoder}'s output or the
* vector used as input to the {@link SpatialPooler}
* @param numDims a number specifying the number of dimensions that
* should be returned.
* @return
*/
public int[] inferInputDimensions(int inputWidth, int numDims) {
double flatSize = inputWidth;
double numColDims = numDims;
int[] retVal = new int[(int)numColDims];
BigDecimal log = new BigDecimal(Math.log10(flatSize));
BigDecimal dimensions = new BigDecimal(numColDims);
double sliceArrangement = new BigDecimal(
Math.pow(10, log.divide(dimensions).doubleValue()),
MathContext.DECIMAL32).doubleValue();
double remainder = sliceArrangement % (int)sliceArrangement;
if(remainder > 0) {
for(int i = 0;i < numColDims - 1;i++)
retVal[i] = 1;
retVal[(int)numColDims - 1] = (int)flatSize;
} else {
for(int i = 0;i < numColDims;i++)
retVal[i] = (int)sliceArrangement;
}
return retVal;
}
/**
* Returns an {@link Observable} that can be subscribed to, or otherwise
* operated upon by another Observable or by an Observable chain.
*
* @return this {@code Layer}'s output {@link Observable}
*/
@SuppressWarnings("unchecked")
public Observable observe() {
// This will be called again after the Network is halted so we have to prepare
// for rebuild of the Observer chain
if(isHalted) {
clearSubscriberObserverLists();
}
if(userObservable == null) {
userObservable = Observable.create(new Observable.OnSubscribe() {
@Override
public void call(Subscriber t1) {
if(observers == null) {
observers = new ArrayList>();
}
observers.add((Observer)t1);
}
});
}
return userObservable;
}
/**
* Called by the {@code Layer} client to receive output {@link Inference}s
* from the configured algorithms.
*
* @param subscriber a {@link Subscriber} to be notified as data is published.
* @return a {@link Subscription}
*/
public Subscription subscribe(final Observer subscriber) {
// This will be called again after the Network is halted so we have to prepare
// for rebuild of the Observer chain
if(isHalted) {
clearSubscriberObserverLists();
}
if(subscriber == null) {
throw new IllegalArgumentException("Subscriber cannot be null.");
}
if(subscribers == null) {
subscribers = new ConcurrentLinkedQueue>();
}
subscribers.add(subscriber);
return createSubscription(subscriber);
}
/**
* Allows the user to define the {@link Connections} object data structure
* to use. Or possibly to share connections between two {@code Layer}s
*
* @param c the {@code Connections} object to use.
* @return this Layer instance (in fluent-style)
*/
public Layer using(Connections c) {
if(isClosed) {
throw new IllegalStateException("Layer already \"closed\"");
}
this.connections = c;
return this;
}
/**
* Allows the user to specify the {@link Parameters} object used by this
* {@code Layer}. If the intent is to share Parameters across multiple
* Layers, it must be kept in mind that two Layers containing the same
* algorithm may require specification of locally different parameter
* settings. In this case, one could use
* {@link #alterParameter(KEY, Object)} method to change a local setting
* without impacting the same setting in the source parameters object. This
* is made possible because the {@link #alterParameter(KEY, Object)} method
* first makes a local copy of the {@link Parameters} object, then modifies
* the specified parameter.
*
* @param p the {@link Parameters} to use in this {@code Layer}
* @return this {@code Layer}
*/
public Layer using(Parameters p) {
if(isClosed) {
throw new IllegalStateException("Layer already \"closed\"");
}
this.params = p;
return this;
}
/**
* Adds an {@link HTMSensor} to this {@code Layer}. An HTMSensor is a regular
* {@link Sensor} (i.e. {@link FileSensor}, {@link URISensor}, or {@link ObservableSensor})
* which has had an {@link Encoder} configured and added to it. HTMSensors are
* HTM Aware, where as regular Sensors have no knowledge of HTM requirements.
*
* @param sensor the {@link HTMSensor}
* @return this Layer instance (in fluent-style)
*/
@SuppressWarnings("rawtypes")
public Layer add(Sensor sensor) {
if(isClosed) {
throw new IllegalStateException("Layer already \"closed\"");
}
this.sensor = (HTMSensor)sensor;
// Configure the parent Network's reference to its sensor Region.
if(parentNetwork != null && parentRegion != null) {
parentNetwork.setSensorRegion(parentRegion);
parentNetwork.setSensor(this.sensor);
}
// Store the SensorParams for Sensor rebuild after deserialization
this.sensorParams = this.sensor.getSensorParams();
return this;
}
/**
* Adds a {@link MultiEncoder} to this {@code Layer}
*
* @param encoder the added MultiEncoder
* @return this Layer instance (in fluent-style)
*/
public Layer add(MultiEncoder encoder) {
if(isClosed) {
throw new IllegalStateException("Layer already \"closed\"");
}
this.encoder = encoder;
return this;
}
/**
* Adds a {@link SpatialPooler} to this {@code Layer}
*
* @param sp the added SpatialPooler
* @return this Layer instance (in fluent-style)
*/
public Layer add(SpatialPooler sp) {
if(isClosed) {
throw new IllegalStateException("Layer already \"closed\"");
}
// Preserve addition order
addedItems.add(sp);
this.algo_content_mask |= SPATIAL_POOLER;
this.spatialPooler = sp;
return this;
}
/**
* Adds a {@link TemporalMemory} to this {@code Layer}
*
* @param tm the added TemporalMemory
* @return this Layer instance (in fluent-style)
*/
public Layer add(TemporalMemory tm) {
if(isClosed) {
throw new IllegalStateException("Layer already \"closed\"");
}
// Preserve addition order
addedItems.add(tm);
this.algo_content_mask |= TEMPORAL_MEMORY;
this.temporalMemory = tm;
return this;
}
/**
* Adds an {@link Anomaly} computer to this {@code Layer}
*
* @param anomalyComputer the Anomaly instance
* @return this Layer instance (in fluent-style)
*/
public Layer add(Anomaly anomalyComputer) {
if(isClosed) {
throw new IllegalStateException("Layer already \"closed\"");
}
// Preserve addition order
addedItems.add(anomalyComputer);
this.algo_content_mask |= ANOMALY_COMPUTER;
this.anomalyComputer = anomalyComputer;
return this;
}
/**
* Adds a "generic" processing node into this {@code Layer}'s processing
* chain.
*
* NOTE: When adding a generic node, the order of calls to
* the addXXX() methods becomes crucially important. Make sure you
* have added items in a valid order in your "fluent" add call declarations. add(Func1 func) {
if(isClosed) {
throw new IllegalStateException("Layer already \"closed\"");
}
if(func == null) {
throw new IllegalArgumentException("Cannot add a null Function");
}
hasGenericProcess = true;
// Preserve addition order
addedItems.add(func);
return this;
}
/**
* Adds the ability to alter a given parameter in place during a fluent
* creation statement. This {@code Layer}'s {@link Parameters} object is
* copied and then the specified key/value pair are set on the internal
* copy. This call does not affect the original Parameters object so that
* local modifications may be made without having to reset them afterward
* for subsequent use with another network structure.
*
* @param key the {@link Parameters} key.
* @param value the value of the parameter
* @return this Layer instance (in fluent-style)
*/
public Layer alterParameter(KEY key, Object value) {
if(isClosed) {
throw new IllegalStateException("Layer already \"closed\"");
}
// Preserve any input dimensions that might have been set prior to this
// in
// previous layers
int[] inputDims = (int[])params.get(KEY.INPUT_DIMENSIONS);
this.params = this.params.copy();
this.params.set(key, value);
this.params.set(KEY.INPUT_DIMENSIONS, inputDims);
if(key == KEY.AUTO_CLASSIFY) {
this.autoCreateClassifiers = value == null ? false : ((Boolean)value).booleanValue();
// Note the addition of a classifier
algo_content_mask |= CLA_CLASSIFIER;
}
return this;
}
/**
* Returns the configured {@link Sensor} if any exists in this {@code Layer},
* or null if one does not.
*
* @return any existing HTMSensor applied to this {@code Layer}
*/
public HTMSensor getSensor() {
return sensor;
}
/**
* Returns the {@link Connections} object being used by this {@link Layer}
*
* @return this {@code Layer}'s {@link Connections}
*/
public Connections getConnections() {
return this.connections;
}
/**
* Processes a single element, sending the specified input up the configured
* chain of algorithms or components within this {@code Layer}; resulting in
* any {@link Subscriber}s or {@link Observer}s being notified of results
* corresponding to the specified input (unless a {@link SpatialPooler}
* "primer delay" has been configured).
*
* The first input to the Layer invokes a method to resolve the transformer
* at the bottom of the input chain, therefore the "type" (<T>) of the
* input cannot be changed once this method is called for the first time.
*
* @param t the input object who's type is generic.
*/
public void compute(T t) {
if(!isClosed) {
close();
}
increment();
if(!dispatchCompleted()) {
completeDispatch(t);
}
publisher.onNext(t);
}
/**
* Stops the processing of this {@code Layer}'s processing thread.
*/
public void halt() {
Object supplier = null;
if(sensor != null && (supplier = sensor.getSensorParams().get("ONSUB")) != null) {
if(supplier instanceof PublisherSupplier) {
((PublisherSupplier)supplier).clearSuppliedInstance();
}
}
// Signal the Observer chain to complete
if(LAYER_THREAD == null) {
publisher.onCompleted();
if(next != null) {
next.halt();
}
}
this.isHalted = true;
}
/**
* Returns a flag indicating whether this layer's processing thread has been
* halted or not.
*
* @return
*/
public boolean isHalted() {
return isHalted;
}
/**
* Sets the learning mode.
*
* @param isLearn
*/
public void setLearn(boolean isLearn) {
this.isLearn = isLearn;
}
/**
* Returns the learning mode setting.
*
* @return
*/
public boolean isLearn() {
return isLearn;
}
/**
* Completes the dispatch chain of algorithm {@link Observable}s with
* specialized {@link Transformer}s for each algorithm contained within this
* Layer. This method then starts the output stream processing of its
* {@link Sensor} in a separate {@link Thread} (if it exists) - logging this
* event.
*
* Calling this method sets a flag on the underlying Sensor marking it as
* "Terminal" meaning that it cannot be restarted and its output stream
* cannot be accessed again.
*/
@SuppressWarnings("unchecked")
public void start() {
if(isHalted) {
restart(true);
return;
}
// Save boilerplate setup steps by automatically closing when start is
// called.
if(!isClosed) {
close();
}
if(sensor == null) {
throw new IllegalStateException("A sensor must be added when the mode is not Network.Mode.MANUAL");
}
this.encoder = encoder == null ? sensor.getEncoder() : encoder;
try {
completeDispatch((T)new int[] {});
} catch(Exception e) {
notifyError(e);
}
startLayerThread();
LOGGER.debug("Start called on Layer thread {}", LAYER_THREAD);
}
/**
* Restarts this {@code Layer}
*
* {@link #restart()} is to be called after a call to {@link #halt()}, to begin
* processing again. The {@link Network} will continue from where it previously
* left off after the last call to halt().
*
* @param startAtIndex flag indicating whether the Layer should be started and
* run from the previous save point or not.
*/
@SuppressWarnings("unchecked")
public void restart(boolean startAtIndex) {
isHalted = false;
if(!isClosed) {
start();
}else{
if(sensor == null) {
throw new IllegalStateException("A sensor must be added when the mode is not Network.Mode.MANUAL");
}
// Re-init the Sensor only if we're halted and haven't already been initialized
// following a deserialization.
if(!isPostSerialized) {
// Recreate the Sensor and its underlying Stream
recreateSensors();
}
if(parentNetwork != null) {
parentNetwork.setSensor(sensor);
}
observableDispatch = createDispatchMap();
this.encoder = encoder == null ? sensor.getEncoder() : encoder;
skip = startAtIndex ?
(sensor.getSensorParams().get("ONSUB")) != null ? -1 : recordNum :
(recordNum = -1);
try {
completeDispatch((T)new int[] {});
} catch(Exception e) {
notifyError(e);
}
startLayerThread();
LOGGER.debug("Re-Start called on Layer thread {}", LAYER_THREAD);
}
}
/**
* Sets a pointer to the "next" Layer in this {@code Layer}'s
* {@link Observable} sequence.
*
* @param l
*/
public void next(Layer l) {
this.next = l;
}
/**
* Returns the next Layer following this Layer in order of process flow.
*
* @return
*/
public Layer getNext() {
return next;
}
/**
* Sets a pointer to the "previous" Layer in this {@code Layer}'s
* {@link Observable} sequence.
*
* @param l
*/
public void previous(Layer l) {
this.previous = l;
}
/**
* Returns the previous Layer preceding this Layer in order of process flow.
*
* @return
*/
public Layer getPrevious() {
return previous;
}
/**
* Returns a flag indicating whether this {@code Layer} is configured with a
* {@link Sensor} which requires starting up.
*
* @return
*/
public boolean hasSensor() {
return sensor != null;
}
/**
* Returns the {@link Thread} from which this {@code Layer} is currently
* outputting data.
*
* Warning: This method returns the current thread if the Layer's processing
* thread has never been started and therefore is null!
*
* @return
*/
public Thread getLayerThread() {
if(LAYER_THREAD != null) {
return LAYER_THREAD;
}
return Thread.currentThread();
}
/**
* Returns the {@link Parameters} used to configure this layer.
*
* @return
*/
public Parameters getParameters() {
return this.params;
}
/**
* Returns the current predictive {@link Cell}s
*
* @return the binary vector representing the current prediction.
*/
public Set getPredictiveCells() {
return currentInference.getPredictiveCells();
}
/**
* Returns the previous predictive {@link Cells}
*
* @return the binary vector representing the current prediction.
*/
public Set getPreviousPredictiveCells() {
return currentInference.getPreviousPredictiveCells();
}
/**
* Returns the current (dense) array of column indexes which represent
* columns which have become activated during the current input sequence
* from the SpatialPooler.
*
* @return the array of active column indexes
*/
public int[] getFeedForwardActiveColumns() {
return currentInference.getFeedForwardActiveColumns();
}
/**
* Returns the {@link Cell}s activated in the {@link TemporalMemory} at time
* "t"
*
* @return
*/
public Set getActiveCells() {
return currentInference.getActiveCells();
}
/**
* Returns the SpatialPooler column activations in sparse form (indexes of
* the on bits).
*
* @return
*/
public int[] getFeedForwardSparseActives() {
return currentInference.getFeedForwardSparseActives();
}
/**
* Returns the {@link Connections} object being used as the structural
* matrix and state.
*/
public Connections getMemory() {
return connections;
}
/**
* Returns the count of records historically inputted into this
* {@code Layer}
*
* @return the current record input count
*/
public int getRecordNum() {
return recordNum;
}
/**
* Returns a flag indicating whether this {@code Layer} has had
* its {@link #close} method called, or not.
* @return
*/
public boolean isClosed() {
return isClosed;
}
/**
* Resets the internal record count to zero
*
* @return this {@code LayerImpl}
*/
public Layer resetRecordNum() {
recordNum = 0;
return this;
}
/**
* Resets the {@link TemporalMemory} if it exists.
*/
public void reset() {
if(temporalMemory == null) {
LOGGER.debug("Attempt to reset Layer: " + getName() + "without TemporalMemory");
} else {
temporalMemory.reset(connections);
}
}
/**
* Returns a flag indicating whether this {@code Layer} contains a
* {@link TemporalMemory}.
*
* @return
*/
public boolean hasTemporalMemory() {
return temporalMemory != null;
}
/**
* Increments the current record sequence number.
*/
public Layer increment() {
if(skip > -1) {
--skip;
} else {
++recordNum;
}
return this;
}
/**
* Sets the name and returns this Layer.
*
* @param name
* @return
*/
public Layer name(String name) {
this.name = name;
return this;
}
/**
* Returns the String identifier of this {@code Layer}
*
* @return
*/
public String getName() {
return name;
}
/**
* Returns the last computed {@link Inference} of this {@code Layer}
*
* @return the last computed inference.
*/
public Inference getInference() {
return currentInference;
}
/**
* Returns the resident {@link MultiEncoder} or the encoder residing in this
* {@code Layer}'s {@link Sensor}, if any.
*
* @return
*/
public MultiEncoder getEncoder() {
if(encoder != null) {
return encoder;
}
if(hasSensor()) {
return sensor.getEncoder();
}
MultiEncoder e = parentNetwork.getEncoder();
if(e != null) {
return e;
}
return null;
}
/**
* Returns the values submitted to this {@code Layer} in an array whose
* indexes correspond to the indexes of probabilities returned when calling
* {@link #getAllPredictions(String, int)}.
*
* @param field The field name of the required prediction
* @param step The step for the required prediction
* @return
*/
@SuppressWarnings("unchecked")
public V[] getAllValues(String field, int step) {
if(currentInference == null || currentInference.getClassifiers() == null) {
throw new IllegalStateException("Predictions not available. " + "Either classifiers unspecified or inferencing has not yet begun.");
}
Classification c = currentInference.getClassification(field);
if(c == null) {
LOGGER.debug("No ClassifierResult exists for the specified field: {}", field);
}
return (V[])c.getActualValues();
}
/**
* Returns a double[] containing a prediction confidence measure for each
* bucket (unique entry as determined by an encoder). In order to relate the
* probability to an actual value, call {@link #getAllValues(String, int)}
* which returns an array containing the actual values submitted to this
* {@code Layer} - the indexes of each probability will match the index of
* each actual value entered.
*
* @param field The field name of the required prediction
* @param step The step for the required prediction
* @return
*/
public double[] getAllPredictions(String field, int step) {
if(currentInference == null || currentInference.getClassifiers() == null) {
throw new IllegalStateException("Predictions not available. " + "Either classifiers unspecified or inferencing has not yet begun.");
}
Classification c = currentInference.getClassification(field);
if(c == null) {
LOGGER.debug("No ClassifierResult exists for the specified field: {}", field);
}
return c.getStats(step);
}
/**
* Returns the value whose probability is calculated to be the highest for
* the specified field and step.
*
* @param field The field name of the required prediction
* @param step The step for the required prediction
* @return
*/
@SuppressWarnings("unchecked")
public K getMostProbableValue(String field, int step) {
if(currentInference == null || currentInference.getClassifiers() == null) {
throw new IllegalStateException("Predictions not available. " + "Either classifiers unspecified or inferencing has not yet begun.");
}
Classification c = currentInference.getClassification(field);
if(c == null) {
LOGGER.debug("No ClassifierResult exists for the specified field: {}", field);
}
return (K)c.getMostProbableValue(step);
}
/**
* Returns the bucket index of the value with the highest calculated
* probability for the specified field and step.
*
* @param field The field name of the required prediction
* @param step The step for the required prediction
* @return
*/
public int getMostProbableBucketIndex(String field, int step) {
if(currentInference == null || currentInference.getClassifiers() == null) {
throw new IllegalStateException("Predictions not available. " + "Either classifiers unspecified or inferencing has not yet begun.");
}
Classification c = currentInference.getClassification(field);
if(c == null) {
LOGGER.debug("No ClassifierResult exists for the specified field: {}", field);
}
return c.getMostProbableBucketIndex(step);
}
//////////////////////////////////////////////////////////////
// PRIVATE METHODS AND CLASSES BELOW HERE //
//////////////////////////////////////////////////////////////
/**
* Notify all subscribers through the delegate that stream processing has
* been completed or halted.
*/
void notifyComplete() {
for(Observer o : subscribers) {
o.onCompleted();
}
for(Observer o : observers) {
o.onCompleted();
}
publisher.onCompleted();
}
/**
* Called internally to propagate the specified {@link Exception} up the
* network hierarchy
*
* @param e the exception to notify users of
*/
void notifyError(Exception e) {
for(Observer o : subscribers) {
o.onError(e);
}
for(Observer o : observers) {
o.onError(e);
}
publisher.onError(e);
}
/**
*
* Returns the content mask used to indicate what algorithm contents this
* {@code Layer} has. This is used to determine whether the
* {@link Inference} object passed between layers should share values.
*
*
* If any algorithms are repeated then {@link Inference}s will
* NOT NEVER
*
* @return
*/
byte getMask() {
return algo_content_mask;
}
/**
* Initializes the algorithm content mask used for detection of repeated
* algorithms among {@code Layer}s in a {@link Region}. see
* {@link #getMask()} for more information.
*/
private void initializeMask() {
algo_content_mask |= (this.spatialPooler == null ? 0 : SPATIAL_POOLER);
algo_content_mask |= (this.temporalMemory == null ? 0 : TEMPORAL_MEMORY);
algo_content_mask |= (this.autoCreateClassifiers == null || !autoCreateClassifiers ? 0 : CLA_CLASSIFIER);
algo_content_mask |= (this.anomalyComputer == null ? 0 : ANOMALY_COMPUTER);
}
/**
* Returns a flag indicating whether we've connected the first observable in
* the sequence (which lazily does the input type of <T> to
* {@link Inference} transformation) to the Observables connecting the rest
* of the algorithm components.
*
* @return flag indicating all observables connected. True if so, false if
* not
*/
private boolean dispatchCompleted() {
return observableDispatch == null;
}
/**
* Connects the first observable which does the transformation of input
* types, to the rest of the sequence - then clears the helper map and sets
* it to null.
*
* @param t
*/
private void completeDispatch(T t) {
// Get the Input Transformer for the specified input type
Observable sequence = resolveObservableSequence(t);
// If this Layer has a Sensor, map its encoder buckets
sequence = mapEncoderBuckets(sequence);
// Add the rest of the chain observables for the other added algorithms.
sequence = fillInSequence(sequence);
// All subscribers and observers are notified from a single delegate.
if(subscribers == null) subscribers = new ConcurrentLinkedQueue>();
subscribers.add(getDelegateObserver());
subscription = sequence.subscribe(getDelegateSubscriber());
// The map of input types to transformers is no longer needed.
observableDispatch.clear();
observableDispatch = null;
// Handle global network sensor access.
if(sensor == null && parentNetwork != null && parentNetwork.isTail(this)) {
sensor = parentNetwork == null ? null : parentNetwork.getSensor();
} else if(parentNetwork != null && sensor != null) {
parentNetwork.setSensor(sensor);
}
}
/**
* We cannot create the {@link Observable} sequence all at once because the
* first step is to transform the input type to the type the rest of the
* sequence uses (Observable<Inference> ). This can only happen
* during the actual call to {@link #compute(Object)} which presents the
* input type - so we create a map of all types of expected inputs, and then
* connect the sequence at execution time; being careful to only incur the
* cost of sequence assembly on the first call to {@link #compute(Object)}.
* After the first call, we dispose of this map and its contents.
*
* @return the map of input types to {@link Transformer}
*/
@SuppressWarnings("unchecked")
private Map, Observable> createDispatchMap() {
Map, Observable> observableDispatch = Collections.synchronizedMap(new HashMap, Observable>());
publisher = PublishSubject.create();
observableDispatch.put((Class)Map.class, factory.createMultiMapFunc(publisher));
observableDispatch.put((Class)ManualInput.class, factory.createManualInputFunc(publisher));
observableDispatch.put((Class)String[].class, factory.createEncoderFunc(publisher));
observableDispatch.put((Class)int[].class, factory.createVectorFunc(publisher));
return observableDispatch;
}
/**
* If this Layer has a Sensor, map its encoder's buckets
*
* @param sequence
* @return
*/
private Observable mapEncoderBuckets(Observable sequence) {
if(hasSensor()) {
if(getSensor().getMetaInfo().getFieldTypes().stream().anyMatch(ft -> {
return ft == FieldMetaType.SARR || ft == FieldMetaType.DARR || ft == FieldMetaType.COORD || ft == FieldMetaType.GEO;
})) {
if(autoCreateClassifiers) {
throw new IllegalStateException("Cannot autoclassify with raw array input or " + " Coordinate based encoders... Remove auto classify setting.");
}
return sequence;
}
sequence = sequence.map(m -> {
doEncoderBucketMapping(m, getSensor().getInputMap());
return m;
});
}
return sequence;
}
/**
* This method is necessary to be able to retrieve the mapped
* {@link Observable} types to input types or their subclasses if any.
*
* @param t the input type. The "expected" types are:
*
* {@link Map}
* {@link ManualInput}
* String[]
* int[]
*
* or their subclasses.
* @return an Observable suitable for subscribing or operating on.
*/
private Observable resolveObservableSequence(T t) {
Observable sequenceStart = null;
if(observableDispatch == null) {
observableDispatch = createDispatchMap();
}
if(observableDispatch != null) {
if(ManualInput.class.isAssignableFrom(t.getClass())) {
sequenceStart = observableDispatch.get(ManualInput.class);
} else if(Map.class.isAssignableFrom(t.getClass())) {
sequenceStart = observableDispatch.get(Map.class);
} else if(t.getClass().isArray()) {
if(t.getClass().equals(String[].class)) {
sequenceStart = observableDispatch.get(String[].class);
} else if(t.getClass().equals(int[].class)) {
sequenceStart = observableDispatch.get(int[].class);
}
}
}
// Insert skip observable operator if initializing with an advanced record number
// (i.e. Serialized Network)
if(recordNum > 0 && skip != -1) {
sequenceStart = sequenceStart.skip(recordNum + 1);
Integer skipCount;
if(((skipCount = (Integer)params.get(KEY.SP_PRIMER_DELAY)) != null)) {
// No need to "warm up" the SpatialPooler if we're deserializing an SP
// that has been running... However "skipCount - recordNum" is there so
// we make sure the Network has run at least long enough to satisfy the
// original requested "primer delay".
params.set(KEY.SP_PRIMER_DELAY, Math.max(0, skipCount - recordNum));
}
}
sequenceStart = sequenceStart.filter(m -> {
if(!checkPointOpObservers.isEmpty() && parentNetwork != null) {
// Execute check point logic
doCheckPoint();
}
return true;
});
return sequenceStart;
}
/**
* Executes the check point logic, handles the return of the serialized byte array
* by delegating the call to {@link rx.Observer#onNext(byte[])} of all the currently queued
* Observers; then clears the list of Observers.
*/
private void doCheckPoint() {
byte[] bytes = parentNetwork.internalCheckPointOp();
if(bytes != null) {
LOGGER.debug("Layer [" + getName() + "] checkPointed file: " +
Persistence.get().getLastCheckPointFileName());
}else{
LOGGER.debug("Layer [" + getName() + "] checkPoint F A I L E D at: " + (new DateTime()));
}
for(Observer o : checkPointOpObservers) {
o.onNext(bytes);
o.onCompleted();
}
checkPointOpObservers.clear();
}
/**
* Stores a {@link NamedTuple} which contains the input values and bucket
* information - keyed to the encoded field name so that a classifier can
* retrieve it later on in the processing sequence.
*
* @param inference
* @param encoderInputMap
*/
private void doEncoderBucketMapping(Inference inference, Map encoderInputMap) {
if(encoderTuples == null) {
encoderTuples = encoder.getEncoders(encoder);
}
// Store the encoding
int[] encoding = inference.getEncoding();
for(EncoderTuple t : encoderTuples) {
String name = t.getName();
Encoder e = t.getEncoder();
int bucketIdx = -1;
Object o = encoderInputMap.get(name);
if(DateTime.class.isAssignableFrom(o.getClass())) {
bucketIdx = ((DateEncoder)e).getBucketIndices((DateTime)o)[0];
} else if(Number.class.isAssignableFrom(o.getClass())) {
bucketIdx = e.getBucketIndices((double)o)[0];
} else {
bucketIdx = e.getBucketIndices((String)o)[0];
}
int offset = t.getOffset();
int[] tempArray = new int[e.getWidth()];
System.arraycopy(encoding, offset, tempArray, 0, tempArray.length);
inference.getClassifierInput().put(name, new NamedTuple(new String[] { "name", "inputValue", "bucketIdx", "encoding" }, name, o, bucketIdx, tempArray));
}
}
/**
* Connects the {@link Transformer} to the rest of the {@link Observable}
* sequence.
*
* @param o the Transformer part of the sequence.
* @return the completed {@link Observable} sequence.
*/
private Observable fillInSequence(Observable o) {
// Route to ordered dispatching if required.
if(hasGenericProcess) {
return fillInOrderedSequence(o);
}
// Spatial Pooler config
if(spatialPooler != null) {
Integer skipCount = 0;
if((skipCount = ((Integer)params.get(KEY.SP_PRIMER_DELAY))) != null) {
o = o.map(factory.createSpatialFunc(spatialPooler)).skip(skipCount.intValue());
} else {
o = o.map(factory.createSpatialFunc(spatialPooler));
}
}
// Temporal Memory config
if(temporalMemory != null) {
o = o.map(factory.createTemporalFunc(temporalMemory));
}
// Classifier config
if(autoCreateClassifiers != null && autoCreateClassifiers.booleanValue()) {
o = o.map(factory.createClassifierFunc());
}
// Anomaly config
if(anomalyComputer != null) {
o = o.map(factory.createAnomalyFunc(anomalyComputer));
}
return o;
}
/**
* Connects {@link Observable} or {@link Transformer} emissions in the order
* they are declared.
*
* @param o first {@link Observable} in sequence.
* @return
*/
@SuppressWarnings("unchecked")
private Observable fillInOrderedSequence(Observable o) {
Collections.reverse(addedItems);
for(Object node : addedItems) {
if(node instanceof Func1) {
o = o.map((Func1)node);
} else if(node instanceof SpatialPooler) {
Integer skipCount = 0;
if((skipCount = ((Integer)params.get(KEY.SP_PRIMER_DELAY))) != null) {
o = o.map(factory.createSpatialFunc(spatialPooler)).skip(skipCount.intValue());
} else {
o = o.map(factory.createSpatialFunc(spatialPooler));
}
} else if(node instanceof TemporalMemory) {
o = o.map(factory.createTemporalFunc(temporalMemory));
}
}
// Classifier config
if(autoCreateClassifiers != null && autoCreateClassifiers.booleanValue()) {
o = o.map(factory.createClassifierFunc());
}
// Anomaly config
if(anomalyComputer != null) {
o = o.map(factory.createAnomalyFunc(anomalyComputer));
}
return o;
}
/**
* Called internally to create a subscription on behalf of the specified
* {@link LayerObserver}
*
* @param sub the LayerObserver (subscriber).
* @return
*/
private Subscription createSubscription(final Observer sub) {
return new Subscription() {
private Observer observer = sub;
@Override
public void unsubscribe() {
subscribers.remove(observer);
if(subscribers.isEmpty()) {
subscription.unsubscribe();
}
}
@Override
public boolean isUnsubscribed() {
return subscribers.contains(observer);
}
};
}
/**
* Returns the {@link PublishSubject}'s subscriber which delegates to all
* the {@link Layer}'s subscribers.
*
* @return
*/
private Observer getDelegateSubscriber() {
return new Observer() {
@Override
public void onCompleted() {
for(Observer o : subscribers) {
o.onCompleted();
}
}
@Override
public void onError(Throwable e) {
for(Observer o : subscribers) {
o.onError(e);
}
}
@Override
public void onNext(Inference i) {
currentInference = i;
for(Observer o : subscribers) {
o.onNext(i);
}
}
};
}
/**
* Returns the {@link PublishSubject}'s subscriber which delegates to all
* the {@link Layer}'s subscribers.
*
* @return
*/
private Observer getDelegateObserver() {
return new Observer() {
@Override
public void onCompleted() {
for(Observer o : observers) {
o.onCompleted();
}
}
@Override
public void onError(Throwable e) {
for(Observer o : observers) {
o.onError(e);
e.printStackTrace();
}
}
@Override
public void onNext(Inference i) {
currentInference = i;
for(Observer o : observers) {
o.onNext(i);
}
}
};
}
/**
* Clears the subscriber and observer lists so they can be rebuilt
* during restart or deserialization.
*/
private void clearSubscriberObserverLists() {
if(observers == null) observers = new ArrayList>();
if(subscribers == null) subscribers = new ConcurrentLinkedQueue>();
subscribers.clear();
userObservable = null;
}
/**
* Creates the {@link NamedTuple} of names to encoders used in the
* observable sequence.
*
* @param encoder
* @return
*/
NamedTuple makeClassifiers(MultiEncoder encoder) {
String[] names = new String[encoder.getEncoders(encoder).size()];
CLAClassifier[] ca = new CLAClassifier[names.length];
int i = 0;
for(EncoderTuple et : encoder.getEncoders(encoder)) {
names[i] = et.getName();
ca[i] = new CLAClassifier();
i++;
}
return new NamedTuple(names, (Object[])ca);
}
/**
* Called internally to invoke the {@link SpatialPooler}
*
* @param input
* @return
*/
protected int[] spatialInput(int[] input) {
if(input == null) {
LOGGER.info("Layer ".concat(getName()).concat(" received null input"));
} else if(input.length < 1) {
LOGGER.info("Layer ".concat(getName()).concat(" received zero length bit vector"));
return input;
}
int[] activeColumns = new int[numColumns];
spatialPooler.compute(connections, input, activeColumns, isLearn || (sensor != null && sensor.getMetaInfo().isLearn()));
return activeColumns;
}
/**
* Called internally to invoke the {@link TemporalMemory}
*
* @param input the current input vector
* @param mi the current input inference container
* @return
*/
protected int[] temporalInput(int[] input, ManualInput mi) {
ComputeCycle cc = null;
if(sensor != null) {
if(sensor.getMetaInfo().isReset()) {
temporalMemory.reset(connections);
}
cc = temporalMemory.compute(connections, input, sensor.getMetaInfo().isLearn());
} else {
cc = temporalMemory.compute(connections, input, isLearn);
}
// Store the predictive columns / simultaneously storing previous predictive in this method
mi.predictiveCells(cc.predictiveCells());
// Store activeCells
mi.activeCells(cc.activeCells);
// Store the Compute Cycle
mi.computeCycle = cc;
return SDR.asCellIndices(cc.activeCells);
}
/**
* Starts this {@code Layer}'s thread
*/
protected void startLayerThread() {
(LAYER_THREAD = new Thread("Sensor Layer [" + getName() + "] Thread") {
@SuppressWarnings("unchecked")
public void run() {
LOGGER.debug("Layer [" + getName() + "] started Sensor output stream processing.");
// Applies "terminal" function, at this point the input stream
// is "sealed".
sensor.getOutputStream().filter(i -> {
if(isHalted) {
notifyComplete();
if(next != null) {
next.halt();
}
return false;
}
if(Thread.currentThread().isInterrupted()) {
notifyError(new RuntimeException("Unknown Exception while filtering input"));
}
return true;
}).forEach(intArray -> {
((ManualInput)Layer.this.factory.inference).encoding(intArray);
Layer.this.compute((T)intArray);
// Notify all downstream observers that the stream is closed
if(!sensor.hasNext()) {
notifyComplete();
}
});
}
}).start();
}
/**
* Returns an {@link rx.Observable} operator that when subscribed to, invokes an operation
* that stores the state of this {@code Network} while keeping the Network up and running.
* The Network will be stored at the pre-configured location (in binary form only, not JSON).
*
* @param network the {@link Network} to check point.
* @return the {@link CheckPointOp} operator
*/
@SuppressWarnings("unchecked")
CheckPointOp getCheckPointOperator() {
if(checkPointOp == null) {
checkPointOp = new CheckPointOperator(Layer.this);
}
return (CheckPointOp)checkPointOp;
}
//////////////////////////////////////////////////////////////
// Inner Class Definition for CheckPointer (Observable) //
//////////////////////////////////////////////////////////////
/**
*
* Implementation of the CheckPointOp interface which serves to checkpoint
* and register a listener at the same time. The {@link rx.Observer} will be
* notified with the byte array of the {@link Network} being serialized.
*
* The layer thread automatically tests for the list of observers to
* contain > 0 elements, which indicates a check point operation should
* be executed.
*
*
* @param {@link rx.Observer}'s return type
*/
static class CheckPointOperator extends Observable implements CheckPointOp {
private CheckPointOperator(Layer l) {
this(new Observable.OnSubscribe() {
@SuppressWarnings({ "unchecked" })
@Override public void call(Subscriber r) {
if(l.LAYER_THREAD != null) {
// The layer thread automatically tests for the list of observers to
// contain > 0 elements, which indicates a check point operation should
// be executed.
l.checkPointOpObservers.add((Observer)r);
}else{
l.doCheckPoint();
}
}
});
}
/**
* Constructs this {@code CheckPointOperator}
* @param f a subscriber function
*/
protected CheckPointOperator(rx.Observable.OnSubscribe f) {
super(f);
}
/**
* Queues the specified {@link rx.Observer} for notification upon
* completion of a check point operation.
*/
public Subscription checkPoint(Observer t) {
return super.subscribe(t);
}
}
//////////////////////////////////////////////////////////////
// Inner Class Definition Transformer Example //
//////////////////////////////////////////////////////////////
/**
* Factory which returns an {@link Observable} capable of transforming known
* input formats to the universal format object passed between all
* Observables in the Observable chains making up the processing steps
* involving HTM algorithms.
*
* The {@link Transformer} implementations are used to transform various
* inputs into a {@link ManualInput}, and thus are used at the beginning of
* any Observable chain; each succeding Observable in a given chain would
* then communicate via passed ManualInputs or {@link Inference}s (which are
* the same thing).
*
* @author David Ray
* @see Layer#completeDispatch(Object)
* @see Layer#resolveObservableSequence(Object)
* @see Layer#fillInSequence(Observable)
*/
class FunctionFactory implements Persistable {
private static final long serialVersionUID = 1L;
ManualInput inference = new ManualInput();
//////////////////////////////////////////////////////////////////////////////
// TRANSFORMERS //
//////////////////////////////////////////////////////////////////////////////
/**
* WARNING: UNIMPLEMENTED
*
*
* Emits an {@link Observable} which is transformed from a String[] of
* csv input to one that emits {@link Inference}s.
*
*
* This class currently lacks the implementation of csv parsing into
* distinct Object types - which is necessary to compose a "multi-map"
* necessary to input data into the {@link MultiEncoder} necessary to
* encode multiple field inputs.
*
*
* TODO: Implement later
*
*/
class String2Inference implements Transformer {
@Override
public Observable call(Observable t1) {
return t1.map(new Func1() {
@Override
public ManualInput call(String[] t1) {
////////////////////////////////////////////////////////////////////////
// Do transformative work here //
// //
// In "real life", this will send data through the MultiEncoder //
// Below is simply a faked out place holder... //
////////////////////////////////////////////////////////////////////////
int[] sdr = new int[t1.length];
for(int i = 0;i < sdr.length;i++) {
sdr[i] = Integer.parseInt(t1[i]);
}
return inference.recordNum(getRecordNum()).sdr(sdr).layerInput(sdr);
}
});
}
}
/**
*
* Emits an {@link Observable} which is transformed from a Map input
* type to one that emits {@link Inference}s.
*
*
* This {@link Transformer} is used when the input to a given
* {@link Layer} is a map of fields to input Objects. It is typically
* used when a Layer is configured with a {@link MultiEncoder} (which is
* the only encoder type that may be contained within a Layer, because
* it can be used to hold any combination of encoders required.).
*
*
*/
@SuppressWarnings("rawtypes")
class Map2Inference implements Transformer {
@Override
public Observable call(Observable t1) {
return t1.map(new Func1() {
@SuppressWarnings("unchecked")
@Override
public ManualInput call(Map t1) {
if(encoderTuples == null) {
encoderTuples = encoder.getEncoders(encoder);
}
// Store the encoding
int[] encoding = encoder.encode(t1);
inference.sdr(encoding).encoding(encoding);
doEncoderBucketMapping(inference, t1);
return inference.recordNum(getRecordNum()).layerInput(t1);
}
});
}
}
/**
*
* Emits an {@link Observable} which is transformed from a binary vector
* input type to one that emits {@link Inference}s.
*
*
* This type is used when bypassing an encoder and possibly any other
* algorithm usually connected in a sequence of algorithms within a
* {@link Layer}
*
*/
class Vector2Inference implements Transformer {
@Override
public Observable call(Observable t1) {
return t1.map(new Func1() {
@Override
public ManualInput call(int[] t1) {
// Indicates a value that skips the encoding step
return inference.recordNum(getRecordNum()).sdr(t1).layerInput(t1);
}
});
}
}
/**
* Emits an {@link Observable} which copies an Inference input to the
* output, storing relevant information in this layer's inference object
* along the way.
*/
class Copy2Inference implements Transformer {
@Override
public Observable call(Observable t1) {
return t1.map(new Func1() {
NamedTuple swap;
boolean swapped;
@Override
public ManualInput call(ManualInput t1) {
// Inference is shared along entire network
if(!swapped) {
swap = inference.getClassifiers();
inference = t1;
inference.classifiers(swap);
swapped = true;
}
// Indicates a value that skips the encoding step
return inference.recordNum(getRecordNum()).sdr(t1.getSDR()).recordNum(t1.getRecordNum()).layerInput(t1);
}
});
}
}
//////////////////////////////////////////////////////////////////////////////
// INPUT TRANSFORMATION FUNCTIONS //
//////////////////////////////////////////////////////////////////////////////
public Observable createEncoderFunc(Observable in) {
return in.ofType(String[].class).compose(new String2Inference());
}
public Observable createMultiMapFunc(Observable in) {
return in.ofType(Map.class).compose(new Map2Inference());
}
public Observable createVectorFunc(Observable in) {
return in.ofType(int[].class).compose(new Vector2Inference());
}
public Observable createManualInputFunc(Observable in) {
return in.ofType(ManualInput.class).compose(new Copy2Inference());
}
//////////////////////////////////////////////////////////////////////////////
// OBSERVABLE COMPONENT CREATION METHODS //
//////////////////////////////////////////////////////////////////////////////
public Func1 createSpatialFunc(final SpatialPooler sp) {
return new Func1() {
int inputWidth = -1;
@Override
public ManualInput call(ManualInput t1) {
int[] sdr = t1.getSDR();
if(sdr.length > 0 && ArrayUtils.isSparse(sdr)) {
if(inputWidth == -1) {
inputWidth = calculateInputWidth();
}
sdr = ArrayUtils.asDense(sdr, inputWidth);
}
sdr = spatialInput(sdr);
return t1.sdr(sdr).feedForwardActiveColumns(sdr);
}
};
}
public Func1 createTemporalFunc(final TemporalMemory tm) {
return new Func1() {
@Override
public ManualInput call(ManualInput t1) {
int[] sdr = t1.getSDR();
if(!ArrayUtils.isSparse(sdr)) {
// Set on Layer, then set sparse actives as the sdr,
// then set on Manual Input (t1)
sdr = ArrayUtils.where(sdr, ArrayUtils.WHERE_1);
t1.sdr(sdr).feedForwardSparseActives(sdr);
}
return t1.sdr(temporalInput(sdr, t1));
}
};
}
public Func1 createClassifierFunc() {
return new Func1() {
private Object bucketIdx;
private Object actValue;
Map inputMap = new HashMap() {
private static final long serialVersionUID = 1L;
@Override
public Object get(Object o) {
return o.equals("bucketIdx") ? bucketIdx : actValue;
}
};
@Override
public ManualInput call(ManualInput t1) {
Map ci = t1.getClassifierInput();
int recordNum = getRecordNum();
for(String key : ci.keySet()) {
NamedTuple inputs = ci.get(key);
bucketIdx = inputs.get("bucketIdx");
actValue = inputs.get("inputValue");
CLAClassifier c = (CLAClassifier)t1.getClassifiers().get(key);
Classification result = c.compute(recordNum, inputMap, t1.getSDR(), isLearn, true);
t1.recordNum(recordNum).storeClassification((String)inputs.get("name"), result);
}
return t1;
}
};
}
public Func1 createAnomalyFunc(final Anomaly an) {
return new Func1() {
int isArrayInput = -1;
int cellsPerColumn = connections.getCellsPerColumn();
@Override
public ManualInput call(ManualInput t1) {
if((hasSP() && t1.getFeedForwardSparseActives() == null) || t1.getPreviousPredictiveCells() == null) {
return t1.anomalyScore(1.0);
}else if(!hasSP() && (isArrayInput == 1 || t1.getLayerInput().getClass().equals(int[].class))) {
isArrayInput = 1;
t1.feedForwardSparseActives((int[])t1.getLayerInput());
}
return t1.anomalyScore(anomalyComputer.compute(t1.getFeedForwardSparseActives(),
SDR.cellsAsColumnIndices(t1.getPreviousPredictiveCells(), cellsPerColumn), 0, 0));
}
};
}
}
/**
* Re-initializes the {@link HTMSensor} following deserialization or restart
* after halt.
*/
private void recreateSensors() {
if(sensor != null) {
// Recreate the Sensor and its underlying Stream
Class sensorKlass = sensor.getSensorClass();
if(sensorKlass.toString().indexOf("File") != -1) {
Object path = sensor.getSensorParams().get("PATH");
sensor = (HTMSensor)Sensor.create(
FileSensor::create, SensorParams.create(Keys::path, "", path));
}else if(sensorKlass.toString().indexOf("Observ") != -1) {
Object supplierOfObservable = sensor.getSensorParams().get("ONSUB");
sensor = (HTMSensor)Sensor.create(
ObservableSensor::create, SensorParams.create(Keys::obs, "", supplierOfObservable));
}else if(sensorKlass.toString().indexOf("URI") != -1) {
Object url = sensor.getSensorParams().get("URI");
sensor = (HTMSensor)Sensor.create(
URISensor::create, SensorParams.create(Keys::uri, "", url));
}
}
}
@Override
public int hashCode() {
final int prime = 31;
int result = 1;
result = prime * result + ((name == null) ? 0 : name.hashCode());
result = prime * result + recordNum;
result = prime * result + algo_content_mask;
result = prime * result + ((currentInference == null) ? 0 : currentInference.hashCode());
result = prime * result + (hasGenericProcess ? 1231 : 1237);
result = prime * result + (isClosed ? 1231 : 1237);
result = prime * result + (isHalted ? 1231 : 1237);
result = prime * result + (isLearn ? 1231 : 1237);
result = prime * result + ((parentRegion == null) ? 0 : parentRegion.hashCode());
result = prime * result + ((sensorParams == null) ? 0 : sensorParams.hashCode());
return result;
}
@Override
public boolean equals(Object obj) {
if(this == obj)
return true;
if(obj == null)
return false;
if(getClass() != obj.getClass())
return false;
Layer other = (Layer)obj;
if(name == null) {
if(other.name != null)
return false;
} else if(!name.equals(other.name))
return false;
if(algo_content_mask != other.algo_content_mask)
return false;
if(currentInference == null) {
if(other.currentInference != null)
return false;
} else if(!currentInference.equals(other.currentInference))
return false;
if(recordNum != other.recordNum)
return false;
if(hasGenericProcess != other.hasGenericProcess)
return false;
if(isClosed != other.isClosed)
return false;
if(isHalted != other.isHalted)
return false;
if(isLearn != other.isLearn)
return false;
if(parentRegion == null) {
if(other.parentRegion != null)
return false;
} else if(other.parentRegion == null || !parentRegion.getName().equals(other.parentRegion.getName()))
return false;
if(sensorParams == null) {
if(other.sensorParams != null)
return false;
} else if(!sensorParams.equals(other.sensorParams))
return false;
return true;
}
}
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