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The Apache Commons Math project is a library of lightweight, self-contained mathematics and statistics components addressing the most common practical problems not immediately available in the Java programming language or commons-lang.
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.commons.math3.ml.neuralnet;
import java.io.Serializable;
import java.io.ObjectInputStream;
import java.util.concurrent.atomic.AtomicReference;
import java.util.concurrent.atomic.AtomicLong;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.util.Precision;
/**
* Describes a neuron element of a neural network.
*
* This class aims to be thread-safe.
*
* @since 3.3
*/
public class Neuron implements Serializable {
/** Serializable. */
private static final long serialVersionUID = 20130207L;
/** Identifier. */
private final long identifier;
/** Length of the feature set. */
private final int size;
/** Neuron data. */
private final AtomicReference features;
/** Number of attempts to update a neuron. */
private final AtomicLong numberOfAttemptedUpdates = new AtomicLong(0);
/** Number of successful updates of a neuron. */
private final AtomicLong numberOfSuccessfulUpdates = new AtomicLong(0);
/**
* Creates a neuron.
* The size of the feature set is fixed to the length of the given
* argument.
*
* Constructor is package-private: Neurons must be
* {@link Network#createNeuron(double[]) created} by the network
* instance to which they will belong.
*
* @param identifier Identifier (assigned by the {@link Network}).
* @param features Initial values of the feature set.
*/
Neuron(long identifier,
double[] features) {
this.identifier = identifier;
this.size = features.length;
this.features = new AtomicReference(features.clone());
}
/**
* Performs a deep copy of this instance.
* Upon return, the copied and original instances will be independent:
* Updating one will not affect the other.
*
* @return a new instance with the same state as this instance.
* @since 3.6
*/
public synchronized Neuron copy() {
final Neuron copy = new Neuron(getIdentifier(),
getFeatures());
copy.numberOfAttemptedUpdates.set(numberOfAttemptedUpdates.get());
copy.numberOfSuccessfulUpdates.set(numberOfSuccessfulUpdates.get());
return copy;
}
/**
* Gets the neuron's identifier.
*
* @return the identifier.
*/
public long getIdentifier() {
return identifier;
}
/**
* Gets the length of the feature set.
*
* @return the number of features.
*/
public int getSize() {
return size;
}
/**
* Gets the neuron's features.
*
* @return a copy of the neuron's features.
*/
public double[] getFeatures() {
return features.get().clone();
}
/**
* Tries to atomically update the neuron's features.
* Update will be performed only if the expected values match the
* current values.
* In effect, when concurrent threads call this method, the state
* could be modified by one, so that it does not correspond to the
* the state assumed by another.
* Typically, a caller {@link #getFeatures() retrieves the current state},
* and uses it to compute the new state.
* During this computation, another thread might have done the same
* thing, and updated the state: If the current thread were to proceed
* with its own update, it would overwrite the new state (which might
* already have been used by yet other threads).
* To prevent this, the method does not perform the update when a
* concurrent modification has been detected, and returns {@code false}.
* When this happens, the caller should fetch the new current state,
* redo its computation, and call this method again.
*
* @param expect Current values of the features, as assumed by the caller.
* Update will never succeed if the contents of this array does not match
* the values returned by {@link #getFeatures()}.
* @param update Features's new values.
* @return {@code true} if the update was successful, {@code false}
* otherwise.
* @throws DimensionMismatchException if the length of {@code update} is
* not the same as specified in the {@link #Neuron(long,double[])
* constructor}.
*/
public boolean compareAndSetFeatures(double[] expect,
double[] update) {
if (update.length != size) {
throw new DimensionMismatchException(update.length, size);
}
// Get the internal reference. Note that this must not be a copy;
// otherwise the "compareAndSet" below will always fail.
final double[] current = features.get();
if (!containSameValues(current, expect)) {
// Some other thread already modified the state.
return false;
}
// Increment attempt counter.
numberOfAttemptedUpdates.incrementAndGet();
if (features.compareAndSet(current, update.clone())) {
// The current thread could atomically update the state (attempt succeeded).
numberOfSuccessfulUpdates.incrementAndGet();
return true;
} else {
// Some other thread came first (attempt failed).
return false;
}
}
/**
* Retrieves the number of calls to the
* {@link #compareAndSetFeatures(double[],double[]) compareAndSetFeatures}
* method.
* Note that if the caller wants to use this method in combination with
* {@link #getNumberOfSuccessfulUpdates()}, additional synchronization
* may be required to ensure consistency.
*
* @return the number of update attempts.
* @since 3.6
*/
public long getNumberOfAttemptedUpdates() {
return numberOfAttemptedUpdates.get();
}
/**
* Retrieves the number of successful calls to the
* {@link #compareAndSetFeatures(double[],double[]) compareAndSetFeatures}
* method.
* Note that if the caller wants to use this method in combination with
* {@link #getNumberOfAttemptedUpdates()}, additional synchronization
* may be required to ensure consistency.
*
* @return the number of successful updates.
* @since 3.6
*/
public long getNumberOfSuccessfulUpdates() {
return numberOfSuccessfulUpdates.get();
}
/**
* Checks whether the contents of both arrays is the same.
*
* @param current Current values.
* @param expect Expected values.
* @throws DimensionMismatchException if the length of {@code expected}
* is not the same as specified in the {@link #Neuron(long,double[])
* constructor}.
* @return {@code true} if the arrays contain the same values.
*/
private boolean containSameValues(double[] current,
double[] expect) {
if (expect.length != size) {
throw new DimensionMismatchException(expect.length, size);
}
for (int i = 0; i < size; i++) {
if (!Precision.equals(current[i], expect[i])) {
return false;
}
}
return true;
}
/**
* Prevents proxy bypass.
*
* @param in Input stream.
*/
private void readObject(ObjectInputStream in) {
throw new IllegalStateException();
}
/**
* Custom serialization.
*
* @return the proxy instance that will be actually serialized.
*/
private Object writeReplace() {
return new SerializationProxy(identifier,
features.get());
}
/**
* Serialization.
*/
private static class SerializationProxy implements Serializable {
/** Serializable. */
private static final long serialVersionUID = 20130207L;
/** Features. */
private final double[] features;
/** Identifier. */
private final long identifier;
/**
* @param identifier Identifier.
* @param features Features.
*/
SerializationProxy(long identifier,
double[] features) {
this.identifier = identifier;
this.features = features;
}
/**
* Custom serialization.
*
* @return the {@link Neuron} for which this instance is the proxy.
*/
private Object readResolve() {
return new Neuron(identifier,
features);
}
}
}