/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 Heaton Research, Inc.
*
* Licensed 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.util;
import org.encog.ml.data.MLDataSet;
import org.encog.neural.NeuralNetworkError;
import org.encog.neural.networks.ContainsFlat;
/**
* Used to validate if training is valid.
*/
public final class EncogValidate {
/**
* Validate a network for training.
*
* @param network
* The network to validate.
* @param training
* The training set to validate.
*/
public static void validateNetworkForTraining(final ContainsFlat network,
final MLDataSet training) {
int inputCount = network.getFlat().getInputCount();
int outputCount = network.getFlat().getOutputCount();
if (inputCount != training.getInputSize()) {
throw new NeuralNetworkError("The input layer size of "
+ inputCount
+ " must match the training input size of "
+ training.getInputSize() + ".");
}
if ((training.getIdealSize() > 0)
&& (outputCount != training.getIdealSize())) {
throw new NeuralNetworkError("The output layer size of "
+ outputCount
+ " must match the training input size of "
+ training.getIdealSize() + ".");
}
}
/**
* Private constructor.
*/
private EncogValidate() {
}
}