//Methods
/// <summary>
/// Adds a new member network and updates the cluster error statistics.
/// </summary>
/// <param name="newMemberNet">The new member network.</param>
/// <param name="scopeID">The ID of a network's scope.</param>
/// <param name="testData">The testing data bundle (unseen by the network to be added).</param>
/// <param name="filters">The filters to be used to denormalize outputs.</param>
public void AddMember(TNRNet newMemberNet, int scopeID, VectorBundle testData, FeatureFilterBase[] filters)
{
//Check the network output
if (Output != newMemberNet.Output)
{
throw new ArgumentException("Inconsistent output type of the network to be added.", "newMemberNet");
}
//Check number of outputs consistency
if (_memberNetCollection.Count > 0)
{
if (newMemberNet.Network.NumOfOutputValues != NumOfOutputs)
{
throw new ArgumentException("Number of outputs of the network differs from already clustered networks.", "newMemberNet");
}
}
//Add member to inner collection
_memberNetCollection.Add(newMemberNet);
_memberNetScopeIDCollection.Add(scopeID);
//Update cluster error statistics
for (int sampleIdx = 0; sampleIdx < testData.OutputVectorCollection.Count; sampleIdx++)
{
double[] nrmComputedValues = newMemberNet.Network.Compute(testData.InputVectorCollection[sampleIdx]);
for (int outIdx = 0; outIdx < nrmComputedValues.Length; outIdx++)
{
double naturalComputedValue = filters != null ? filters[outIdx].ApplyReverse(nrmComputedValues[outIdx]) : nrmComputedValues[outIdx];
double naturalIdealValue = filters != null ? filters[outIdx].ApplyReverse(testData.OutputVectorCollection[sampleIdx][outIdx]) : testData.OutputVectorCollection[sampleIdx][outIdx];
ErrorStats.Update(nrmComputedValues[outIdx],
testData.OutputVectorCollection[sampleIdx][outIdx],
naturalComputedValue,
naturalIdealValue
);
} //outIdx
} //sampleIdx
return;
}
private void CalculateErrorPercents(ErrorStats errors)
{
errors.SortedValues = new double[errors.Values.Length];
Array.Copy(errors.Values, errors.SortedValues, errors.Values.Length);
Array.Sort(errors.SortedValues);
errors.Mean = MathUtils.Mean(errors.Values);
errors.AbsMean = MathUtils.Mean(MathUtils.Abs(errors.Values));
errors.Std = MathUtils.StandardDeviation(errors.Values, errors.Mean);
errors.Minimum = errors.SortedValues[0];
errors.Percent25 = errors.SortedValues[errors.SortedValues.Length * 25 / 100];
errors.Percent50 = errors.SortedValues[errors.SortedValues.Length * 50 / 100];
errors.Percent75 = errors.SortedValues[errors.SortedValues.Length * 75 / 100];
errors.Maximum = errors.SortedValues[errors.SortedValues.Length - 1];
}
