public Prediction Classify(DataMining.Data.Example example)
{
example = example.Clone() as DataMining.Data.Example;
example.Label = -1;
double[] classProbabilities = new double[this._metadata.Target.Values.Length];
int resultIndex = 0;
for (int classIndex = 0; classIndex < classProbabilities.Length; classIndex++)
{
double probability = this.GetProbability(example, classIndex);
classProbabilities[classIndex] = probability;
if (probability > classProbabilities[resultIndex])
{
resultIndex = classIndex;
}
}
double sum = classProbabilities.Sum();
for (int i = 0; i < classProbabilities.Length; i++)
{
classProbabilities[i] /= sum;
}
return(new Prediction(resultIndex, classProbabilities[resultIndex]));
}
public Prediction Classify(DataMining.Data.Example example)
{
example = example.Clone() as DataMining.Data.Example;
example.Label = -1;
this._classProbabilities = new double[this._metadata.Target.Values.Length];
int resultIndex = 0;
for (int classIndex = 0; classIndex < this._metadata.Target.Values.Length; classIndex++)
{
BayesianNetwork bayesianNetwork = this._bayesianNetworks[classIndex];
double probability = this._metadata.Target.ValueCounts[classIndex] / (double)this._metadata.Size;
probability *= bayesianNetwork.GetProbability(example);
this._classProbabilities[classIndex] = probability;
if (probability > this._classProbabilities[resultIndex])
{
resultIndex = classIndex;
}
}
double sum = this._classProbabilities.Sum();
for (int i = 0; i < this._classProbabilities.Length; i++)
{
this._classProbabilities[i] /= sum;
}
return(new Prediction(resultIndex, this._classProbabilities[resultIndex]));
}
public Prediction Classify(DataMining.Data.Example example)
{
example = example.Clone() as DataMining.Data.Example;
example.Label = -1;
int clusterIndex = this._clusteringSolution.GetExampleNativeCluster(example);
Prediction prediction = this._bayesianNetworkClassfiers[clusterIndex].Classify(example);
return(prediction);
}
