/// <summary>
/// Constructor of McBoostTreeLoss
/// </summary>
/// <param name="dp">the input data used to train boosted regression trees</param>
/// <param name="learnRate">the input learning rate specified in training</param>
public McBoostTreeLoss(LabelFeatureDataCoded labelFeatureDataCoded, LabelConverter labelConvert,
float learnRate)
{
this.learnRate = learnRate;
this.numClass = 0;
this.numSamples = labelFeatureDataCoded.NumDataPoint;
this.classLabels = new int[labelFeatureDataCoded.NumDataPoint];
for (int i = 0; i < this.numSamples; i++)
{
this.classLabels[i] = (int)labelConvert.convert(labelFeatureDataCoded.GetLabel(i));
if (this.classLabels[i] + 1 > this.numClass)
this.numClass = this.classLabels[i] + 1;
}
//qiangwu: we probably don't need a matrix to store the label information though it is more convinent coding-wise
this.classLabelsMatrix = BulidClassLabelsMatrix(this.classLabels, this.numClass);
this.classProb = new float[this.numClass][];
this.classFunValue = new float[this.numClass][];
this.pseudoResponses = new float[this.numClass][];
this.weights = new float[this.numClass][];
for (int k = 0; k < this.numClass; k++)
{
this.classProb[k] = new float[this.numSamples];
this.classFunValue[k] = new float[this.numSamples];
this.pseudoResponses[k] = new float[this.numSamples];
this.weights[k] = new float[this.numSamples];
}
//float[] weightsByLabel = WeightsByLabel(this.classLabels);
//float[] probWeights = new float[this.numSamples];
}
/// <summary>
/// Constructor of BoostTreeRankNetLoss
/// </summary>
/// <param name="dp">the input data used to train boosted regression trees</param>
/// <param name="learnRate">the input learning rate specified in training</param>
public BoostTreeNoGainLambdaLoss(LabelFeatureDataCoded labelFeatureDataCoded, LabelConverter labelConvert,
float learnRate, float labelWeight, StepSizeType stepSizeType, FindStepLib fs,
float labelForUnlabeled, double scoreForDegenerateQuery, int truncLevel)
{
this.learnRate = learnRate;
this.labelWeight = labelWeight;
this.labelForUnlabeled = labelForUnlabeled;
this.scoreForDegenerateQuery = scoreForDegenerateQuery;
this.truncLevel = truncLevel;
this.labels = new float[labelFeatureDataCoded.NumDataPoint];
for (int i = 0; i < labelFeatureDataCoded.NumDataPoint; i++)
{
this.labels[i] = labelConvert.convert(labelFeatureDataCoded.GetLabel(i));
}
this.numSamples = labels.Length;
this.score = new float[this.numSamples];
this.funValue = new float[this.numSamples];
this.pseudoResponses = new float[this.numSamples];
this.weights = new float[this.numSamples];
this.labelFeatureDataCoded = labelFeatureDataCoded;
//data member to compute the optimal adjustment factor (step size) for leaf nodes response
this.qcAccum = this.CreateQueryCollection();
this.qcCurrent = this.CreateQueryCollection();
this.fs = fs;
this.stepSizeType = stepSizeType;
}
public BoostTreeRegressionLoss(LabelFeatureDataCoded labelFeatureDataCoded,
float learnRate)
{
this.learnRate = learnRate;
this.numSamples = labelFeatureDataCoded.NumDataPoint;
this.labels = new float[labelFeatureDataCoded.NumDataPoint];
//for (int i = 0; i < labelFeatureDataCoded.NumDataPoint; i++)
//{
// this.labels[i] = labelConvert.convert(labelFeatureDataCoded.GetLabel(i));
//}
for (int i = 0; i < labelFeatureDataCoded.NumDataPoint; i++)
{
this.labels[i] = labelFeatureDataCoded.GetLabel(i);
}
this.scores = new float[this.numSamples];
this.funValues = new float[this.numSamples];
this.pseudoResponses = new float[this.numSamples];
//this.weights = new float[this.numSamples];
this.labelFeatureDataCoded = labelFeatureDataCoded;
}