/**
* 训练
*/
public override void Train(int IteratCnt)
{
if (FeatureSummary == null)
{
FeatureSummary = new MLFeatureFunctionsSummary <int, int>();
}
for (int k = 0; k < (IteratCnt > 0? IteratCnt:ITERATIONS); k++)
{
int i = 0;
//for (int i = 0; i < functions.Count; i++)
//分组线程
List <Thread> thds = new List <Thread>();
foreach (string key in functions.Keys)
{
double delta = iis_solve_delta(empirical_expects[i], key);
if (k == 0)
{
wNames[i] = key;
}
w[i] += delta;
OnPeriodEvent(_GrpId, k, i, wNames, w);
i++;
}
//if (DEBUG) System.out.println("ITERATIONS: " + k + " " + Arrays.toString(w));
}
FeatureSummary.Keys.Add(w);
if (_GrpId == 0)
{
FeatureSummary.FuncList.AddRange(functions.Values.ToArray());
}
//FeatureSummary.Functions = functions;
this.OnTrainFinished();
}
public void LoadSummary()
{
string txt = OnLoadLocalFile();
List <MLFeatureFunctionsSummary <LabelT, FeatureT> > ret = DetailStringClass.getObjectListByXml <MLFeatureFunctionsSummary <LabelT, FeatureT> >(txt);
if (ret != null && ret.Count > 0)
{
FeatureSummary = ret[0];
}
}
/**
* 创建特征函数
* @param instances 实例
*/
private void createFeatFunctions(InstanceList instances)
{
int maxLabel = 0;
int minLabel = int.MaxValue;
int[] maxFeatures = new int[instances[0].Feature.Count];
List <MLFeature <int> > featureSet = new List <MLFeature <int> >();
foreach (MLInstance <int, int> instance in instances)
{
if (instance.Label > maxLabel)
{
maxLabel = instance.Label;
}
if (instance.Label < minLabel)
{
minLabel = instance.Label;
}
for (int i = 0; i < instance.Feature.Count; i++)
{
if (instance.Feature[i] > maxFeatures[i])
{
maxFeatures[i] = instance.Feature[i];
}
}
featureSet.Add(instance.Feature);
}
features = new List <MLFeature <int> >();
List <List <int> > flist = Feature.getNextFeature("1234567890", maxFeatures.Length);
flist.ForEach(p => features.Add(new Feature(p)));
//featureSet
maxY = maxLabel;
minY = minLabel;
for (int i = 0; i < maxFeatures.Length; i++)
{
for (int x = 0; x <= maxFeatures[i]; x++)
{
for (int y = minY; y <= maxLabel; y++)
{
functions.Add(string.Format("{0}_{1}_{2}", i, x, y), new MLFeatureFunctionsClass <int, int>(i, x, y));
}
}
}
if (_GrpId == 0)
{
if (FeatureSummary == null)
{
FeatureSummary = new MLFeatureFunctionsSummary <int, int>();
}
FeatureSummary.TrainCnt = N;
FeatureSummary.FeatureCnt = maxFeatures.Length;
for (int i = 0; i < maxFeatures.Length; i++)
{
List <int> list = new List <int>();
for (int j = 0; j <= maxFeatures[i]; j++)
{
list.Add(j);
}
FeatureSummary.FeatureList.Add(list);
}
FeatureSummary.LabelCnt = maxY - minY + 1;
for (int i = minY; i <= maxY; i++)
{
FeatureSummary.LabelList.Add(i);
}
}
if (DEBUG)
{
//System.out.println("# features = " + features.size());
//System.out.println("# functions = " + functions.size());
}
}
