/// <summary> Generates the classifier.
///
/// </summary>
/// <param name="instances">set of instances serving as training data
/// </param>
/// <exception cref="Exception">if the classifier has not been generated successfully
/// </exception>
public override void buildClassifier(Instances instances)
{
double sumOfWeights = 0;
m_Class = instances.classAttribute();
m_ClassValue = 0;
switch (instances.classAttribute().type())
{
case weka.core.Attribute.NUMERIC:
m_Counts = null;
break;
case weka.core.Attribute.NOMINAL:
m_Counts = new double[instances.numClasses()];
for (int i = 0; i < m_Counts.Length; i++)
{
m_Counts[i] = 1;
}
sumOfWeights = instances.numClasses();
break;
default:
throw new System.Exception("ZeroR can only handle nominal and numeric class" + " attributes.");
}
System.Collections.IEnumerator enu = instances.enumerateInstances();
//UPGRADE_TODO: Method 'java.util.Enumeration.hasMoreElements' was converted to 'System.Collections.IEnumerator.MoveNext' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationhasMoreElements'"
while (enu.MoveNext())
{
//UPGRADE_TODO: Method 'java.util.Enumeration.nextElement' was converted to 'System.Collections.IEnumerator.Current' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationnextElement'"
Instance instance = (Instance) enu.Current;
if (!instance.classIsMissing())
{
if (instances.classAttribute().Nominal)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1042'"
m_Counts[(int) instance.classValue()] += instance.weight();
}
else
{
m_ClassValue += instance.weight() * instance.classValue();
}
sumOfWeights += instance.weight();
}
}
if (instances.classAttribute().Numeric)
{
if (Utils.gr(sumOfWeights, 0))
{
m_ClassValue /= sumOfWeights;
}
}
else
{
m_ClassValue = Utils.maxIndex(m_Counts);
Utils.normalize(m_Counts, sumOfWeights);
}
}
public void Build(weka.core.Instances instances)
{
WekaUtils.DebugAssert(instances.numClasses() == 3, "instance's numClasses should be 3.");
m_counts = new int[instances.numClasses()];
for (int i = 0; i < m_counts.Length; i++)
{
m_counts[i] = 0;
}
foreach (Instance instance in instances)
{
int v = (int)instance.classValue();
m_counts[v]++;
}
}
/// <summary> Initializes all the counters for the evaluation and also takes a
/// cost matrix as parameter.
/// Use <code>useNoPriors()</code> if the dataset is the test set and you
/// can't initialize with the priors from the training set via
/// <code>setPriors(Instances)</code>.
///
/// </summary>
/// <param name="data"> set of training instances, to get some header
/// information and prior class distribution information
/// </param>
/// <param name="costMatrix"> the cost matrix---if null, default costs will be used
/// </param>
/// <throws> Exception if cost matrix is not compatible with </throws>
/// <summary> data, the class is not defined or the class is numeric
/// </summary>
/// <seealso cref="useNoPriors()">
/// </seealso>
/// <seealso cref="setPriors(Instances)">
/// </seealso>
public Evaluation(Instances data, CostMatrix costMatrix)
{
m_NumClasses = data.numClasses();
m_NumFolds = 1;
m_ClassIsNominal = data.classAttribute().Nominal;
if (m_ClassIsNominal)
{
double[][] tmpArray = new double[m_NumClasses][];
for (int i = 0; i < m_NumClasses; i++)
{
tmpArray[i] = new double[m_NumClasses];
}
m_ConfusionMatrix = tmpArray;
m_ClassNames = new System.String[m_NumClasses];
for (int i = 0; i < m_NumClasses; i++)
{
m_ClassNames[i] = data.classAttribute().value_Renamed(i);
}
}
m_CostMatrix = costMatrix;
if (m_CostMatrix != null)
{
if (!m_ClassIsNominal)
{
throw new System.Exception("Class has to be nominal if cost matrix " + "given!");
}
if (m_CostMatrix.size() != m_NumClasses)
{
throw new System.Exception("Cost matrix not compatible with data!");
}
}
m_ClassPriors = new double[m_NumClasses];
Priors = data;
m_MarginCounts = new double[k_MarginResolution + 1];
}
/// <summary> Creates split on numeric attribute.
///
/// </summary>
/// <exception cref="Exception">if something goes wrong
/// </exception>
private void handleNumericAttribute(Instances trainInstances)
{
int firstMiss;
int next = 1;
int last = 0;
int splitIndex = - 1;
double currentInfoGain;
double defaultEnt;
double minSplit;
Instance instance;
int i;
// Current attribute is a numeric attribute.
m_distribution = new Distribution(2, trainInstances.numClasses());
// Only Instances with known values are relevant.
System.Collections.IEnumerator enu = trainInstances.enumerateInstances();
i = 0;
//UPGRADE_TODO: Method 'java.util.Enumeration.hasMoreElements' was converted to 'System.Collections.IEnumerator.MoveNext' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationhasMoreElements'"
while (enu.MoveNext())
{
//UPGRADE_TODO: Method 'java.util.Enumeration.nextElement' was converted to 'System.Collections.IEnumerator.Current' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationnextElement'"
instance = (Instance) enu.Current;
if (instance.isMissing(m_attIndex))
break;
m_distribution.add(1, instance);
i++;
}
firstMiss = i;
// Compute minimum number of Instances required in each
// subset.
minSplit = 0.1 * (m_distribution.total()) / ((double) trainInstances.numClasses());
if (Utils.smOrEq(minSplit, m_minNoObj))
minSplit = m_minNoObj;
else if (Utils.gr(minSplit, 25))
minSplit = 25;
// Enough Instances with known values?
if (Utils.sm((double) firstMiss, 2 * minSplit))
return ;
// Compute values of criteria for all possible split
// indices.
defaultEnt = infoGainCrit.oldEnt(m_distribution);
while (next < firstMiss)
{
if (trainInstances.instance(next - 1).value_Renamed(m_attIndex) + 1e-5 < trainInstances.instance(next).value_Renamed(m_attIndex))
{
// Move class values for all Instances up to next
// possible split point.
m_distribution.shiftRange(1, 0, trainInstances, last, next);
// Check if enough Instances in each subset and compute
// values for criteria.
if (Utils.grOrEq(m_distribution.perBag(0), minSplit) && Utils.grOrEq(m_distribution.perBag(1), minSplit))
{
currentInfoGain = infoGainCrit.splitCritValue(m_distribution, m_sumOfWeights, defaultEnt);
if (Utils.gr(currentInfoGain, m_infoGain))
{
m_infoGain = currentInfoGain;
splitIndex = next - 1;
}
m_index++;
}
last = next;
}
next++;
}
// Was there any useful split?
if (m_index == 0)
return ;
// Compute modified information gain for best split.
m_infoGain = m_infoGain - (Utils.log2(m_index) / m_sumOfWeights);
if (Utils.smOrEq(m_infoGain, 0))
return ;
// Set instance variables' values to values for
// best split.
m_numSubsets = 2;
m_splitPoint = (trainInstances.instance(splitIndex + 1).value_Renamed(m_attIndex) + trainInstances.instance(splitIndex).value_Renamed(m_attIndex)) / 2;
// In case we have a numerical precision problem we need to choose the
// smaller value
if (m_splitPoint == trainInstances.instance(splitIndex + 1).value_Renamed(m_attIndex))
{
m_splitPoint = trainInstances.instance(splitIndex).value_Renamed(m_attIndex);
}
// Restore distributioN for best split.
m_distribution = new Distribution(2, trainInstances.numClasses());
m_distribution.addRange(0, trainInstances, 0, splitIndex + 1);
m_distribution.addRange(1, trainInstances, splitIndex + 1, firstMiss);
// Compute modified gain ratio for best split.
m_gainRatio = gainRatioCrit.splitCritValue(m_distribution, m_sumOfWeights, m_infoGain);
}
/// <summary> Creates split on enumerated attribute.
///
/// </summary>
/// <exception cref="Exception">if something goes wrong
/// </exception>
private void handleEnumeratedAttribute(Instances trainInstances)
{
Instance instance;
m_distribution = new Distribution(m_complexityIndex, trainInstances.numClasses());
// Only Instances with known values are relevant.
System.Collections.IEnumerator enu = trainInstances.enumerateInstances();
//UPGRADE_TODO: Method 'java.util.Enumeration.hasMoreElements' was converted to 'System.Collections.IEnumerator.MoveNext' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationhasMoreElements'"
while (enu.MoveNext())
{
//UPGRADE_TODO: Method 'java.util.Enumeration.nextElement' was converted to 'System.Collections.IEnumerator.Current' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationnextElement'"
instance = (Instance) enu.Current;
if (!instance.isMissing(m_attIndex))
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1042'"
m_distribution.add((int) instance.value_Renamed(m_attIndex), instance);
}
}
// Check if minimum number of Instances in at least two
// subsets.
if (m_distribution.check(m_minNoObj))
{
m_numSubsets = m_complexityIndex;
m_infoGain = infoGainCrit.splitCritValue(m_distribution, m_sumOfWeights);
m_gainRatio = gainRatioCrit.splitCritValue(m_distribution, m_sumOfWeights, m_infoGain);
}
}
/// <summary> Creates split on enumerated attribute.
///
/// </summary>
/// <exception cref="Exception">if something goes wrong
/// </exception>
private void handleEnumeratedAttribute(Instances trainInstances)
{
Distribution newDistribution, secondDistribution;
int numAttValues;
double currIG, currGR;
Instance instance;
int i;
numAttValues = trainInstances.attribute(m_attIndex).numValues();
newDistribution = new Distribution(numAttValues, trainInstances.numClasses());
// Only Instances with known values are relevant.
System.Collections.IEnumerator enu = trainInstances.enumerateInstances();
//UPGRADE_TODO: Method 'java.util.Enumeration.hasMoreElements' was converted to 'System.Collections.IEnumerator.MoveNext' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationhasMoreElements'"
while (enu.MoveNext())
{
//UPGRADE_TODO: Method 'java.util.Enumeration.nextElement' was converted to 'System.Collections.IEnumerator.Current' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationnextElement'"
instance = (Instance) enu.Current;
if (!instance.isMissing(m_attIndex))
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1042'"
newDistribution.add((int) instance.value_Renamed(m_attIndex), instance);
}
}
m_distribution = newDistribution;
// For all values
for (i = 0; i < numAttValues; i++)
{
if (Utils.grOrEq(newDistribution.perBag(i), m_minNoObj))
{
secondDistribution = new Distribution(newDistribution, i);
// Check if minimum number of Instances in the two
// subsets.
if (secondDistribution.check(m_minNoObj))
{
m_numSubsets = 2;
currIG = m_infoGainCrit.splitCritValue(secondDistribution, m_sumOfWeights);
currGR = m_gainRatioCrit.splitCritValue(secondDistribution, m_sumOfWeights, currIG);
if ((i == 0) || Utils.gr(currGR, m_gainRatio))
{
m_gainRatio = currGR;
m_infoGain = currIG;
m_splitPoint = (double) i;
m_distribution = secondDistribution;
}
}
}
}
}
public override void buildClassifier(Instances insts)
{
if (insts.checkForStringAttributes())
{
throw new Exception("Cannot handle string attributes!");
}
if (insts.numClasses() > 2)
{
throw new System.Exception("Can only handle two-class datasets!");
}
if (insts.classAttribute().Numeric)
{
throw new Exception("Can't handle a numeric class!");
}
// Filter data
m_Train = new Instances(insts);
m_Train.deleteWithMissingClass();
m_ReplaceMissingValues = new ReplaceMissingValues();
m_ReplaceMissingValues.setInputFormat(m_Train);
m_Train = Filter.useFilter(m_Train, m_ReplaceMissingValues);
m_NominalToBinary = new NominalToBinary();
m_NominalToBinary.setInputFormat(m_Train);
m_Train = Filter.useFilter(m_Train, m_NominalToBinary);
/** Randomize training data */
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.util.Random.Random' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
m_Train.randomize(new System.Random((System.Int32) m_Seed));
/** Make space to store perceptrons */
m_Additions = new int[m_MaxK + 1];
m_IsAddition = new bool[m_MaxK + 1];
m_Weights = new int[m_MaxK + 1];
/** Compute perceptrons */
m_K = 0;
for (int it = 0; it < m_NumIterations; it++)
{
for (int i = 0; i < m_Train.numInstances(); i++)
{
Instance inst = m_Train.instance(i);
if (!inst.classIsMissing())
{
int prediction = makePrediction(m_K, inst);
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1042'"
int classValue = (int) inst.classValue();
if (prediction == classValue)
{
m_Weights[m_K]++;
}
else
{
m_IsAddition[m_K] = (classValue == 1);
m_Additions[m_K] = i;
m_K++;
m_Weights[m_K]++;
}
if (m_K == m_MaxK)
{
//UPGRADE_NOTE: Labeled break statement was changed to a goto statement. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1012'"
goto out_brk;
}
}
}
}
//UPGRADE_NOTE: Label 'out_brk' was added. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1011'"
out_brk: ;
}
/// <summary> Creates a distribution according to given instances and
/// split model.
///
/// </summary>
/// <exception cref="Exception">if something goes wrong
/// </exception>
public Distribution(Instances source, ClassifierSplitModel modelToUse)
{
int index;
Instance instance;
double[] weights;
m_perClassPerBag = new double[modelToUse.numSubsets()][];
for (int i = 0; i < modelToUse.numSubsets(); i++)
{
m_perClassPerBag[i] = new double[0];
}
m_perBag = new double[modelToUse.numSubsets()];
totaL = 0;
m_perClass = new double[source.numClasses()];
for (int i = 0; i < modelToUse.numSubsets(); i++)
m_perClassPerBag[i] = new double[source.numClasses()];
System.Collections.IEnumerator enu = source.enumerateInstances();
//UPGRADE_TODO: Method 'java.util.Enumeration.hasMoreElements' was converted to 'System.Collections.IEnumerator.MoveNext' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationhasMoreElements'"
while (enu.MoveNext())
{
//UPGRADE_TODO: Method 'java.util.Enumeration.nextElement' was converted to 'System.Collections.IEnumerator.Current' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationnextElement'"
instance = (Instance) enu.Current;
index = modelToUse.whichSubset(instance);
if (index != - 1)
add(index, instance);
else
{
weights = modelToUse.GetWeights(instance);
addWeights(instance, weights);
}
}
}
/// <summary> Creates a distribution with only one bag according
/// to instances in source.
///
/// </summary>
/// <exception cref="Exception">if something goes wrong
/// </exception>
public Distribution(Instances source)
{
m_perClassPerBag = new double[1][];
for (int i = 0; i < 1; i++)
{
m_perClassPerBag[i] = new double[0];
}
m_perBag = new double[1];
totaL = 0;
m_perClass = new double[source.numClasses()];
m_perClassPerBag[0] = new double[source.numClasses()];
System.Collections.IEnumerator enu = source.enumerateInstances();
//UPGRADE_TODO: Method 'java.util.Enumeration.hasMoreElements' was converted to 'System.Collections.IEnumerator.MoveNext' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationhasMoreElements'"
while (enu.MoveNext())
{
//UPGRADE_TODO: Method 'java.util.Enumeration.nextElement' was converted to 'System.Collections.IEnumerator.Current' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationnextElement'"
add(0, (Instance) enu.Current);
}
}
/// <summary> Builds the boosted classifier</summary>
public virtual void buildClassifier(Instances data)
{
m_RandomInstance = new Random(m_Seed);
Instances boostData;
int classIndex = data.classIndex();
if (data.classAttribute().Numeric)
{
throw new Exception("LogitBoost can't handle a numeric class!");
}
if (m_Classifier == null)
{
throw new System.Exception("A base classifier has not been specified!");
}
if (!(m_Classifier is WeightedInstancesHandler) && !m_UseResampling)
{
m_UseResampling = true;
}
if (data.checkForStringAttributes())
{
throw new Exception("Cannot handle string attributes!");
}
if (m_Debug)
{
System.Console.Error.WriteLine("Creating copy of the training data");
}
m_NumClasses = data.numClasses();
m_ClassAttribute = data.classAttribute();
// Create a copy of the data
data = new Instances(data);
data.deleteWithMissingClass();
// Create the base classifiers
if (m_Debug)
{
System.Console.Error.WriteLine("Creating base classifiers");
}
m_Classifiers = new Classifier[m_NumClasses][];
for (int j = 0; j < m_NumClasses; j++)
{
m_Classifiers[j] = Classifier.makeCopies(m_Classifier, this.NumIterations);
}
// Do we want to select the appropriate number of iterations
// using cross-validation?
int bestNumIterations = this.NumIterations;
if (m_NumFolds > 1)
{
if (m_Debug)
{
System.Console.Error.WriteLine("Processing first fold.");
}
// Array for storing the results
double[] results = new double[this.NumIterations];
// Iterate throught the cv-runs
for (int r = 0; r < m_NumRuns; r++)
{
// Stratify the data
data.randomize(m_RandomInstance);
data.stratify(m_NumFolds);
// Perform the cross-validation
for (int i = 0; i < m_NumFolds; i++)
{
// Get train and test folds
Instances train = data.trainCV(m_NumFolds, i, m_RandomInstance);
Instances test = data.testCV(m_NumFolds, i);
// Make class numeric
Instances trainN = new Instances(train);
trainN.ClassIndex = - 1;
trainN.deleteAttributeAt(classIndex);
trainN.insertAttributeAt(new weka.core.Attribute("'pseudo class'"), classIndex);
trainN.ClassIndex = classIndex;
m_NumericClassData = new Instances(trainN, 0);
// Get class values
int numInstances = train.numInstances();
double[][] tmpArray = new double[numInstances][];
for (int i2 = 0; i2 < numInstances; i2++)
{
tmpArray[i2] = new double[m_NumClasses];
}
double[][] trainFs = tmpArray;
double[][] tmpArray2 = new double[numInstances][];
for (int i3 = 0; i3 < numInstances; i3++)
{
tmpArray2[i3] = new double[m_NumClasses];
}
double[][] trainYs = tmpArray2;
for (int j = 0; j < m_NumClasses; j++)
{
for (int k = 0; k < numInstances; k++)
{
trainYs[k][j] = (train.instance(k).classValue() == j)?1.0 - m_Offset:0.0 + (m_Offset / (double) m_NumClasses);
}
}
// Perform iterations
double[][] probs = initialProbs(numInstances);
m_NumGenerated = 0;
double sumOfWeights = train.sumOfWeights();
for (int j = 0; j < this.NumIterations; j++)
{
performIteration(trainYs, trainFs, probs, trainN, sumOfWeights);
Evaluation eval = new Evaluation(train);
eval.evaluateModel(this, test);
results[j] += eval.correct();
}
}
}
// Find the number of iterations with the lowest error
//UPGRADE_TODO: The equivalent in .NET for field 'java.lang.Double.MAX_VALUE' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
double bestResult = - System.Double.MaxValue;
for (int j = 0; j < this.NumIterations; j++)
{
if (results[j] > bestResult)
{
bestResult = results[j];
bestNumIterations = j;
}
}
if (m_Debug)
{
System.Console.Error.WriteLine("Best result for " + bestNumIterations + " iterations: " + bestResult);
}
}
// Build classifier on all the data
int numInstances2 = data.numInstances();
double[][] trainFs2 = new double[numInstances2][];
for (int i4 = 0; i4 < numInstances2; i4++)
{
trainFs2[i4] = new double[m_NumClasses];
}
double[][] trainYs2 = new double[numInstances2][];
for (int i5 = 0; i5 < numInstances2; i5++)
{
trainYs2[i5] = new double[m_NumClasses];
}
for (int j = 0; j < m_NumClasses; j++)
{
for (int i = 0, k = 0; i < numInstances2; i++, k++)
{
trainYs2[i][j] = (data.instance(k).classValue() == j)?1.0 - m_Offset:0.0 + (m_Offset / (double) m_NumClasses);
}
}
// Make class numeric
data.ClassIndex = - 1;
data.deleteAttributeAt(classIndex);
data.insertAttributeAt(new weka.core.Attribute("'pseudo class'"), classIndex);
data.ClassIndex = classIndex;
m_NumericClassData = new Instances(data, 0);
// Perform iterations
double[][] probs2 = initialProbs(numInstances2);
double logLikelihood = CalculateLogLikelihood(trainYs2, probs2);
m_NumGenerated = 0;
if (m_Debug)
{
System.Console.Error.WriteLine("Avg. log-likelihood: " + logLikelihood);
}
double sumOfWeights2 = data.sumOfWeights();
for (int j = 0; j < bestNumIterations; j++)
{
double previousLoglikelihood = logLikelihood;
performIteration(trainYs2, trainFs2, probs2, data, sumOfWeights2);
logLikelihood = CalculateLogLikelihood(trainYs2, probs2);
if (m_Debug)
{
System.Console.Error.WriteLine("Avg. log-likelihood: " + logLikelihood);
}
if (System.Math.Abs(previousLoglikelihood - logLikelihood) < m_Precision)
{
return ;
}
}
}
/// <summary> Generates the classifier.
///
/// </summary>
/// <param name="instances">set of instances serving as training data
/// </param>
/// <exception cref="Exception">if the classifier has not been generated successfully
/// </exception>
public override void buildClassifier(Instances instances)
{
//UPGRADE_TODO: The equivalent in .NET for field 'java.lang.Double.MAX_VALUE' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
double bestVal = System.Double.MaxValue, currVal;
//UPGRADE_TODO: The equivalent in .NET for field 'java.lang.Double.MAX_VALUE' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
double bestPoint = - System.Double.MaxValue, sum;
int bestAtt = - 1, numClasses;
if (instances.checkForStringAttributes())
{
throw new Exception("Can't handle string attributes!");
}
double[][] bestDist = new double[3][];
for (int i = 0; i < 3; i++)
{
bestDist[i] = new double[instances.numClasses()];
}
m_Instances = new Instances(instances);
m_Instances.deleteWithMissingClass();
if (m_Instances.numInstances() == 0)
{
throw new System.ArgumentException("No instances without missing " + "class values in training file!");
}
if (instances.numAttributes() == 1)
{
throw new System.ArgumentException("Attribute missing. Need at least one " + "attribute other than class attribute!");
}
if (m_Instances.classAttribute().Nominal)
{
numClasses = m_Instances.numClasses();
}
else
{
numClasses = 1;
}
// For each attribute
bool first = true;
for (int i = 0; i < m_Instances.numAttributes(); i++)
{
if (i != m_Instances.classIndex())
{
// Reserve space for distribution.
double[][] tmpArray = new double[3][];
for (int i2 = 0; i2 < 3; i2++)
{
tmpArray[i2] = new double[numClasses];
}
m_Distribution = tmpArray;
// Compute value of criterion for best split on attribute
if (m_Instances.attribute(i).Nominal)
{
currVal = findSplitNominal(i);
}
else
{
currVal = findSplitNumeric(i);
}
if ((first) || (currVal < bestVal))
{
bestVal = currVal;
bestAtt = i;
bestPoint = m_SplitPoint;
for (int j = 0; j < 3; j++)
{
Array.Copy(m_Distribution[j], 0, bestDist[j], 0, numClasses);
}
}
// First attribute has been investigated
first = false;
}
}
// Set attribute, split point and distribution.
m_AttIndex = bestAtt;
m_SplitPoint = bestPoint;
m_Distribution = bestDist;
if (m_Instances.classAttribute().Nominal)
{
for (int i = 0; i < m_Distribution.Length; i++)
{
double sumCounts = Utils.sum(m_Distribution[i]);
if (sumCounts == 0)
{
// This means there were only missing attribute values
Array.Copy(m_Distribution[2], 0, m_Distribution[i], 0, m_Distribution[2].Length);
Utils.normalize(m_Distribution[i]);
}
else
{
Utils.normalize(m_Distribution[i], sumCounts);
}
}
}
// Save memory
m_Instances = new Instances(m_Instances, 0);
}
