prtEvalMinCost   Returns the minimum cost of classification
 
    cost = prtEvalMinCost(prtClassifier, prtDataSet, costMat) returns the
    minimum cost of classification of dataset prtDataSet with classifier
    prtClassifier according to the cost matrix costMat. prtDataSet must be
    a labeled, prtDataSetStandard object. prtClassifier must be a prtClass
    object. costMat must be a 2x2 matrix consisting of the costs. costMat
    = [C00, C10; C01 C11], where Cij is the cost for deciding i when j is
    the truth.
 
    cost = prtEvalMinCost(prtClassifier, prtDataSet,costMat, nFolds)
    returns the minimum cost of classification of dataset prtDataSet with
    classifier prtClassifier according to the cost matrix costMat with
    K-fold cross-validation. prtDataSet must be a labeled, binary
    prtDataSetStandard object. prtClassifier must be a prtClass object.
    nFolds is the number of folds in the K-fold cross-validation.
 
    cost = prtEvalMinCost(prtClassifier, prtDataSet,costMat, xValInds) same
    as above, but use crossValidation with specified indices instead of
    random folds.
 
    [cost, PF, PD] =  prtScoreMinCost(...) returns the probability of false
    alarm PF and the probability of detection PD.
 
    Example: 
    dataSet = prtDataGenSpiral; 
    classifier = prtClassDlrt;
    cost = prtEvalMinCost(classifier, dataSet,[ 0 1; 1 0])