/*
"getBestStump()" function saves the best decision stump's threshold, weak hypothesis and its error;
*/
void DSC::getBestStump(Threshold &threshold, ivec &hypothesis, double &error)
{
/* Separating weights by classes: */
mat class1Weights = weights(find(classes == -1));
mat class2Weights = weights(find(classes == +1));
/* Accumulate weights for given thresholds ("number of thresholds" x "number of features"): */
mat thresholdWeights1 = accumarray(join_rows(class1Thresholds, featureIndexes1),
repmat(class1Weights, features.n_cols, 1),
SizeMat(N_THRESHOLDS, features.n_cols));
mat thresholdWeights2 = accumarray(join_rows(class2Thresholds, featureIndexes2),
repmat(class2Weights, features.n_cols, 1),
SizeMat(N_THRESHOLDS, features.n_cols));
/*
Looking for threshold with minimum error;
Here we construct cummulative sum of weights for seaprate classes, then we add them;
In order to find the smallest error, we consider both directions of a threshold;
*/
mat thresholdErrors = flipud(cumsum(flipud(thresholdWeights1))) + cumsum(thresholdWeights2);
thresholdErrors = join_cols(thresholdErrors, sum(weights) - thresholdErrors);
uword index;
uword featureType;
error = thresholdErrors.min(index, featureType);
threshold.featureType = featureType;
if (index > N_THRESHOLDS - 1)
{
threshold.direction = -1;
index -= N_THRESHOLDS;
}
else
{
threshold.direction = +1;
}
threshold.value = minFeatures(featureType) + (ranges(featureType) / N_THRESHOLDS) * index;
/* Evaluating hypothesis for derived threshold: */
classify(threshold, features, hypothesis);
return;
}
void rotate(vector<vector<int> > &matrix) {
flipud(matrix);
transpose(matrix);
}
