double Score(const Kernel &kernel,
const typename Kernel::Model &model,
const std::vector<T> &samples,
std::vector<T> *inliers) const
{
double cost = 0.0;
for (size_t j = 0; j < samples.size(); ++j) {
double error = kernel.Error(samples[j], model);
if (error < threshold_) {
cost += error;
inliers->push_back(samples[j]);
} else {
cost += threshold_;
}
}
return cost;
}
double LeastMedianOfSquares(const Kernel &kernel,
typename Kernel::Model * model = NULL,
double* outlierThreshold = NULL,
double outlierRatio=0.5,
double minProba=0.99)
{
const size_t min_samples = Kernel::MINIMUM_SAMPLES;
const size_t total_samples = kernel.NumSamples();
std::vector<double> residuals(total_samples); // Array for storing residuals
std::vector<size_t> vec_sample(min_samples);
double dBestMedian = std::numeric_limits<double>::max();
// Required number of iterations is evaluated from outliers ratio
const size_t N = (min_samples<total_samples)?
getNumSamples(minProba, outlierRatio, min_samples): 0;
for (size_t i=0; i < N; i++) {
// Get Samples indexes
UniformSample(min_samples, total_samples, &vec_sample);
// Estimate parameters: the solutions are stored in a vector
std::vector<typename Kernel::Model> models;
kernel.Fit(vec_sample, &models);
// Now test the solutions on the whole data
for (size_t k = 0; k < models.size(); ++k) {
//Compute Residuals :
for (size_t l = 0; l < total_samples; ++l) {
double error = kernel.Error(l, models[k]);
residuals[l] = error;
}
// Compute median
std::vector<double>::iterator itMedian = residuals.begin() +
std::size_t( total_samples*(1.-outlierRatio) );
std::nth_element(residuals.begin(), itMedian, residuals.end());
double median = *itMedian;
// Store best solution
if(median < dBestMedian) {
dBestMedian = median;
if (model) (*model) = models[k];
}
}
}
// This array of precomputed values corresponds to the inverse
// cumulative function for a normal distribution. For more information
// consult the litterature (Robust Regression for Outlier Detection,
// rouseeuw-leroy). The values are computed for each 5%
static const double ICDF[21] = {
1.4e16, 15.94723940, 7.957896558, 5.287692054,
3.947153876, 3.138344200, 2.595242369, 2.203797543,
1.906939402, 1.672911853, 1.482602218, 1.323775627,
1.188182950, 1.069988721, 0.9648473415, 0.8693011162,
0.7803041458, 0.6946704675, 0.6079568319,0.5102134568,
0.3236002672
};
// Evaluate the outlier threshold
if(outlierThreshold) {
double sigma = ICDF[int((1.-outlierRatio)*20.)] *
(1. + 5. / double(total_samples - min_samples));
*outlierThreshold = (double)(sigma * sigma * dBestMedian * 4.);
if (N==0) *outlierThreshold = std::numeric_limits<double>::max();
}
return dBestMedian;
}
