private Polynomial2D Ransac(double[][] xdata, double[] ydata,
double threshold = 1.0, int maxEvaluations = 500, int maxSamplings = 30)
{
// We are going to find the best model (which fits
// the maximum number of inlier points as possible).
Polynomial2D bestModel = null;
int[] bestInliers = null;
int maxInliers = 0;
// For this we are going to search for random samples
// of the original points which contains no outliers.
int count = 0; // Total number of trials performed
double N = maxEvaluations; // Estimative of number of trials needed.
// While the number of trials is less than our estimative,
// and we have not surpassed the maximum number of trials
while (count < N && count < maxEvaluations)
{
int[] idx;
Polynomial2D model = null;
// Select at random s datapoints to form a trial model.
idx = Vector.Sample(ydata.Length).Take(maxSamplings).ToArray();
double[][] inlierX = xdata.Get(idx);
double[] inlierY = ydata.Get(idx);
model = Polynomial2D.Fit(inlierX, inlierY);
// Now, evaluate the distances between total points and the model returning the
// indices of the points that are inliers (according to a distance threshold t).
idx = xdata
.Where((x, i) => ydata[i] - model.Evaluate(x[0], x[1]) < threshold)
.Select((x, i) => i).ToArray();
// Check if the model was the model which highest number of inliers:
if (idx.Length > maxInliers)
{
maxInliers = idx.Length; // Set the new maximum,
bestModel = model; // This is the best model found so far,
bestInliers = idx; // Store the indices of the current inliers.
}
count++; // Increase the trial counter.
}
return(bestModel);
}
public double Warp(double x1, double x2)
{
return(model.Evaluate(x1, x2));
}
