public void CollinearTest()
{
{
Point3 p1 = new Point3(0, 0, 0);
Point3 p2 = new Point3(0, 0, 1);
Point3 p3 = new Point3(0, 0, 2);
bool expected = true;
bool actual = Point3.Collinear(p1, p2, p3);
Assert.AreEqual(expected, actual);
}
{
Point3 p1 = new Point3(1, 0, 0);
Point3 p2 = new Point3(0, 2, 1);
Point3 p3 = new Point3(0, 0, 2);
bool expected = false;
bool actual = Point3.Collinear(p1, p2, p3);
Assert.AreEqual(expected, actual);
}
{
Point3 p1 = new Point3(134, 268, 402);
Point3 p2 = new Point3(329, 658, 98);
Point3 p3 = new Point3(125, 250, 375);
bool expected = false;
bool actual = Point3.Collinear(p1, p2, p3);
Assert.AreEqual(expected, actual);
}
}
private bool degenerate(int[] indices)
{
Point3 p1 = points[indices[0]];
Point3 p2 = points[indices[1]];
Point3 p3 = points[indices[2]];
return(Point3.Collinear(p1, p2, p3));
}
private void computeInliers()
{
int[] bestInliers = null;
int maxInliers = 0;
int size = this.points.Length;
Plane plane = null;
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)
{
int[] sample = null;
int samplings = 0;
// While the number of samples attempted is less
// than the maximum limit of attempts
while (samplings < maxSamplings)
{
// Select at random s data points to form a trial model.
sample = Accord.Statistics.Tools.RandomSample(size, 3);
if (!Point3.Collinear(points[sample[0]], points[sample[1]], points[sample[2]]))
{
Point3[] randPoints = { points[sample[0]], points[sample[1]], points[sample[2]] };
// Fit model using the random selection of points
plane = fitting(randPoints);
break;
}
samplings++; // Increase the samplings counter
}
if (plane == null)
{
throw new ConvergenceException("A model could not be inferred from the data points");
}
// 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).
inliers = distance(plane, this.threshold);
// Check if the model was the model which highest number of inliers:
if (bestInliers == null || inliers.Length > maxInliers)
{
// Yes, this model has the highest number of inliers.
maxInliers = inliers.Length; // Set the new maximum,
bestPlane = plane; // This is the best model found so far,
bestInliers = inliers; // Store the indices of the current inliers.
// Update estimate of N, the number of trials to ensure we pick,
// with probability p, a data set with no outliers.
double pInlier = (double)inliers.Length / (double)size;
double pNoOutliers = 1.0 - System.Math.Pow(pInlier, 3);
N = System.Math.Ceiling(System.Math.Log(1.0 - 0.99) / System.Math.Log(pNoOutliers));
}
Console.WriteLine("trail " + count + " out of " + N);
count++; // Increase the trial counter.
if (count > maxEvaluations)
{
int[] temp = { };
inliers = temp;
return;
}
}
inliers = bestInliers;
}