public void MeanShiftConstructorTest()
{
Accord.Math.Tools.SetupGenerator(0);
// Test Samples
double[][] samples =
{
new double[] { 0, 1 },
new double[] { 1, 2 },
new double[] { 1, 1 },
new double[] { 0, 7 },
new double[] { 1, 1 },
new double[] { 6, 2 },
new double[] { 6, 5 },
new double[] { 5, 1 },
new double[] { 7, 1 },
new double[] { 5, 1 }
};
var kernel = new GaussianKernel(dimension: 2);
MeanShift meanShift = new MeanShift(2, kernel, 3);
// Compute the model (estimate)
int[] labels = meanShift.Compute(samples);
int a = 0;
int b = 1;
if (0.2358896594197982.IsRelativelyEqual(meanShift.Clusters.Modes[1][0], 1e-10))
{
a = 1;
b = 0;
}
for (int i = 0; i < 5; i++)
Assert.AreEqual(a, labels[i]);
for (int i = 5; i < samples.Length; i++)
Assert.AreEqual(b, labels[i]);
Assert.AreEqual(0.2358896594197982, meanShift.Clusters.Modes[a][0], 1e-10);
Assert.AreEqual(1.0010865560750339, meanShift.Clusters.Modes[a][1], 1e-10);
Assert.AreEqual(6.7284908155626031, meanShift.Clusters.Modes[b][0], 1e-10);
Assert.AreEqual(1.2713970467590967, meanShift.Clusters.Modes[b][1], 1e-10);
Assert.AreEqual(2, meanShift.Clusters.Count);
Assert.AreEqual(2, meanShift.Clusters.Modes.Length);
}
public void MeanShiftConstructorTest()
{
Accord.Math.Random.Generator.Seed = 0;
// Test Samples
double[][] samples =
{
new double[] { 0, 1 },
new double[] { 1, 2 },
new double[] { 1, 1 },
new double[] { 0, 7 },
new double[] { 1, 1 },
new double[] { 6, 2 },
new double[] { 6, 5 },
new double[] { 5, 1 },
new double[] { 7, 1 },
new double[] { 5, 1 }
};
var kernel = new GaussianKernel(dimension: 2);
MeanShift meanShift = new MeanShift(2, kernel, 2.0);
meanShift.UseParallelProcessing = false;
// Compute the model (estimate)
int[] labels = meanShift.Compute(samples);
int a = labels[0];
int b = (a == 0) ? 1 : 0;
for (int i = 0; i < 5; i++)
Assert.AreEqual(a, labels[i]);
for (int i = 5; i < samples.Length; i++)
Assert.AreEqual(b, labels[i]);
Assert.AreEqual(1.1922811512028066, meanShift.Clusters.Modes[a][0], 1e-3);
Assert.AreEqual(1.2567196159235963, meanShift.Clusters.Modes[a][1], 1e-3);
Assert.AreEqual(5.2696337859175868, meanShift.Clusters.Modes[b][0], 1e-3);
Assert.AreEqual(1.4380326532534968, meanShift.Clusters.Modes[b][1], 1e-3);
Assert.AreEqual(2, meanShift.Clusters.Count);
Assert.AreEqual(2, meanShift.Clusters.Modes.Length);
Assert.AreEqual(0.5, meanShift.Clusters.Proportions[0]);
Assert.AreEqual(0.5, meanShift.Clusters.Proportions[1]);
}
/// <summary>
/// Runs the Mean-Shift algorithm.
/// </summary>
///
private void runMeanShift()
{
int pixelSize = 3;
// Retrieve the kernel bandwidth
double sigma = (double)numBandwidth.Value;
// Load original image
Bitmap image = Properties.Resources.leaf;
// Create converters
ImageToArray imageToArray = new ImageToArray(min: -1, max: +1);
ArrayToImage arrayToImage = new ArrayToImage(image.Width, image.Height, min: -1, max: +1);
// Transform the image into an array of pixel values
double[][] pixels; imageToArray.Convert(image, out pixels);
// Create a MeanShift algorithm using the given bandwidth
// and a Gaussian density kernel as the kernel function:
IRadiallySymmetricKernel kernel = new GaussianKernel(pixelSize);
var meanShift = new MeanShift(pixelSize, kernel, sigma)
{
Tolerance = 0.05,
MaxIterations = 10
};
// Compute the mean-shift algorithm until the difference
// in shift vectors between two iterations is below 0.05
int[] idx = meanShift.Compute(pixels);
// Replace every pixel with its corresponding centroid
pixels.ApplyInPlace((x, i) => meanShift.Clusters.Modes[idx[i]]);
// Show resulting image in the picture box
Bitmap result; arrayToImage.Convert(pixels, out result);
pictureBox.Image = result;
}