private void btnInitialize_Click(object sender, EventArgs e)
{
kmeans = new KMeans(k);
kmeans.Compute(mixture);
// Classify all instances in mixture data
int[] classifications = kmeans.Classify(mixture);
// Draw the classifications
updateGraph(classifications);
}
public void KMeansConstructorTest()
{
Accord.Math.Tools.SetupGenerator(0);
// Declare some observations
double[][] observations =
{
new double[] { -5, -2, -1 },
new double[] { -5, -5, -6 },
new double[] { 2, 1, 1 },
new double[] { 1, 1, 2 },
new double[] { 1, 2, 2 },
new double[] { 3, 1, 2 },
new double[] { 11, 5, 4 },
new double[] { 15, 5, 6 },
new double[] { 10, 5, 6 },
};
// Create a new K-Means algorithm with 3 clusters
KMeans kmeans = new KMeans(3);
// Compute the algorithm, retrieving an integer array
// containing the labels for each of the observations
int[] labels = kmeans.Compute(observations);
// As a result, the first two observations should belong to the
// same cluster (thus having the same label). The same should
// happen to the next four observations and to the last three.
Assert.AreEqual(labels[0], labels[1]);
Assert.AreEqual(labels[2], labels[3]);
Assert.AreEqual(labels[2], labels[4]);
Assert.AreEqual(labels[2], labels[5]);
Assert.AreEqual(labels[6], labels[7]);
Assert.AreEqual(labels[6], labels[8]);
Assert.AreNotEqual(labels[0], labels[2]);
Assert.AreNotEqual(labels[2], labels[6]);
Assert.AreNotEqual(labels[0], labels[6]);
int[] labels2 = kmeans.Classify(observations);
Assert.IsTrue(labels.IsEqual(labels2));
}
public Bitmap ProcessImage(Bitmap image, List <Color> cenotridColorList)
{
Bitmap resultImage = new Bitmap(image.Width, image.Height); //resultimage bitmapinin uzunluk ve genişliğini tanımladık
for (int i = 0; i < resultImage.Height; i++) //yüksekliğine kadar aldık
{
for (int j = 0; j < resultImage.Width; j++) //genişliğine kadar aldık
{
Color c = image.GetPixel(i, j); ////image in pixellerini c ye atadık
double[] pixelArray = new double[] { c.R, c.G, c.B }; // bu pixelleri rneklerine ayırdık
int resultCentroid = _kmeans.Classify(pixelArray);
Color centroidColor = cenotridColorList[resultCentroid];
resultImage.SetPixel(i, j, centroidColor);
}
}
return(resultImage);
}
private void btnPresentResults_Click(object sender, EventArgs e)
{
frmPresenter presenter = new frmPresenter();
int cluster = 0;
foreach (Centroid c in _centroids)
{
presenter.AddList(String.Format("Cluster: {0}", cluster));
cluster++;
}
foreach (string file in _files)
{
Bitmap bmpImage = ResizeImage(file, new Size(400, 200));
double[] processResult = GetAverageRGB(bmpImage);
int clusterIndex = _kMeans.Classify(processResult);
presenter.AddItemToList(clusterIndex, file);
}
presenter.Show();
}
public void KMeansConstructorTest()
{
Accord.Math.Tools.SetupGenerator(0);
// Declare some observations
double[][] observations =
{
new double[] { -5, -2, -1 },
new double[] { -5, -5, -6 },
new double[] { 2, 1, 1 },
new double[] { 1, 1, 2 },
new double[] { 1, 2, 2 },
new double[] { 3, 1, 2 },
new double[] { 11, 5, 4 },
new double[] { 15, 5, 6 },
new double[] { 10, 5, 6 },
};
// Create a new K-Means algorithm with 3 clusters
KMeans kmeans = new KMeans(3);
// Compute the algorithm, retrieving an integer array
// containing the labels for each of the observations
int[] labels = kmeans.Compute(observations);
// As a result, the first two observations should belong to the
// same cluster (thus having the same label). The same should
// happen to the next four observations and to the last three.
Assert.AreEqual(labels[0], labels[1]);
Assert.AreEqual(labels[2], labels[3]);
Assert.AreEqual(labels[2], labels[4]);
Assert.AreEqual(labels[2], labels[5]);
Assert.AreEqual(labels[6], labels[7]);
Assert.AreEqual(labels[6], labels[8]);
Assert.AreNotEqual(labels[0], labels[2]);
Assert.AreNotEqual(labels[2], labels[6]);
Assert.AreNotEqual(labels[0], labels[6]);
int[] labels2 = kmeans.Classify(observations);
Assert.IsTrue(labels.IsEqual(labels2));
}
private void btnInitialize_Click(object sender, EventArgs e)
{
kmeans = new KMeans(k);
kmeans.Compute(mixture);
// Classify all instances in mixture data
int[] classifications = kmeans.Classify(mixture);
// Draw the classifications
updateGraph(classifications);
}
