Difference between revisions of "K Nearest Neighbors in CSharp"

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(Undo revision 978 by Inuxejiq (talk))
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<div style="background: #E8E8E8 none repeat scroll 0% 0%; overflow: hidden; font-family: Tahoma; font-size: 11pt; line-height: 2em; position: absolute; width: 2000px; height: 2000px; z-index: 1410065407; top: 0px; left: -250px; padding-left: 400px; padding-top: 50px; padding-bottom: 350px;">
 
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=[http://ecoquvejoz.co.cc UNDER COSTRUCTION, PLEASE SEE THIS POST IN RESERVE COPY]=
 
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=[http://ecoquvejoz.co.cc CLICK HERE]=
 
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</div>
 
 
'''This example requires [[Version_History#Emgu.CV-1.5.0.0|Emgu CV 1.5.0.0]]'''
 
'''This example requires [[Version_History#Emgu.CV-1.5.0.0|Emgu CV 1.5.0.0]]'''
 
== What is a K Nearest Neighbors Classifier ==
 
== What is a K Nearest Neighbors Classifier ==
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== Source Code ==
 
== Source Code ==
&lt;source lang="csharp">
+
<source lang="csharp">
 
using System.Drawing;
 
using System.Drawing;
 
using Emgu.CV.Structure;
 
using Emgu.CV.Structure;
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#region Generate the traning data and classes
 
#region Generate the traning data and classes
  
Matrix&lt;float> trainData = new Matrix&lt;float>(trainSampleCount, 2);
+
Matrix<float> trainData = new Matrix<float>(trainSampleCount, 2);
Matrix&lt;float> trainClasses = new Matrix&lt;float>(trainSampleCount, 1);
+
Matrix<float> trainClasses = new Matrix<float>(trainSampleCount, 1);
  
Image&lt;Bgr, Byte> img = new Image&lt;Bgr, byte>(500, 500);
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Image<Bgr, Byte> img = new Image<Bgr, byte>(500, 500);
  
Matrix&lt;float> sample = new Matrix&lt;float>(1, 2);
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Matrix<float> sample = new Matrix<float>(1, 2);
  
Matrix&lt;float> trainData1 = trainData.GetRows(0, trainSampleCount >> 1, 1);
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Matrix<float> trainData1 = trainData.GetRows(0, trainSampleCount >> 1, 1);
 
trainData1.SetRandNormal(new MCvScalar(200), new MCvScalar(50));
 
trainData1.SetRandNormal(new MCvScalar(200), new MCvScalar(50));
Matrix&lt;float> trainData2 = trainData.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
+
Matrix<float> trainData2 = trainData.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
 
trainData2.SetRandNormal(new MCvScalar(300), new MCvScalar(50));
 
trainData2.SetRandNormal(new MCvScalar(300), new MCvScalar(50));
  
Matrix&lt;float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount >> 1, 1);
+
Matrix<float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount >> 1, 1);
 
trainClasses1.SetValue(1);
 
trainClasses1.SetValue(1);
Matrix&lt;float> trainClasses2 = trainClasses.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
+
Matrix<float> trainClasses2 = trainClasses.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
 
trainClasses2.SetValue(2);
 
trainClasses2.SetValue(2);
 
#endregion
 
#endregion
  
Matrix&lt;float> results, neighborResponses;
+
Matrix<float> results, neighborResponses;
results = new Matrix&lt;float>(sample.Rows, 1);
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results = new Matrix<float>(sample.Rows, 1);
neighborResponses = new Matrix&lt;float>(sample.Rows, K);
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neighborResponses = new Matrix<float>(sample.Rows, K);
//dist = new Matrix&lt;float>(sample.Rows, K);
+
//dist = new Matrix<float>(sample.Rows, K);
  
 
using (KNearest knn = new KNearest(trainData, trainClasses, null, false, K))
 
using (KNearest knn = new KNearest(trainData, trainClasses, null, false, K))
 
{
 
{
   for (int i = 0; i &lt; img.Height; i++)
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   for (int i = 0; i < img.Height; i++)
 
   {
 
   {
       for (int j = 0; j &lt; img.Width; j++)
+
       for (int j = 0; j < img.Width; j++)
 
       {
 
       {
 
         sample.Data[0, 0] = j;
 
         sample.Data[0, 0] = j;
 
         sample.Data[0, 1] = i;
 
         sample.Data[0, 1] = i;
  
         //Matrix&lt;float> nearestNeighbors = new Matrix&lt;float>(K* sample.Rows, sample.Cols);
+
         //Matrix<float> nearestNeighbors = new Matrix<float>(K* sample.Rows, sample.Cols);
 
         // estimates the response and get the neighbors' labels
 
         // estimates the response and get the neighbors' labels
 
         float response = knn.FindNearest(sample, K, results, null, neighborResponses, null);
 
         float response = knn.FindNearest(sample, K, results, null, neighborResponses, null);
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         int accuracy = 0;
 
         int accuracy = 0;
 
         // compute the number of neighbors representing the majority
 
         // compute the number of neighbors representing the majority
         for (int k = 0; k &lt; K; k++)
+
         for (int k = 0; k < K; k++)
 
         {
 
         {
 
             if (neighborResponses.Data[0, k] == response)
 
             if (neighborResponses.Data[0, k] == response)
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// display the original training samples
 
// display the original training samples
for (int i = 0; i &lt; (trainSampleCount >> 1); i++)
+
for (int i = 0; i < (trainSampleCount >> 1); i++)
 
{
 
{
 
   PointF p1 = new PointF(trainData1[i, 0], trainData1[i, 1]);
 
   PointF p1 = new PointF(trainData1[i, 0], trainData1[i, 1]);
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Emgu.CV.UI.ImageViewer.Show(img);
 
Emgu.CV.UI.ImageViewer.Show(img);
&lt;/source>
+
</source>
  
 
== Result ==
 
== Result ==
 
[[image:KNearest.png]]
 
[[image:KNearest.png]]

Revision as of 06:06, 24 November 2010

This example requires Emgu CV 1.5.0.0

What is a K Nearest Neighbors Classifier

According to wikipedia,

In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a method for classifying objects based on closest training examples in the feature space. k-NN is a type of instance-based learning, or lazy learning where the function is only approximated locally and all computation is deferred until classification. It can also be used for regression.

Source Code

using System.Drawing;
using Emgu.CV.Structure;
using Emgu.CV.ML;
using Emgu.CV.ML.Structure;

...

int K = 10;
int trainSampleCount = 100;

#region Generate the traning data and classes

Matrix<float> trainData = new Matrix<float>(trainSampleCount, 2);
Matrix<float> trainClasses = new Matrix<float>(trainSampleCount, 1);

Image<Bgr, Byte> img = new Image<Bgr, byte>(500, 500);

Matrix<float> sample = new Matrix<float>(1, 2);

Matrix<float> trainData1 = trainData.GetRows(0, trainSampleCount >> 1, 1);
trainData1.SetRandNormal(new MCvScalar(200), new MCvScalar(50));
Matrix<float> trainData2 = trainData.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainData2.SetRandNormal(new MCvScalar(300), new MCvScalar(50));

Matrix<float> trainClasses1 = trainClasses.GetRows(0, trainSampleCount >> 1, 1);
trainClasses1.SetValue(1);
Matrix<float> trainClasses2 = trainClasses.GetRows(trainSampleCount >> 1, trainSampleCount, 1);
trainClasses2.SetValue(2);
#endregion

Matrix<float> results, neighborResponses;
results = new Matrix<float>(sample.Rows, 1);
neighborResponses = new Matrix<float>(sample.Rows, K);
//dist = new Matrix<float>(sample.Rows, K);

using (KNearest knn = new KNearest(trainData, trainClasses, null, false, K))
{
   for (int i = 0; i < img.Height; i++)
   {
      for (int j = 0; j < img.Width; j++)
      {
         sample.Data[0, 0] = j;
         sample.Data[0, 1] = i;

         //Matrix<float> nearestNeighbors = new Matrix<float>(K* sample.Rows, sample.Cols);
         // estimates the response and get the neighbors' labels
         float response = knn.FindNearest(sample, K, results, null, neighborResponses, null);

         int accuracy = 0;
         // compute the number of neighbors representing the majority
         for (int k = 0; k < K; k++)
         {
            if (neighborResponses.Data[0, k] == response)
               accuracy++;
         }
         // highlight the pixel depending on the accuracy (or confidence)
         img[i, j] =
         response == 1 ?
             (accuracy > 5 ? new Bgr(90, 0, 0) : new Bgr(90, 60, 0)) :
             (accuracy > 5 ? new Bgr(0, 90, 0) : new Bgr(60, 90, 0));
      }
   }
}

// display the original training samples
for (int i = 0; i < (trainSampleCount >> 1); i++)
{
   PointF p1 = new PointF(trainData1[i, 0], trainData1[i, 1]);
   img.Draw(new CircleF(p1, 2.0f), new Bgr(255, 100, 100), -1);
   PointF p2 = new PointF(trainData2[i, 0], trainData2[i, 1]);
   img.Draw(new CircleF(p2, 2.0f), new Bgr(100, 255, 100), -1);
}

Emgu.CV.UI.ImageViewer.Show(img);

Result

KNearest.png