public void ComputeTest()
{
double[][] inputs =
{
// Class 0
new double[] { 4, 1 },
new double[] { 2, 4 },
new double[] { 2, 3 },
new double[] { 3, 6 },
new double[] { 4, 4 },
// Class 1
new double[] { 9, 10 },
new double[] { 6, 8 },
new double[] { 9, 5 },
new double[] { 8, 7 },
new double[] { 10, 8 }
};
int[] output =
{
0, 0, 0, 0, 0, // The first five are from class 0
1, 1, 1, 1, 1 // The last five are from class 1
};
// Then, we will create a LDA for the given instances.
var mmdc = new MinimumMeanDistanceClassifier(inputs, output);
double[][] projection = mmdc.Scores(inputs);
// Or perform classification using:
int[] results = mmdc.Decide(inputs);
Assert.IsTrue(results.IsEqual(output));
//string str = projection.ToCSharp();
double[][] expected = new double[][] {
new double[] { -7.76, -62.92 },
new double[] { -1.16, -53.92 },
new double[] { -1.36, -62.12 },
new double[] { -5.76, -31.72 },
new double[] { -1.16, -32.32 },
new double[] { -76.96, -6.12 },
new double[] { -28.36, -5.92 },
new double[] { -37.96, -7.12 },
new double[] { -36.56, -0.52 },
new double[] { -68.36, -2.72 }
};
Assert.IsTrue(expected.IsEqual(projection, 1e-6));
}
private void btnFeature_Click(object sender, EventArgs e)
{
if (pca == null)
{
MessageBox.Show("Please compute the analysis first!");
return;
}
ImageToArray converter = new ImageToArray(min: -1, max: +1);
int rows = dataGridView3.Rows.Count;
double[][] inputs = new double[rows][];
double[][] features = new double[rows][];
int[] outputs = new int[rows];
int index = 0;
foreach (DataGridViewRow row in dataGridView3.Rows)
{
Bitmap image = row.Cells["colHand2"].Value as Bitmap;
int label = (int)row.Cells["colLabel2"].Value;
double[] input;
converter.Convert(image, out input);
double[] feature = pca.Transform(input);
row.Cells["colProjection"].Value = feature.ToString("N2");
row.Tag = feature;
inputs[index] = input;
features[index] = feature;
outputs[index] = label;
index++;
}
classifier = new MinimumMeanDistanceClassifier(features, outputs);
btnClassify.Enabled = true;
}
public void new_method()
{
#region doc_learn
// Create some sample input data instances.
double[][] inputs =
{
// Class 0
new double[] { 4, 1 },
new double[] { 2, 4 },
new double[] { 2, 3 },
// Class 1
new double[] { 5, 5 },
new double[] { 5, 6 },
// Class 2
new double[] { 10, 8 }
};
int[] output =
{
0, 0, 0, // The first three are from class 0
1, 1, // The second two are from class 1
2 // The last is from class 2
};
// We will create a MMDC object for the data
var mmdc = new MinimumMeanDistanceClassifier();
// Compute the analysis and create a classifier
mmdc.Learn(inputs, output);
// Now we can project the data into mean distance space:
double[][] projection = mmdc.Scores(inputs);
// Or perform classification using:
int[] results = mmdc.Decide(inputs);
#endregion
Assert.IsTrue(results.IsEqual(output));
string str = projection.ToCSharp();
double[][] expected = new double[][]
{
new double[] { -4.55555555555556, -21.25, -85 },
new double[] { -2.22222222222222, -11.25, -80 },
new double[] { -0.555555555555555, -15.25, -89 },
new double[] { -10.8888888888889, -0.25, -34 },
new double[] { -16.5555555555556, -0.25, -29 },
new double[] { -82.2222222222222, -31.25, 0 }
};
Assert.IsTrue(expected.IsEqual(projection, 1e-6));
}
