public void ComputeTest3()
{
// Schölkopf KPCA toy example
double[][] inputs = scholkopf().ToJagged();
int[] output = Matrix.Expand(new int[, ] {
{ 1 }, { 2 }, { 3 }
}, new int[] { 30, 30, 30 }).GetColumn(0);
IKernel kernel = new Gaussian(0.2);
var target = new KernelDiscriminantAnalysis(inputs, output, kernel);
target.Compute();
double[][] actual = target.Transform(inputs, 2);
double[][] expected1 =
{
new double[] { 1.2785801485080475, 0.20539157505913622 },
new double[] { 1.2906613255489541, 0.20704272225753775 },
new double[] { 1.2978134597266808, 0.20802649628632208 },
};
double[][] actual1 = actual.Submatrix(0, 2, 0, 1);
Assert.IsTrue(Matrix.IsEqual(actual1, expected1, 0.0000001));
// Assert the result equals the transformation of the input
double[][] result = target.Result.ToJagged();
double[][] projection = target.Transform(inputs);
Assert.IsTrue(Matrix.IsEqual(result, projection));
int[] actual2 = target.Classify(inputs);
Assert.IsTrue(Matrix.IsEqual(actual2, output));
int[] actual3 = new int[inputs.Length];
double[][] scores = new double[inputs.Length][];
for (int i = 0; i < inputs.Length; i++)
{
actual3[i] = target.Classify(inputs[i], out scores[i]);
}
Assert.IsTrue(Matrix.IsEqual(actual3, output));
scores = scores.Get(0, 5, null);
double[][] expected = new double[][] {
new double[] { -6.23928931356786E-06, -5.86731829543872, -4.76988430445096 },
new double[] { -9.44593697210785E-05, -5.92312597750504, -4.82189359956088 },
new double[] { -0.000286839977573986, -5.95629842504978, -4.85283341267476 },
new double[] { -4.38986003009456E-05, -5.84990179343448, -4.75189423787298 },
new double[] { -0.000523817959022851, -5.77534144986199, -4.683120454667 }
};
//Assert.IsTrue(Matrix.IsEqual(scores, expected, 1e-6));
}
private void btnClassify_Click(object sender, EventArgs e)
{
if (dgvAnalysisTesting.Rows.Count == 0)
{
MessageBox.Show("Please load the testing data before clicking this button");
return;
}
else if (kda == null)
{
MessageBox.Show("Please perform the analysis before attempting classification");
return;
}
lbStatus.Text = "Classification started. This may take a while...";
Application.DoEvents();
int hits = 0;
progressBar.Visible = true;
progressBar.Value = 0;
progressBar.Step = 1;
progressBar.Maximum = dgvAnalysisTesting.Rows.Count;
// Extract inputs
foreach (DataGridViewRow row in dgvAnalysisTesting.Rows)
{
double[] input = (double[])row.Cells["colTestingFeatures"].Value;
int expected = (int)row.Cells["colTestingExpected"].Value;
int output = kda.Classify(input);
row.Cells["colTestingOutput"].Value = output;
if (expected == output)
{
row.Cells[0].Style.BackColor = Color.LightGreen;
row.Cells[1].Style.BackColor = Color.LightGreen;
row.Cells[2].Style.BackColor = Color.LightGreen;
hits++;
}
else
{
row.Cells[0].Style.BackColor = Color.White;
row.Cells[1].Style.BackColor = Color.White;
row.Cells[2].Style.BackColor = Color.White;
}
progressBar.PerformStep();
}
progressBar.Visible = false;
lbStatus.Text = String.Format("Classification complete. Hits: {0}/{1} ({2:0%})",
hits, dgvAnalysisTesting.Rows.Count, (double)hits / dgvAnalysisTesting.Rows.Count);
}
public void ClassifyTest1()
{
// Create some sample input data
// This is the same data used in the example by Gutierrez-Osuna
// http://research.cs.tamu.edu/prism/lectures/pr/pr_l10.pdf
double[,] inputs =
{
{ 4, 1 }, // Class 1
{ 2, 4 },
{ 2, 3 },
{ 3, 6 },
{ 4, 4 },
{ 9, 10 }, // Class 2
{ 6, 8 },
{ 9, 5 },
{ 8, 7 },
{ 10, 8 }
};
int[] output =
{
1, 1, 1, 1, 1, // Class labels for the input vectors
2, 2, 2, 2, 2
};
// Create a new Linear Discriminant Analysis object
var lda = new KernelDiscriminantAnalysis(inputs, output, new Linear());
// Compute the analysis
lda.Compute();
// Test the classify method
for (int i = 0; i < 5; i++)
{
int expected = 1;
int actual = lda.Classify(inputs.GetRow(i));
Assert.AreEqual(expected, actual);
}
for (int i = 5; i < 10; i++)
{
int expected = 2;
int actual = lda.Classify(inputs.GetRow(i));
Assert.AreEqual(expected, actual);
}
}
private void graphMapInput_MouseMove(object sender, MouseEventArgs e)
{
double x;
double y;
graphMapInput.GraphPane.ReverseTransform(new PointF(e.X, e.Y), out x, out y);
double[,] data = new double[1, 2];
data[0, 0] = x;
data[0, 1] = y;
double[,] result = kda.Transform(data);
int c = kda.Classify(new double[] { x, y });
graphMapFeature.GraphPane.CurveList["M1"].Clear();
graphMapFeature.GraphPane.CurveList["M2"].Clear();
graphMapFeature.GraphPane.CurveList["M3"].Clear();
if (c == 1)
{
graphMapFeature.GraphPane.CurveList["M1"].AddPoint(result[0, 0], result[0, 1]);
}
else if (c == 2)
{
graphMapFeature.GraphPane.CurveList["M2"].AddPoint(result[0, 0], result[0, 1]);
}
else
{
graphMapFeature.GraphPane.CurveList["M3"].AddPoint(result[0, 0], result[0, 1]);
}
graphMapFeature.Invalidate();
}
public void ClassifyTest()
{
// Create some sample input data instances. This is the same
// data used in the Gutierrez-Osuna's example available on:
// http://research.cs.tamu.edu/prism/lectures/pr/pr_l10.pdf
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
};
// Now we can chose a kernel function to
// use, such as a linear kernel function.
IKernel kernel = new Linear();
// Then, we will create a KDA using this linear kernel.
var kda = new KernelDiscriminantAnalysis(inputs, output, kernel);
kda.Compute(); // Compute the analysis
// Now we can project the data into KDA space:
double[][] projection = kda.Transform(inputs);
double[][] classifierProjection = kda.Classifier.First.Transform(inputs);
Assert.IsTrue(projection.IsEqual(classifierProjection));
// Or perform classification using:
int[] results = kda.Classify(inputs);
string str = projection.ToCSharp();
double[][] expected = new double[][] {
new double[] { 80.7607049998409, -5.30485371541545E-06, 6.61304584781419E-06, 4.52807990036774E-06, -3.44409628150189E-06, 3.69094504515388E-06, -1.33641000168438E-05, -0.000132874977040842, -0.000261921590627878, 1.22137997452386 },
new double[] { 67.6629612351861, 6.80622743409742E-06, -8.48466262226566E-06, -5.80961187779394E-06, 4.4188405141643E-06, -4.73555212510135E-06, 1.71463925084936E-05, 0.000170481102685471, 0.000336050342774286, -1.5670535522193 },
new double[] { 59.8679301679674, 4.10375477777336E-06, -5.11575246520124E-06, -3.50285421113483E-06, 2.66430090034575E-06, -2.85525936627451E-06, 1.03382660725515E-05, 0.00010279007663172, 0.000202618589039361, -0.944841112367518 },
new double[] { 101.494441852779, 1.02093411395998E-05, -1.27269939227403E-05, -8.71441780958548E-06, 6.62826077091339E-06, -7.10332818965043E-06, 2.57195887591877E-05, 0.000255721654028207, 0.000504075514164981, -2.35058032832894 },
new double[] { 104.145798201497, 2.80256425000402E-06, -3.49368461627364E-06, -2.39219308895144E-06, 1.81952256639306E-06, -1.94993321933623E-06, 7.06027928387698E-06, 7.01981011275166E-05, 0.000138373670580449, -0.645257345031474 },
new double[] { 242.123077020588, 9.00824221261587E-06, -1.12297005614437E-05, -7.689192102589E-06, 5.84846541151762E-06, -6.26764250277745E-06, 2.26937548148953E-05, 0.000225636753569347, 0.000444772512580016, -2.07404146617259 },
new double[] { 171.808759436683, 9.60879168943052E-06, -1.19783472456447E-05, -8.2018049702981E-06, 6.23836308744075E-06, -6.68548535731617E-06, 2.42066717959233E-05, 0.000240679203812988, 0.000474424013376051, -2.21231089725078 },
new double[] { 203.147921684494, -4.5041210583463E-06, 5.61485022387842E-06, 3.8445962076139E-06, -2.92423269243614E-06, 3.13382127359318E-06, -1.13468773577097E-05, -0.000112818376692303, -0.000222386256126583, 1.03702073308629 },
new double[] { 200.496565335776, 2.90265583302585E-06, -3.61845908969372E-06, -2.47762852723099E-06, 1.88450551963371E-06, -2.01957368695105E-06, 7.31243213181187E-06, 7.27051762225983E-05, 0.000143315587422421, -0.668302250211177 },
new double[] { 244.774433369306, 1.60146531058558E-06, -1.99639123366069E-06, -1.36696743169296E-06, 1.0397271781315E-06, -1.11424755644407E-06, 4.03444536090092E-06, 4.01132006970784E-05, 7.90706689741683E-05, -0.368718482875124 }
};
Assert.IsTrue(expected.IsEqual(projection, 1e-6));
// Test the classify method
for (int i = 0; i < 5; i++)
{
int actual = results[i];
Assert.AreEqual(0, actual);
}
for (int i = 5; i < 10; i++)
{
int actual = results[i];
Assert.AreEqual(1, actual);
}
}
public void ClassifyTest()
{
// Create some sample input data instances. This is the same
// data used in the Gutierrez-Osuna's example available on:
// http://research.cs.tamu.edu/prism/lectures/pr/pr_l10.pdf
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
};
// Now we can chose a kernel function to
// use, such as a linear kernel function.
IKernel kernel = new Linear();
// Then, we will create a KDA using this linear kernel.
var kda = new KernelDiscriminantAnalysis(inputs, output, kernel);
kda.Compute(); // Compute the analysis
// Now we can project the data into KDA space:
double[][] projection = kda.Transform(inputs);
// Or perform classification using:
int[] results = kda.Classify(inputs);
// Test the classify method
for (int i = 0; i < 5; i++)
{
int expected = 0;
int actual = results[i];
Assert.AreEqual(expected, actual);
}
for (int i = 5; i < 10; i++)
{
int expected = 1;
int actual = results[i];
Assert.AreEqual(expected, actual);
}
}
public int computeNew(double[] input)
{
return(kda.Classify(input));
}
