import static java.lang.System.out;

import java.util.*;

import javastat.*;

import javastat.multivariate.*;

import static javastat.util.Output.*;

import javastat.util.*;

/**

*

* <p>Example: class DiscriminantAnalysis.</p>

* <p>Data Source: Fisher's iris data</p>

*/

public class DiscriminantAnalysisExample

{

public static void main(String arg[])

{

DataManager dm = new DataManager();

double[][] testdata = new double[4][];

dm.scanFileToMatrix(System.getProperty("user.dir") +

System.getProperty("file.separator") +

"iris.txt", testdata, 4);

double[] testgroup = new double[150];

for (int j = 0; j < 3; j++)

{

for (int i = 0; i < 50; i++)

{

testgroup[j * 50 + i] = j + 1;

}

}

DiscriminantAnalysis testclass1 = new DiscriminantAnalysis(testgroup,

testdata);

double[][] linearDiscriminants1 = {testclass1.linearDiscriminants[0],

testclass1.linearDiscriminants[1]};

DiscriminantAnalysis testclass2 = new DiscriminantAnalysis();

double[][] linearDiscriminants2 = testclass2.linearDiscriminants(

testgroup, testdata);

double[][] predata = new double[4][];

dm.scanFileToMatrix(System.getProperty("user.dir") +

System.getProperty("file.separator") +

"iris2.txt", predata, 4);

int[] predictedGroup2 = testclass2.predictedGroup(testgroup, testdata,

predata);

double errorRate = 0.0;

for (int i = 0; i < predictedGroup2.length; i++)

{

if ((int) testgroup[i] != predictedGroup2[i])

{

errorRate += 1.0;

}

}

errorRate /= predictedGroup2.length;

DiscriminantAnalysis testclass3 = new DiscriminantAnalysis(testgroup,

testdata, testdata);

double[][] linearDiscriminants3 = {testclass3.linearDiscriminants[0],

testclass3.linearDiscriminants[1]};

int[] predictedGroup3 = testclass3.predictedGroup;

int preg = predictedGroup3[predictedGroup3.length - 1];

Hashtable argument1 = new Hashtable();

StatisticalAnalysis testclass4 = new DiscriminantAnalysis(argument1,

testgroup, testdata).statisticalAnalysis;

linearDiscriminants1 = (double[][]) testclass4.output.get(

LINEAR_DISCRIMINANTS);

Hashtable argument2 = new Hashtable();

DiscriminantAnalysis testclass5 =

new DiscriminantAnalysis(argument2, null);

linearDiscriminants2 =

testclass5.linearDiscriminants(argument2, testgroup, testdata);

predictedGroup2 = testclass5.predictedGroup(argument2, testgroup,

testdata, predata);

errorRate = 0.0;

for (int i = 0; i < predictedGroup2.length; i++)

{

if ((int) testgroup[i] != predictedGroup2[i])

{

errorRate += 1.0;

}

}

errorRate /= predictedGroup2.length;

Hashtable argument3 = new Hashtable();

StatisticalAnalysis testclass6 = new DiscriminantAnalysis(argument3,

testgroup, testdata, testdata).statisticalAnalysis;

linearDiscriminants3 = (double[][]) testclass6.output.get(

LINEAR_DISCRIMINANTS);

predictedGroup3 =

(int[]) testclass6.output.get(PREDICTED_GROUP);

preg = predictedGroup3[predictedGroup3.length - 1];

}

}

Results:

The first discriminant based on non-null constructor =

[-0.946 , -1.803 , 1.523 , 4.002]

The second discriminant based on non-null constructor =

[0.031 , 1.382 , -1.551 , 4.32]

The first discriminant based on null constructor = [-0.946 , -1.803 , 1.523 , 4.002]

The second discriminant based on null constructor = [0.031 , 1.382 , -1.551 , 4.32]

The error rate based on null constructor = 0.02

{LINEAR_DISCRIMINANTS=[[D@8813f2,

PREDICTED_GROUP=[I@1d58aae}

The first discriminant based on non-null constructor =

[-0.946 , -1.803 , 1.523 , 4.002]

The second discriminant based on non-null constructor =

[0.031 , 1.382 , -1.551 , 4.32]

The last observation of Iris data was assigned to = group 3.0

{LINEAR_DISCRIMINANTS=[[D@83cc67,

PREDICTED_GROUP=[I@e09713}