natas_hw
/
jidt
mirror of https://github.com/jlizier/jidt
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Adding more unit testing on Kraskov MI, including for dependent data sets

This commit is contained in:
joseph.lizier 2012-12-20 13:23:52 +00:00
parent d95f04ec70
commit 8fe32003ab
1 changed files with 147 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

150
java/unittests/infodynamics/measures/continuous/kraskov/MutualInfoMultiVariateTester.java
View File

@ -105,7 +105,7 @@ public class MutualInfoMultiVariateTester
*
* To run Kraskov's tool (http://www.klab.caltech.edu/~kraskov/MILCA/) for this
* data, run:
* ./MIhigherdim <dataFile> 2 1 1 3000 <kNearestNeighbours> 0
* ./MIxnyn <dataFile> 1 1 3000 <kNearestNeighbours> 0
*
* @throws Exception if file not found
*
@ -152,14 +152,14 @@ public class MutualInfoMultiVariateTester
*
* To run Kraskov's tool (http://www.klab.caltech.edu/~kraskov/MILCA/) for this
* data, run:
* ./MIhigherdim <dataFile> 4 1 1 3000 <kNearestNeighbours> 0
* ./MIxnyn <dataFile> 2 2 3000 <kNearestNeighbours> 0
*
* @throws Exception if file not found
*
*/
public void testMultivariateMIforRandomVariablesFromFile() throws Exception {
// Test set 1:
// Test set 3:
// We'll just take the first two columns from this data set
ArrayFileReader afr = new ArrayFileReader("demos/data/4randomCols-1.txt");
@ -177,4 +177,148 @@ public class MutualInfoMultiVariateTester
kNNs, expectedFromMILCA_2);
}
/**
* Test the computed multivariate MI against that calculated by Kraskov's own MILCA
* tool on the same data.
*
* To run Kraskov's tool (http://www.klab.caltech.edu/~kraskov/MILCA/) for this
* data, run:
* ./MIxnyn <dataFile> 1 3 3000 <kNearestNeighbours> 0
*
* @throws Exception if file not found
*
*/
public void testImbalancedMultivariateMIforRandomVariablesFromFile() throws Exception {
// Test set 4:
// We'll take MI from first column to the next 3:
ArrayFileReader afr = new ArrayFileReader("demos/data/4randomCols-1.txt");
double[][] data = afr.getDouble2DMatrix();
// Use various Kraskov k nearest neighbours parameter
int[] kNNs = {1, 2, 3, 4, 5, 6, 10, 15};
// Expected values from Kraskov's MILCA toolkit:
double[] expectedFromMILCA = {0.02473475, 0.00404451, -0.00454679,
-0.00737512, -0.00464896, -0.00610772, -0.00881741, -0.01306668};
System.out.println("Kraskov comparison 4 - multivariate random data 2 (1 var to 3 vars)");
checkMIForGivenData(MatrixUtils.selectColumns(data, new int[] {0}),
MatrixUtils.selectColumns(data, new int[] {1, 2, 3}),
kNNs, expectedFromMILCA);
}
/**
* Test the computed multivariate MI against that calculated by Kraskov's own MILCA
* tool on the same data.
*
* To run Kraskov's tool (http://www.klab.caltech.edu/~kraskov/MILCA/) for this
* data, run:
* ./MIxnyn <dataFile> 2 2 3030 <kNearestNeighbours> 0
* where the file has the first 30 rows repeated.
*
* Kraskov et al recommend that a small amount of noise should be
* added to the data to avoid issues with repeated scores; we have not
* implemented this yet.
*
* @throws Exception if file not found
*
*/
public void testMultivariateMIforRandomVariablesRepeatedDataFromFile() throws Exception {
// Test set 5:
// We'll just take the first two columns from this data set
ArrayFileReader afr = new ArrayFileReader("demos/data/4randomCols-1.txt");
double[][] data = afr.getDouble2DMatrix();
double[][] data2 = new double[data.length + 30][data[0].length];
for (int r = 0; r < data.length; r++) {
for (int c = 0; c < data[r].length; c++) {
data2[r][c] = data[r][c];
}
}
// Repeat the first 30 rows:
for (int r = 0; r < 30; r++) {
for (int c = 0; c < data[r].length; c++) {
data2[r+data.length][c] = data[r][c];
}
}
// Use various Kraskov k nearest neighbours parameter
int[] kNNs = {1, 2, 3, 4, 5, 6, 10, 15};
// Expected values from Kraskov's MILCA toolkit:
double[] expectedFromMILCA_2 = {0.16846374, 0.04091779, 0.02069109,
0.00700680, 0.00121768, -0.00134164, -0.00870685, -0.00966508};
System.out.println("Kraskov comparison 5 - multivariate random data 1 with 30 repeated rows");
checkMIForGivenData(MatrixUtils.selectColumns(data2, new int[] {0, 1}),
MatrixUtils.selectColumns(data2, new int[] {2, 3}),
kNNs, expectedFromMILCA_2);
}
/**
* Test the computed multivariate MI against that calculated by Kraskov's own MILCA
* tool on the same data.
*
* To run Kraskov's tool (http://www.klab.caltech.edu/~kraskov/MILCA/) for this
* data, run:
* ./MIxnyn <dataFile> 2 2 3000 <kNearestNeighbours> 0
*
* @throws Exception if file not found
*
*/
public void testMultivariateMIforDependentVariablesFromFile() throws Exception {
// Test set 6:
// We'll just take the first two columns from this data set
ArrayFileReader afr = new ArrayFileReader("demos/data/4ColsPairedDirectDependence-1.txt");
double[][] data = afr.getDouble2DMatrix();
// Use various Kraskov k nearest neighbours parameter
int[] kNNs = {1, 2, 3, 4, 5, 6, 10, 15};
// Expected values from Kraskov's MILCA toolkit:
double[] expectedFromMILCA_2 = {5.00322122, 4.29011291, 3.91312749,
3.69192886, 3.52807488, 3.39865354, 3.05327646, 2.79951639};
System.out.println("Kraskov comparison 6 - multivariate dependent data 1");
checkMIForGivenData(MatrixUtils.selectColumns(data, new int[] {0, 1}),
MatrixUtils.selectColumns(data, new int[] {2, 3}),
kNNs, expectedFromMILCA_2);
}
/**
* Test the computed multivariate MI against that calculated by Kraskov's own MILCA
* tool on the same data.
*
* To run Kraskov's tool (http://www.klab.caltech.edu/~kraskov/MILCA/) for this
* data, run:
* ./MIxnyn <dataFile> 2 2 3000 <kNearestNeighbours> 0
*
* @throws Exception if file not found
*
*/
public void testMultivariateMIforNoisyDependentVariablesFromFile() throws Exception {
// Test set 6:
// We'll just take the first two columns from this data set
ArrayFileReader afr = new ArrayFileReader("demos/data/4ColsPairedNoisyDependence-1.txt");
double[][] data = afr.getDouble2DMatrix();
// Use various Kraskov k nearest neighbours parameter
int[] kNNs = {1, 2, 3, 4, 5, 6, 10, 15};
// Expected values from Kraskov's MILCA toolkit:
double[] expectedFromMILCA_2 = {0.33738970, 0.36251531, 0.34708687,
0.36200563, 0.35766125, 0.35007623, 0.35023664, 0.33728287};
System.out.println("Kraskov comparison 6 - multivariate dependent data 1");
checkMIForGivenData(MatrixUtils.selectColumns(data, new int[] {0, 1}),
MatrixUtils.selectColumns(data, new int[] {2, 3}),
kNNs, expectedFromMILCA_2);
}
}