mirror of https://github.com/jlizier/jidt
Adding property to allow used to set random seed for noise addition to data in MI, CMI and wrapped estimators. Closes issue #99
This commit is contained in:
Joseph Lizier
parent
2130985c34
commit
2a18cd0e74
|
|
@ -92,6 +92,16 @@ public interface ConditionalMutualInfoCalculatorMultiVariate
|
|||
* where it is 1e-8, matching the MILCA toolkit)
|
||||
*/
|
||||
public static final String PROP_ADD_NOISE = "NOISE_LEVEL_TO_ADD";
|
||||
/**
|
||||
* Property name for the seed for the random number generator for noise to be
|
||||
* added to the data (default is no seed)
|
||||
*/
|
||||
public static final String PROP_NOISE_SEED = "NOISE_SEED";
|
||||
/**
|
||||
* Property value to indicate no seed for the random number generator for noise to be
|
||||
* added to the data
|
||||
*/
|
||||
public static final String NOISE_NO_SEED_VALUE = "NONE";
|
||||
|
||||
/**
|
||||
* Initialise the calculator for (re-)use, clearing PDFs,
|
||||
|
|
|
|||
|
|
@ -166,6 +166,14 @@ public abstract class ConditionalMutualInfoMultiVariateCommon implements
|
|||
* and 1e-8 is used to match MILCA toolkit)
|
||||
*/
|
||||
protected double noiseLevel = (double) 0;
|
||||
/**
|
||||
* Has the user set a seed for the random noise
|
||||
*/
|
||||
protected boolean noiseSeedSet = false;
|
||||
/**
|
||||
* Seed that the user set for the random noise
|
||||
*/
|
||||
protected long noiseSeed = 0;
|
||||
|
||||
/**
|
||||
* Cache for the means of each dimension in variable 1, in case we need to normalise
|
||||
|
|
@ -250,6 +258,8 @@ public abstract class ConditionalMutualInfoMultiVariateCommon implements
|
|||
* (Default is 0, except for KSG estimators where it is recommended by Kraskov
|
||||
* and so they use 1e-8 to match the MILCA toolkit, although that adds in
|
||||
* a random amount of noise in [0,noiseLevel) ).</li>
|
||||
* <li>{@link #PROP_NOISE_SEED} -- a long value seed for the random noise generator or
|
||||
* the string {@link ConditionalMutualInfoCalculatorMultiVariate#NOISE_NO_SEED_VALUE} for no seed (default)</li>
|
||||
* </ul>
|
||||
*
|
||||
* <p>Unknown property values are ignored.</p>
|
||||
|
|
@ -260,6 +270,8 @@ public abstract class ConditionalMutualInfoMultiVariateCommon implements
|
|||
*/
|
||||
@Override
|
||||
public void setProperty(String propertyName, String propertyValue) {
|
||||
|
||||
boolean propertySet = true;
|
||||
if (propertyName.equalsIgnoreCase(PROP_NORMALISE)) {
|
||||
normalise = Boolean.parseBoolean(propertyValue);
|
||||
} else if (propertyName.equalsIgnoreCase(PROP_ADD_NOISE)) {
|
||||
|
|
@ -271,6 +283,20 @@ public abstract class ConditionalMutualInfoMultiVariateCommon implements
|
|||
addNoise = true;
|
||||
noiseLevel = Double.parseDouble(propertyValue);
|
||||
}
|
||||
} else if (propertyName.equalsIgnoreCase(PROP_NOISE_SEED)) {
|
||||
if (propertyValue.equals(NOISE_NO_SEED_VALUE)) {
|
||||
noiseSeedSet = false;
|
||||
} else {
|
||||
noiseSeedSet = true;
|
||||
noiseSeed = Long.parseLong(propertyValue);
|
||||
}
|
||||
} else {
|
||||
// No property was set here
|
||||
propertySet = false;
|
||||
}
|
||||
if (debug && propertySet) {
|
||||
System.out.println(this.getClass().getSimpleName() + ": Set property " + propertyName +
|
||||
" to " + propertyValue);
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -280,6 +306,12 @@ public abstract class ConditionalMutualInfoMultiVariateCommon implements
|
|||
return Boolean.toString(normalise);
|
||||
} else if (propertyName.equalsIgnoreCase(PROP_ADD_NOISE)) {
|
||||
return Double.toString(noiseLevel);
|
||||
} else if (propertyName.equalsIgnoreCase(PROP_NOISE_SEED)) {
|
||||
if (noiseSeedSet) {
|
||||
return Long.toString(noiseSeed);
|
||||
} else {
|
||||
return NOISE_NO_SEED_VALUE;
|
||||
}
|
||||
} else {
|
||||
// No property matches for this class
|
||||
return null;
|
||||
|
|
@ -733,6 +765,9 @@ public abstract class ConditionalMutualInfoMultiVariateCommon implements
|
|||
// Add Gaussian noise of std dev noiseLevel to the data if required
|
||||
if (addNoise) {
|
||||
Random random = new Random();
|
||||
if (noiseSeedSet) {
|
||||
random.setSeed(noiseSeed);
|
||||
}
|
||||
for (int r = 0; r < var1Observations.length; r++) {
|
||||
for (int c = 0; c < dimensionsVar1; c++) {
|
||||
var1Observations[r][c] +=
|
||||
|
|
|
|||
|
|
@ -91,6 +91,16 @@ public interface MutualInfoCalculatorMultiVariate
|
|||
* if the data is to be normalised, that will be done before adding this noise.
|
||||
*/
|
||||
public static final String PROP_ADD_NOISE = "NOISE_LEVEL_TO_ADD";
|
||||
/**
|
||||
* Property name for the seed for the random number generator for noise to be
|
||||
* added to the data (default is no seed)
|
||||
*/
|
||||
public static final String PROP_NOISE_SEED = "NOISE_SEED";
|
||||
/**
|
||||
* Property value to indicate no seed for the random number generator for noise to be
|
||||
* added to the data
|
||||
*/
|
||||
public static final String NOISE_NO_SEED_VALUE = "NONE";
|
||||
|
||||
/**
|
||||
* <p>As per {@link #addObservations(double[][], double[][])};
|
||||
|
|
|
|||
|
|
@ -167,9 +167,19 @@ public abstract class MutualInfoMultiVariateCommon implements
|
|||
*/
|
||||
protected boolean addNoise = false;
|
||||
/**
|
||||
* Amount of random Gaussian noise to add to the incoming data
|
||||
* Amount of random Gaussian noise to add to the incoming data.
|
||||
* 0 by default except for KSG estimators (where it is recommended
|
||||
* and 1e-8 is used to match MILCA toolkit)
|
||||
*/
|
||||
protected double noiseLevel = (double) 0.0;
|
||||
/**
|
||||
* Has the user set a seed for the random noise
|
||||
*/
|
||||
protected boolean noiseSeedSet = false;
|
||||
/**
|
||||
* Seed that the user set for the random noise
|
||||
*/
|
||||
protected long noiseSeed = 0;
|
||||
|
||||
/* (non-Javadoc)
|
||||
* @see infodynamics.measures.continuous.ChannelCalculatorCommon#initialise()
|
||||
|
|
@ -222,6 +232,8 @@ public abstract class MutualInfoMultiVariateCommon implements
|
|||
* by Kraskov for the KSG method though, so for that estimator we
|
||||
* use 1e-8 to match the MILCA toolkit (though note it adds in
|
||||
* a random amount of noise in [0,noiseLevel) ).</li>
|
||||
* <li>{@link #PROP_NOISE_SEED} -- a long value seed for the random noise generator or
|
||||
* the string {@link MutualInfoCalculatorMultiVariate#NOISE_NO_SEED_VALUE} for no seed (default)</li>
|
||||
* </ul>
|
||||
*
|
||||
* <p>Unknown property values are ignored.</p>
|
||||
|
|
@ -250,6 +262,13 @@ public abstract class MutualInfoMultiVariateCommon implements
|
|||
addNoise = true;
|
||||
noiseLevel = Double.parseDouble(propertyValue);
|
||||
}
|
||||
} else if (propertyName.equalsIgnoreCase(PROP_NOISE_SEED)) {
|
||||
if (propertyValue.equals(NOISE_NO_SEED_VALUE)) {
|
||||
noiseSeedSet = false;
|
||||
} else {
|
||||
noiseSeedSet = true;
|
||||
noiseSeed = Long.parseLong(propertyValue);
|
||||
}
|
||||
} else {
|
||||
// No property was set here
|
||||
propertySet = false;
|
||||
|
|
@ -270,6 +289,12 @@ public abstract class MutualInfoMultiVariateCommon implements
|
|||
return Boolean.toString(normalise);
|
||||
} else if (propertyName.equalsIgnoreCase(PROP_ADD_NOISE)) {
|
||||
return Double.toString(noiseLevel);
|
||||
} else if (propertyName.equalsIgnoreCase(PROP_NOISE_SEED)) {
|
||||
if (noiseSeedSet) {
|
||||
return Long.toString(noiseSeed);
|
||||
} else {
|
||||
return NOISE_NO_SEED_VALUE;
|
||||
}
|
||||
} else {
|
||||
// No property was recognised here
|
||||
return null;
|
||||
|
|
@ -601,6 +626,9 @@ public abstract class MutualInfoMultiVariateCommon implements
|
|||
// Add Gaussian noise of std dev noiseLevel to the data if required
|
||||
if (addNoise) {
|
||||
Random random = new Random();
|
||||
if (noiseSeedSet) {
|
||||
random.setSeed(noiseSeed);
|
||||
}
|
||||
for (int r = 0; r < sourceObservations.length; r++) {
|
||||
for (int c = 0; c < dimensionsSource; c++) {
|
||||
sourceObservations[r][c] +=
|
||||
|
|
|
|||
|
|
@ -98,10 +98,11 @@ public class ConditionalMutualInfoMultiVariateTester
|
|||
* @param var2 dest multivariate data set
|
||||
* @param kNNs array of Kraskov k nearest neighbours parameter to check
|
||||
* @param expectedResults array of expected results for each k
|
||||
* @return errors of the computed values against expectedResults
|
||||
*/
|
||||
protected void checkTEForGivenData(double[][] var1, double[][] var2,
|
||||
protected double[] checkTEForGivenData(double[][] var1, double[][] var2,
|
||||
int[] kNNs, double[] expectedResults) throws Exception {
|
||||
checkTEForGivenData(var1, var2, 1, 1, kNNs, expectedResults);
|
||||
return checkTEForGivenData(var1, var2, 1, 1, kNNs, expectedResults);
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
@ -115,11 +116,37 @@ public class ConditionalMutualInfoMultiVariateTester
|
|||
* @param historyL history length l of source
|
||||
* @param kNNs array of Kraskov k nearest neighbours parameter to check
|
||||
* @param expectedResults array of expected results for each k
|
||||
* @return errors of the computed values against expectedResults
|
||||
*/
|
||||
protected void checkTEForGivenData(double[][] var1, double[][] var2,
|
||||
protected double[] checkTEForGivenData(double[][] var1, double[][] var2,
|
||||
int historyK, int historyL, int[] kNNs, double[] expectedResults) throws Exception {
|
||||
return checkTEForGivenData(var1, var2, historyK, historyL, kNNs, expectedResults,
|
||||
0, "NONE", 0.000001);
|
||||
}
|
||||
|
||||
/**
|
||||
* Utility function to run Kraskov conditional MI algorithm 1
|
||||
* as transfer entropy for data with known results
|
||||
* from TRENTOOL.
|
||||
*
|
||||
* @param var1 source multivariate data set
|
||||
* @param var2 dest multivariate data set
|
||||
* @param historyK history length k of destination
|
||||
* @param historyL history length l of source
|
||||
* @param kNNs array of Kraskov k nearest neighbours parameter to check
|
||||
* @param expectedResults array of expected results for each k
|
||||
* @param noiseLevel noise to add to the data - set to 0 if we
|
||||
* need to exactly reproduce calculations (most cases in unit tests for consistency)
|
||||
* @param noiseSeed seed for the random noise generator (either "NONE" or Long string)
|
||||
* @param tolerance tolerance to accept the calculation
|
||||
* @return errors of the computed values against expectedResults
|
||||
*/
|
||||
protected double[] checkTEForGivenData(double[][] var1, double[][] var2,
|
||||
int historyK, int historyL, int[] kNNs, double[] expectedResults,
|
||||
double noiseLevel, String noiseSeed, double tolerance) throws Exception {
|
||||
|
||||
ConditionalMutualInfoCalculatorMultiVariateKraskov condMiCalc = getNewCalc(1);
|
||||
double[] errors = new double[expectedResults.length];
|
||||
|
||||
// Which is the first time index for the dest next state?
|
||||
// It depends on the values of k and l for embedding the past state
|
||||
|
|
@ -148,7 +175,9 @@ public class ConditionalMutualInfoMultiVariateTester
|
|||
condMiCalc.setProperty(
|
||||
ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_NUM_THREADS,
|
||||
NUM_THREADS_TO_USE);
|
||||
condMiCalc.setProperty(ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_ADD_NOISE, "0"); // Need consistency of results for unit test
|
||||
condMiCalc.setProperty(ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_ADD_NOISE,
|
||||
Double.toString(noiseLevel));
|
||||
condMiCalc.setProperty(ConditionalMutualInfoCalculatorMultiVariateKraskov.PROP_NOISE_SEED, noiseSeed);
|
||||
condMiCalc.initialise(var1[0].length * historyL,
|
||||
var2[0].length, var2[0].length * historyK);
|
||||
// Construct the joint vectors of the source states
|
||||
|
|
@ -193,8 +222,10 @@ public class ConditionalMutualInfoMultiVariateTester
|
|||
System.out.printf("k=%d: Average MI %.8f (expected %.8f)\n",
|
||||
k, condMi, expectedResults[kIndex]);
|
||||
// 6 decimal places is Matlab accuracy
|
||||
assertEquals(expectedResults[kIndex], condMi, 0.000001);
|
||||
assertEquals(expectedResults[kIndex], condMi, tolerance);
|
||||
errors[kIndex] = condMi - expectedResults[kIndex];
|
||||
}
|
||||
return errors;
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
@ -812,4 +843,68 @@ public class ConditionalMutualInfoMultiVariateTester
|
|||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Extends testUnivariateTEforCoupledVariablesFromFile to test
|
||||
* using seed for random number generator
|
||||
*
|
||||
* @throws Exception if file not found
|
||||
*
|
||||
*/
|
||||
public void testWithSeed() throws Exception {
|
||||
|
||||
// Test set 1:
|
||||
|
||||
ArrayFileReader afr = new ArrayFileReader("demos/data/2coupledRandomCols-1.txt");
|
||||
double[][] data = afr.getDouble2DMatrix();
|
||||
|
||||
// Use various Kraskov k nearest neighbours parameter
|
||||
int[] kNNs = {4};
|
||||
// Expected values from TRENTOOL:
|
||||
double[] expectedFromTRENTOOL = {0.3058006};
|
||||
|
||||
System.out.println("Kraskov Cond MI as TE comparison 1 - univariate coupled data 1");
|
||||
double[] noNoiseError = checkTEForGivenData(MatrixUtils.selectColumns(data, new int[] {0}),
|
||||
MatrixUtils.selectColumns(data, new int[] {1}),
|
||||
kNNs, expectedFromTRENTOOL);
|
||||
double noNoiseResult = expectedFromTRENTOOL[0] + noNoiseError[0];
|
||||
|
||||
// And now in the reverse direction:
|
||||
double[] expectedFromTRENTOOLRev = new double[] {-0.0029744};
|
||||
|
||||
System.out.println(" reverse direction:");
|
||||
double[] noNoiseErrorRev = checkTEForGivenData(MatrixUtils.selectColumns(data, new int[] {1}),
|
||||
MatrixUtils.selectColumns(data, new int[] {0}),
|
||||
kNNs, expectedFromTRENTOOLRev);
|
||||
double noNoiseResultRev = expectedFromTRENTOOLRev[0] + noNoiseErrorRev[0];
|
||||
|
||||
// Check that changing the random number generator still returns close to those with no noise
|
||||
// results, but with larger tolerance
|
||||
System.out.println("\n Kraskov Cond MI as TE comparison 1 - univariate coupled data 1 - seed 1");
|
||||
double[] withSeed1Error = checkTEForGivenData(MatrixUtils.selectColumns(data, new int[] {0}),
|
||||
MatrixUtils.selectColumns(data, new int[] {1}),
|
||||
1, 1, kNNs, expectedFromTRENTOOL,
|
||||
1e-8, "1", 0.01);
|
||||
double[] withSeed1Results = new double[] {expectedFromTRENTOOL[0] + withSeed1Error[0]};
|
||||
// Now check that the results are exact when we repeat with the same seed:
|
||||
System.out.println("\n Kraskov Cond MI as TE comparison 1 - univariate coupled data 1 - seed 1 repeat want exact");
|
||||
checkTEForGivenData(MatrixUtils.selectColumns(data, new int[] {0}),
|
||||
MatrixUtils.selectColumns(data, new int[] {1}),
|
||||
1, 1, kNNs, withSeed1Results,
|
||||
1e-8, "1", 1e-10);
|
||||
|
||||
// And in reverse direction:
|
||||
System.out.println("\n Kraskov Cond MI as TE comparison 1 - univariate coupled data 1 - seed 1 reverse");
|
||||
double[] withSeed1ErrorRev = checkTEForGivenData(MatrixUtils.selectColumns(data, new int[] {1}),
|
||||
MatrixUtils.selectColumns(data, new int[] {0}),
|
||||
1, 1, kNNs, expectedFromTRENTOOLRev,
|
||||
1e-8, "1", 0.01);
|
||||
double[] withSeed1ResultsRev = new double[] {expectedFromTRENTOOLRev[0] + withSeed1ErrorRev[0]};
|
||||
// Now check that the results are exact when we repeat with the same seed:
|
||||
System.out.println("\n Kraskov Cond MI as TE comparison 1 - univariate coupled data 1 - seed 1 reverse repeat want exact");
|
||||
checkTEForGivenData(MatrixUtils.selectColumns(data, new int[] {1}),
|
||||
MatrixUtils.selectColumns(data, new int[] {0}),
|
||||
1, 1, kNNs, withSeed1ResultsRev,
|
||||
1e-8, "1", 1e-10);
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -96,6 +96,23 @@ public class MutualInfoMultiVariateTester
|
|||
checkComputeSignificanceDoesntAlterAverage(2);
|
||||
}
|
||||
|
||||
/**
|
||||
* Utility function to run Kraskov MI for data with known results.
|
||||
* Sets to use no noise in the calculation and a tolerance of 0.0000001
|
||||
*
|
||||
* @param var1
|
||||
* @param var2
|
||||
* @param kNNs array of Kraskov k nearest neighbours parameter to check
|
||||
* @param expectedResults array of expected results for each k
|
||||
* @return errors of the computed values against expectedResults
|
||||
*/
|
||||
protected double[] checkMIForGivenData(double[][] var1, double[][] var2,
|
||||
int[] kNNs, double[] expectedResults) throws Exception {
|
||||
// Dropping required accuracy by one order of magnitude, due
|
||||
// to faster but slightly less accurate digamma estimator change
|
||||
return checkMIForGivenData(var1, var2, kNNs, expectedResults, 0, "NONE", 0.0000001);
|
||||
}
|
||||
|
||||
/**
|
||||
* Utility function to run Kraskov MI for data with known results
|
||||
*
|
||||
|
|
@ -103,13 +120,20 @@ public class MutualInfoMultiVariateTester
|
|||
* @param var2
|
||||
* @param kNNs array of Kraskov k nearest neighbours parameter to check
|
||||
* @param expectedResults array of expected results for each k
|
||||
* @param noiseLevel noise to add to the data - set to 0 if we
|
||||
* need to exactly reproduce calculations (most cases in unit tests for consistency)
|
||||
* @param noiseSeed seed for the random noise generator (either "NONE" or Long string)
|
||||
* @param tolerance tolerance to accept the calculation
|
||||
* @return errors of the computed values against expectedResults
|
||||
*/
|
||||
protected void checkMIForGivenData(double[][] var1, double[][] var2,
|
||||
int[] kNNs, double[] expectedResults) throws Exception {
|
||||
protected double[] checkMIForGivenData(double[][] var1, double[][] var2,
|
||||
int[] kNNs, double[] expectedResults,
|
||||
double noiseLevel, String noiseSeed, double tolerance) throws Exception {
|
||||
|
||||
// The Kraskov MILCA toolkit MIhigherdim executable
|
||||
// uses algorithm 2 by default (this is what it means by rectangular):
|
||||
MutualInfoCalculatorMultiVariateKraskov miCalc = getNewCalc(2);
|
||||
double[] errors = new double[expectedResults.length];
|
||||
|
||||
for (int kIndex = 0; kIndex < kNNs.length; kIndex++) {
|
||||
int k = kNNs[kIndex];
|
||||
|
|
@ -122,7 +146,9 @@ public class MutualInfoMultiVariateTester
|
|||
// No longer need to set this property as it's set by default:
|
||||
//miCalc.setProperty(MutualInfoCalculatorMultiVariateKraskov.PROP_NORM_TYPE,
|
||||
// EuclideanUtils.NORM_MAX_NORM_STRING);
|
||||
miCalc.setProperty(MutualInfoCalculatorMultiVariateKraskov.PROP_ADD_NOISE, "0"); // Need consistency for unit tests
|
||||
miCalc.setProperty(MutualInfoCalculatorMultiVariateKraskov.PROP_ADD_NOISE,
|
||||
Double.toString(noiseLevel));
|
||||
miCalc.setProperty(MutualInfoCalculatorMultiVariateKraskov.PROP_NOISE_SEED, noiseSeed);
|
||||
miCalc.initialise(var1[0].length, var2[0].length);
|
||||
miCalc.setObservations(var1, var2);
|
||||
miCalc.setDebug(true);
|
||||
|
|
@ -131,10 +157,10 @@ public class MutualInfoMultiVariateTester
|
|||
|
||||
System.out.printf("k=%d: Average MI %.8f (expected %.8f)\n",
|
||||
k, mi, expectedResults[kIndex]);
|
||||
// Dropping required accuracy by one order of magnitude, due
|
||||
// to faster but slightly less accurate digamma estimator change
|
||||
assertEquals(expectedResults[kIndex], mi, 0.0000001);
|
||||
assertEquals(expectedResults[kIndex], mi, tolerance);
|
||||
errors[kIndex] = mi - expectedResults[kIndex];
|
||||
}
|
||||
return errors;
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
@ -801,5 +827,47 @@ public class MutualInfoMultiVariateTester
|
|||
double miWithDynCorrExclAndSeperateSets = miCalc.computeAverageLocalOfObservations();
|
||||
assertEquals(expectedMIWithoutDynCorrExcl, miWithDynCorrExclAndSeperateSets, 0.00001);
|
||||
}
|
||||
|
||||
/**
|
||||
* Extends the tests from testUnivariateMIforRandomVariablesFromFile to use
|
||||
* a random seed for noise and check that results are repeatable.
|
||||
*
|
||||
* @throws Exception if file not found
|
||||
*
|
||||
*/
|
||||
public void testUnivariateMIWithSeed() throws Exception {
|
||||
|
||||
// Test set 1:
|
||||
|
||||
ArrayFileReader afr = new ArrayFileReader("demos/data/2randomCols-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 = {-0.05294175, -0.03944338, -0.02190217,
|
||||
0.00120807, -0.00924771, -0.00316402, -0.00778205, -0.00565778};
|
||||
|
||||
System.out.println("Kraskov comparison 1 - univariate random data 1 - no seed");
|
||||
double[] noNoiseErrors = checkMIForGivenData(MatrixUtils.selectColumns(data, new int[] {0}),
|
||||
MatrixUtils.selectColumns(data, new int[] {1}),
|
||||
kNNs, expectedFromMILCA);
|
||||
double[] noNoiseResults = MatrixUtils.add(expectedFromMILCA, noNoiseErrors);
|
||||
|
||||
// Check that changing the random number generator still returns close to those with no noise
|
||||
// results, but with larger tolerance
|
||||
System.out.println("\n Kraskov comparison 1 - univariate random data 1 - seed 1");
|
||||
double[] withSeed1Errors = checkMIForGivenData(MatrixUtils.selectColumns(data, new int[] {0}),
|
||||
MatrixUtils.selectColumns(data, new int[] {1}),
|
||||
kNNs, noNoiseResults,
|
||||
1e-8, "1", 0.01);
|
||||
double[] withSeed1Results = MatrixUtils.add(noNoiseResults, withSeed1Errors);
|
||||
// Now check that the results are exact when we repeat with the same seed:
|
||||
System.out.println("\n Kraskov comparison 1 - univariate random data 1 - seed 1 repeat");
|
||||
checkMIForGivenData(MatrixUtils.selectColumns(data, new int[] {0}),
|
||||
MatrixUtils.selectColumns(data, new int[] {1}),
|
||||
kNNs, withSeed1Results,
|
||||
1e-8, "1", 1e-10);
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue