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Re-engineered Discrete Measure Calculation #103

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Re-engineered Discrete Measure Calculation #103

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ac8326a
Discrete entropy is complete I think. Maybe a few more things moved a…
Aug 22, 2024
de44f67
Final commit for Brad's thesis. All remaining TODO comments are real …
Dec 24, 2024
1fcf2d0
Merge branch 'master' of https://github.com/bwootwoot/jidt-discrete-r…
bwootwoot Jan 23, 2025
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90 changes: 52 additions & 38 deletions java/source/infodynamics/measures/discrete/ActiveInformationCalculatorDiscrete.java
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Original file line number Diff line number Diff line change
Expand Up @@ -72,7 +72,7 @@
* @author Joseph Lizier (<a href="joseph.lizier at gmail.com">email</a>,
* <a href="http://lizier.me/joseph/">www</a>)
*/
public class ActiveInformationCalculatorDiscrete extends SingleAgentMeasureDiscreteInContextOfPastCalculator
public class ActiveInformationCalculatorDiscrete extends UnivariateMeasureDiscreteInContextOfPastCalculator
implements EmpiricalNullDistributionComputer, AnalyticNullDistributionComputer {

protected boolean aisComputed = false;
Expand Down Expand Up @@ -111,7 +111,7 @@ public ActiveInformationCalculatorDiscrete(int base, int history) {

@Override
public void initialise() {
initialise(base, k);
initialise(alphabetSize, k);
}

public void initialise(int base, int history) {
Expand All @@ -128,7 +128,7 @@ public void addObservations(int states[]) {
// Initialise and store the current previous value for each column
int prevVal = 0;
for (int p = 0; p < k; p++) {
prevVal *= base;
prevVal *= alphabetSize;
prevVal += states[p];
}

Expand All @@ -142,7 +142,7 @@ public void addObservations(int states[]) {
nextCount[nextVal]++;
// Update the previous value:
prevVal -= maxShiftedValue[states[t-k]];
prevVal *= base;
prevVal *= alphabetSize;
prevVal += states[t];
}
}
Expand All @@ -159,7 +159,7 @@ public void addObservations(int states[][]) {
for (int c = 0; c < columns; c++) {
prevVal[c] = 0;
for (int p = 0; p < k; p++) {
prevVal[c] *= base;
prevVal[c] *= alphabetSize;
prevVal[c] += states[p][c];
}
}
Expand All @@ -176,13 +176,12 @@ public void addObservations(int states[][]) {
nextCount[nextVal]++;
// Update the previous value:
prevVal[c] -= maxShiftedValue[states[r-k][c]];
prevVal[c] *= base;
prevVal[c] *= alphabetSize;
prevVal[c] += states[r][c];
}
}
}

@Override
public void addObservations(int states[][][]) {
int timeSteps = states.length;
if (timeSteps == 0) {
Expand All @@ -202,7 +201,7 @@ public void addObservations(int states[][][]) {
for (int c = 0; c < agentColumns; c++) {
prevVal[r][c] = 0;
for (int p = 0; p < k; p++) {
prevVal[r][c] *= base;
prevVal[r][c] *= alphabetSize;
prevVal[r][c] += states[p][r][c];
}
}
Expand All @@ -221,14 +220,13 @@ public void addObservations(int states[][][]) {
nextCount[nextVal]++;
// Update the previous value:
prevVal[r][c] -= maxShiftedValue[states[t-k][r][c]];
prevVal[r][c] *= base;
prevVal[r][c] *= alphabetSize;
prevVal[r][c] += states[t][r][c];
}
}
}
}

@Override
public void addObservations(int states[][], int col) {
int rows = states.length;
// increment the count of observations:
Expand All @@ -238,7 +236,7 @@ public void addObservations(int states[][], int col) {
int prevVal = 0;
prevVal = 0;
for (int p = 0; p < k; p++) {
prevVal *= base;
prevVal *= alphabetSize;
prevVal += states[p][col];
}

Expand All @@ -253,12 +251,11 @@ public void addObservations(int states[][], int col) {
nextCount[nextVal]++;
// Update the previous value:
prevVal -= maxShiftedValue[states[r-k][col]];
prevVal *= base;
prevVal *= alphabetSize;
prevVal += states[r][col];
}
}

@Override
public void addObservations(int states[][][], int agentIndex1, int agentIndex2) {
int timeSteps = states.length;
// increment the count of observations:
Expand All @@ -268,7 +265,7 @@ public void addObservations(int states[][][], int agentIndex1, int agentIndex2)
int prevVal = 0;
prevVal = 0;
for (int p = 0; p < k; p++) {
prevVal *= base;
prevVal *= alphabetSize;
prevVal += states[p][agentIndex1][agentIndex2];
}

Expand All @@ -283,7 +280,7 @@ public void addObservations(int states[][][], int agentIndex1, int agentIndex2)
nextCount[nextVal]++;
// Update the previous value:
prevVal -= maxShiftedValue[states[t-k][agentIndex1][agentIndex2]];
prevVal *= base;
prevVal *= alphabetSize;
prevVal += states[t][agentIndex1][agentIndex2];
}
}
Expand All @@ -295,7 +292,7 @@ public double computeAverageLocalOfObservations() {

max = 0;
min = 0;
for (int nextVal = 0; nextVal < base; nextVal++) {
for (int nextVal = 0; nextVal < alphabetSize; nextVal++) {
// compute p_next
double p_next = (double) nextCount[nextVal] / (double) observations;
for (int prevVal = 0; prevVal < base_power_k; prevVal++) {
Expand Down Expand Up @@ -335,7 +332,7 @@ public double computeAverageLocalEntropyRateOfObservations() {
double entRate = 0.0;
double entRateCont = 0.0;

for (int nextVal = 0; nextVal < base; nextVal++) {
for (int nextVal = 0; nextVal < alphabetSize; nextVal++) {
for (int prevVal = 0; prevVal < base_power_k; prevVal++) {
// compute p_prev
double p_prev = (double) pastCount[prevVal] / (double) observations;
Expand Down Expand Up @@ -389,7 +386,7 @@ public double[] computeLocalFromPreviousObservations(int states[]){
// Initialise and store the current previous value for each column
int prevVal = 0;
for (int p = 0; p < k; p++) {
prevVal *= base;
prevVal *= alphabetSize;
prevVal += states[p];
}

Expand All @@ -414,7 +411,7 @@ public double[] computeLocalFromPreviousObservations(int states[]){
}
// Update the previous value:
prevVal -= maxShiftedValue[states[t-k]];
prevVal *= base;
prevVal *= alphabetSize;
prevVal += states[t];
}
average = average/(double) (timeSteps - k);
Expand All @@ -423,7 +420,6 @@ public double[] computeLocalFromPreviousObservations(int states[]){

}

@Override
public double[][] computeLocalFromPreviousObservations(int states[][]){
int rows = states.length;
int columns = states[0].length;
Expand All @@ -439,7 +435,7 @@ public double[][] computeLocalFromPreviousObservations(int states[][]){
for (int c = 0; c < columns; c++) {
prevVal[c] = 0;
for (int p = 0; p < k; p++) {
prevVal[c] *= base;
prevVal[c] *= alphabetSize;
prevVal[c] += states[p][c];
}
}
Expand All @@ -465,7 +461,7 @@ public double[][] computeLocalFromPreviousObservations(int states[][]){
}
// Update the previous value:
prevVal[c] -= maxShiftedValue[states[r-k][c]];
prevVal[c] *= base;
prevVal[c] *= alphabetSize;
prevVal[c] += states[r][c];
}
}
Expand All @@ -475,7 +471,7 @@ public double[][] computeLocalFromPreviousObservations(int states[][]){

}

@Override

public double[][][] computeLocalFromPreviousObservations(int states[][][]){
int timeSteps = states.length;
int agentRows = states[0].length;
Expand All @@ -493,7 +489,7 @@ public double[][][] computeLocalFromPreviousObservations(int states[][][]){
for (int c = 0; c < agentColumns; c++) {
prevVal[r][c] = 0;
for (int p = 0; p < k; p++) {
prevVal[r][c] *= base;
prevVal[r][c] *= alphabetSize;
prevVal[r][c] += states[p][r][c];
}
}
Expand Down Expand Up @@ -521,7 +517,7 @@ public double[][][] computeLocalFromPreviousObservations(int states[][][]){
}
// Update the previous value:
prevVal[r][c] -= maxShiftedValue[states[t-k][r][c]];
prevVal[r][c] *= base;
prevVal[r][c] *= alphabetSize;
prevVal[r][c] += states[t][r][c];
}
}
Expand All @@ -531,7 +527,7 @@ public double[][][] computeLocalFromPreviousObservations(int states[][][]){
return localActive;
}

@Override

public double[] computeLocalFromPreviousObservations(int states[][], int col){
int rows = states.length;
//int columns = states[0].length;
Expand All @@ -546,7 +542,7 @@ public double[] computeLocalFromPreviousObservations(int states[][], int col){
int prevVal = 0;
prevVal = 0;
for (int p = 0; p < k; p++) {
prevVal *= base;
prevVal *= alphabetSize;
prevVal += states[p][col];
}
int nextVal;
Expand All @@ -570,7 +566,7 @@ public double[] computeLocalFromPreviousObservations(int states[][], int col){
}
// Update the previous value:
prevVal -= maxShiftedValue[states[r-k][col]];
prevVal *= base;
prevVal *= alphabetSize;
prevVal += states[r][col];
}
average = average/(double) (rows - k);
Expand All @@ -579,7 +575,7 @@ public double[] computeLocalFromPreviousObservations(int states[][], int col){

}

@Override

public double[] computeLocalFromPreviousObservations(int states[][][],
int agentIndex1, int agentIndex2){
int timeSteps = states.length;
Expand All @@ -595,7 +591,7 @@ public double[] computeLocalFromPreviousObservations(int states[][][],
int prevVal = 0;
prevVal = 0;
for (int p = 0; p < k; p++) {
prevVal *= base;
prevVal *= alphabetSize;
prevVal += states[p][agentIndex1][agentIndex2];
}
int nextVal;
Expand All @@ -619,7 +615,7 @@ public double[] computeLocalFromPreviousObservations(int states[][][],
}
// Update the previous value:
prevVal -= maxShiftedValue[states[t-k][agentIndex1][agentIndex2]];
prevVal *= base;
prevVal *= alphabetSize;
prevVal += states[t][agentIndex1][agentIndex2];
}
average = average/(double) (timeSteps - k);
Expand Down Expand Up @@ -709,14 +705,14 @@ public EmpiricalMeasurementDistribution computeSignificance(int[][] newOrderings
MatrixUtils.fill(prevValues, prevVal, t_prev, numberOfSamplesPrev);
t_prev += numberOfSamplesPrev;
}
for (int nextVal = 0; nextVal < base; nextVal++) {
for (int nextVal = 0; nextVal < alphabetSize; nextVal++) {
int numberOfSamplesNext = nextCount[nextVal];
MatrixUtils.fill(nextValues, nextVal, t_next, numberOfSamplesNext);
t_next += numberOfSamplesNext;
}

ActiveInformationCalculatorDiscrete ais2;
ais2 = new ActiveInformationCalculatorDiscrete(base, k);
ais2 = new ActiveInformationCalculatorDiscrete(alphabetSize, k);
ais2.initialise();
ais2.observations = observations;
ais2.pastCount = pastCount;
Expand Down Expand Up @@ -754,7 +750,7 @@ public AnalyticMeasurementDistribution computeSignificance()
}
return new ChiSquareMeasurementDistribution(average,
observations,
(base - 1) * (base_power_k - 1));
(alphabetSize - 1) * (base_power_k - 1));
}

/**
Expand All @@ -767,7 +763,7 @@ public void writePdfs() {
double miCont = 0.0;

System.out.println("nextVal p(next) prevVal p(prev) p(joint) logTerm localVal");
for (int nextVal = 0; nextVal < base; nextVal++) {
for (int nextVal = 0; nextVal < alphabetSize; nextVal++) {
// compute p_next
double p_next = (double) nextCount[nextVal] / (double) observations;
for (int prevVal = 0; prevVal < base_power_k; prevVal++) {
Expand Down Expand Up @@ -811,7 +807,7 @@ public void writePdfs() {
public int computePastValue(int[] x, int t) {
int pastVal = 0;
for (int p = 0; p < k; p++) {
pastVal *= base;
pastVal *= alphabetSize;
pastVal += x[t - k + 1 + p];
}
return pastVal;
Expand All @@ -835,7 +831,7 @@ public int computePastValue(int[] x, int t) {
public int computePastValue(int[][] data, int column, int t) {
int pastVal = 0;
for (int p = 0; p < k; p++) {
pastVal *= base;
pastVal *= alphabetSize;
pastVal += data[t - k + 1 + p][column];
}
return pastVal;
Expand Down Expand Up @@ -863,9 +859,27 @@ public int computePastValue(int[][][] data, int agentRow,
int agentColumn, int t) {
int pastVal = 0;
for (int p = 0; p < k; p++) {
pastVal *= base;
pastVal *= alphabetSize;
pastVal += data[t - k + 1 + p][agentRow][agentColumn];
}
return pastVal;
}

@Override
public void setObservations(Object[] observations) throws Exception {
// TODO Auto-generated method stub
throw new UnsupportedOperationException("Unimplemented method 'setObservations'");
}

@Override
public void startAddObservations() {
// TODO Auto-generated method stub
throw new UnsupportedOperationException("Unimplemented method 'startAddObservations'");
}

@Override
public void finaliseAddObservations() throws Exception {
// TODO Auto-generated method stub
throw new UnsupportedOperationException("Unimplemented method 'finaliseAddObservations'");
}
}
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