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A Java based Neuron Modeling framework
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package model.MARK_II.generalAlgorithm;
import java.math.BigInteger;
import java.text.DecimalFormat;
/**
* @author Quinn Liu ([email protected])
* @version 3/10/2015
*/
public class SDRStatistics {
// notes on below variables @ https://github.com/WalnutiQ/WalnutiQ/issues/152
private int n; // number of neurons
private int w; // number of activeNeurons and also upper bound of b
private int theta; // theta = lower bound of b
public SDRStatistics(int numberOfNeurons, int numberOfActiveNeurons,
int minimumNumberOfOverlapNeuronsForMatch) {
this.n = numberOfNeurons;
this.w = numberOfActiveNeurons;
this.theta = minimumNumberOfOverlapNeuronsForMatch;
if (this.n < 0) {
throw new IllegalArgumentException("n must be >= 0");
}
if (this.n < this.w || this.n <= this.theta) {
throw new IllegalArgumentException("n must be >= than w and n " +
"must be >= theta");
}
}
/**
* This method assumes there is a SDR set M that is a union of SDRs to
* represent a specific idea. SDR x is usually intersected with SDR set M
* to find how many "bits" are in common. However if SDR set M is the union
* of too many SDRs than any SDR x will "match" with M. This method
* calculates the probability of false positive when SDR set M gets too
* large for exact matches between SDR x and SDRs within M where theta = w.
*
* @param SDRSetMSize Number of SDRs in M. In HTM's SDRSetMSize usually > 1024.
*
* @return Probability of false positive for SDR x "matching" with
* SDR set M.
*/
double probabilityOfFalsePositiveForSDRxIntersectSDRSetM(int SDRSetMSize) {
// s = Percentage of ON bits in all SDRs x and in M.
double s = (double) this.w / (double) this.n;
double probabilityAGivenBitIsStill0 = Math.pow(1 - s, SDRSetMSize);
// In this case w = Number of active "bits" in all SDRs.
double answer = Math.pow(1 - probabilityAGivenBitIsStill0, this.w);
return answer;
}
/**
* @return a double value between 0 and 100 of the probability of
* a false positive.
*/
String probabilityOfFalsePositive() {
BigInteger numerator = new BigInteger("0");
for (int b = this.theta; b <= this.w; b++) {
numerator = numerator.add(overlapSet(b));
}
BigInteger denominator = combination(this.n, this.w);
double probabilityOfFalsePositive = (numerator.floatValue() /
denominator.floatValue()) * 100;
double percentageOfNoise = (Double.valueOf(this.theta) / Double.valueOf(this.w)) * 100;
DecimalFormat df1 = new DecimalFormat("#.00");
DecimalFormat df2 = new DecimalFormat("#.000000");
String statistics = "With " + df1.format(percentageOfNoise) + "% " +
"noise there is a " + df2.format(probabilityOfFalsePositive)
+ "% of false positive";
return statistics;
}
BigInteger overlapSet(int b) {
if (b > this.w) {
throw new IllegalArgumentException("b must be <= than w");
}
return combination(this.w, b).multiply(combination(this.n - this.w, this.w - b));
}
/**
* n choose k = n!/(k!(n-k)!)
*
* @param n Total number of elements.
* @param k Number of elements to combine at one time.
* @return The number of times k elements can be arranged out of n elements
* where order of arranging things do NOT make arrangement unique.
*/
static BigInteger combination(int n, int k) {
return factorial(n).divide(factorial(k).multiply(factorial(n - k)));
}
static BigInteger factorial(long n) {
if (n < 0) {
throw new IllegalArgumentException("You cannot take the factorial" +
"of the negative integer " + n);
}
BigInteger result = BigInteger.valueOf(1);
for (int i = 1; i <= n; i++) {
result = result.multiply(BigInteger.valueOf(i));
}
return result;
}
}