/**
* Update the boost factors for all columns. The boost factors are used to increase the overlap of
* inactive columns to improve their chances of becoming active. and hence encourage participation
* of more columns in the learning process. This is a line defined as: y = mx + b boost =
* (1-maxBoost)/minDuty * dutyCycle + maxFiringBoost. Intuitively this means that columns that
* have been active enough have a boost factor of 1, meaning their overlap is not boosted. Columns
* whose active duty cycle drops too much below that of their neighbors are boosted depending on
* how infrequently they have been active. The more infrequent, the more they are boosted. The
* exact boost factor is linearly interpolated between the points (dutyCycle:0,
* boost:maxFiringBoost) and (dutyCycle:minDuty, boost:1.0).
*
* <p>boostFactor ^ maxBoost _ | |\ | \ 1 _ | \ _ _ _ _ _ _ _ | +-------------------->
* activeDutyCycle | minActiveDutyCycle
*/
public void updateBoostFactors(Connections c) {
double[] activeDutyCycles = c.getActiveDutyCycles();
double[] minActiveDutyCycles = c.getMinActiveDutyCycles();
// Indexes of values > 0
int[] mask = ArrayUtils.where(minActiveDutyCycles, ArrayUtils.GREATER_THAN_0);
double[] boostInterim;
if (mask.length < 1) {
boostInterim = c.getBoostFactors();
} else {
double[] numerator = new double[c.getNumColumns()];
Arrays.fill(numerator, 1 - c.getMaxBoost());
boostInterim = ArrayUtils.divide(numerator, minActiveDutyCycles, 0, 0);
boostInterim = ArrayUtils.multiply(boostInterim, activeDutyCycles, 0, 0);
boostInterim = ArrayUtils.d_add(boostInterim, c.getMaxBoost());
}
ArrayUtils.setIndexesTo(
boostInterim,
ArrayUtils.where(
activeDutyCycles,
new Condition.Adapter<Object>() {
int i = 0;
@Override
public boolean eval(double d) {
return d > minActiveDutyCycles[i++];
}
}),
1.0d);
c.setBoostFactors(boostInterim);
}
/**
* Updates the minimum duty cycles in a global fashion. Sets the minimum duty cycles for the
* overlap and activation of all columns to be a percent of the maximum in the region, specified
* by {@link Connections#getMinOverlapDutyCycles()} and minPctActiveDutyCycle respectively.
* Functionality it is equivalent to {@link #updateMinDutyCyclesLocal(Connections)}, but this
* function exploits the globalness of the computation to perform it in a straightforward, and
* more efficient manner.
*
* @param c
*/
public void updateMinDutyCyclesGlobal(Connections c) {
Arrays.fill(
c.getMinOverlapDutyCycles(),
c.getMinPctOverlapDutyCycles() * ArrayUtils.max(c.getOverlapDutyCycles()));
Arrays.fill(
c.getMinActiveDutyCycles(),
c.getMinPctActiveDutyCycles() * ArrayUtils.max(c.getActiveDutyCycles()));
}
/**
* Removes the set of columns who have never been active from the set of active columns selected
* in the inhibition round. Such columns cannot represent learned pattern and are therefore
* meaningless if only inference is required. This should not be done when using a random,
* unlearned SP since you would end up with no active columns.
*
* @param activeColumns An array containing the indices of the active columns
* @return a list of columns with a chance of activation
*/
public int[] stripUnlearnedColumns(Connections c, int[] activeColumns) {
TIntHashSet active = new TIntHashSet(activeColumns);
TIntHashSet aboveZero = new TIntHashSet();
int numCols = c.getNumColumns();
double[] colDutyCycles = c.getActiveDutyCycles();
for (int i = 0; i < numCols; i++) {
if (colDutyCycles[i] <= 0) {
aboveZero.add(i);
}
}
active.removeAll(aboveZero);
TIntArrayList l = new TIntArrayList(active);
l.sort();
// return l;
return Arrays.stream(activeColumns).filter(i -> c.getActiveDutyCycles()[i] > 0).toArray();
}
/**
* Updates the minimum duty cycles. The minimum duty cycles are determined locally. Each column's
* minimum duty cycles are set to be a percent of the maximum duty cycles in the column's
* neighborhood. Unlike {@link #updateMinDutyCyclesGlobal(Connections)}, here the values can be
* quite different for different columns.
*
* @param c
*/
public void updateMinDutyCyclesLocal(Connections c) {
int len = c.getNumColumns();
for (int i = 0; i < len; i++) {
int[] maskNeighbors =
getNeighborsND(c, i, c.getMemory(), c.getInhibitionRadius(), true).toArray();
c.getMinOverlapDutyCycles()[i] =
ArrayUtils.max(ArrayUtils.sub(c.getOverlapDutyCycles(), maskNeighbors))
* c.getMinPctOverlapDutyCycles();
c.getMinActiveDutyCycles()[i] =
ArrayUtils.max(ArrayUtils.sub(c.getActiveDutyCycles(), maskNeighbors))
* c.getMinPctActiveDutyCycles();
}
}
/**
* Updates the duty cycles for each column. The OVERLAP duty cycle is a moving average of the
* number of inputs which overlapped with each column. The ACTIVITY duty cycles is a moving
* average of the frequency of activation for each column.
*
* @param c the {@link Connections} (spatial pooler memory)
* @param overlaps an array containing the overlap score for each column. The overlap score for a
* column is defined as the number of synapses in a "connected state" (connected synapses)
* that are connected to input bits which are turned on.
* @param activeColumns An array containing the indices of the active columns, the sparse set of
* columns which survived inhibition
*/
public void updateDutyCycles(Connections c, int[] overlaps, int[] activeColumns) {
double[] overlapArray = new double[c.getNumColumns()];
double[] activeArray = new double[c.getNumColumns()];
ArrayUtils.greaterThanXThanSetToYInB(overlaps, overlapArray, 0, 1);
if (activeColumns.length > 0) {
ArrayUtils.setIndexesTo(activeArray, activeColumns, 1);
}
int period = c.getDutyCyclePeriod();
if (period > c.getIterationNum()) {
period = c.getIterationNum();
}
c.setOverlapDutyCycles(
updateDutyCyclesHelper(c, c.getOverlapDutyCycles(), overlapArray, period));
c.setActiveDutyCycles(updateDutyCyclesHelper(c, c.getActiveDutyCycles(), activeArray, period));
}
