/**
* This method ensures that each column has enough connections to input bits to allow it to become
* active. Since a column must have at least 'stimulusThreshold' overlaps in order to be
* considered during the inhibition phase, columns without such minimal number of connections,
* even if all the input bits they are connected to turn on, have no chance of obtaining the
* minimum threshold. For such columns, the permanence values are increased until the minimum
* number of connections are formed.
*
* <p>Note: This method services the "sparse" versions of corresponding methods
*
* @param c The {@link Connections} memory
* @param perm permanence values
*/
public void raisePermanenceToThresholdSparse(Connections c, double[] perm) {
ArrayUtils.clip(perm, c.getSynPermMin(), c.getSynPermMax());
while (true) {
int numConnected = ArrayUtils.valueGreaterCount(c.getSynPermConnected(), perm);
if (numConnected >= c.getStimulusThreshold()) return;
ArrayUtils.raiseValuesBy(c.getSynPermBelowStimulusInc(), perm);
}
}
/**
* This method ensures that each column has enough connections to input bits to allow it to become
* active. Since a column must have at least 'stimulusThreshold' overlaps in order to be
* considered during the inhibition phase, columns without such minimal number of connections,
* even if all the input bits they are connected to turn on, have no chance of obtaining the
* minimum threshold. For such columns, the permanence values are increased until the minimum
* number of connections are formed.
*
* @param c the {@link Connections} memory
* @param perm the permanence values
* @param maskPotential
*/
public void raisePermanenceToThreshold(Connections c, double[] perm, int[] maskPotential) {
if (maskPotential.length < c.getStimulusThreshold()) {
throw new IllegalStateException(
"This is likely due to a "
+ "value of stimulusThreshold that is too large relative "
+ "to the input size. [len(mask) < self._stimulusThreshold]");
}
ArrayUtils.clip(perm, c.getSynPermMin(), c.getSynPermMax());
while (true) {
int numConnected =
ArrayUtils.valueGreaterCountAtIndex(c.getSynPermConnected(), perm, maskPotential);
if (numConnected >= c.getStimulusThreshold()) return;
ArrayUtils.raiseValuesBy(c.getSynPermBelowStimulusInc(), perm, maskPotential);
}
}
/**
* This method increases the permanence values of synapses of columns whose activity level has
* been too low. Such columns are identified by having an overlap duty cycle that drops too much
* below those of their peers. The permanence values for such columns are increased.
*
* @param c
*/
public void bumpUpWeakColumns(final Connections c) {
int[] weakColumns =
ArrayUtils.where(
c.getMemory().get1DIndexes(),
new Condition.Adapter<Integer>() {
@Override
public boolean eval(int i) {
return c.getOverlapDutyCycles()[i] < c.getMinOverlapDutyCycles()[i];
}
});
for (int i = 0; i < weakColumns.length; i++) {
Pool pool = c.getPotentialPools().get(weakColumns[i]);
double[] perm = pool.getSparsePermanences();
ArrayUtils.raiseValuesBy(c.getSynPermBelowStimulusInc(), perm);
int[] indexes = pool.getSparsePotential();
Column col = c.getColumn(weakColumns[i]);
updatePermanencesForColumnSparse(c, perm, col, indexes, true);
}
}