/**
* 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 updates the permanence matrix with a column's new permanence values. The column is
* identified by its index, which reflects the row in the matrix, and the permanence is given in
* 'sparse' form, (i.e. an array whose members are associated with specific indexes). It is in
* charge of implementing 'clipping' - ensuring that the permanence values are always between 0
* and 1 - and 'trimming' - enforcing sparseness by zeroing out all permanence values below
* 'synPermTrimThreshold'. Every method wishing to modify the permanence matrix should do so
* through this method.
*
* @param c the {@link Connections} which is the memory model.
* @param perm An array of permanence values for a column. The array is "sparse", i.e. it contains
* an entry for each input bit, even if the permanence value is 0.
* @param column The column in the permanence, potential and connectivity matrices
* @param raisePerm a boolean value indicating whether the permanence values
*/
public void updatePermanencesForColumnSparse(
Connections c, double[] perm, Column column, int[] maskPotential, boolean raisePerm) {
if (raisePerm) {
raisePermanenceToThresholdSparse(c, perm);
}
ArrayUtils.lessThanOrEqualXThanSetToY(perm, c.getSynPermTrimThreshold(), 0);
ArrayUtils.clip(perm, c.getSynPermMin(), c.getSynPermMax());
column.setProximalPermanencesSparse(c, perm, maskPotential);
}
/**
* 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);
}
}