/**
* The primary method in charge of learning. Adapts the permanence values of the synapses based on
* the input vector, and the chosen columns after inhibition round. Permanence values are
* increased for synapses connected to input bits that are turned on, and decreased for synapses
* connected to inputs bits that are turned off.
*
* @param c the {@link Connections} (spatial pooler memory)
* @param inputVector a integer array that comprises the input to the spatial pooler. There exists
* an entry in the array for every input bit.
* @param activeColumns an array containing the indices of the columns that survived inhibition.
*/
public void adaptSynapses(Connections c, int[] inputVector, int[] activeColumns) {
int[] inputIndices = ArrayUtils.where(inputVector, ArrayUtils.INT_GREATER_THAN_0);
double[] permChanges = new double[c.getNumInputs()];
Arrays.fill(permChanges, -1 * c.getSynPermInactiveDec());
ArrayUtils.setIndexesTo(permChanges, inputIndices, c.getSynPermActiveInc());
for (int i = 0; i < activeColumns.length; i++) {
Pool pool = c.getPotentialPools().get(activeColumns[i]);
double[] perm = pool.getDensePermanences(c);
int[] indexes = pool.getSparsePotential();
ArrayUtils.raiseValuesBy(permChanges, perm);
Column col = c.getColumn(activeColumns[i]);
updatePermanencesForColumn(c, perm, col, indexes, true);
}
}
