/**
* Initializes the permanences of a column. The method returns a 1-D array the size of the input,
* where each entry in the array represents the initial permanence value between the input bit at
* the particular index in the array, and the column represented by the 'index' parameter.
*
* @param c the {@link Connections} which is the memory model
* @param potentialPool An array specifying the potential pool of the column. Permanence values
* will only be generated for input bits corresponding to indices for which the mask value is
* 1. WARNING: potentialPool is sparse, not an array of "1's"
* @param index the index of the column being initialized
* @param connectedPct A value between 0 or 1 specifying the percent of the input bits that will
* start off in a connected state.
* @return
*/
public double[] initPermanence(
Connections c, int[] potentialPool, int index, double connectedPct) {
double[] perm = new double[c.getNumInputs()];
for (int idx : potentialPool) {
if (c.random.nextDouble() <= connectedPct) {
perm[idx] = initPermConnected(c);
} else {
perm[idx] = initPermNonConnected(c);
}
perm[idx] = perm[idx] < c.getSynPermTrimThreshold() ? 0 : perm[idx];
}
c.getColumn(index).setProximalPermanences(c, perm);
return perm;
}
/**
* 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);
}
}
/**
* The range of connectedSynapses per column, averaged for each dimension. This value is used to
* calculate the inhibition radius. This variation of the function supports arbitrary column
* dimensions.
*
* @param c the {@link Connections} (spatial pooler memory)
* @param columnIndex the current column for which to avg.
* @return
*/
public double avgConnectedSpanForColumnND(Connections c, int columnIndex) {
int[] dimensions = c.getInputDimensions();
int[] connected = c.getColumn(columnIndex).getProximalDendrite().getConnectedSynapsesSparse(c);
if (connected == null || connected.length == 0) return 0;
int[] maxCoord = new int[c.getInputDimensions().length];
int[] minCoord = new int[c.getInputDimensions().length];
Arrays.fill(maxCoord, -1);
Arrays.fill(minCoord, ArrayUtils.max(dimensions));
SparseMatrix<?> inputMatrix = c.getInputMatrix();
for (int i = 0; i < connected.length; i++) {
maxCoord = ArrayUtils.maxBetween(maxCoord, inputMatrix.computeCoordinates(connected[i]));
minCoord = ArrayUtils.minBetween(minCoord, inputMatrix.computeCoordinates(connected[i]));
}
return ArrayUtils.average(ArrayUtils.add(ArrayUtils.subtract(maxCoord, minCoord), 1));
}
/**
* 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);
}
}
/**
* Step two of pooler initialization kept separate from initialization of static members so that
* they may be set at a different point in the initialization (as sometimes needed by tests).
*
* <p>This step prepares the proximal dendritic synapse pools with their initial permanence values
* and connected inputs.
*
* @param c the {@link Connections} memory
*/
public void connectAndConfigureInputs(Connections c) {
// Initialize the set of permanence values for each column. Ensure that
// each column is connected to enough input bits to allow it to be
// activated.
int numColumns = c.getNumColumns();
for (int i = 0; i < numColumns; i++) {
int[] potential = mapPotential(c, i, true);
Column column = c.getColumn(i);
c.getPotentialPools().set(i, column.createPotentialPool(c, potential));
double[] perm = initPermanence(c, potential, i, c.getInitConnectedPct());
updatePermanencesForColumn(c, perm, column, potential, true);
}
// The inhibition radius determines the size of a column's local
// neighborhood. A cortical column must overcome the overlap score of
// columns in its neighborhood in order to become active. This radius is
// updated every learning round. It grows and shrinks with the average
// number of connected synapses per column.
updateInhibitionRadius(c);
}
