/**
* Called to initialize the structural anatomy with configured values and prepare the anatomical
* entities for activation.
*
* @param c
*/
public void initMatrices(final Connections c) {
SparseObjectMatrix<Column> mem = c.getMemory();
c.setMemory(mem == null ? mem = new SparseObjectMatrix<>(c.getColumnDimensions()) : mem);
c.setInputMatrix(new SparseBinaryMatrix(c.getInputDimensions()));
// Calculate numInputs and numColumns
int numInputs = c.getInputMatrix().getMaxIndex() + 1;
int numColumns = c.getMemory().getMaxIndex() + 1;
if (numColumns <= 0) {
throw new InvalidSPParamValueException("Invalid number of columns: " + numColumns);
}
if (numInputs <= 0) {
throw new InvalidSPParamValueException("Invalid number of inputs: " + numInputs);
}
c.setNumInputs(numInputs);
c.setNumColumns(numColumns);
// Fill the sparse matrix with column objects
for (int i = 0; i < numColumns; i++) {
mem.set(i, new Column(c.getCellsPerColumn(), i));
}
c.setPotentialPools(new SparseObjectMatrix<Pool>(c.getMemory().getDimensions()));
c.setConnectedMatrix(new SparseBinaryMatrix(new int[] {numColumns, numInputs}));
// Initialize state meta-management statistics
c.setOverlapDutyCycles(new double[numColumns]);
c.setActiveDutyCycles(new double[numColumns]);
c.setMinOverlapDutyCycles(new double[numColumns]);
c.setMinActiveDutyCycles(new double[numColumns]);
c.setBoostFactors(new double[numColumns]);
Arrays.fill(c.getBoostFactors(), 1);
}
/**
* 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();
}
}
/**
* Performs inhibition. This method calculates the necessary values needed to actually perform
* inhibition and then delegates the task of picking the active columns to helper functions.
*
* @param c the {@link Connections} matrix
* @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 density The fraction of columns to survive inhibition. This value is only an intended
* target. Since the surviving columns are picked in a local fashion, the exact fraction of
* surviving columns is likely to vary.
* @return indices of the winning columns
*/
public int[] inhibitColumnsLocal(Connections c, double[] overlaps, double density) {
int numCols = c.getNumColumns();
int[] activeColumns = new int[numCols];
double addToWinners = ArrayUtils.max(overlaps) / 1000.0;
for (int i = 0; i < numCols; i++) {
TIntArrayList maskNeighbors =
getNeighborsND(c, i, c.getMemory(), c.getInhibitionRadius(), false);
double[] overlapSlice = ArrayUtils.sub(overlaps, maskNeighbors.toArray());
int numActive = (int) (0.5 + density * (maskNeighbors.size() + 1));
int numBigger = ArrayUtils.valueGreaterCount(overlaps[i], overlapSlice);
if (numBigger < numActive) {
activeColumns[i] = 1;
overlaps[i] += addToWinners;
}
}
return ArrayUtils.where(activeColumns, ArrayUtils.INT_GREATER_THAN_0);
}
/**
* 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);
}
}
/**
* Maps a column to its respective input index, keeping to the topology of the region. It takes
* the index of the column as an argument and determines what is the index of the flattened input
* vector that is to be the center of the column's potential pool. It distributes the columns over
* the inputs uniformly. The return value is an integer representing the index of the input bit.
* Examples of the expected output of this method: * If the topology is one dimensional, and the
* column index is 0, this method will return the input index 0. If the column index is 1, and
* there are 3 columns over 7 inputs, this method will return the input index 3. * If the topology
* is two dimensional, with column dimensions [3, 5] and input dimensions [7, 11], and the column
* index is 3, the method returns input index 8.
*
* @param columnIndex The index identifying a column in the permanence, potential and connectivity
* matrices.
* @return A boolean value indicating that boundaries should be ignored.
*/
public int mapColumn(Connections c, int columnIndex) {
int[] columnCoords = c.getMemory().computeCoordinates(columnIndex);
double[] colCoords = ArrayUtils.toDoubleArray(columnCoords);
double[] ratios =
ArrayUtils.divide(colCoords, ArrayUtils.toDoubleArray(c.getColumnDimensions()), 0, 0);
double[] inputCoords =
ArrayUtils.multiply(ArrayUtils.toDoubleArray(c.getInputDimensions()), ratios, 0, 0);
inputCoords =
ArrayUtils.d_add(
inputCoords,
ArrayUtils.multiply(
ArrayUtils.divide(
ArrayUtils.toDoubleArray(c.getInputDimensions()),
ArrayUtils.toDoubleArray(c.getColumnDimensions()),
0,
0),
0.5));
int[] inputCoordInts =
ArrayUtils.clip(ArrayUtils.toIntArray(inputCoords), c.getInputDimensions(), -1);
return c.getInputMatrix().computeIndex(inputCoordInts);
}
