Beispiel #1
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 /**
  * The average number of columns per input, taking into account the topology of the inputs and
  * columns. This value is used to calculate the inhibition radius. This function supports an
  * arbitrary number of dimensions. If the number of column dimensions does not match the number of
  * input dimensions, we treat the missing, or phantom dimensions as 'ones'.
  *
  * @param c the {@link Connections} (spatial pooler memory)
  * @return
  */
 public double avgColumnsPerInput(Connections c) {
   int[] colDim = Arrays.copyOf(c.getColumnDimensions(), c.getColumnDimensions().length);
   int[] inputDim = Arrays.copyOf(c.getInputDimensions(), c.getInputDimensions().length);
   double[] columnsPerInput =
       ArrayUtils.divide(
           ArrayUtils.toDoubleArray(colDim), ArrayUtils.toDoubleArray(inputDim), 0, 0);
   return ArrayUtils.average(columnsPerInput);
 }
Beispiel #2
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  /**
   * 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);
  }
Beispiel #3
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 /**
  * 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);
 }
Beispiel #4
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  /**
   * 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.
   * @return
   */
  public int[] inhibitColumns(Connections c, double[] overlaps) {
    overlaps = Arrays.copyOf(overlaps, overlaps.length);

    double density;
    double inhibitionArea;
    if ((density = c.getLocalAreaDensity()) <= 0) {
      inhibitionArea = Math.pow(2 * c.getInhibitionRadius() + 1, c.getColumnDimensions().length);
      inhibitionArea = Math.min(c.getNumColumns(), inhibitionArea);
      density = c.getNumActiveColumnsPerInhArea() / inhibitionArea;
      density = Math.min(density, 0.5);
    }

    // Add our fixed little bit of random noise to the scores to help break ties.
    // ArrayUtils.d_add(overlaps, c.getTieBreaker());

    if (c.getGlobalInhibition()
        || c.getInhibitionRadius() > ArrayUtils.max(c.getColumnDimensions())) {
      return inhibitColumnsGlobal(c, overlaps, density);
    }

    return inhibitColumnsLocal(c, overlaps, density);
  }
Beispiel #5
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  /**
   * Update the inhibition radius. The inhibition radius is a measure of the square (or hypersquare)
   * of columns that each a column is "connected to" on average. Since columns are are not connected
   * to each other directly, we determine this quantity by first figuring out how many *inputs* a
   * column is connected to, and then multiplying it by the total number of columns that exist for
   * each input. For multiple dimension the aforementioned calculations are averaged over all
   * dimensions of inputs and columns. This value is meaningless if global inhibition is enabled.
   *
   * @param c the {@link Connections} (spatial pooler memory)
   */
  public void updateInhibitionRadius(Connections c) {
    if (c.getGlobalInhibition()) {
      c.setInhibitionRadius(ArrayUtils.max(c.getColumnDimensions()));
      return;
    }

    TDoubleArrayList avgCollected = new TDoubleArrayList();
    int len = c.getNumColumns();
    for (int i = 0; i < len; i++) {
      avgCollected.add(avgConnectedSpanForColumnND(c, i));
    }
    double avgConnectedSpan = ArrayUtils.average(avgCollected.toArray());
    double diameter = avgConnectedSpan * avgColumnsPerInput(c);
    double radius = (diameter - 1) / 2.0d;
    radius = Math.max(1, radius);
    c.setInhibitionRadius((int) Math.round(radius));
  }