TemporalMemory.hpp

/*
 * Copyright 2013-2016 Numenta Inc.
 *
 * Copyright may exist in Contributors' modifications
 * and/or contributions to the work.
 *
 * Use of this source code is governed by the MIT
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 */

/** @file
 * Definitions for the Temporal Memory in C++
 */

#ifndef NTA_TEMPORAL_MEMORY_HPP
#define NTA_TEMPORAL_MEMORY_HPP

#include <nupic/algorithms/Connections.hpp>
#include <nupic/types/Serializable.hpp>
#include <nupic/types/Types.hpp>
#include <nupic/utils/Random.hpp>
#include <vector>

#include <nupic/proto/TemporalMemoryProto.capnp.h>

using namespace std;
using namespace nupic;
using namespace nupic::algorithms::connections;

namespace nupic {
namespace algorithms {
namespace temporal_memory {

/**
 * Temporal Memory implementation in C++.
 *
 * Example usage:
 *
 *     SpatialPooler sp(inputDimensions, columnDimensions, <parameters>);
 *     TemporalMemory tm(columnDimensions, <parameters>);
 *
 *     while (true) {
 *        <get input vector, streaming spatiotemporal information>
 *        sp.compute(inputVector, learn, activeColumns)
 *        tm.compute(number of activeColumns, activeColumns, learn)
 *        <do something with the tm, e.g. classify tm.getActiveCells()>
 *     }
 *
 * The public API uses C arrays, not std::vectors, as inputs. C arrays are
 * a good lowest common denominator. You can get a C array from a vector,
 * but you can't get a vector from a C array without copying it. This is
 * important, for example, when using numpy arrays. The only way to
 * convert a numpy array into a std::vector is to copy it, but you can
 * access a numpy array's internal C array directly.
 */
class TemporalMemory : public Serializable<TemporalMemoryProto> {
public:
  TemporalMemory();

  /**
   * Initialize the temporal memory (TM) using the given parameters.
   *
   * @param columnDimensions
   * Dimensions of the column space
   *
   * @param cellsPerColumn
   * Number of cells per column
   *
   * @param activationThreshold
   * If the number of active connected synapses on a segment is at least
   * this threshold, the segment is said to be active.
   *
   * @param initialPermanence
   * Initial permanence of a new synapse.
   *
   * @param connectedPermanence
   * If the permanence value for a synapse is greater than this value, it
   * is said to be connected.
   *
   * @param minThreshold
   * If the number of potential synapses active on a segment is at least
   * this threshold, it is said to be "matching" and is eligible for
   * learning.
   *
   * @param maxNewSynapseCount
   * The maximum number of synapses added to a segment during learning.
   *
   * @param permanenceIncrement
   * Amount by which permanences of synapses are incremented during
   * learning.
   *
   * @param permanenceDecrement
   * Amount by which permanences of synapses are decremented during
   * learning.
   *
   * @param predictedSegmentDecrement
   * Amount by which segments are punished for incorrect predictions.
   *
   * @param seed
   * Seed for the random number generator.
   *
   * @param maxSegmentsPerCell
   * The maximum number of segments per cell.
   *
   * @param maxSynapsesPerSegment
   * The maximum number of synapses per segment.
   *
   * @param checkInputs
   * Whether to check that the activeColumns are sorted without
   * duplicates. Disable this for a small speed boost.
   *
   * Notes:
   *
   * predictedSegmentDecrement: A good value is just a bit larger than
   * (the column-level sparsity * permanenceIncrement). So, if column-level
   * sparsity is 2% and permanenceIncrement is 0.01, this parameter should be
   * something like 4% * 0.01 = 0.0004).
   */
  TemporalMemory(vector<UInt> columnDimensions, UInt cellsPerColumn = 32,
                 UInt activationThreshold = 13,
                 Permanence initialPermanence = 0.21,
                 Permanence connectedPermanence = 0.50, UInt minThreshold = 10,
                 UInt maxNewSynapseCount = 20,
                 Permanence permanenceIncrement = 0.10,
                 Permanence permanenceDecrement = 0.10,
                 Permanence predictedSegmentDecrement = 0.0, Int seed = 42,
                 UInt maxSegmentsPerCell = 255,
                 UInt maxSynapsesPerSegment = 255, bool checkInputs = true);

  virtual void
  initialize(vector<UInt> columnDimensions = {2048}, UInt cellsPerColumn = 32,
             UInt activationThreshold = 13, Permanence initialPermanence = 0.21,
             Permanence connectedPermanence = 0.50, UInt minThreshold = 10,
             UInt maxNewSynapseCount = 20,
             Permanence permanenceIncrement = 0.10,
             Permanence permanenceDecrement = 0.10,
             Permanence predictedSegmentDecrement = 0.0, Int seed = 42,
             UInt maxSegmentsPerCell = 255, UInt maxSynapsesPerSegment = 255,
             bool checkInputs = true);

  virtual ~TemporalMemory();

  //----------------------------------------------------------------------
  //  Main functions
  //----------------------------------------------------------------------

  /**
   * Get the version number of for the TM implementation.
   *
   * @returns Integer version number.
   */
  virtual UInt version() const;

  /**
   * This *only* updates _rng to a new Random using seed.
   *
   * @returns Integer version number.
   */
  void seed_(UInt64 seed);

  /**
   * Indicates the start of a new sequence.
   * Resets sequence state of the TM.
   */
  virtual void reset();

  /**
   * Calculate the active cells, using the current active columns and
   * dendrite segments. Grow and reinforce synapses.
   *
   * @param activeColumnsSize
   * Size of activeColumns.
   *
   * @param activeColumns
   * A sorted list of active column indices.
   *
   * @param learn
   * If true, reinforce / punish / grow synapses.
   */
  void activateCells(size_t activeColumnsSize, const UInt activeColumns[],
                     bool learn = true);

  /**
   * Calculate dendrite segment activity, using the current active cells.
   *
   * @param learn
   * If true, segment activations will be recorded. This information is
   * used during segment cleanup.
   */
  void activateDendrites(bool learn = true);

  /**
   * Perform one time step of the Temporal Memory algorithm.
   *
   * This method calls activateCells, then calls activateDendrites. Using
   * the TemporalMemory via its compute method ensures that you'll always
   * be able to call getPredictiveCells to get predictions for the next
   * time step.
   *
   * @param activeColumnsSize
   * Number of active columns.
   *
   * @param activeColumns
   * Sorted list of indices of active columns.
   *
   * @param learn
   * Whether or not learning is enabled.
   */
  virtual void compute(size_t activeColumnsSize, const UInt activeColumns[],
                       bool learn = true);

  // ==============================
  //  Helper functions
  // ==============================

  /**
   * Create a segment on the specified cell. This method calls
   * createSegment on the underlying connections, and it does some extra
   * bookkeeping. Unit tests should call this method, and not
   * connections.createSegment().
   *
   * @param cell
   * Cell to add a segment to.
   *
   * @return Segment
   * The created segment.
   */
  Segment createSegment(CellIdx cell);

  /**
   * Returns the indices of cells that belong to a column.
   *
   * @param column Column index
   *
   * @return (vector<CellIdx>) Cell indices
   */
  vector<CellIdx> cellsForColumn(Int column);

  /**
   * Returns the number of cells in this layer.
   *
   * @return (int) Number of cells
   */
  UInt numberOfCells(void);

  /**
   * Returns the indices of the active cells.
   *
   * @returns (std::vector<CellIdx>) Vector of indices of active cells.
   */
  vector<CellIdx> getActiveCells() const;

  /**
   * Returns the indices of the predictive cells.
   *
   * @returns (std::vector<CellIdx>) Vector of indices of predictive cells.
   */
  vector<CellIdx> getPredictiveCells() const;

  /**
   * Returns the indices of the winner cells.
   *
   * @returns (std::vector<CellIdx>) Vector of indices of winner cells.
   */
  vector<CellIdx> getWinnerCells() const;

  vector<Segment> getActiveSegments() const;
  vector<Segment> getMatchingSegments() const;

  /**
   * Returns the dimensions of the columns in the region.
   *
   * @returns Integer number of column dimension
   */
  vector<UInt> getColumnDimensions() const;

  /**
   * Returns the total number of columns.
   *
   * @returns Integer number of column numbers
   */
  UInt numberOfColumns() const;

  /**
   * Returns the number of cells per column.
   *
   * @returns Integer number of cells per column
   */
  UInt getCellsPerColumn() const;

  /**
   * Returns the activation threshold.
   *
   * @returns Integer number of the activation threshold
   */
  UInt getActivationThreshold() const;
  void setActivationThreshold(UInt);

  /**
   * Returns the initial permanence.
   *
   * @returns Initial permanence
   */
  Permanence getInitialPermanence() const;
  void setInitialPermanence(Permanence);

  /**
   * Returns the connected permanance.
   *
   * @returns Returns the connected permanance
   */
  Permanence getConnectedPermanence() const;
  void setConnectedPermanence(Permanence);

  /**
   * Returns the minimum threshold.
   *
   * @returns Integer number of minimum threshold
   */
  UInt getMinThreshold() const;
  void setMinThreshold(UInt);

  /**
   * Returns the maximum number of synapses that can be added to a segment
   * in a single time step.
   *
   * @returns Integer number of maximum new synapse count
   */
  UInt getMaxNewSynapseCount() const;
  void setMaxNewSynapseCount(UInt);

  /**
   * Get and set the checkInputs parameter.
   */
  bool getCheckInputs() const;
  void setCheckInputs(bool);

  /**
   * Returns the permanence increment.
   *
   * @returns Returns the Permanence increment
   */
  Permanence getPermanenceIncrement() const;
  void setPermanenceIncrement(Permanence);

  /**
   * Returns the permanence decrement.
   *
   * @returns Returns the Permanence decrement
   */
  Permanence getPermanenceDecrement() const;
  void setPermanenceDecrement(Permanence);

  /**
   * Returns the predicted Segment decrement.
   *
   * @returns Returns the segment decrement
   */
  Permanence getPredictedSegmentDecrement() const;
  void setPredictedSegmentDecrement(Permanence);

  /**
   * Returns the maxSegmentsPerCell.
   *
   * @returns Max segments per cell
   */
  UInt getMaxSegmentsPerCell() const;

  /**
   * Returns the maxSynapsesPerSegment.
   *
   * @returns Max synapses per segment
   */
  UInt getMaxSynapsesPerSegment() const;

  /**
   * Raises an error if cell index is invalid.
   *
   * @param cell Cell index
   */
  bool _validateCell(CellIdx cell);

  /**
   * Save (serialize) the current state of the spatial pooler to the
   * specified file.
   *
   * @param fd A valid file descriptor.
   */
  virtual void save(ostream &outStream) const;

  using Serializable::write;
  virtual void write(TemporalMemoryProto::Builder &proto) const override;

  /**
   * Load (deserialize) and initialize the spatial pooler from the
   * specified input stream.
   *
   * @param inStream A valid istream.
   */
  virtual void load(istream &inStream);

  using Serializable::read;
  virtual void read(TemporalMemoryProto::Reader &proto) override;

  /**
   * Returns the number of bytes that a save operation would result in.
   * Note: this method is currently somewhat inefficient as it just does
   * a full save into an ostream and counts the resulting size.
   *
   * @returns Integer number of bytes
   */
  virtual UInt persistentSize() const;

  bool operator==(const TemporalMemory &other);
  bool operator!=(const TemporalMemory &other);

  //----------------------------------------------------------------------
  // Debugging helpers
  //----------------------------------------------------------------------

  /**
   * Print the main TM creation parameters
   */
  void printParameters();

  /**
   * Returns the index of the column that a cell belongs to.
   *
   * @param cell Cell index
   *
   * @return (int) Column index
   */
  Int columnForCell(CellIdx cell);

  /**
   * Print the given UInt array in a nice format
   */
  void printState(vector<UInt> &state);

  /**
   * Print the given Real array in a nice format
   */
  void printState(vector<Real> &state);

protected:
  UInt numColumns_;
  vector<UInt> columnDimensions_;
  UInt cellsPerColumn_;
  UInt activationThreshold_;
  UInt minThreshold_;
  UInt maxNewSynapseCount_;
  bool checkInputs_;
  Permanence initialPermanence_;
  Permanence connectedPermanence_;
  Permanence permanenceIncrement_;
  Permanence permanenceDecrement_;
  Permanence predictedSegmentDecrement_;

  vector<CellIdx> activeCells_;
  vector<CellIdx> winnerCells_;
  vector<Segment> activeSegments_;
  vector<Segment> matchingSegments_;
  vector<UInt32> numActiveConnectedSynapsesForSegment_;
  vector<UInt32> numActivePotentialSynapsesForSegment_;

  UInt maxSegmentsPerCell_;
  UInt maxSynapsesPerSegment_;
  UInt64 iteration_;
  vector<UInt64> lastUsedIterationForSegment_;

  Random rng_;

public:
  Connections connections;
};

} // end namespace temporal_memory
} // end namespace algorithms
} // namespace nupic

#endif // NTA_TEMPORAL_MEMORY_HPP