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| Original file line number | Diff line number | Diff line change |
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| #pragma once | ||
| #include "MemObject.h" | ||
| #include "Segment.h" | ||
| #include "SegmentUpdateInfo.h" | ||
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| class Column; | ||
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| /// A data structure representing a single context sensitive cell. | ||
| class Cell : | ||
| public MemObject | ||
| { | ||
| public: | ||
| Cell(void); | ||
| ~Cell(void); | ||
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| MemObjectType GetMemObjectType() {return MOT_CELL;} | ||
| void Retire(); | ||
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| /// Properties | ||
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| private: | ||
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| int Index, NumPredictionSteps, PrevNumPredictionSteps, PrevActiveTime; | ||
| bool IsActive, WasActive, IsLearning, WasLearning, _isPredicting; | ||
| bool IsSegmentPredicting, WasSegmentPredicted, WasPredicted; | ||
| Column *column; | ||
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| void SetColumn(Column *value) {column = value;} | ||
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| void SetNumPredictionSteps(int value) {NumPredictionSteps = value;} | ||
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| public: | ||
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| FastList Segments; | ||
| FastList _segmentUpdates; | ||
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| /// Position in Column | ||
| int GetIndex() {return Index;} | ||
| void SetIndex(int value) {Index = value;} | ||
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| /// Gets or sets a value indicating whether this Cell is active. | ||
| /// true if this instance is active; otherwise, false. | ||
| bool GetIsActive() {return IsActive;} | ||
| void SetIsActive(bool value); | ||
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| /// Gets a value indicating whether this Cell was active. | ||
| /// true if it was active; otherwise, false. | ||
| bool GetWasActive() {return WasActive;} | ||
| void SetWasActive(bool value) {WasActive = value;} | ||
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| /// Gets or sets a value indicating whether this Cell is learning. | ||
| /// true if it is learning; otherwise, false. | ||
| bool GetIsLearning() {return IsLearning;} | ||
| void SetIsLearning(bool value) {IsLearning = value;} | ||
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| /// Gets a value indicating whether this Cell was learning. | ||
| /// true if it was learning; otherwise, false. | ||
| bool GetWasLearning() {return WasLearning;} | ||
| void SetWasLearning(bool value) {WasLearning = value;} | ||
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| bool GetIsPredicting() {return _isPredicting;} | ||
| void SetIsPredicting(bool value, int numPredictionSteps); | ||
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| bool GetIsSegmentPredicting() {return IsSegmentPredicting;} | ||
| void SetIsSegmentPredicting(bool value) {IsSegmentPredicting = value;} | ||
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| /// Indicates whether this cell was predicted to become active. | ||
| bool GetWasPredicted() {return WasPredicted;} | ||
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| int GetNumPredictionSteps() {return NumPredictionSteps;} | ||
| int GetPrevNumPredictionSteps() {return PrevNumPredictionSteps;} | ||
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| int GetPrevActiveTme() {return PrevActiveTime;} | ||
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| Column *GetColumn() {return column;} | ||
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| /// Methods | ||
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| /// Initialize a new Cell belonging to the specified Column. The index is an | ||
| /// integer id to distinguish this Cell from others in the Column. | ||
| void Initialize(Column *col, int intdx); | ||
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| /// Advances this cell to the next time step. | ||
| /// | ||
| /// The current state of this cell (active, learning, predicting) will be set as the | ||
| /// previous state and the current state will be reset to no cell activity by | ||
| /// default until it can be determined. | ||
| void NextTimeStep(); | ||
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| /// Creates a new segment for this Cell. | ||
| /// learningCells: A set of available learning cells to add to the segmentUpdateList. | ||
| /// Returns created segment. | ||
| /// | ||
| /// The new segment will initially connect to at most newSynapseCount | ||
| /// synapses randomly selected from the set of cells that | ||
| /// were in the learning state at t-1 (specified by the learningCells parameter). | ||
| Segment *CreateSegment(FastList &learningCells, int creationTime); | ||
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| /// For this cell, return a Segment that was active in the previous | ||
| /// time step. If multiple segments were active, sequence segments are given | ||
| /// preference. Otherwise, segments with most activity are given preference. | ||
| Segment *GetPreviousActiveSegment(); | ||
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| /// Add a new SegmentUpdateInfo object to this Cell containing proposed changes to the | ||
| /// specified segment. | ||
| /// | ||
| /// If the segment is NULL, then a new segment is to be added, otherwise | ||
| /// the specified segment is updated. If the segment exists, find all active | ||
| /// synapses for the segment (either at t or t-1 based on the 'previous' parameter) | ||
| /// and mark them as needing to be updated. If newSynapses is true, then | ||
| /// Region.newSynapseCount - len(activeSynapses) new synapses are added to the | ||
| /// segment to be updated. The (new) synapses are randomly chosen from the set | ||
| /// of current learning cells (within Region.predictionRadius if set). | ||
| /// | ||
| /// These segment updates are only applied when the applySegmentUpdates | ||
| /// method is later called on this Cell. | ||
| SegmentUpdateInfo *UpdateSegmentActiveSynapses(bool previous, Segment *segment, bool newSynapses, UpdateType updateType); | ||
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| /// This function reinforces each segment in this Cell's SegmentUpdateInfo. | ||
| /// | ||
| /// Using the segmentUpdateInfo, the following changes are | ||
| /// performed. If positiveReinforcement is true then synapses on the active | ||
| /// list get their permanence counts incremented by permanenceInc. All other | ||
| /// synapses get their permanence counts decremented by permanenceDec. If | ||
| /// positiveReinforcement is false, then synapses on the active list get | ||
| /// their permanence counts decremented by permanenceDec. After this step, | ||
| /// any synapses in segmentUpdate that do yet exist get added with a permanence | ||
| /// count of initialPerm. These new synapses are randomly chosen from the | ||
| /// set of all cells that have learnState output = 1 at time step t. | ||
| ///</remarks> | ||
| void ApplySegmentUpdates(int _curTime, ApplyUpdateTrigger _trigger); | ||
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| /// For this cell in the previous time step (t-1) find the Segment | ||
| /// with the largest number of active synapses. | ||
| /// | ||
| /// However only consider segments that predict activation in the number of | ||
| /// time steps of the active segment of this cell with the least number of | ||
| /// steps until activation + 1. For example if right now this cell is being | ||
| /// predicted to occur in t+2 at the earliest, then we want to find the best | ||
| /// segment from last time step that would predict for t+3. | ||
| /// This routine is aggressive in finding the best match. The permanence | ||
| /// value of synapses is allowed to be below connectedPerm. | ||
| /// The number of active synapses is allowed to be below activationThreshold, | ||
| /// but must be above minThreshold. The routine returns that segment. | ||
| /// If no segments are found, then None is returned. | ||
| Segment *GetBestMatchingPreviousSegment(); | ||
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| /// Gets the best matching Segment. | ||
| /// | ||
| /// numPredictionSteps: Number of time steps in the future an activation will occur. | ||
| /// previous: Defaults to false. | ||
| /// | ||
| /// For this cell (at t-1 if previous=True else at t), find the segmentUpdateList (only | ||
| /// consider sequence segments if isSequence is True, otherwise only consider | ||
| /// non-sequence segments) with the largest number of active synapses. | ||
| /// This routine is aggressive in finding the best match. The permanence | ||
| /// value of synapses is allowed to be below connectedPerm. | ||
| /// The number of active synapses is allowed to be below activationThreshold, | ||
| /// but must be above minThreshold. The routine returns that segment. | ||
| /// If no segments are found, then None is returned. | ||
| Segment *GetBestMatchingSegment(int numPredictionSteps, bool previous); | ||
| }; | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,37 @@ | ||
| #include "Classifier.h" | ||
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| Classifier::Classifier(QString &_id, int _numItems, QString _regionID, QString _inputspaceID, QStringList &_labels) | ||
| : DataSpace(_id), labels(_labels), regionID(_regionID), inputspaceID(_inputspaceID), numItems(_numItems) | ||
| { | ||
| } | ||
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| int Classifier::GetSizeX() | ||
| { | ||
| return 1; | ||
| } | ||
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| int Classifier::GetSizeY() | ||
| { | ||
| return numItems; | ||
| } | ||
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| int Classifier::GetNumValues() | ||
| { | ||
| return 1; | ||
| } | ||
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| int Classifier::GetHypercolumnDiameter() | ||
| { | ||
| return 1; | ||
| } | ||
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| bool Classifier::GetIsActive(int _x, int _y, int _index) | ||
| { | ||
| _ASSERT(_x == 0); | ||
| _ASSERT((_y >= 0) && (_y < numItems)); | ||
| _ASSERT(_index == 0); | ||
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| // TEMP | ||
| return false; | ||
| } | ||
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