Merge pull request #993 from htm-community/tm-repeating-inputs

Fix TM static inputs issue.
htm-community · Jan 23, 2023 · f3d2905 · f3d2905
2 parents 34a6853 + c877b27
commit f3d2905
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Showing 2 changed files with 36 additions and 10 deletions.
diff --git a/bindings/py/tests/algorithms/temporal_memory_test.py b/bindings/py/tests/algorithms/temporal_memory_test.py
@@ -247,6 +247,23 @@ def testGetMethods(self):
     self.assertEqual(parameters1["tm"]["maxSynapsesPerSegment"], maxSynapsesPerSegment, "using Method (getMaxSynapsesPerSegment) failed")
     self.assertEqual(True, checkInputs, "using Method (getCheckInputs) failed")
 
+  def testStaticInputs(self):
+    """ Check that repeating the same input results in the same output. """
+    cols = 100
+    tm = TM([cols])
+    # Train on a square wave.
+    inp_a = SDR(cols).randomize( .2 )
+    inp_b = SDR(cols).randomize( .2 )
+    for i in range(10): tm.compute( inp_a, True )
+    for i in range(10): tm.compute( inp_b, True )
+    # Test that it reached a steady state.
+    self.assertEqual(tm.anomaly, 0.0)
+    out_1 = tm.getActiveCells()
+    tm.compute( inp_b, True )
+    self.assertEqual(tm.anomaly, 0.0)
+    out_2 = tm.getActiveCells()
+    self.assertTrue(all(out_1.sparse == out_2.sparse))
+
 def _print(txt):
     if debugPrint:
         print(txt)

diff --git a/src/htm/algorithms/TemporalMemory.cpp b/src/htm/algorithms/TemporalMemory.cpp
@@ -190,9 +190,9 @@ void TemporalMemory::activatePredictedColumn_(
 
 
 void TemporalMemory::burstColumn_(
-	    const UInt column,
-            vector<Segment>::const_iterator columnMatchingSegmentsBegin,
-            vector<Segment>::const_iterator columnMatchingSegmentsEnd,
+            const UInt column,
+            const vector<Segment>::const_iterator columnMatchingSegmentsBegin,
+            const vector<Segment>::const_iterator columnMatchingSegmentsEnd,
             const SDR &prevActiveCells,
             const vector<CellIdx> &prevWinnerCells,
             const bool learn) {
@@ -208,19 +208,28 @@ void TemporalMemory::burstColumn_(
                                  numActivePotentialSynapsesForSegment_[b]);
                        });
 
-  const CellIdx winnerCell =
-      (bestMatchingSegment != columnMatchingSegmentsEnd)
-          ? connections.cellForSegment(*bestMatchingSegment)
-          : getLeastUsedCell_(column); //TODO replace (with random?) this is extremely costly, removing makes TM 6x faster!
-
+  CellIdx winnerCell;
+  if(bestMatchingSegment != columnMatchingSegmentsEnd) {
+    winnerCell = connections.cellForSegment(*bestMatchingSegment);
+  }
+  else {
+    // Check for previous winner cells in this minicolumn.
+    const auto prevWinnerPtr = std::lower_bound(prevWinnerCells.begin(), prevWinnerCells.end(), column,
+        [&](const CellIdx cell, const UInt col) { return columnForCell(cell) < col; });
+    if(prevWinnerPtr != prevWinnerCells.end() && columnForCell(*prevWinnerPtr) == column) {
+      winnerCell = *prevWinnerPtr;
+    }
+    else {
+      winnerCell = getLeastUsedCell_(column);
+    }
+  }
   winnerCells_.push_back(winnerCell);
 
-  // Learn.
   if (learn) {
     if (bestMatchingSegment != columnMatchingSegmentsEnd) {
       // Learn on the best matching segment.
       connections_.adaptSegment(*bestMatchingSegment, prevActiveCells,
-                   permanenceIncrement_, permanenceDecrement_, true, minThreshold_); //TODO consolidate SP.stimulusThreshold_ & TM.minThreshold_ into Conn.stimulusThreshold ? (replacing segmentThreshold arg used in some methods in Conn) 
+                   permanenceIncrement_, permanenceDecrement_, true, minThreshold_);
 
       const Int32 nGrowDesired = maxNewSynapseCount_ - numActivePotentialSynapsesForSegment_[*bestMatchingSegment];
       if (nGrowDesired > 0) {