Python TP.trimSegments方法代码示例

# 需要导入模块: from nupic.research.TP import TP [as 别名]
# 或者: from nupic.research.TP.TP import trimSegments [as 别名]
  def basicTest2(self, tp, numPatterns=100, numRepetitions=3, activity=15,
                 testTrimming=False, testRebuild=False):
    """Basic test (basic run of learning and inference)"""
    # Create PY TP object that mirrors the one sent in.
    tpPy = TP(numberOfCols=tp.numberOfCols, cellsPerColumn=tp.cellsPerColumn,
              initialPerm=tp.initialPerm, connectedPerm=tp.connectedPerm,
              minThreshold=tp.minThreshold, newSynapseCount=tp.newSynapseCount,
              permanenceInc=tp.permanenceInc, permanenceDec=tp.permanenceDec,
              permanenceMax=tp.permanenceMax, globalDecay=tp.globalDecay,
              activationThreshold=tp.activationThreshold,
              doPooling=tp.doPooling,
              segUpdateValidDuration=tp.segUpdateValidDuration,
              pamLength=tp.pamLength, maxAge=tp.maxAge,
              maxSeqLength=tp.maxSeqLength,
              maxSegmentsPerCell=tp.maxSegmentsPerCell,
              maxSynapsesPerSegment=tp.maxSynapsesPerSegment,
              seed=tp.seed, verbosity=tp.verbosity)

    # Ensure we are copying over learning states for TPDiff
    tp.retrieveLearningStates = True

    verbosity = VERBOSITY

    # Learn

    # Build up sequences
    sequence = fdrutils.generateCoincMatrix(nCoinc=numPatterns,
                                            length=tp.numberOfCols,
                                            activity=activity)
    for r in xrange(numRepetitions):
      for i in xrange(sequence.nRows()):

        #if i > 11:
        #  setVerbosity(6, tp, tpPy)

        if i % 10 == 0:
          tp.reset()
          tpPy.reset()

        if verbosity >= 2:
          print "\n\n    ===================================\nPattern:",
          print i, "Round:", r, "input:", sequence.getRow(i)

        y1 = tp.learn(sequence.getRow(i))
        y2 = tpPy.learn(sequence.getRow(i))

        # Ensure everything continues to work well even if we continuously
        # rebuild outSynapses structure
        if testRebuild:
          tp.cells4.rebuildOutSynapses()

        if testTrimming:
          tp.trimSegments()
          tpPy.trimSegments()

        if verbosity > 2:
          print "\n   ------  CPP states  ------ ",
          tp.printStates()
          print "\n   ------  PY states  ------ ",
          tpPy.printStates()
          if verbosity > 6:
            print "C++ cells: "
            tp.printCells()
            print "PY cells: "
            tpPy.printCells()

        if verbosity >= 3:
          print "Num segments in PY and C++", tpPy.getNumSegments(), \
              tp.getNumSegments()

        # Check if the two TP's are identical or not. This check is slow so
        # we do it every other iteration. Make it every iteration for debugging
        # as needed.
        self.assertTrue(fdrutils.tpDiff2(tp, tpPy, verbosity, False))

        # Check that outputs are identical
        self.assertLess(abs((y1 - y2).sum()), 3)

    print "Learning completed"

    self.assertTrue(fdrutils.tpDiff2(tp, tpPy, verbosity))

    # TODO: Need to check - currently failing this
    #checkCell0(tpPy)

    # Remove unconnected synapses and check TP's again

    # Test rebuild out synapses
    print "Rebuilding outSynapses"
    tp.cells4.rebuildOutSynapses()
    self.assertTrue(fdrutils.tpDiff2(tp, tpPy, VERBOSITY))

    print "Trimming segments"
    tp.trimSegments()
    tpPy.trimSegments()
    self.assertTrue(fdrutils.tpDiff2(tp, tpPy, VERBOSITY))

    # Save and reload after learning
    print "Pickling and unpickling"
    tp.makeCells4Ephemeral = False
#.........这里部分代码省略.........