Python TP.checkPrediction2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 checkPrediction2 [as 别名]

#.........这里部分代码省略.........
          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
    pickle.dump(tp, open("test_tp10x.pkl", "wb"))
    tp2 = pickle.load(open("test_tp10x.pkl"))
    self.assertTrue(fdrutils.tpDiff2(tp, tp2, VERBOSITY, checkStates=False))

    # Infer
    print "Testing inference"

    # Setup for inference
    tp.reset()
    tpPy.reset()
    setVerbosity(INFERENCE_VERBOSITY, tp, tpPy)

    patterns = numpy.zeros((40, tp.numberOfCols), dtype='uint32')
    for i in xrange(4):
      _RGEN.initializeUInt32Array(patterns[i], 2)

    for i, x in enumerate(patterns):

      x = numpy.zeros(tp.numberOfCols, dtype='uint32')
      _RGEN.initializeUInt32Array(x, 2)
      y = tp.infer(x)
      yPy = tpPy.infer(x)

      self.assertTrue(fdrutils.tpDiff2(tp, tpPy, VERBOSITY, checkLearn=False))
      if abs((y - yPy).sum()) > 0:
        print "C++ output", y
        print "Py output", yPy
        assert False

      if i > 0:
        tp.checkPrediction2(patterns)
        tpPy.checkPrediction2(patterns)

    print "Inference completed"
    print "===================================="

    return tp, tpPy