#!/usr/bin/env python
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, Numenta, Inc.  Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero Public License version 3 as
# published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the GNU Affero Public License for more details.
#
# You should have received a copy of the GNU Affero Public License
# along with this program.  If not, see http://www.gnu.org/licenses.
#
# http://numenta.org/licenses/
# ----------------------------------------------------------------------

"""
This file tests that we can learn and predict the particularly vexing case of a
single constant signal!
"""

import numpy as np
import unittest2 as unittest

from nupic.research import fdrutilities as fdrutils
from nupic.research.TP import TP
from nupic.research.TP10X2 import TP10X2
from nupic.support.unittesthelpers.testcasebase import (TestCaseBase,
                                                        TestOptionParser)



def _printOneTrainingVector(x):
  "Print a single vector succinctly."
  print ''.join('1' if k != 0 else '.' for k in x)

def _getSimplePatterns(numOnes, numPatterns):
  """Very simple patterns. Each pattern has numOnes consecutive
  bits on. There are numPatterns*numOnes bits in the vector. These patterns
  are used as elements of sequences when building up a training set."""

  numCols = numOnes * numPatterns
  p = []
  for i in xrange(numPatterns):
    x = np.zeros(numCols, dtype='float32')
    x[i*numOnes:(i + 1)*numOnes] = 1
    p.append(x)

  return p

def _createTps(numCols):
  """Create two instances of temporal poolers (TP.py and TP10X2.py) with
  identical parameter settings."""

  # Keep these fixed:
  minThreshold = 4
  activationThreshold = 5
  newSynapseCount = 7
  initialPerm = 0.3
  connectedPerm = 0.5
  permanenceInc = 0.1
  permanenceDec = 0.05
  globalDecay = 0
  cellsPerColumn = 1

  cppTp = TP10X2(numberOfCols=numCols, cellsPerColumn=cellsPerColumn,
                  initialPerm=initialPerm, connectedPerm=connectedPerm,
                  minThreshold=minThreshold, newSynapseCount=newSynapseCount,
                  permanenceInc=permanenceInc, permanenceDec=permanenceDec,
                  activationThreshold=activationThreshold,
                  globalDecay=globalDecay, burnIn=1,
                  seed=SEED, verbosity=VERBOSITY,
                  checkSynapseConsistency=True,
                  pamLength=1000)

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

  pyTp = TP(numberOfCols=numCols, cellsPerColumn=cellsPerColumn,
             initialPerm=initialPerm, connectedPerm=connectedPerm,
             minThreshold=minThreshold, newSynapseCount=newSynapseCount,
             permanenceInc=permanenceInc, permanenceDec=permanenceDec,
             activationThreshold=activationThreshold,
             globalDecay=globalDecay, burnIn=1,
             seed=SEED, verbosity=VERBOSITY,
             pamLength=1000)

  return cppTp, pyTp

class TPConstantTest(TestCaseBase):

  def setUp(self):
    self.cppTp, self.pyTp = _createTps(100)

  def _basicTest(self, tp=None):
    """Test creation, pickling, and basic run of learning and inference."""

    trainingSet = _getSimplePatterns(10, 10)

    # Learn on several constant sequences, with a reset in between
    for _ in range(2):
      for seq in trainingSet[0:5]:
        for _ in range(10):
          tp.learn(seq)
        tp.reset()

    print "Learning completed"

    # Infer
    print "Running inference"

    tp.collectStats = True
    for seq in trainingSet[0:5]:
      tp.reset()
      tp.resetStats()
      for _ in range(10):
        tp.infer(seq)
        if VERBOSITY > 1 :
          print
          _printOneTrainingVector(seq)
          tp.printStates(False, False)
          print
          print
      if VERBOSITY > 1:
        print tp.getStats()

      # Ensure our predictions are accurate for each sequence
      self.assertGreater(tp.getStats()['predictionScoreAvg2'], 0.8)
      print ("tp.getStats()['predictionScoreAvg2'] = ",
             tp.getStats()['predictionScoreAvg2'])

    print "TPConstant basicTest ok"

  def testCppTpBasic(self):
    self._basicTest(self.cppTp)

  def testPyTpBasic(self):
    self._basicTest(self.pyTp)

  def testIdenticalTps(self):
    self.assertTrue(fdrutils.tpDiff2(self.cppTp, self.pyTp))


if __name__=="__main__":
  parser = TestOptionParser()
  options, _ = parser.parse_args()
  SEED = options.seed
  VERBOSITY = options.verbosity

  np.random.seed(SEED)

  unittest.main()
