class SMSequences(object):
"""
Class generates sensorimotor sequences
"""
def __init__(
self,
sensoryInputElements,
spatialConfig,
sensoryInputElementsPool=list("ABCDEFGHIJKLMNOPQRSTUVWXYZ" "abcdefghijklmnopqrstuvwxyz0123456789"),
minDisplacement=1,
maxDisplacement=1,
numActiveBitsSensoryInput=9,
numActiveBitsMotorInput=9,
seed=42,
verbosity=False,
useRandomEncoder=False,
):
"""
@param sensoryInputElements (list)
Strings or numbers representing the sensory elements that exist in your
world. Elements can be repeated if multiple of the same exist.
@param spatialConfig (numpy.array)
Array of size: (1, len(sensoryInputElements), dimension). It has a
coordinate for every element in sensoryInputElements.
@param sensoryInputElementsPool (list)
List of strings representing a readable version of all possible sensory
elements in this world. Elements don't need to be in any order and there
should be no duplicates. By default this contains the set of
alphanumeric characters.
@param maxDisplacement (int)
Maximum `distance` for a motor command. Distance is defined by the
largest difference along any coordinate dimension.
@param minDisplacement (int)
Minimum `distance` for a motor command. Distance is defined by the
largest difference along any coordinate dimension.
@param numActiveBitsSensoryInput (int)
Number of active bits for each sensory input.
@param numActiveBitsMotorInput (int)
Number of active bits for each dimension of the motor input.
@param seed (int)
Random seed for nupic.bindings.Random.
@param verbosity (int)
Verbosity
@param useRandomEncoder (boolean)
if True, use the random encoder SDRCategoryEncoder. If False,
use CategoryEncoder. CategoryEncoder encodes categories using contiguous
non-overlapping bits for each category, which makes it easier to debug.
"""
# ---------------------------------------------------------------------------------
# Store creation parameters
self.sensoryInputElements = sensoryInputElements
self.sensoryInputElementsPool = sensoryInputElementsPool
self.spatialConfig = spatialConfig.astype(int)
self.spatialLength = len(spatialConfig)
self.maxDisplacement = maxDisplacement
self.minDisplacement = minDisplacement
self.numActiveBitsSensoryInput = numActiveBitsSensoryInput
self.numActiveBitsMotorInput = numActiveBitsMotorInput
self.verbosity = verbosity
self.seed = seed
self.initialize(useRandomEncoder)
def initialize(self, useRandomEncoder):
"""
Initialize the various data structures.
"""
self.setRandomSeed(self.seed)
self.dim = numpy.shape(self.spatialConfig)[-1]
self.spatialMap = dict(zip(map(tuple, list(self.spatialConfig)), self.sensoryInputElements))
self.lengthMotorInput1D = (2 * self.maxDisplacement + 1) * self.numActiveBitsMotorInput
uniqueSensoryElements = list(set(self.sensoryInputElementsPool))
if useRandomEncoder:
self.sensoryEncoder = SDRCategoryEncoder(
n=1024, w=self.numActiveBitsSensoryInput, categoryList=uniqueSensoryElements, forced=True
)
self.lengthSensoryInput = self.sensoryEncoder.getWidth()
else:
self.lengthSensoryInput = (len(self.sensoryInputElementsPool) + 1) * self.numActiveBitsSensoryInput
self.sensoryEncoder = CategoryEncoder(
w=self.numActiveBitsSensoryInput, categoryList=uniqueSensoryElements, forced=True
)
motorEncoder1D = ScalarEncoder(
n=self.lengthMotorInput1D,
w=self.numActiveBitsMotorInput,
minval=-self.maxDisplacement,
maxval=self.maxDisplacement,
clipInput=True,
forced=True,
)
self.motorEncoder = VectorEncoder(length=self.dim, encoder=motorEncoder1D)
def generateSensorimotorSequence(self, sequenceLength):
"""
Generate sensorimotor sequences of length sequenceLength.
@param sequenceLength (int)
Length of the sensorimotor sequence.
@return (tuple) Contains:
sensorySequence (list)
Encoded sensory input for whole sequence.
motorSequence (list)
Encoded motor input for whole sequence.
sensorimotorSequence (list)
Encoder sensorimotor input for whole sequence. This is useful
when you want to give external input to temporal memory.
"""
motorSequence = []
sensorySequence = []
sensorimotorSequence = []
currentEyeLoc = self.nupicRandomChoice(self.spatialConfig)
for i in xrange(sequenceLength):
currentSensoryInput = self.spatialMap[tuple(currentEyeLoc)]
nextEyeLoc, currentEyeV = self.getNextEyeLocation(currentEyeLoc)
if self.verbosity:
print "sensory input = ", currentSensoryInput, "eye location = ", currentEyeLoc, " motor command = ", currentEyeV
sensoryInput = self.encodeSensoryInput(currentSensoryInput)
motorInput = self.encodeMotorInput(list(currentEyeV))
sensorimotorInput = numpy.concatenate((sensoryInput, motorInput))
sensorySequence.append(sensoryInput)
motorSequence.append(motorInput)
sensorimotorSequence.append(sensorimotorInput)
currentEyeLoc = nextEyeLoc
return (sensorySequence, motorSequence, sensorimotorSequence)
def encodeSensorimotorSequence(self, eyeLocs):
"""
Encode sensorimotor sequence given the eye movements. Sequence will have
length len(eyeLocs) - 1 because only the differences of eye locations can be
used to encoder motor commands.
@param eyeLocs (list)
Numpy coordinates describing where the eye is looking.
@return (tuple) Contains:
sensorySequence (list)
Encoded sensory input for whole sequence.
motorSequence (list)
Encoded motor input for whole sequence.
sensorimotorSequence (list)
Encoder sensorimotor input for whole sequence. This is useful
when you want to give external input to temporal memory.
"""
sequenceLength = len(eyeLocs) - 1
motorSequence = []
sensorySequence = []
sensorimotorSequence = []
for i in xrange(sequenceLength):
currentEyeLoc = eyeLocs[i]
nextEyeLoc = eyeLocs[i + 1]
currentSensoryInput = self.spatialMap[currentEyeLoc]
currentEyeV = nextEyeLoc - currentEyeLoc
if self.verbosity:
print "sensory input = ", currentSensoryInput, "eye location = ", currentEyeLoc, " motor command = ", currentEyeV
sensoryInput = self.encodeSensoryInput(currentSensoryInput)
motorInput = self.encodeMotorInput(list(currentEyeV))
sensorimotorInput = numpy.concatenate((sensoryInput, motorInput))
sensorySequence.append(sensoryInput)
motorSequence.append(motorInput)
sensorimotorSequence.append(sensorimotorInput)
return (sensorySequence, motorSequence, sensorimotorSequence)
def getNextEyeLocation(self, currentEyeLoc):
"""
Generate next eye location based on current eye location.
@param currentEyeLoc (numpy.array)
Current coordinate describing the eye location in the world.
@return (tuple) Contains:
nextEyeLoc (numpy.array)
Coordinate of the next eye location.
eyeDiff (numpy.array)
Vector describing change from currentEyeLoc to nextEyeLoc.
"""
possibleEyeLocs = []
for loc in self.spatialConfig:
shift = abs(max(loc - currentEyeLoc))
if self.minDisplacement <= shift <= self.maxDisplacement:
possibleEyeLocs.append(loc)
nextEyeLoc = self.nupicRandomChoice(possibleEyeLocs)
eyeDiff = nextEyeLoc - currentEyeLoc
return nextEyeLoc, eyeDiff
def setRandomSeed(self, seed):
"""
Reset the nupic random generator. This is necessary to reset random seed to
generate new sequences.
@param seed (int)
Seed for nupic.bindings.Random.
"""
self.seed = seed
self._random = Random()
self._random.setSeed(seed)
def nupicRandomChoice(self, array):
"""
Chooses a random element from an array using the nupic random number
generator.
@param array (list or numpy.array)
Array to choose random element from.
@return (element)
Element chosen at random.
"""
return array[self._random.getUInt32(len(array))]
def encodeMotorInput(self, motorInput):
"""
Encode motor command to bit vector.
@param motorInput (1D numpy.array)
Motor command to be encoded.
@return (1D numpy.array)
Encoded motor command.
"""
if not hasattr(motorInput, "__iter__"):
motorInput = list([motorInput])
return self.motorEncoder.encode(motorInput)
def decodeMotorInput(self, motorInputPattern):
"""
Decode motor command from bit vector.
@param motorInputPattern (1D numpy.array)
Encoded motor command.
@return (1D numpy.array)
Decoded motor command.
"""
key = self.motorEncoder.decode(motorInputPattern)[0].keys()[0]
motorCommand = self.motorEncoder.decode(motorInputPattern)[0][key][1][0]
return motorCommand
def encodeSensoryInput(self, sensoryInputElement):
"""
Encode sensory input to bit vector
@param sensoryElement (1D numpy.array)
Sensory element to be encoded.
@return (1D numpy.array)
Encoded sensory element.
"""
return self.sensoryEncoder.encode(sensoryInputElement)
def decodeSensoryInput(self, sensoryInputPattern):
"""
Decode sensory input from bit vector.
@param sensoryInputPattern (1D numpy.array)
Encoded sensory element.
@return (1D numpy.array)
Decoded sensory element.
"""
return self.sensoryEncoder.decode(sensoryInputPattern)[0]["category"][1]
def printSensoryCodingScheme(self):
"""
Print sensory inputs along with their encoded versions.
"""
print "\nsensory coding scheme: "
for loc in self.spatialConfig:
sensoryElement = self.spatialMap[tuple(loc)]
print sensoryElement, "%s : " % loc,
printSequence(self.encodeSensoryInput(sensoryElement))
def printMotorCodingScheme(self):
"""
Print motor commands (displacement vector) along with their encoded
versions.
"""
print "\nmotor coding scheme: "
self.build(self.dim, [])
def build(self, n, vec):
"""
Recursive function to help print motor coding scheme.
"""
for i in range(-self.maxDisplacement, self.maxDisplacement + 1):
next = vec + [i]
if n == 1:
print "{:>5}\t".format(next), " = ",
printSequence(self.encodeMotorInput(next))
else:
self.build(n - 1, next)
class RandomizedLetterEncoder(CustomEncoder):
"""
Encoder for strings. It encodes each letter into binary and appends
a random chain of bits at the end.
"""
def __init__(self, width, nRandBits, actBitsPerLetter=1):
"""
@param width: The size of the encoded list of bits output.
@param nRandBits: The number of random bits that the output
will have after the binary representation of the
string. 0 for a pure string to binary conversion.
@param actBitsPerLetter: The number of active bits per letter.
"""
random.seed(SEED)
if nRandBits > width:
raise ValueError("nRandBits can't be greater than width.")
if actBitsPerLetter < 1:
raise ValueError("There must be at least 1 active bit per letter")
self.width = width
self.nRandBits = nRandBits
self.actBitsPerLetter = actBitsPerLetter
self.alreadyEncoded = dict()
self.catEnc = CategoryEncoder(self.actBitsPerLetter,
list(string.ascii_lowercase),
forced=True)
def getWidth(self):
return self.width
def encode(self, inputData, verbose=0):
"""
@param inputData
@param verbose=0
"""
strBinaryLen = len(inputData) * self.catEnc.getWidth()
if (strBinaryLen + self.nRandBits) > self.width:
raise ValueError("The string is too long to be encoded with the"\
"current parameters.")
strBinary = []
# Encode each char of the string
for letter in inputData:
strBinary.extend(list(self.catEnc.encode(letter)))
if inputData not in self.alreadyEncoded:
self.alreadyEncoded[inputData] = (
[random.randrange(strBinaryLen, self.width) \
for _ in xrange(self.nRandBits)],
(len(self.alreadyEncoded) + 1)
)
output = numpy.zeros((self.width, ), dtype=numpy.uint8)
output[:strBinaryLen] = strBinary
output[self.alreadyEncoded[inputData][0]] = 1
return output
from Utils.CLAClassifierCond import CLAClassifierCond
from nupic.encoders.category import CategoryEncoder
from nupic.research.spatial_pooler import SpatialPooler
from nupic.research.temporal_memory import TemporalMemory
import numpy
encoder1 = CategoryEncoder(5, ['a', 'b', 'c'], forced=True)
encoder2 = CategoryEncoder(5, ['z', 'x', 'y', 'a'], forced=True)
sp = SpatialPooler(inputDimensions=(2,
max(encoder1.getWidth(),
encoder2.getWidth())),
columnDimensions=(2, 20),
potentialRadius=12,
potentialPct=0.5,
globalInhibition=True,
localAreaDensity=-1.0,
numActiveColumnsPerInhArea=5.0,
stimulusThreshold=0,
synPermInactiveDec=0.1,
synPermActiveInc=0.1,
synPermConnected=0.1,
minPctOverlapDutyCycle=0.1,
minPctActiveDutyCycle=0.1,
dutyCyclePeriod=10,
maxBoost=3,
seed=42,
spVerbosity=0)
tm = TemporalMemory(
columnDimensions=sp.getColumnDimensions(),
class SMSequences(object):
"""
Class generates sensorimotor sequences
"""
def __init__(self,
sensoryInputElements,
spatialConfig,
sensoryInputElementsPool=list(
"ABCDEFGHIJKLMNOPQRSTUVWXYZ"
"abcdefghijklmnopqrstuvwxyz0123456789"),
minDisplacement=1,
maxDisplacement=1,
numActiveBitsSensoryInput=9,
numActiveBitsMotorInput=9,
seed=42,
verbosity=False,
useRandomEncoder=False):
"""
@param sensoryInputElements (list)
Strings or numbers representing the sensory elements that exist in your
world. Elements can be repeated if multiple of the same exist.
@param spatialConfig (numpy.array)
Array of size: (1, len(sensoryInputElements), dimension). It has a
coordinate for every element in sensoryInputElements.
@param sensoryInputElementsPool (list)
List of strings representing a readable version of all possible sensory
elements in this world. Elements don't need to be in any order and there
should be no duplicates. By default this contains the set of
alphanumeric characters.
@param maxDisplacement (int)
Maximum `distance` for a motor command. Distance is defined by the
largest difference along any coordinate dimension.
@param minDisplacement (int)
Minimum `distance` for a motor command. Distance is defined by the
largest difference along any coordinate dimension.
@param numActiveBitsSensoryInput (int)
Number of active bits for each sensory input.
@param numActiveBitsMotorInput (int)
Number of active bits for each dimension of the motor input.
@param seed (int)
Random seed for nupic.bindings.Random.
@param verbosity (int)
Verbosity
@param useRandomEncoder (boolean)
if True, use the random encoder SDRCategoryEncoder. If False,
use CategoryEncoder. CategoryEncoder encodes categories using contiguous
non-overlapping bits for each category, which makes it easier to debug.
"""
#---------------------------------------------------------------------------------
# Store creation parameters
self.sensoryInputElements = sensoryInputElements
self.sensoryInputElementsPool = sensoryInputElementsPool
self.spatialConfig = spatialConfig.astype(int)
self.spatialLength = len(spatialConfig)
self.maxDisplacement = maxDisplacement
self.minDisplacement = minDisplacement
self.numActiveBitsSensoryInput = numActiveBitsSensoryInput
self.numActiveBitsMotorInput = numActiveBitsMotorInput
self.verbosity = verbosity
self.seed = seed
self.initialize(useRandomEncoder)
def initialize(self, useRandomEncoder):
"""
Initialize the various data structures.
"""
self.setRandomSeed(self.seed)
self.dim = numpy.shape(self.spatialConfig)[-1]
self.spatialMap = dict(
zip(map(tuple, list(self.spatialConfig)),
self.sensoryInputElements))
self.lengthMotorInput1D = (2*self.maxDisplacement + 1) * \
self.numActiveBitsMotorInput
uniqueSensoryElements = list(set(self.sensoryInputElementsPool))
if useRandomEncoder:
self.sensoryEncoder = SDRCategoryEncoder(
n=1024,
w=self.numActiveBitsSensoryInput,
categoryList=uniqueSensoryElements,
forced=True)
self.lengthSensoryInput = self.sensoryEncoder.getWidth()
else:
self.lengthSensoryInput = (len(self.sensoryInputElementsPool)+1) * \
self.numActiveBitsSensoryInput
self.sensoryEncoder = CategoryEncoder(
w=self.numActiveBitsSensoryInput,
categoryList=uniqueSensoryElements,
forced=True)
motorEncoder1D = ScalarEncoder(n=self.lengthMotorInput1D,
w=self.numActiveBitsMotorInput,
minval=-self.maxDisplacement,
maxval=self.maxDisplacement,
clipInput=True,
forced=True)
self.motorEncoder = VectorEncoder(length=self.dim,
encoder=motorEncoder1D)
def generateSensorimotorSequence(self, sequenceLength):
"""
Generate sensorimotor sequences of length sequenceLength.
@param sequenceLength (int)
Length of the sensorimotor sequence.
@return (tuple) Contains:
sensorySequence (list)
Encoded sensory input for whole sequence.
motorSequence (list)
Encoded motor input for whole sequence.
sensorimotorSequence (list)
Encoder sensorimotor input for whole sequence. This is useful
when you want to give external input to temporal memory.
"""
motorSequence = []
sensorySequence = []
sensorimotorSequence = []
currentEyeLoc = self.nupicRandomChoice(self.spatialConfig)
for i in xrange(sequenceLength):
currentSensoryInput = self.spatialMap[tuple(currentEyeLoc)]
nextEyeLoc, currentEyeV = self.getNextEyeLocation(currentEyeLoc)
if self.verbosity:
print "sensory input = ", currentSensoryInput, \
"eye location = ", currentEyeLoc, \
" motor command = ", currentEyeV
sensoryInput = self.encodeSensoryInput(currentSensoryInput)
motorInput = self.encodeMotorInput(list(currentEyeV))
sensorimotorInput = numpy.concatenate((sensoryInput, motorInput))
sensorySequence.append(sensoryInput)
motorSequence.append(motorInput)
sensorimotorSequence.append(sensorimotorInput)
currentEyeLoc = nextEyeLoc
return (sensorySequence, motorSequence, sensorimotorSequence)
def encodeSensorimotorSequence(self, eyeLocs):
"""
Encode sensorimotor sequence given the eye movements. Sequence will have
length len(eyeLocs) - 1 because only the differences of eye locations can be
used to encoder motor commands.
@param eyeLocs (list)
Numpy coordinates describing where the eye is looking.
@return (tuple) Contains:
sensorySequence (list)
Encoded sensory input for whole sequence.
motorSequence (list)
Encoded motor input for whole sequence.
sensorimotorSequence (list)
Encoder sensorimotor input for whole sequence. This is useful
when you want to give external input to temporal memory.
"""
sequenceLength = len(eyeLocs) - 1
motorSequence = []
sensorySequence = []
sensorimotorSequence = []
for i in xrange(sequenceLength):
currentEyeLoc = eyeLocs[i]
nextEyeLoc = eyeLocs[i + 1]
currentSensoryInput = self.spatialMap[currentEyeLoc]
currentEyeV = nextEyeLoc - currentEyeLoc
if self.verbosity:
print "sensory input = ", currentSensoryInput, \
"eye location = ", currentEyeLoc, \
" motor command = ", currentEyeV
sensoryInput = self.encodeSensoryInput(currentSensoryInput)
motorInput = self.encodeMotorInput(list(currentEyeV))
sensorimotorInput = numpy.concatenate((sensoryInput, motorInput))
sensorySequence.append(sensoryInput)
motorSequence.append(motorInput)
sensorimotorSequence.append(sensorimotorInput)
return (sensorySequence, motorSequence, sensorimotorSequence)
def getNextEyeLocation(self, currentEyeLoc):
"""
Generate next eye location based on current eye location.
@param currentEyeLoc (numpy.array)
Current coordinate describing the eye location in the world.
@return (tuple) Contains:
nextEyeLoc (numpy.array)
Coordinate of the next eye location.
eyeDiff (numpy.array)
Vector describing change from currentEyeLoc to nextEyeLoc.
"""
possibleEyeLocs = []
for loc in self.spatialConfig:
shift = abs(max(loc - currentEyeLoc))
if self.minDisplacement <= shift <= self.maxDisplacement:
possibleEyeLocs.append(loc)
nextEyeLoc = self.nupicRandomChoice(possibleEyeLocs)
eyeDiff = nextEyeLoc - currentEyeLoc
return nextEyeLoc, eyeDiff
def setRandomSeed(self, seed):
"""
Reset the nupic random generator. This is necessary to reset random seed to
generate new sequences.
@param seed (int)
Seed for nupic.bindings.Random.
"""
self.seed = seed
self._random = Random()
self._random.setSeed(seed)
def nupicRandomChoice(self, array):
"""
Chooses a random element from an array using the nupic random number
generator.
@param array (list or numpy.array)
Array to choose random element from.
@return (element)
Element chosen at random.
"""
return array[self._random.getUInt32(len(array))]
def encodeMotorInput(self, motorInput):
"""
Encode motor command to bit vector.
@param motorInput (1D numpy.array)
Motor command to be encoded.
@return (1D numpy.array)
Encoded motor command.
"""
if not hasattr(motorInput, "__iter__"):
motorInput = list([motorInput])
return self.motorEncoder.encode(motorInput)
def decodeMotorInput(self, motorInputPattern):
"""
Decode motor command from bit vector.
@param motorInputPattern (1D numpy.array)
Encoded motor command.
@return (1D numpy.array)
Decoded motor command.
"""
key = self.motorEncoder.decode(motorInputPattern)[0].keys()[0]
motorCommand = self.motorEncoder.decode(
motorInputPattern)[0][key][1][0]
return motorCommand
def encodeSensoryInput(self, sensoryInputElement):
"""
Encode sensory input to bit vector
@param sensoryElement (1D numpy.array)
Sensory element to be encoded.
@return (1D numpy.array)
Encoded sensory element.
"""
return self.sensoryEncoder.encode(sensoryInputElement)
def decodeSensoryInput(self, sensoryInputPattern):
"""
Decode sensory input from bit vector.
@param sensoryInputPattern (1D numpy.array)
Encoded sensory element.
@return (1D numpy.array)
Decoded sensory element.
"""
return self.sensoryEncoder.decode(
sensoryInputPattern)[0]['category'][1]
def printSensoryCodingScheme(self):
"""
Print sensory inputs along with their encoded versions.
"""
print "\nsensory coding scheme: "
for loc in self.spatialConfig:
sensoryElement = self.spatialMap[tuple(loc)]
print sensoryElement, "%s : " % loc,
printSequence(self.encodeSensoryInput(sensoryElement))
def printMotorCodingScheme(self):
"""
Print motor commands (displacement vector) along with their encoded
versions.
"""
print "\nmotor coding scheme: "
self.build(self.dim, [])
def build(self, n, vec):
"""
Recursive function to help print motor coding scheme.
"""
for i in range(-self.maxDisplacement, self.maxDisplacement + 1):
next = vec + [i]
if n == 1:
print '{:>5}\t'.format(next), " = ",
printSequence(self.encodeMotorInput(next))
else:
self.build(n - 1, next)
class RandomizedLetterEncoder(CustomEncoder):
"""
Encoder for strings. It encodes each letter into binary and appends
a random chain of bits at the end.
"""
def __init__(self, width, nRandBits, actBitsPerLetter=1):
"""
@param width: The size of the encoded list of bits output.
@param nRandBits: The number of random bits that the output
will have after the binary representation of the
string. 0 for a pure string to binary conversion.
@param actBitsPerLetter: The number of active bits per letter.
"""
random.seed(SEED)
if nRandBits > width:
raise ValueError("nRandBits can't be greater than width.")
if actBitsPerLetter < 1:
raise ValueError("There must be at least 1 active bit per letter")
self.width = width
self.nRandBits = nRandBits
self.actBitsPerLetter = actBitsPerLetter
self.alreadyEncoded = dict()
self.catEnc = CategoryEncoder(self.actBitsPerLetter,
list(string.ascii_lowercase),
forced=True)
def getWidth(self):
return self.width
def encode(self, inputData, verbose=0):
"""
@param inputData
@param verbose=0
"""
strBinaryLen = len(inputData) * self.catEnc.getWidth()
if (strBinaryLen + self.nRandBits) > self.width:
raise ValueError("The string is too long to be encoded with the"\
"current parameters.")
strBinary = []
# Encode each char of the string
for letter in inputData:
strBinary.extend(list(self.catEnc.encode(letter)))
if inputData not in self.alreadyEncoded:
self.alreadyEncoded[inputData] = (
[random.randrange(strBinaryLen, self.width) \
for _ in xrange(self.nRandBits)],
(len(self.alreadyEncoded) + 1)
)
output = numpy.zeros((self.width,), dtype=numpy.uint8)
output[:strBinaryLen] = strBinary
output[self.alreadyEncoded[inputData][0]] = 1
return output