def topDownCompute(self, encoded):
"""[ScalarEncoder class method override]"""
#Decode to delta scalar
if self._prevAbsolute==None or self._prevDelta==None:
return [EncoderResult(value=0, scalar=0,
encoding=numpy.zeros(self.n))]
ret = self._adaptiveScalarEnc.topDownCompute(encoded)
if self._prevAbsolute != None:
ret = [EncoderResult(value=ret[0].value+self._prevAbsolute,
scalar=ret[0].scalar+self._prevAbsolute,
encoding=ret[0].encoding)]
# ret[0].value+=self._prevAbsolute
# ret[0].scalar+=self._prevAbsolute
return ret
def getBucketInfo(self, buckets):
""" See the function description in base.py """
# The "category" is simply the bucket index
category = buckets[0]
encoding = numpy.zeros(self.n)
encoding[self.letterToBitmap[category]] = 1
return [EncoderResult(value=category, scalar=ord(category), encoding=encoding)]
def getBucketInfo(self, buckets):
""" See the function description in base.py """
binIndex = buckets[0]
value = numpy.mean(self.bins[binIndex, :])
return [EncoderResult(value=value, scalar=value,
encoding=self._topDownMappingM.getRow(binIndex))]
def getBucketInfo(self, buckets):
"""Required base class override."""
b = self.buckets[buckets[0]]
encoding = numpy.zeros(self.n, dtype=DEFAULT_DTYPE)
self._encode(encoding, [buckets[0]])
return [
EncoderResult(value=b.minVal, scalar=b.minVal, encoding=encoding)
]
def getBucketInfo(self, buckets): """ [overrides overrides nupic.encoders.base.Encoder.getBucketInfo] """ b = self.buckets[buckets[0]] encoding = numpy.zeros(self.n, dtype=DEFAULT_DTYPE) self._encode(encoding, [buckets[0]]) return [EncoderResult(value=b.minVal, scalar=b.minVal, encoding=encoding)]
def topDownCompute(self, encoded):
""" See the function description in base.py """
topDownMappingM = self._getTopDownMapping()
binIndex = topDownMappingM.rightVecProd(encoded).argmax()
value = numpy.mean(self.bins[binIndex, :])
return [EncoderResult(value=value, scalar=value,
encoding=self._topDownMappingM.getRow(binIndex))]
def topDownCompute(self, encoded):
""" See the function description in base.py
"""
encoderResult = self.encoder.topDownCompute(encoded)[0]
value = encoderResult.value
categoryIndex = int(round(value))
category = self.indexToCategory[categoryIndex]
return EncoderResult(value=category, scalar=categoryIndex,
encoding=encoderResult.encoding)
def topDownCompute(self, encoded):
"""
See the function description in base.py
"""
scaledResult = self.encoder.topDownCompute(encoded)[0]
scaledValue = scaledResult.value
value = math.pow(10, scaledValue)
return EncoderResult(value=value, scalar=value,
encoding = scaledResult.encoding)
def getBucketInfo(self, buckets):
"""
See the function description in base.py
"""
scaledResult = self.encoder.getBucketInfo(buckets)[0]
scaledValue = scaledResult.value
value = math.pow(10, scaledValue)
return [EncoderResult(value=value, scalar=value,
encoding = scaledResult.encoding)]
def getBucketInfo(self, buckets):
""" See the function description in base.py
"""
# For the category encoder, the bucket index is the category index
bucketInfo = self.encoder.getBucketInfo(buckets)[0]
categoryIndex = int(round(bucketInfo.value))
category = self.indexToCategory[categoryIndex]
return [EncoderResult(value=category, scalar=categoryIndex,
encoding=bucketInfo.encoding)]
def topDownCompute(self, encoded):
""" See the function description in base.py
"""
if self.ncategories==0:
return 0
topDownMappingM = self._getTopDownMapping()
categoryIndex = topDownMappingM.rightVecProd(encoded).argmax()
category = self.categories[categoryIndex]
encoding = topDownMappingM.getRow(categoryIndex)
return EncoderResult(value=category, scalar=categoryIndex, encoding=encoding)
def getBucketInfo(self, buckets):
""" See the function description in base.py
"""
if self.ncategories==0:
return 0
topDownMappingM = self._getTopDownMapping()
categoryIndex = buckets[0]
category = self.categories[categoryIndex]
encoding = topDownMappingM.getRow(categoryIndex)
return [EncoderResult(value=category, scalar=categoryIndex,
encoding=encoding)]
def getBucketInfo(self, buckets):
""" See the function description in base.py """
# Get/generate the topDown mapping table
#NOTE: although variable topDownMappingM is unused, some (bad-style) actions
#are executed during _getTopDownMapping() so this line must stay here
topDownMappingM = self._getTopDownMapping()
# The "category" is simply the bucket index
category = buckets[0]
encoding = self._topDownMappingM.getRow(category)
# Which input value does this correspond to?
if self.periodic:
inputVal = self.minval + self.resolution/2 + category * self.resolution
else:
inputVal = self.minval + category * self.resolution
return [EncoderResult(value=inputVal, scalar=inputVal, encoding=encoding)]
