class LogEncoder(Encoder):
"""
This class wraps the ScalarEncoder class.
A Log encoder represents a floating point value on a logarithmic scale.
valueToEncode = log10(input)
w -- number of bits to set in output
minval -- minimum input value. must be greater than 0. Lower values are
reset to this value
maxval -- maximum input value (input is strictly less if periodic == True)
periodic -- If true, then the input value "wraps around" such that minval =
maxval For a periodic value, the input must be strictly less than
maxval, otherwise maxval is a true upper bound.
Exactly one of n, radius, resolution must be set. "0" is a special
value that means "not set".
n -- number of bits in the representation (must be > w)
radius -- inputs separated by more than this distance in log space will have
non-overlapping representations
resolution -- The minimum change in scaled value needed to produce a change
in encoding. This should be specified in log space. For
example, the scaled values 10 and 11 will be distinguishable
in the output. In terms of the original input values, this
means 10^1 (1) and 10^1.1 (1.25) will be distinguishable.
name -- an optional string which will become part of the description
verbosity -- level of debugging output you want the encoder to provide.
clipInput -- if true, non-periodic inputs smaller than minval or greater
than maxval will be clipped to minval/maxval
forced -- (default False), if True, skip some safety checks
"""
def __init__(self,
w=5,
minval=1e-07,
maxval=10000,
periodic=False,
n=0,
radius=0,
resolution=0,
name="log",
verbosity=0,
clipInput=True,
forced=False):
# Lower bound for log encoding near machine precision limit
lowLimit = 1e-07
# Limit minval as log10(0) is undefined.
if minval < lowLimit:
minval = lowLimit
# Check that minval is still lower than maxval
if not minval < maxval:
raise ValueError(
"Max val must be larger than min val or the lower limit "
"for this encoder %.7f" % lowLimit)
self.encoders = None
self.verbosity = verbosity
# Scale values for calculations within the class
self.minScaledValue = math.log10(minval)
self.maxScaledValue = math.log10(maxval)
if not self.maxScaledValue > self.minScaledValue:
raise ValueError(
"Max val must be larger, in log space, than min val.")
self.clipInput = clipInput
self.minval = minval
self.maxval = maxval
self.encoder = ScalarEncoder(w=w,
minval=self.minScaledValue,
maxval=self.maxScaledValue,
periodic=False,
n=n,
radius=radius,
resolution=resolution,
verbosity=self.verbosity,
clipInput=self.clipInput,
forced=forced)
self.width = self.encoder.getWidth()
self.description = [(name, 0)]
self.name = name
# This list is created by getBucketValues() the first time it is called,
# and re-created whenever our buckets would be re-arranged.
self._bucketValues = None
############################################################################
def getWidth(self):
return self.width
############################################################################
def getDescription(self):
return self.description
############################################################################
def getDecoderOutputFieldTypes(self):
"""
Encoder class virtual method override
"""
return (FieldMetaType.float, )
############################################################################
def _getScaledValue(self, inpt):
"""
Convert the input, which is in normal space, into log space
"""
if inpt == SENTINEL_VALUE_FOR_MISSING_DATA:
return None
else:
val = inpt
if val < self.minval:
val = self.minval
elif val > self.maxval:
val = self.maxval
scaledVal = math.log10(val)
return scaledVal
############################################################################
def getBucketIndices(self, inpt):
"""
See the function description in base.py
"""
# Get the scaled value
scaledVal = self._getScaledValue(inpt)
if scaledVal is None:
return [None]
else:
return self.encoder.getBucketIndices(scaledVal)
############################################################################
def encodeIntoArray(self, inpt, output):
"""
See the function description in base.py
"""
# Get the scaled value
scaledVal = self._getScaledValue(inpt)
if scaledVal is None:
output[0:] = 0
else:
self.encoder.encodeIntoArray(scaledVal, output)
if self.verbosity >= 2:
print "input:", inpt, "scaledVal:", scaledVal, "output:", output
print "decoded:", self.decodedToStr(self.decode(output))
############################################################################
def decode(self, encoded, parentFieldName=''):
"""
See the function description in base.py
"""
# Get the scalar values from the underlying scalar encoder
(fieldsDict, fieldNames) = self.encoder.decode(encoded)
if len(fieldsDict) == 0:
return (fieldsDict, fieldNames)
# Expect only 1 field
assert (len(fieldsDict) == 1)
# Convert each range into normal space
(inRanges, inDesc) = fieldsDict.values()[0]
outRanges = []
for (minV, maxV) in inRanges:
outRanges.append((math.pow(10, minV), math.pow(10, maxV)))
# Generate a text description of the ranges
desc = ""
numRanges = len(outRanges)
for i in xrange(numRanges):
if outRanges[i][0] != outRanges[i][1]:
desc += "%.2f-%.2f" % (outRanges[i][0], outRanges[i][1])
else:
desc += "%.2f" % (outRanges[i][0])
if i < numRanges - 1:
desc += ", "
# Return result
if parentFieldName != '':
fieldName = "%s.%s" % (parentFieldName, self.name)
else:
fieldName = self.name
return ({fieldName: (outRanges, desc)}, [fieldName])
############################################################################
def getBucketValues(self):
"""
See the function description in base.py
"""
# Need to re-create?
if self._bucketValues is None:
scaledValues = self.encoder.getBucketValues()
self._bucketValues = []
for scaledValue in scaledValues:
value = math.pow(10, scaledValue)
self._bucketValues.append(value)
return self._bucketValues
############################################################################
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 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 closenessScores(self, expValues, actValues, fractional=True):
"""
See the function description in base.py
"""
# Compute the percent error in log space
if expValues[0] > 0:
expValue = math.log10(expValues[0])
else:
expValue = self.minScaledValue
if actValues[0] > 0:
actValue = math.log10(actValues[0])
else:
actValue = self.minScaledValue
if fractional:
err = abs(expValue - actValue)
pctErr = err / (self.maxScaledValue - self.minScaledValue)
pctErr = min(1.0, pctErr)
closeness = 1.0 - pctErr
else:
err = abs(expValue - actValue)
closeness = err
#print "log::", "expValue:", expValues[0], "actValue:", actValues[0], \
# "closeness", closeness
#import pdb; pdb.set_trace()
return numpy.array([closeness])
@classmethod
def read(cls, proto):
encoder = object.__new__(cls)
encoder.verbosity = proto.verbosity
encoder.minScaledValue = proto.minScaledValue
encoder.maxScaledValue = proto.maxScaledValue
encoder.clipInput = proto.clipInput
encoder.minval = proto.minval
encoder.maxval = proto.maxval
encoder.encoder = ScalarEncoder.read(proto.encoder)
encoder.name = proto.name
encoder.width = encoder.encoder.getWidth()
encoder.description = [(encoder.name, 0)]
encoder._bucketValues = None
return encoder
def write(self, proto):
proto.verbosity = self.verbosity
proto.minScaledValue = self.minScaledValue
proto.maxScaledValue = self.maxScaledValue
proto.clipInput = self.clipInput
proto.minval = self.minval
proto.maxval = self.maxval
self.encoder.write(proto.encoder)
proto.name = self.name
class CategoryEncoder(Encoder):
"""
Encodes a list of discrete categories (described by strings), that aren't
related to each other, so we never emit a mixture of categories.
The value of zero is reserved for "unknown category"
Internally we use a :class:`.ScalarEncoder` with a radius of 1, but since we
only encode integers, we never get mixture outputs.
The :class:`.SDRCategoryEncoder` uses a different method to encode categories.
:param categoryList: list of discrete string categories
:param forced: if True, skip checks for parameters' settings; see
:class:`.ScalarEncoder` for details. (default False)
"""
def __init__(self, w, categoryList, name="category", verbosity=0, forced=False):
self.encoders = None
self.verbosity = verbosity
# number of categories includes "unknown"
self.ncategories = len(categoryList) + 1
self.categoryToIndex = dict()
self.indexToCategory = dict()
self.indexToCategory[0] = UNKNOWN
for i in xrange(len(categoryList)):
self.categoryToIndex[categoryList[i]] = i+1
self.indexToCategory[i+1] = categoryList[i]
self.encoder = ScalarEncoder(w, minval=0, maxval=self.ncategories - 1,
radius=1, periodic=False, forced=forced)
self.width = w * self.ncategories
assert self.encoder.getWidth() == self.width
self.description = [(name, 0)]
self.name = name
# These are used to support the topDownCompute method
self._topDownMappingM = None
# This gets filled in by getBucketValues
self._bucketValues = None
def getDecoderOutputFieldTypes(self):
""" [Encoder class virtual method override]
"""
# TODO: change back to string meta-type after the decoding logic is fixed
# to output strings instead of internal index values.
#return (FieldMetaType.string,)
return (FieldMetaType.integer,)
def getWidth(self):
return self.width
def getDescription(self):
return self.description
def getScalars(self, input):
""" See method description in base.py """
if input == SENTINEL_VALUE_FOR_MISSING_DATA:
return numpy.array([None])
else:
return numpy.array([self.categoryToIndex.get(input, 0)])
def getBucketIndices(self, input):
""" See method description in base.py """
# Get the bucket index from the underlying scalar encoder
if input == SENTINEL_VALUE_FOR_MISSING_DATA:
return [None]
else:
return self.encoder.getBucketIndices(self.categoryToIndex.get(input, 0))
def encodeIntoArray(self, input, output):
# if not found, we encode category 0
if input == SENTINEL_VALUE_FOR_MISSING_DATA:
output[0:] = 0
val = "<missing>"
else:
val = self.categoryToIndex.get(input, 0)
self.encoder.encodeIntoArray(val, output)
if self.verbosity >= 2:
print "input:", input, "va:", val, "output:", output
print "decoded:", self.decodedToStr(self.decode(output))
def decode(self, encoded, parentFieldName=''):
""" See the function description in base.py
"""
# Get the scalar values from the underlying scalar encoder
(fieldsDict, fieldNames) = self.encoder.decode(encoded)
if len(fieldsDict) == 0:
return (fieldsDict, fieldNames)
# Expect only 1 field
assert(len(fieldsDict) == 1)
# Get the list of categories the scalar values correspond to and
# generate the description from the category name(s).
(inRanges, inDesc) = fieldsDict.values()[0]
outRanges = []
desc = ""
for (minV, maxV) in inRanges:
minV = int(round(minV))
maxV = int(round(maxV))
outRanges.append((minV, maxV))
while minV <= maxV:
if len(desc) > 0:
desc += ", "
desc += self.indexToCategory[minV]
minV += 1
# Return result
if parentFieldName != '':
fieldName = "%s.%s" % (parentFieldName, self.name)
else:
fieldName = self.name
return ({fieldName: (outRanges, desc)}, [fieldName])
def closenessScores(self, expValues, actValues, fractional=True,):
""" See the function description in base.py
kwargs will have the keyword "fractional", which is ignored by this encoder
"""
expValue = expValues[0]
actValue = actValues[0]
if expValue == actValue:
closeness = 1.0
else:
closeness = 0.0
if not fractional:
closeness = 1.0 - closeness
return numpy.array([closeness])
def getBucketValues(self):
""" See the function description in base.py """
if self._bucketValues is None:
numBuckets = len(self.encoder.getBucketValues())
self._bucketValues = []
for bucketIndex in range(numBuckets):
self._bucketValues.append(self.getBucketInfo([bucketIndex])[0].value)
return self._bucketValues
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
"""
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)
@classmethod
def getSchema(cls):
return CategoryEncoderProto
@classmethod
def read(cls, proto):
encoder = object.__new__(cls)
encoder.verbosity = proto.verbosity
encoder.encoder = ScalarEncoder.read(proto.encoder)
encoder.width = proto.width
encoder.description = [(proto.name, 0)]
encoder.name = proto.name
encoder.indexToCategory = {x.index: x.category
for x in proto.indexToCategory}
encoder.categoryToIndex = {category: index
for index, category
in encoder.indexToCategory.items()
if category != UNKNOWN}
encoder._topDownMappingM = None
encoder.ncategories = len(proto.indexToCategory)
encoder._bucketValues = None
encoder.encoders = None
return encoder
def write(self, proto):
proto.width = self.width
proto.indexToCategory = [
{"index": index, "category": category}
for index, category in self.indexToCategory.items()
]
proto.name = self.name
proto.verbosity = self.verbosity
self.encoder.write(proto.encoder)
class CategoryEncoder(Encoder):
"""Encodes a list of discrete categories (described by strings), that aren't
related to each other, so we never emit a mixture of categories.
The value of zero is reserved for "unknown category"
Internally we use a ScalarEncoder with a radius of 1, but since we only encode
integers, we never get mixture outputs.
The SDRCategoryEncoder uses a different method to encode categories"""
def __init__(self,
w,
categoryList,
name="category",
verbosity=0,
forced=False):
"""params:
forced (default False) : if True, skip checks for parameters' settings; see encoders/scalar.py for details
"""
self.encoders = None
self.verbosity = verbosity
# number of categories includes "unknown"
self.ncategories = len(categoryList) + 1
self.categoryToIndex = dict()
self.indexToCategory = dict()
self.indexToCategory[0] = UNKNOWN
for i in xrange(len(categoryList)):
self.categoryToIndex[categoryList[i]] = i + 1
self.indexToCategory[i + 1] = categoryList[i]
self.encoder = ScalarEncoder(w,
minval=0,
maxval=self.ncategories - 1,
radius=1,
periodic=False,
forced=forced)
self.width = w * self.ncategories
assert self.encoder.getWidth() == self.width
self.description = [(name, 0)]
self.name = name
# These are used to support the topDownCompute method
self._topDownMappingM = None
# This gets filled in by getBucketValues
self._bucketValues = None
def getDecoderOutputFieldTypes(self):
""" [Encoder class virtual method override]
"""
# TODO: change back to string meta-type after the decoding logic is fixed
# to output strings instead of internal index values.
#return (FieldMetaType.string,)
return (FieldMetaType.integer, )
def getWidth(self):
return self.width
def getDescription(self):
return self.description
def getScalars(self, input):
""" See method description in base.py """
if input == SENTINEL_VALUE_FOR_MISSING_DATA:
return numpy.array([None])
else:
return numpy.array([self.categoryToIndex.get(input, 0)])
def getBucketIndices(self, input):
""" See method description in base.py """
# Get the bucket index from the underlying scalar encoder
if input == SENTINEL_VALUE_FOR_MISSING_DATA:
return [None]
else:
return self.encoder.getBucketIndices(
self.categoryToIndex.get(input, 0))
def encodeIntoArray(self, input, output):
# if not found, we encode category 0
if input == SENTINEL_VALUE_FOR_MISSING_DATA:
output[0:] = 0
val = "<missing>"
else:
val = self.categoryToIndex.get(input, 0)
self.encoder.encodeIntoArray(val, output)
if self.verbosity >= 2:
print "input:", input, "va:", val, "output:", output
print "decoded:", self.decodedToStr(self.decode(output))
def decode(self, encoded, parentFieldName=''):
""" See the function description in base.py
"""
# Get the scalar values from the underlying scalar encoder
(fieldsDict, fieldNames) = self.encoder.decode(encoded)
if len(fieldsDict) == 0:
return (fieldsDict, fieldNames)
# Expect only 1 field
assert (len(fieldsDict) == 1)
# Get the list of categories the scalar values correspond to and
# generate the description from the category name(s).
(inRanges, inDesc) = fieldsDict.values()[0]
outRanges = []
desc = ""
for (minV, maxV) in inRanges:
minV = int(round(minV))
maxV = int(round(maxV))
outRanges.append((minV, maxV))
while minV <= maxV:
if len(desc) > 0:
desc += ", "
desc += self.indexToCategory[minV]
minV += 1
# Return result
if parentFieldName != '':
fieldName = "%s.%s" % (parentFieldName, self.name)
else:
fieldName = self.name
return ({fieldName: (outRanges, desc)}, [fieldName])
def closenessScores(
self,
expValues,
actValues,
fractional=True,
):
""" See the function description in base.py
kwargs will have the keyword "fractional", which is ignored by this encoder
"""
expValue = expValues[0]
actValue = actValues[0]
if expValue == actValue:
closeness = 1.0
else:
closeness = 0.0
if not fractional:
closeness = 1.0 - closeness
return numpy.array([closeness])
def getBucketValues(self):
""" See the function description in base.py """
if self._bucketValues is None:
numBuckets = len(self.encoder.getBucketValues())
self._bucketValues = []
for bucketIndex in range(numBuckets):
self._bucketValues.append(
self.getBucketInfo([bucketIndex])[0].value)
return self._bucketValues
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
"""
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)
@classmethod
def read(cls, proto):
encoder = object.__new__(cls)
encoder.verbosity = proto.verbosity
encoder.encoder = ScalarEncoder.read(proto.encoder)
encoder.width = proto.width
encoder.description = [(proto.name, 0)]
encoder.name = proto.name
encoder.indexToCategory = {
x.index: x.category
for x in proto.indexToCategory
}
encoder.categoryToIndex = {
category: index
for index, category in encoder.indexToCategory.items()
if category != UNKNOWN
}
encoder._topDownMappingM = None
encoder._bucketValues = None
return encoder
def write(self, proto):
proto.width = self.width
proto.indexToCategory = [{
"index": index,
"category": category
} for index, category in self.indexToCategory.items()]
proto.name = self.name
proto.verbosity = self.verbosity
self.encoder.write(proto.encoder)
class LogEncoder(Encoder):
"""
This class wraps the ScalarEncoder class.
A Log encoder represents a floating point value on a logarithmic scale.
valueToEncode = log10(input)
w -- number of bits to set in output
minval -- minimum input value. must be greater than 0. Lower values are
reset to this value
maxval -- maximum input value (input is strictly less if periodic == True)
periodic -- If true, then the input value "wraps around" such that minval =
maxval For a periodic value, the input must be strictly less than
maxval, otherwise maxval is a true upper bound.
Exactly one of n, radius, resolution must be set. "0" is a special
value that means "not set".
n -- number of bits in the representation (must be > w)
radius -- inputs separated by more than this distance in log space will have
non-overlapping representations
resolution -- The minimum change in scaled value needed to produce a change
in encoding. This should be specified in log space. For
example, the scaled values 10 and 11 will be distinguishable
in the output. In terms of the original input values, this
means 10^1 (1) and 10^1.1 (1.25) will be distinguishable.
name -- an optional string which will become part of the description
verbosity -- level of debugging output you want the encoder to provide.
clipInput -- if true, non-periodic inputs smaller than minval or greater
than maxval will be clipped to minval/maxval
forced -- (default False), if True, skip some safety checks
"""
def __init__(
self,
w=5,
minval=1e-07,
maxval=10000,
periodic=False,
n=0,
radius=0,
resolution=0,
name="log",
verbosity=0,
clipInput=True,
forced=False,
):
# Lower bound for log encoding near machine precision limit
lowLimit = 1e-07
# Limit minval as log10(0) is undefined.
if minval < lowLimit:
minval = lowLimit
# Check that minval is still lower than maxval
if not minval < maxval:
raise ValueError(
"Max val must be larger than min val or the lower limit " "for this encoder %.7f" % lowLimit
)
self.encoders = None
self.verbosity = verbosity
# Scale values for calculations within the class
self.minScaledValue = math.log10(minval)
self.maxScaledValue = math.log10(maxval)
if not self.maxScaledValue > self.minScaledValue:
raise ValueError("Max val must be larger, in log space, than min val.")
self.clipInput = clipInput
self.minval = minval
self.maxval = maxval
self.encoder = ScalarEncoder(
w=w,
minval=self.minScaledValue,
maxval=self.maxScaledValue,
periodic=False,
n=n,
radius=radius,
resolution=resolution,
verbosity=self.verbosity,
clipInput=self.clipInput,
forced=forced,
)
self.width = self.encoder.getWidth()
self.description = [(name, 0)]
self.name = name
# This list is created by getBucketValues() the first time it is called,
# and re-created whenever our buckets would be re-arranged.
self._bucketValues = None
############################################################################
def getWidth(self):
return self.width
############################################################################
def getDescription(self):
return self.description
############################################################################
def getDecoderOutputFieldTypes(self):
"""
Encoder class virtual method override
"""
return (FieldMetaType.float,)
############################################################################
def _getScaledValue(self, inpt):
"""
Convert the input, which is in normal space, into log space
"""
if inpt == SENTINEL_VALUE_FOR_MISSING_DATA:
return None
else:
val = inpt
if val < self.minval:
val = self.minval
elif val > self.maxval:
val = self.maxval
scaledVal = math.log10(val)
return scaledVal
############################################################################
def getBucketIndices(self, inpt):
"""
See the function description in base.py
"""
# Get the scaled value
scaledVal = self._getScaledValue(inpt)
if scaledVal is None:
return [None]
else:
return self.encoder.getBucketIndices(scaledVal)
############################################################################
def encodeIntoArray(self, inpt, output):
"""
See the function description in base.py
"""
# Get the scaled value
scaledVal = self._getScaledValue(inpt)
if scaledVal is None:
output[0:] = 0
else:
self.encoder.encodeIntoArray(scaledVal, output)
if self.verbosity >= 2:
print "input:", inpt, "scaledVal:", scaledVal, "output:", output
print "decoded:", self.decodedToStr(self.decode(output))
############################################################################
def decode(self, encoded, parentFieldName=""):
"""
See the function description in base.py
"""
# Get the scalar values from the underlying scalar encoder
(fieldsDict, fieldNames) = self.encoder.decode(encoded)
if len(fieldsDict) == 0:
return (fieldsDict, fieldNames)
# Expect only 1 field
assert len(fieldsDict) == 1
# Convert each range into normal space
(inRanges, inDesc) = fieldsDict.values()[0]
outRanges = []
for (minV, maxV) in inRanges:
outRanges.append((math.pow(10, minV), math.pow(10, maxV)))
# Generate a text description of the ranges
desc = ""
numRanges = len(outRanges)
for i in xrange(numRanges):
if outRanges[i][0] != outRanges[i][1]:
desc += "%.2f-%.2f" % (outRanges[i][0], outRanges[i][1])
else:
desc += "%.2f" % (outRanges[i][0])
if i < numRanges - 1:
desc += ", "
# Return result
if parentFieldName != "":
fieldName = "%s.%s" % (parentFieldName, self.name)
else:
fieldName = self.name
return ({fieldName: (outRanges, desc)}, [fieldName])
############################################################################
def getBucketValues(self):
"""
See the function description in base.py
"""
# Need to re-create?
if self._bucketValues is None:
scaledValues = self.encoder.getBucketValues()
self._bucketValues = []
for scaledValue in scaledValues:
value = math.pow(10, scaledValue)
self._bucketValues.append(value)
return self._bucketValues
############################################################################
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 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 closenessScores(self, expValues, actValues, fractional=True):
"""
See the function description in base.py
"""
# Compute the percent error in log space
if expValues[0] > 0:
expValue = math.log10(expValues[0])
else:
expValue = self.minScaledValue
if actValues[0] > 0:
actValue = math.log10(actValues[0])
else:
actValue = self.minScaledValue
if fractional:
err = abs(expValue - actValue)
pctErr = err / (self.maxScaledValue - self.minScaledValue)
pctErr = min(1.0, pctErr)
closeness = 1.0 - pctErr
else:
err = abs(expValue - actValue)
closeness = err
# print "log::", "expValue:", expValues[0], "actValue:", actValues[0], \
# "closeness", closeness
# import pdb; pdb.set_trace()
return numpy.array([closeness])
class CategoryEncoder(Encoder):
"""Encodes a list of discrete categories (described by strings), that aren't
related to each other, so we never emit a mixture of categories.
The value of zero is reserved for "unknown category"
Internally we use a ScalarEncoder with a radius of 1, but since we only encode
integers, we never get mixture outputs.
The SDRCategoryEncoder uses a different method to encode categories"""
def __init__(self, w, categoryList, name="category", verbosity=0, forced=False):
"""params:
forced (default False) : if True, skip checks for parameters' settings; see encoders/scalar.py for details
"""
self.encoders = None
self.verbosity = verbosity
# number of categories includes "unknown"
self.ncategories = len(categoryList) + 1
self.categoryToIndex = dict() # check_later: what is the purpose of categoryToIndex and indexToCategory?
self.indexToCategory = dict()
self.indexToCategory[0] = UNKNOWN
for i in xrange(len(categoryList)):
self.categoryToIndex[categoryList[i]] = i+1
self.indexToCategory[i+1] = categoryList[i]
self.encoder = ScalarEncoder(w, minval=0, maxval=self.ncategories - 1,
radius=1, periodic=False, forced=forced)
self.width = w * self.ncategories
assert self.encoder.getWidth() == self.width
self.description = [(name, 0)]
self.name = name
# These are used to support the topDownCompute method
self._topDownMappingM = None
# This gets filled in by getBucketValues
self._bucketValues = None
############################################################################
def getDecoderOutputFieldTypes(self):
""" [Encoder class virtual method override]
"""
# TODO: change back to string meta-type after the decoding logic is fixed
# to output strings instead of internal index values.
#return (FieldMetaType.string,)
return (FieldMetaType.integer,)
############################################################################
def getWidth(self):
return self.width
############################################################################
def getDescription(self):
return self.description
############################################################################
def getScalars(self, input):
""" See method description in base.py """
if input == SENTINEL_VALUE_FOR_MISSING_DATA:
return numpy.array([None])
else:
return numpy.array([self.categoryToIndex.get(input, 0)]) # to_note: returns the scalar value of the input, as stored in the categoryToIndex
# Will return in the format of a numpy array (e.g. [1] or [2]), return [0] if the
# input does not match with any of the key in categoryToIndex dictionary
############################################################################
def getBucketIndices(self, input):
""" See method description in base.py """
# Get the bucket index from the underlying scalar encoder
if input == SENTINEL_VALUE_FOR_MISSING_DATA:
return [None]
else:
return self.encoder.getBucketIndices(self.categoryToIndex.get(input, 0)) # to_note: get the first ON bit from the ScalarEncoder for a given input.
# Unknown value will have the first ON bit at position 1, then other values at k*w
# Value NONE will have all 0s
# problem_with_this_approach: this approach might be fast, but treating
# category encoding as rigid scalar encoding might make it hard for learning
############################################################################
def encodeIntoArray(self, input, output):
# if not found, we encode category 0
if input == SENTINEL_VALUE_FOR_MISSING_DATA:
output[0:] = 0
val = "<missing>"
else:
val = self.categoryToIndex.get(input, 0)
self.encoder.encodeIntoArray(val, output)
if self.verbosity >= 2:
print "input:", input, "va:", val, "output:", output
print "decoded:", self.decodedToStr(self.decode(output))
############################################################################
def decode(self, encoded, parentFieldName=''):
""" See the function description in base.py
"""
# Get the scalar values from the underlying scalar encoder
(fieldsDict, fieldNames) = self.encoder.decode(encoded)
if len(fieldsDict) == 0:
return (fieldsDict, fieldNames)
# Expect only 1 field
assert(len(fieldsDict) == 1)
# Get the list of categories the scalar values correspond to and
# generate the description from the category name(s).
(inRanges, inDesc) = fieldsDict.values()[0] # to_note: dict.values() returns values in [list] form, that's why we need [0]
outRanges = []
desc = ""
for (minV, maxV) in inRanges:
minV = int(round(minV))
maxV = int(round(maxV))
outRanges.append((minV, maxV))
while minV <= maxV:
if len(desc) > 0:
desc += ", "
desc += self.indexToCategory[minV]
minV += 1
"""
## Test with noisy encoding (very likely if such encoding comes from output of the predicting process)
catfish = numpy.zeros(20, 'int')
# catfish[5:10] = 1
# catfish[15:20] = 1
catfish[5:10] = 1
catfish[8] = 1
catfish[11] = 1
catfish[14] = 1
catfish[17] = 1
print "Cat fish =", catfish; print
## Note: this kind of encoding is highly unstable, for a little noisy output like this [0 0 0 0 0 1 1 1 1 1 0 1 0 0 1 0 0 1 0 0],
## it should safely generate 'cat'. However, it generates 'cat', 'dog', 'fish'. To improve this encoding/decoding scheme,
## we might need to replace the filling in process, even might need to think about other way to process information.
## problem_with_this_approach
"""
# Return result
if parentFieldName != '':
fieldName = "%s.%s" % (parentFieldName, self.name)
else:
fieldName = self.name
return ({fieldName: (outRanges, desc)}, [fieldName])
############################################################################
def closenessScores(self, expValues, actValues, fractional=True,):
""" See the function description in base.py
kwargs will have the keyword "fractional", which is ignored by this encoder
"""
expValue = expValues[0]
actValue = actValues[0]
if expValue == actValue:
closeness = 1.0
else:
closeness = 0.0
if not fractional:
closeness = 1.0 - closeness
return numpy.array([closeness])
############################################################################
def getBucketValues(self):
""" See the function description in base.py """
if self._bucketValues is None:
numBuckets = len(self.encoder.getBucketValues())
self._bucketValues = []
for bucketIndex in range(numBuckets):
self._bucketValues.append(self.getBucketInfo([bucketIndex])[0].value) # to_note: list of category corresponding to bucket indices
# each bucket is a number that is spaced (radius/w) each other
return self._bucketValues
############################################################################
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] # to_note: map the bucket index to category
return [EncoderResult(value=category, scalar=categoryIndex,
encoding=bucketInfo.encoding)]
############################################################################
def topDownCompute(self, encoded):
""" See the function description in base.py
"""
encoderResult = self.encoder.topDownCompute(encoded)[0] # to_note: return EncoderResult, which includes the value (depend on ScalarEncoder)
value = encoderResult.value
categoryIndex = int(round(value))
category = self.indexToCategory[categoryIndex]
return EncoderResult(value=category, scalar=categoryIndex,
encoding=encoderResult.encoding)
@classmethod
def read(cls, proto):
encoder = object.__new__(cls)
encoder.verbosity = proto.verbosity
encoder.encoder = ScalarEncoder.read(proto.encoder)
encoder.width = proto.width
encoder.description = [(proto.name, 0)]
encoder.name = proto.name
encoder.indexToCategory = {x.index: x.category
for x in proto.indexToCategory}
encoder.categoryToIndex = {category: index
for index, category
in encoder.indexToCategory.items()
if category != UNKNOWN}
encoder._topDownMappingM = None
encoder._bucketValues = None
return encoder
def write(self, proto):
proto.width = self.width
proto.indexToCategory = [
{"index": index, "category": category}
for index, category in self.indexToCategory.items()
]
proto.name = self.name
proto.verbosity = self.verbosity
self.encoder.write(proto.encoder)
class LogEncoder(Encoder):
"""
This class wraps the :class:`.ScalarEncoder`.
A Log encoder represents a floating point value on a logarithmic scale.
.. code-block:: python
valueToEncode = log10(input)
:param resolution: The minimum change in scaled value needed to produce a
change in encoding. This should be specified in log space.
For example, the scaled values 10 and 11 will be
distinguishable in the output. In terms of the original
input values, this means 10^1 (1) and 10^1.1 (1.25) will be
distinguishable.
:param radius: inputs separated by more than this distance in log space will
have non-overlapping representations
"""
def __init__(self,
w=5,
minval=1e-07,
maxval=10000,
periodic=False,
n=0,
radius=0,
resolution=0,
name="log",
verbosity=0,
clipInput=True,
forced=False):
# Lower bound for log encoding near machine precision limit
lowLimit = 1e-07
# Limit minval as log10(0) is undefined.
if minval < lowLimit:
minval = lowLimit
# Check that minval is still lower than maxval
if not minval < maxval:
raise ValueError("Max val must be larger than min val or the lower limit "
"for this encoder %.7f" % lowLimit)
self.encoders = None
self.verbosity = verbosity
# Scale values for calculations within the class
self.minScaledValue = math.log10(minval)
self.maxScaledValue = math.log10(maxval)
if not self.maxScaledValue > self.minScaledValue:
raise ValueError("Max val must be larger, in log space, than min val.")
self.clipInput = clipInput
self.minval = minval
self.maxval = maxval
self.encoder = ScalarEncoder(w=w,
minval=self.minScaledValue,
maxval=self.maxScaledValue,
periodic=False,
n=n,
radius=radius,
resolution=resolution,
verbosity=self.verbosity,
clipInput=self.clipInput,
forced=forced)
self.width = self.encoder.getWidth()
self.description = [(name, 0)]
self.name = name
# This list is created by getBucketValues() the first time it is called,
# and re-created whenever our buckets would be re-arranged.
self._bucketValues = None
def getWidth(self):
return self.width
def getDescription(self):
return self.description
def getDecoderOutputFieldTypes(self):
"""
Encoder class virtual method override
"""
return (FieldMetaType.float, )
def _getScaledValue(self, inpt):
"""
Convert the input, which is in normal space, into log space
"""
if inpt == SENTINEL_VALUE_FOR_MISSING_DATA:
return None
else:
val = inpt
if val < self.minval:
val = self.minval
elif val > self.maxval:
val = self.maxval
scaledVal = math.log10(val)
return scaledVal
def getBucketIndices(self, inpt):
"""
See the function description in base.py
"""
# Get the scaled value
scaledVal = self._getScaledValue(inpt)
if scaledVal is None:
return [None]
else:
return self.encoder.getBucketIndices(scaledVal)
def encodeIntoArray(self, inpt, output):
"""
See the function description in base.py
"""
# Get the scaled value
scaledVal = self._getScaledValue(inpt)
if scaledVal is None:
output[0:] = 0
else:
self.encoder.encodeIntoArray(scaledVal, output)
if self.verbosity >= 2:
print "input:", inpt, "scaledVal:", scaledVal, "output:", output
print "decoded:", self.decodedToStr(self.decode(output))
def decode(self, encoded, parentFieldName=''):
"""
See the function description in base.py
"""
# Get the scalar values from the underlying scalar encoder
(fieldsDict, fieldNames) = self.encoder.decode(encoded)
if len(fieldsDict) == 0:
return (fieldsDict, fieldNames)
# Expect only 1 field
assert(len(fieldsDict) == 1)
# Convert each range into normal space
(inRanges, inDesc) = fieldsDict.values()[0]
outRanges = []
for (minV, maxV) in inRanges:
outRanges.append((math.pow(10, minV),
math.pow(10, maxV)))
# Generate a text description of the ranges
desc = ""
numRanges = len(outRanges)
for i in xrange(numRanges):
if outRanges[i][0] != outRanges[i][1]:
desc += "%.2f-%.2f" % (outRanges[i][0], outRanges[i][1])
else:
desc += "%.2f" % (outRanges[i][0])
if i < numRanges-1:
desc += ", "
# Return result
if parentFieldName != '':
fieldName = "%s.%s" % (parentFieldName, self.name)
else:
fieldName = self.name
return ({fieldName: (outRanges, desc)}, [fieldName])
def getBucketValues(self):
"""
See the function description in base.py
"""
# Need to re-create?
if self._bucketValues is None:
scaledValues = self.encoder.getBucketValues()
self._bucketValues = []
for scaledValue in scaledValues:
value = math.pow(10, scaledValue)
self._bucketValues.append(value)
return self._bucketValues
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 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 closenessScores(self, expValues, actValues, fractional=True):
"""
See the function description in base.py
"""
# Compute the percent error in log space
if expValues[0] > 0:
expValue = math.log10(expValues[0])
else:
expValue = self.minScaledValue
if actValues [0] > 0:
actValue = math.log10(actValues[0])
else:
actValue = self.minScaledValue
if fractional:
err = abs(expValue - actValue)
pctErr = err / (self.maxScaledValue - self.minScaledValue)
pctErr = min(1.0, pctErr)
closeness = 1.0 - pctErr
else:
err = abs(expValue - actValue)
closeness = err
#print "log::", "expValue:", expValues[0], "actValue:", actValues[0], \
# "closeness", closeness
#import pdb; pdb.set_trace()
return numpy.array([closeness])
@classmethod
def read(cls, proto):
encoder = object.__new__(cls)
encoder.verbosity = proto.verbosity
encoder.minScaledValue = proto.minScaledValue
encoder.maxScaledValue = proto.maxScaledValue
encoder.clipInput = proto.clipInput
encoder.minval = proto.minval
encoder.maxval = proto.maxval
encoder.encoder = ScalarEncoder.read(proto.encoder)
encoder.name = proto.name
encoder.width = encoder.encoder.getWidth()
encoder.description = [(encoder.name, 0)]
encoder._bucketValues = None
return encoder
def write(self, proto):
proto.verbosity = self.verbosity
proto.minScaledValue = self.minScaledValue
proto.maxScaledValue = self.maxScaledValue
proto.clipInput = self.clipInput
proto.minval = self.minval
proto.maxval = self.maxval
self.encoder.write(proto.encoder)
proto.name = self.name
def main():
DIR = "./sim_data"
# Odom Encoder
xSDR = ScalarEncoder(w=21,minval=0,maxval=20,n=256)
ySDR = ScalarEncoder(w=21,minval=0,maxval=20,n=256)
xyWidth = xSDR.getWidth() + ySDR.getWidth()
# Visual input
D = np.loadtxt(DIR + '/seq_multi_loop_noise05_al5.txt', dtype='i', delimiter=',')
numberImages = D[:,0].size
nColumns = D[0,:].size
#time.sleep(10)
# Odom input
odom = np.loadtxt(DIR + '/seq_multi_loop_noise05_al5_gt.txt', dtype='f', delimiter=',')
x = odom[:,0]
y = odom[:,1]
# Encoder Odom input
odomSDR = np.zeros((numberImages,xyWidth), dtype=int)
for i in range(1):
_xSDR = np.zeros(xSDR.getWidth(), dtype=int)
xSDR.encodeIntoArray(x[i], _xSDR)
_ySDR = np.zeros(ySDR.getWidth(), dtype=int)
ySDR.encodeIntoArray(y[i], _ySDR)
odomSDR[i,:] = np.concatenate([_xSDR, _ySDR])
tm0 = TM(
columnCount=nColumns,
cellsPerColumn=4,
initialPermanence=0.21,
connectedPermanence=0.5,
permanenceIncrement=0.1,
permanenceDecrement=0.1,
minThreshold=15,
basalInputSize= 512,
reducedBasalThreshold=1000,
activationThreshold=1000,
apicalInputSize=0,
maxSynapsesPerSegment=-1,
sampleSize=1,
seed = 42
)
tm = TemporalMemory(
# Must be the same dimensions as the SP
columnDimensions=(2048,),
# How many cells in each mini-column.
cellsPerColumn=4,
# A segment is active if it has >= activationThreshold connected synapses
# that are active due to infActiveState
activationThreshold=13,
initialPermanence=0.21,
connectedPermanence=0.5,
# Minimum number of active synapses for a segment to be considered during
# search for the best-matching segments.
minThreshold=1,
# The max number of synapses added to a segment during learning
maxNewSynapseCount=3,
#permanenceIncrement=0.01,
#permanenceDecrement=0.01,
predictedSegmentDecrement=0.0005,
maxSegmentsPerCell=3,
maxSynapsesPerSegment=3,
seed=42
)
#time.sleep(10)
# Simple HTM parameters
params = Params()
params.maxPredDepth = 0
params.probAdditionalCon = 0.05 # probability for random connection
params.nCellPerCol = 32 # number of cells per minicolumn
params.nInConPerCol = int(round(np.count_nonzero(D) / D.shape[0]))
#print params.nInConPerCol
params.minColumnActivity = int(round(0.25*params.nInConPerCol))
params.nColsPerPattern = 10 # minimum number of active minicolumns k_min
params.kActiveColumn = 100 # maximum number of active minicolumns k_max
params.kMin = 1
# run HTM
t = time.time()
print ('Simple HTM')
htm = MCN('htm',params)
outputSDR = []
max_index = []
for i in range (min(numberImages,D.shape[0])):
loop = 0
#print('\n-------- ITERATION %d ---------' %i)
# skip empty vectors
if np.count_nonzero(D[i,:]) == 0:
print('empty vector, skip\n')
continue
loop += 1
#print D[i,:]
htm.compute(D[i,:])
max_index.append(max(htm.winnerCells))
outputSDR.append(htm.winnerCells)
elapsed = time.time() - t
print("Elapsed time: %f seconds\n" %elapsed)
# create output SDR matrix from HTM winner cell output
M = np.zeros((len(outputSDR),max(max_index)+1), dtype=int)
for i in range(len(outputSDR)):
for j in range(len(outputSDR[i])):
winner = outputSDR[i][j]
M[i][winner] = 1
# Temporal Pooler descriptors
print 'Temporal Pooler descriptors'
D1_tm=[]
id_max1=[]
t = time.time()
for i in range(min(numberImages,D.shape[0])):
D1_sp = np.nonzero(D[i,:])[0]
tm.compute(D1_sp, learn=True)
activeCells = tm.getWinnerCells()
D1_tm.append(activeCells)
id_max1.append(max(activeCells))
elapsed = time.time() - t
print( "Elapsed time: %f seconds\n" %elapsed)
# create output SDR matrix from HTM winner cell output
T = np.zeros((len(D1_tm),max(id_max1)+1), dtype=int)
for i in range(len(D1_tm)):
for j in range(len(D1_tm[i])):
winner = D1_tm[i][j]
T[i][winner] = 1
# Temporal Pooler - Distal connections
print 'Temporal Pooler - Distal connections'
D2_tm=[]
id_max2=[]
t = time.time()
for i in range(min(numberImages,D.shape[0])):
D2_sp = np.nonzero(D[i,:])[0]
basalInputs = np.nonzero(odomSDR[i,:])[0]
tm0.compute(sorted(D2_sp), sorted(basalInputs), apicalInput=(), basalGrowthCandidates=None, apicalGrowthCandidates=None, learn=True)
activeCells2 = tm0.getWinnerCells()
D2_tm.append(activeCells2)
id_max2.append(max(activeCells2))
elapsed = time.time() - t
print( "Elapsed time: %f seconds\n" %elapsed)
# create output SDR matrix from HTM winner cell output
T2 = np.zeros((len(D2_tm),max(id_max2)+1), dtype=int)
for i in range(len(D2_tm)):
for j in range(len(D2_tm[i])):
winner = D2_tm[i][j]
T2[i][winner] = 1
# Create ground truth and show precision-recall curves
GT_data = np.loadtxt(DIR + '/seq_multi_loop_noNoise_gt.txt', dtype='i', delimiter=',',skiprows=1)
GT = np.zeros((numberImages,numberImages), dtype=int)
for i in range(GT.shape[0]):
for j in range(i,GT.shape[1]):
GT[i,j] = (np.any(GT_data[i,:] != GT_data[j,:])==False)
# Results
print ('Results')
fig, ax = plt.subplots()
S0 = evaluateSimilarity(D)
P, R = createPR(S0,GT)
ax.plot(R, P, label='InputSDR: (avgP=%f)' %np.trapz(P,R))
S1 = evaluateSimilarity(M)
P, R = createPR(S1,GT)
ax.plot(R, P, label='MCN (avgP=%f)' %np.trapz(P,R))
S2 = evaluateSimilarity(T)
P, R = createPR(S2,GT)
ax.plot(R, P, label='HTM (avgP=%f)' %np.trapz(P,R))
S3 = evaluateSimilarity(T2)
P, R = createPR(S3,GT)
ax.plot(R, P, label='HTM Distal (avgP=%f)' %np.trapz(P,R))
ax.legend()
ax.grid(True)
plt.xlabel("Recall")
plt.ylabel("Precision")
plt.show()
'''
def buildSDR(arrayOfShapeTypes,arrayOfDistances,arrayOfAreas,numCentroids): #Scalar Encoder for the area areaEncoder = ScalarEncoder(5, 1e3, 3.8e3, periodic=False, n=80, radius=0, resolution=0, name=None, verbosity=0, clipInput=False, forced=True) #Scalar encoder for the distances between centroids distanceEncoder = ScalarEncoder(5, 30, 240, periodic=False, n=80, radius=0, resolution=0, name=None, verbosity=0, clipInput=False, forced=True) if numCentroids ==3: distanceBits0 = np.zeros(distanceEncoder.getWidth()) distanceEncoder.encodeIntoArray(arrayOfDistances[0],distanceBits0) distanceBits1 = np.zeros(distanceEncoder.getWidth()) distanceEncoder.encodeIntoArray(arrayOfDistances[1],distanceBits1) distanceBits2 = np.zeros(distanceEncoder.getWidth()) distanceEncoder.encodeIntoArray(arrayOfDistances[2],distanceBits2) if numCentroids ==2: distanceBits0 = np.zeros(distanceEncoder.getWidth()) distanceEncoder.encodeIntoArray(arrayOfDistances[0],distanceBits0) # Build the Triangle's base SDR. One hot encoding used for all SDRs. TriangleSDR = np.zeros(10) # Part A: Number of Sides [0 - 4] TriangleSDR[2] = 1 # Part B: Number of Neighbors [5 - 9] TriangleSDR[7] = 1 # Build the Circle's base SDR CircleSDR = np.zeros(10) # Part A: Number of Sides [0 - 4] CircleSDR[0] = 1 # Part B: Number of Neighbors [5 - 9] CircleSDR[7] = 1 # Build the Square's base SDR SquareSDR = np.zeros(10) # Part A: Number of Sides [0 - 4] SquareSDR[3] = 1 # Part B: Number of Neighbors [5 - 9] SquareSDR[7] = 1 arrayOfSDRs = np.array([]) for i in range(numCentroids): # Encode the area areaBits = np.zeros(areaEncoder.getWidth()) areaEncoder.encodeIntoArray(arrayOfAreas[i],areaBits) # Figure out the shape type via the CNN output then us an # if, elseif tree to decide which SDR to concatenate if arrayOfShapeTypes[i] == 0: #Its a Triangle tempSDR = np.concatenate(( TriangleSDR, areaBits)) elif arrayOfShapeTypes[i] == 1: #Its a Circle tempSDR = np.concatenate(( CircleSDR, areaBits)) elif arrayOfShapeTypes[i] == 2: #Its a Square tempSDR = np.concatenate(( SquareSDR, areaBits)) if numCentroids == 3: if i == 0: #Its the first item tempSDR = np.concatenate((tempSDR,distanceBits0,distanceBits2)) elif i == 1: #Its the second tempSDR = np.concatenate((tempSDR,distanceBits0,distanceBits1)) elif i == 2: #Its the third tempSDR = np.concatenate((tempSDR,distanceBits1,distanceBits2)) elif numCentroids == 2: if i == 0: #Its the first item tempSDR = np.concatenate((tempSDR,distanceBits0,np.zeros(80))) elif i == 1: #Its the second tempSDR = np.concatenate((tempSDR,distanceBits0,np.zeros(80))) elif numCentroids == 1: tempSDR = np.concatenate((tempSDR,np.zeros(160))) arrayOfSDRs = np.append(arrayOfSDRs,tempSDR) if numCentroids == 2: arrayOfSDRs = np.append(arrayOfSDRs,np.zeros(250)) if numCentroids == 1: arrayOfSDRs = np.append(arrayOfSDRs,np.zeros(500)) # Concatenate all three SDRs #print(arrayOfSDRs) imageSDR = arrayOfSDRs return imageSDR
