class KNNClassifier(Serializable):
"""
This class implements NuPIC's k Nearest Neighbor Classifier. KNN is very
useful as a basic classifier for many situations. This implementation contains
many enhancements that are useful for HTM experiments. These enhancements
include an optimized C++ class for sparse vectors, support for continuous
online learning, support for various distance methods (including Lp-norm and
raw overlap), support for performing SVD on the input vectors (very useful for
large vectors), support for a fixed-size KNN, and a mechanism to store custom
ID's for each vector.
:param k: (int) The number of nearest neighbors used in the classification
of patterns. Must be odd.
:param exact: (boolean) If true, patterns must match exactly when assigning
class labels
:param distanceNorm: (int) When distance method is "norm", this specifies
the p value of the Lp-norm
:param distanceMethod: (string) The method used to compute distance between
input patterns and prototype patterns. The possible options are:
- ``norm``: When distanceNorm is 2, this is the euclidean distance,
When distanceNorm is 1, this is the manhattan distance
In general: sum(abs(x-proto) ^ distanceNorm) ^ (1/distanceNorm)
The distances are normalized such that farthest prototype from
a given input is 1.0.
- ``rawOverlap``: Only appropriate when inputs are binary. This computes:
(width of the input) - (# bits of overlap between input
and prototype).
- ``pctOverlapOfInput``: Only appropriate for binary inputs. This computes
1.0 - (# bits overlap between input and prototype) /
(# ON bits in input)
- ``pctOverlapOfProto``: Only appropriate for binary inputs. This computes
1.0 - (# bits overlap between input and prototype) /
(# ON bits in prototype)
- ``pctOverlapOfLarger``: Only appropriate for binary inputs. This computes
1.0 - (# bits overlap between input and prototype) /
max(# ON bits in input, # ON bits in prototype)
:param distThreshold: (float) A threshold on the distance between learned
patterns and a new pattern proposed to be learned. The distance must be
greater than this threshold in order for the new pattern to be added to
the classifier's memory.
:param doBinarization: (boolean) If True, then scalar inputs will be
binarized.
:param binarizationThreshold: (float) If doBinarization is True, this
specifies the threshold for the binarization of inputs
:param useSparseMemory: (boolean) If True, classifier will use a sparse
memory matrix
:param sparseThreshold: (float) If useSparseMemory is True, input variables
whose absolute values are less than this threshold will be stored as
zero
:param relativeThreshold: (boolean) Flag specifying whether to multiply
sparseThreshold by max value in input
:param numWinners: (int) Number of elements of the input that are stored. If
0, all elements are stored
:param numSVDSamples: (int) Number of samples the must occur before a SVD
(Singular Value Decomposition) transformation will be performed. If 0,
the transformation will never be performed
:param numSVDDims: (string) Controls dimensions kept after SVD
transformation. If "adaptive", the number is chosen automatically
:param fractionOfMax: (float) If numSVDDims is "adaptive", this controls the
smallest singular value that is retained as a fraction of the largest
singular value
:param verbosity: (int) Console verbosity level where 0 is no output and
larger integers provide increasing levels of verbosity
:param maxStoredPatterns: (int) Limits the maximum number of the training
patterns stored. When KNN learns in a fixed capacity mode, the unused
patterns are deleted once the number of stored patterns is greater than
maxStoredPatterns. A value of -1 is no limit
:param replaceDuplicates: (bool) A boolean flag that determines whether,
during learning, the classifier replaces duplicates that match exactly,
even if distThreshold is 0. Should be True for online learning
:param cellsPerCol: (int) If >= 1, input is assumed to be organized into
columns, in the same manner as the temporal memory AND whenever a new
prototype is stored, only the start cell (first cell) is stored in any
bursting column
:param minSparsity: (float) If useSparseMemory is set, only vectors with
sparsity >= minSparsity will be stored during learning. A value of 0.0
implies all vectors will be stored. A value of 0.1 implies only vectors
with at least 10% sparsity will be stored
"""
def __init__(self, k=1,
exact=False,
distanceNorm=2.0,
distanceMethod="norm",
distThreshold=0,
doBinarization=False,
binarizationThreshold=0.5,
useSparseMemory=True,
sparseThreshold=0.1,
relativeThreshold=False,
numWinners=0,
numSVDSamples=None,
numSVDDims=None,
fractionOfMax=None,
verbosity=0,
maxStoredPatterns=-1,
replaceDuplicates=False,
cellsPerCol=0,
minSparsity=0.0):
self.version = KNNCLASSIFIER_VERSION
self.k = k
self.exact = exact
self.distanceNorm = distanceNorm
assert (distanceMethod in ("norm", "rawOverlap", "pctOverlapOfLarger",
"pctOverlapOfProto", "pctOverlapOfInput"))
self.distanceMethod = distanceMethod
self.distThreshold = distThreshold
self.doBinarization = doBinarization
self.binarizationThreshold = binarizationThreshold
self.useSparseMemory = useSparseMemory
self.sparseThreshold = sparseThreshold
self.relativeThreshold = relativeThreshold
self.numWinners = numWinners
self.numSVDSamples = numSVDSamples
self.numSVDDims = numSVDDims
self.fractionOfMax = fractionOfMax
if self.numSVDDims=="adaptive":
self._adaptiveSVDDims = True
else:
self._adaptiveSVDDims = False
self.verbosity = verbosity
self.replaceDuplicates = replaceDuplicates
self.cellsPerCol = cellsPerCol
self.maxStoredPatterns = maxStoredPatterns
self.minSparsity = minSparsity
self.clear()
def clear(self):
"""Clears the state of the KNNClassifier."""
self._Memory = None
self._numPatterns = 0
self._M = None
self._categoryList = []
self._partitionIdList = []
self._partitionIdMap = {}
self._finishedLearning = False
self._iterationIdx = -1
# Fixed capacity KNN
if self.maxStoredPatterns > 0:
assert self.useSparseMemory, ("Fixed capacity KNN is implemented only "
"in the sparse memory mode")
self.fixedCapacity = True
self._categoryRecencyList = []
else:
self.fixedCapacity = False
# Cached value of the store prototype sizes
self._protoSizes = None
# Used by PCA
self._s = None
self._vt = None
self._nc = None
self._mean = None
# Used by Network Builder
self._specificIndexTraining = False
self._nextTrainingIndices = None
def _doubleMemoryNumRows(self):
m = 2 * self._Memory.shape[0]
n = self._Memory.shape[1]
self._Memory = numpy.resize(self._Memory,(m,n))
self._M = self._Memory[:self._numPatterns]
def _sparsifyVector(self, inputPattern, doWinners=False):
# Do sparsification, using a relative or absolute threshold
if not self.relativeThreshold:
inputPattern = inputPattern*(abs(inputPattern) > self.sparseThreshold)
elif self.sparseThreshold > 0:
inputPattern = inputPattern * \
(abs(inputPattern) > (self.sparseThreshold * abs(inputPattern).max()))
# Do winner-take-all
if doWinners:
if (self.numWinners>0) and (self.numWinners < (inputPattern > 0).sum()):
sparseInput = numpy.zeros(inputPattern.shape)
# Don't consider strongly negative numbers as winners.
sorted = inputPattern.argsort()[0:self.numWinners]
sparseInput[sorted] += inputPattern[sorted]
inputPattern = sparseInput
# Do binarization
if self.doBinarization:
# Don't binarize negative numbers to positive 1.
inputPattern = (inputPattern > self.binarizationThreshold).astype(float)
return inputPattern
def prototypeSetCategory(self, idToCategorize, newCategory):
"""
Allows ids to be assigned a category and subsequently enables users to use:
- :meth:`~.KNNClassifier.KNNClassifier.removeCategory`
- :meth:`~.KNNClassifier.KNNClassifier.closestTrainingPattern`
- :meth:`~.KNNClassifier.KNNClassifier.closestOtherTrainingPattern`
"""
if idToCategorize not in self._categoryRecencyList:
return
recordIndex = self._categoryRecencyList.index(idToCategorize)
self._categoryList[recordIndex] = newCategory
def removeIds(self, idsToRemove):
"""
There are two caveats. First, this is a potentially slow operation. Second,
pattern indices will shift if patterns before them are removed.
:param idsToRemove: A list of row indices to remove.
"""
# Form a list of all categories to remove
rowsToRemove = [k for k, rowID in enumerate(self._categoryRecencyList) \
if rowID in idsToRemove]
# Remove rows from the classifier
self._removeRows(rowsToRemove)
def removeCategory(self, categoryToRemove):
"""
There are two caveats. First, this is a potentially slow operation. Second,
pattern indices will shift if patterns before them are removed.
:param categoryToRemove: Category label to remove
"""
removedRows = 0
if self._Memory is None:
return removedRows
# The internal category indices are stored in float
# format, so we should compare with a float
catToRemove = float(categoryToRemove)
# Form a list of all categories to remove
rowsToRemove = [k for k, catID in enumerate(self._categoryList) \
if catID == catToRemove]
# Remove rows from the classifier
self._removeRows(rowsToRemove)
assert catToRemove not in self._categoryList
def _removeRows(self, rowsToRemove):
"""
A list of row indices to remove. There are two caveats. First, this is
a potentially slow operation. Second, pattern indices will shift if
patterns before them are removed.
"""
# Form a numpy array of row indices to be removed
removalArray = numpy.array(rowsToRemove)
# Remove categories
self._categoryList = numpy.delete(numpy.array(self._categoryList),
removalArray).tolist()
if self.fixedCapacity:
self._categoryRecencyList = numpy.delete(
numpy.array(self._categoryRecencyList), removalArray).tolist()
# Remove the partition ID, if any for these rows and rebuild the id map.
for row in reversed(rowsToRemove): # Go backwards
# Remove these patterns from partitionList
self._partitionIdList.pop(row)
self._rebuildPartitionIdMap(self._partitionIdList)
# Remove actual patterns
if self.useSparseMemory:
# Delete backwards
for rowIndex in rowsToRemove[::-1]:
self._Memory.deleteRow(rowIndex)
else:
self._M = numpy.delete(self._M, removalArray, 0)
numRemoved = len(rowsToRemove)
# Sanity checks
numRowsExpected = self._numPatterns - numRemoved
if self.useSparseMemory:
if self._Memory is not None:
assert self._Memory.nRows() == numRowsExpected
else:
assert self._M.shape[0] == numRowsExpected
assert len(self._categoryList) == numRowsExpected
self._numPatterns -= numRemoved
return numRemoved
def doIteration(self):
"""
Utility method to increment the iteration index. Intended for models that
don't learn each timestep.
"""
self._iterationIdx += 1
def learn(self, inputPattern, inputCategory, partitionId=None, isSparse=0,
rowID=None):
"""
Train the classifier to associate specified input pattern with a
particular category.
:param inputPattern: (list) The pattern to be assigned a category. If
isSparse is 0, this should be a dense array (both ON and OFF bits
present). Otherwise, if isSparse > 0, this should be a list of the
indices of the non-zero bits in sorted order
:param inputCategory: (int) The category to be associated to the training
pattern
:param partitionId: (int) partitionID allows you to associate an id with each
input vector. It can be used to associate input patterns stored in the
classifier with an external id. This can be useful for debugging or
visualizing. Another use case is to ignore vectors with a specific id
during inference (see description of infer() for details). There can be
at most one partitionId per stored pattern (i.e. if two patterns are
within distThreshold, only the first partitionId will be stored). This
is an optional parameter.
:param isSparse: (int) If 0, the input pattern is a dense representation. If
isSparse > 0, the input pattern is a list of non-zero indices and
isSparse is the length of the dense representation
:param rowID: (int) UNKNOWN
:returns: The number of patterns currently stored in the classifier
"""
if self.verbosity >= 1:
print("%s learn:" % g_debugPrefix)
print(" category:", int(inputCategory))
print(" active inputs:", _labeledInput(inputPattern,
cellsPerCol=self.cellsPerCol))
if isSparse > 0:
assert all(inputPattern[i] <= inputPattern[i+1]
for i in range(len(inputPattern)-1)), \
"Sparse inputPattern must be sorted."
assert all(bit < isSparse for bit in inputPattern), \
("Sparse inputPattern must not index outside the dense "
"representation's bounds.")
if rowID is None:
rowID = self._iterationIdx
# Dense vectors
if not self.useSparseMemory:
# Not supported
assert self.cellsPerCol == 0, "not implemented for dense vectors"
# If the input was given in sparse form, convert it to dense
if isSparse > 0:
denseInput = numpy.zeros(isSparse)
denseInput[inputPattern] = 1.0
inputPattern = denseInput
if self._specificIndexTraining and not self._nextTrainingIndices:
# Specific index mode without any index provided - skip training
return self._numPatterns
if self._Memory is None:
# Initialize memory with 100 rows and numPatterns = 0
inputWidth = len(inputPattern)
self._Memory = numpy.zeros((100,inputWidth))
self._numPatterns = 0
self._M = self._Memory[:self._numPatterns]
addRow = True
if self._vt is not None:
# Compute projection
inputPattern = numpy.dot(self._vt, inputPattern - self._mean)
if self.distThreshold > 0:
# Check if input is too close to an existing input to be accepted
dist = self._calcDistance(inputPattern)
minDist = dist.min()
addRow = (minDist >= self.distThreshold)
if addRow:
self._protoSizes = None # need to re-compute
if self._numPatterns == self._Memory.shape[0]:
# Double the size of the memory
self._doubleMemoryNumRows()
if not self._specificIndexTraining:
# Normal learning - append the new input vector
self._Memory[self._numPatterns] = inputPattern
self._numPatterns += 1
self._categoryList.append(int(inputCategory))
else:
# Specific index training mode - insert vector in specified slot
vectorIndex = self._nextTrainingIndices.pop(0)
while vectorIndex >= self._Memory.shape[0]:
self._doubleMemoryNumRows()
self._Memory[vectorIndex] = inputPattern
self._numPatterns = max(self._numPatterns, vectorIndex + 1)
if vectorIndex >= len(self._categoryList):
self._categoryList += [-1] * (vectorIndex -
len(self._categoryList) + 1)
self._categoryList[vectorIndex] = int(inputCategory)
# Set _M to the "active" part of _Memory
self._M = self._Memory[0:self._numPatterns]
self._addPartitionId(self._numPatterns-1, partitionId)
# Sparse vectors
else:
# If the input was given in sparse form, convert it to dense if necessary
if isSparse > 0 and (self._vt is not None or self.distThreshold > 0 \
or self.numSVDDims is not None or self.numSVDSamples is not None \
or self.numWinners > 0):
denseInput = numpy.zeros(isSparse)
denseInput[inputPattern] = 1.0
inputPattern = denseInput
isSparse = 0
# Get the input width
if isSparse > 0:
inputWidth = isSparse
else:
inputWidth = len(inputPattern)
# Allocate storage if this is the first training vector
if self._Memory is None:
self._Memory = NearestNeighbor(0, inputWidth)
# Support SVD if it is on
if self._vt is not None:
inputPattern = numpy.dot(self._vt, inputPattern - self._mean)
# Threshold the input, zeroing out entries that are too close to 0.
# This is only done if we are given a dense input.
if isSparse == 0:
thresholdedInput = self._sparsifyVector(inputPattern, True)
addRow = True
# If given the layout of the cells, then turn on the logic that stores
# only the start cell for bursting columns.
if self.cellsPerCol >= 1:
burstingCols = thresholdedInput.reshape(-1,
self.cellsPerCol).min(axis=1).nonzero()[0]
for col in burstingCols:
thresholdedInput[(col * self.cellsPerCol) + 1 :
(col * self.cellsPerCol) + self.cellsPerCol] = 0
# Don't learn entries that are too close to existing entries.
if self._Memory.nRows() > 0:
dist = None
# if this vector is a perfect match for one we already learned, then
# replace the category - it may have changed with online learning on.
if self.replaceDuplicates:
dist = self._calcDistance(thresholdedInput, distanceNorm=1)
if dist.min() == 0:
rowIdx = dist.argmin()
self._categoryList[rowIdx] = int(inputCategory)
if self.fixedCapacity:
self._categoryRecencyList[rowIdx] = rowID
addRow = False
# Don't add this vector if it matches closely with another we already
# added
if self.distThreshold > 0:
if dist is None or self.distanceNorm != 1:
dist = self._calcDistance(thresholdedInput)
minDist = dist.min()
addRow = (minDist >= self.distThreshold)
if not addRow:
if self.fixedCapacity:
rowIdx = dist.argmin()
self._categoryRecencyList[rowIdx] = rowID
# If sparsity is too low, we do not want to add this vector
if addRow and self.minSparsity > 0.0:
if isSparse==0:
sparsity = ( float(len(thresholdedInput.nonzero()[0])) /
len(thresholdedInput) )
else:
sparsity = float(len(inputPattern)) / isSparse
if sparsity < self.minSparsity:
addRow = False
# Add the new sparse vector to our storage
if addRow:
self._protoSizes = None # need to re-compute
if isSparse == 0:
self._Memory.addRow(thresholdedInput)
else:
self._Memory.addRowNZ(inputPattern, [1]*len(inputPattern))
self._numPatterns += 1
self._categoryList.append(int(inputCategory))
self._addPartitionId(self._numPatterns-1, partitionId)
if self.fixedCapacity:
self._categoryRecencyList.append(rowID)
if self._numPatterns > self.maxStoredPatterns and \
self.maxStoredPatterns > 0:
leastRecentlyUsedPattern = numpy.argmin(self._categoryRecencyList)
self._Memory.deleteRow(leastRecentlyUsedPattern)
self._categoryList.pop(leastRecentlyUsedPattern)
self._categoryRecencyList.pop(leastRecentlyUsedPattern)
self._numPatterns -= 1
if self.numSVDDims is not None and self.numSVDSamples is not None \
and self._numPatterns == self.numSVDSamples:
self.computeSVD()
return self._numPatterns
def getOverlaps(self, inputPattern):
"""
Return the degree of overlap between an input pattern and each category
stored in the classifier. The overlap is computed by computing:
.. code-block:: python
logical_and(inputPattern != 0, trainingPattern != 0).sum()
:param inputPattern: pattern to check overlap of
:returns: (overlaps, categories) Two numpy arrays of the same length, where:
* overlaps: an integer overlap amount for each category
* categories: category index for each element of overlaps
"""
assert self.useSparseMemory, "Not implemented yet for dense storage"
overlaps = self._Memory.rightVecSumAtNZ(inputPattern)
return (overlaps, self._categoryList)
def getDistances(self, inputPattern):
"""Return the distances between the input pattern and all other
stored patterns.
:param inputPattern: pattern to check distance with
:returns: (distances, categories) numpy arrays of the same length.
- overlaps: an integer overlap amount for each category
- categories: category index for each element of distances
"""
dist = self._getDistances(inputPattern)
return (dist, self._categoryList)
def infer(self, inputPattern, computeScores=True, overCategories=True,
partitionId=None):
"""Finds the category that best matches the input pattern. Returns the
winning category index as well as a distribution over all categories.
:param inputPattern: (list) A pattern to be classified
:param computeScores: NO EFFECT
:param overCategories: NO EFFECT
:param partitionId: (int) If provided, all training vectors with partitionId
equal to that of the input pattern are ignored.
For example, this may be used to perform k-fold cross validation
without repopulating the classifier. First partition all the data into
k equal partitions numbered 0, 1, 2, ... and then call learn() for each
vector passing in its partitionId. Then, during inference, by passing
in the partition ID in the call to infer(), all other vectors with the
same partitionId are ignored simulating the effect of repopulating the
classifier while ommitting the training vectors in the same partition.
:returns: 4-tuple with these keys:
- ``winner``: The category with the greatest number of nearest neighbors
within the kth nearest neighbors. If the inferenceResult contains no
neighbors, the value of winner is None. This can happen, for example,
in cases of exact matching, if there are no stored vectors, or if
minSparsity is not met.
- ``inferenceResult``: A list of length numCategories, each entry contains
the number of neighbors within the top k neighbors that are in that
category.
- ``dist``: A list of length numPrototypes. Each entry is the distance
from the unknown to that prototype. All distances are between 0.0 and
1.0.
- ``categoryDist``: A list of length numCategories. Each entry is the
distance from the unknown to the nearest prototype of
that category. All distances are between 0 and 1.0.
"""
# Calculate sparsity. If sparsity is too low, we do not want to run
# inference with this vector
sparsity = 0.0
if self.minSparsity > 0.0:
sparsity = ( float(len(inputPattern.nonzero()[0])) /
len(inputPattern) )
if len(self._categoryList) == 0 or sparsity < self.minSparsity:
# No categories learned yet; i.e. first inference w/ online learning or
# insufficient sparsity
winner = None
inferenceResult = numpy.zeros(1)
dist = numpy.ones(1)
categoryDist = numpy.ones(1)
else:
maxCategoryIdx = max(self._categoryList)
inferenceResult = numpy.zeros(maxCategoryIdx+1)
dist = self._getDistances(inputPattern, partitionId=partitionId)
validVectorCount = len(self._categoryList) - self._categoryList.count(-1)
# Loop through the indices of the nearest neighbors.
if self.exact:
# Is there an exact match in the distances?
exactMatches = numpy.where(dist<0.00001)[0]
if len(exactMatches) > 0:
for i in exactMatches[:min(self.k, validVectorCount)]:
inferenceResult[self._categoryList[i]] += 1.0
else:
sorted = dist.argsort()
for j in sorted[:min(self.k, validVectorCount)]:
inferenceResult[self._categoryList[j]] += 1.0
# Prepare inference results.
if inferenceResult.any():
winner = inferenceResult.argmax()
inferenceResult /= inferenceResult.sum()
else:
winner = None
categoryDist = min_score_per_category(maxCategoryIdx,
self._categoryList, dist)
categoryDist.clip(0, 1.0, categoryDist)
if self.verbosity >= 1:
print("%s infer:" % (g_debugPrefix))
print(" active inputs:", _labeledInput(inputPattern,
cellsPerCol=self.cellsPerCol))
print(" winner category:", winner)
print(" pct neighbors of each category:", inferenceResult)
print(" dist of each prototype:", dist)
print(" dist of each category:", categoryDist)
result = (winner, inferenceResult, dist, categoryDist)
return result
def getClosest(self, inputPattern, topKCategories=3):
"""Returns the index of the pattern that is closest to inputPattern,
the distances of all patterns to inputPattern, and the indices of the k
closest categories.
"""
inferenceResult = numpy.zeros(max(self._categoryList)+1)
dist = self._getDistances(inputPattern)
sorted = dist.argsort()
validVectorCount = len(self._categoryList) - self._categoryList.count(-1)
for j in sorted[:min(self.k, validVectorCount)]:
inferenceResult[self._categoryList[j]] += 1.0
winner = inferenceResult.argmax()
topNCats = []
for i in range(topKCategories):
topNCats.append((self._categoryList[sorted[i]], dist[sorted[i]] ))
return winner, dist, topNCats
def closestTrainingPattern(self, inputPattern, cat):
"""Returns the closest training pattern to inputPattern that belongs to
category "cat".
:param inputPattern: The pattern whose closest neighbor is sought
:param cat: The required category of closest neighbor
:returns: A dense version of the closest training pattern, or None if no
such patterns exist
"""
dist = self._getDistances(inputPattern)
sorted = dist.argsort()
for patIdx in sorted:
patternCat = self._categoryList[patIdx]
# If closest pattern belongs to desired category, return it
if patternCat == cat:
if self.useSparseMemory:
closestPattern = self._Memory.getRow(int(patIdx))
else:
closestPattern = self._M[patIdx]
return closestPattern
# No patterns were found!
return None
def closestOtherTrainingPattern(self, inputPattern, cat):
"""Return the closest training pattern that is *not* of the given
category "cat".
:param inputPattern: The pattern whose closest neighbor is sought
:param cat: Training patterns of this category will be ignored no matter
their distance to inputPattern
:returns: A dense version of the closest training pattern, or None if no
such patterns exist
"""
dist = self._getDistances(inputPattern)
sorted = dist.argsort()
for patIdx in sorted:
patternCat = self._categoryList[patIdx]
# If closest pattern does not belong to specified category, return it
if patternCat != cat:
if self.useSparseMemory:
closestPattern = self._Memory.getRow(int(patIdx))
else:
closestPattern = self._M[patIdx]
return closestPattern
# No patterns were found!
return None
def getPattern(self, idx, sparseBinaryForm=False, cat=None):
"""Gets a training pattern either by index or category number.
:param idx: Index of the training pattern
:param sparseBinaryForm: If true, returns a list of the indices of the
non-zero bits in the training pattern
:param cat: If not None, get the first pattern belonging to category cat. If
this is specified, idx must be None.
:returns: The training pattern with specified index
"""
if cat is not None:
assert idx is None
idx = self._categoryList.index(cat)
if not self.useSparseMemory:
pattern = self._Memory[idx]
if sparseBinaryForm:
pattern = pattern.nonzero()[0]
else:
(nz, values) = self._Memory.rowNonZeros(idx)
if not sparseBinaryForm:
pattern = numpy.zeros(self._Memory.nCols())
numpy.put(pattern, nz, 1)
else:
pattern = nz
return pattern
def getPartitionId(self, i):
"""
Gets the partition id given an index.
:param i: index of partition
:returns: the partition id associated with pattern i. Returns None if no id
is associated with it.
"""
if (i < 0) or (i >= self._numPatterns):
raise RuntimeError("index out of bounds")
partitionId = self._partitionIdList[i]
if partitionId == numpy.inf:
return None
else:
return partitionId
def getPartitionIdList(self):
"""
:returns: a list of complete partition id objects
"""
return self._partitionIdList
def getNumPartitionIds(self):
"""
:returns: the number of unique partition Ids stored.
"""
return len(self._partitionIdMap)
def getPartitionIdKeys(self):
"""
:returns: a list containing unique (non-None) partition Ids (just the keys)
"""
return list(self._partitionIdMap.keys())
def getPatternIndicesWithPartitionId(self, partitionId):
"""
:returns: a list of pattern indices corresponding to this partitionId.
Return an empty list if there are none.
"""
return self._partitionIdMap.get(partitionId, [])
def _addPartitionId(self, index, partitionId=None):
"""
Adds partition id for pattern index
"""
if partitionId is None:
self._partitionIdList.append(numpy.inf)
else:
self._partitionIdList.append(partitionId)
indices = self._partitionIdMap.get(partitionId, [])
indices.append(index)
self._partitionIdMap[partitionId] = indices
def _rebuildPartitionIdMap(self, partitionIdList):
"""
Rebuilds the partition Id map using the given partitionIdList
"""
self._partitionIdMap = {}
for row, partitionId in enumerate(partitionIdList):
indices = self._partitionIdMap.get(partitionId, [])
indices.append(row)
self._partitionIdMap[partitionId] = indices
def _calcDistance(self, inputPattern, distanceNorm=None):
"""Calculate the distances from inputPattern to all stored patterns. All
distances are between 0.0 and 1.0
:param inputPattern The pattern from which distances to all other patterns
are calculated
:param distanceNorm Degree of the distance norm
"""
if distanceNorm is None:
distanceNorm = self.distanceNorm
# Sparse memory
if self.useSparseMemory:
if self._protoSizes is None:
self._protoSizes = self._Memory.rowSums()
overlapsWithProtos = self._Memory.rightVecSumAtNZ(inputPattern)
inputPatternSum = inputPattern.sum()
if self.distanceMethod == "rawOverlap":
dist = inputPattern.sum() - overlapsWithProtos
elif self.distanceMethod == "pctOverlapOfInput":
dist = inputPatternSum - overlapsWithProtos
if inputPatternSum > 0:
dist /= inputPatternSum
elif self.distanceMethod == "pctOverlapOfProto":
overlapsWithProtos /= self._protoSizes
dist = 1.0 - overlapsWithProtos
elif self.distanceMethod == "pctOverlapOfLarger":
maxVal = numpy.maximum(self._protoSizes, inputPatternSum)
if maxVal.all() > 0:
overlapsWithProtos /= maxVal
dist = 1.0 - overlapsWithProtos
elif self.distanceMethod == "norm":
dist = self._Memory.vecLpDist(self.distanceNorm, inputPattern)
distMax = dist.max()
if distMax > 0:
dist /= distMax
else:
raise RuntimeError("Unimplemented distance method %s" %
self.distanceMethod)
# Dense memory
else:
if self.distanceMethod == "norm":
dist = numpy.power(numpy.abs(self._M - inputPattern), self.distanceNorm)
dist = dist.sum(1)
dist = numpy.power(dist, 1.0/self.distanceNorm)
dist /= dist.max()
else:
raise RuntimeError ("Not implemented yet for dense storage....")
return dist
def _getDistances(self, inputPattern, partitionId=None):
"""Return the distances from inputPattern to all stored patterns.
:param inputPattern The pattern from which distances to all other patterns
are returned
:param partitionId If provided, ignore all training vectors with this
partitionId.
"""
if not self._finishedLearning:
self.finishLearning()
self._finishedLearning = True
if self._vt is not None and len(self._vt) > 0:
inputPattern = numpy.dot(self._vt, inputPattern - self._mean)
sparseInput = self._sparsifyVector(inputPattern)
# Compute distances
dist = self._calcDistance(sparseInput)
# Invalidate results where category is -1
if self._specificIndexTraining:
dist[numpy.array(self._categoryList) == -1] = numpy.inf
# Ignore vectors with this partition id by setting their distances to inf
if partitionId is not None:
dist[self._partitionIdMap.get(partitionId, [])] = numpy.inf
return dist
def finishLearning(self):
"""
Used for batch scenarios. This method needs to be called between learning
and inference.
"""
if self.numSVDDims is not None and self._vt is None:
self.computeSVD()
def computeSVD(self, numSVDSamples=None, finalize=True):
"""
Compute the singular value decomposition (SVD). The SVD is a factorization
of a real or complex matrix. It factors the matrix `a` as
`u * np.diag(s) * v`, where `u` and `v` are unitary and `s` is a 1-d array
of `a`'s singular values.
**Reason for computing the SVD:**
There are cases where you want to feed a lot of vectors to the
KNNClassifier. However, this can be slow. You can speed up training by (1)
computing the SVD of the input patterns which will give you the
eigenvectors, (2) only keeping a fraction of the eigenvectors, and (3)
projecting the input patterns onto the remaining eigenvectors.
Note that all input patterns are projected onto the eigenvectors in the same
fashion. Keeping only the highest eigenvectors increases training
performance since it reduces the dimensionality of the input.
:param numSVDSamples: (int) the number of samples to use for the SVD
computation.
:param finalize: (bool) whether to apply SVD to the input patterns.
:returns: (array) The singular values for every matrix, sorted in
descending order.
"""
if numSVDSamples is None:
numSVDSamples = self._numPatterns
if not self.useSparseMemory:
self._a = self._Memory[:self._numPatterns]
else:
self._a = self._Memory.toDense()[:self._numPatterns]
self._mean = numpy.mean(self._a, axis=0)
self._a -= self._mean
u,self._s,self._vt = numpy.linalg.svd(self._a[:numSVDSamples])
if finalize:
self._finalizeSVD()
return self._s
def getAdaptiveSVDDims(self, singularValues, fractionOfMax=0.001):
"""
Compute the number of eigenvectors (singularValues) to keep.
:param singularValues:
:param fractionOfMax:
:return:
"""
v = singularValues/singularValues[0]
idx = numpy.where(v<fractionOfMax)[0]
if len(idx):
print("Number of PCA dimensions chosen: ", idx[0], "out of ", len(v))
return idx[0]
else:
print("Number of PCA dimensions chosen: ", len(v)-1, "out of ", len(v))
return len(v)-1
def _finalizeSVD(self, numSVDDims=None):
"""
Called by finalizeLearning(). This will project all the patterns onto the
SVD eigenvectors.
:param numSVDDims: (int) number of egeinvectors used for projection.
:return:
"""
if numSVDDims is not None:
self.numSVDDims = numSVDDims
if self.numSVDDims=="adaptive":
if self.fractionOfMax is not None:
self.numSVDDims = self.getAdaptiveSVDDims(self._s, self.fractionOfMax)
else:
self.numSVDDims = self.getAdaptiveSVDDims(self._s)
if self._vt.shape[0] < self.numSVDDims:
print("******************************************************************")
print ("Warning: The requested number of PCA dimensions is more than "
"the number of pattern dimensions.")
print("Setting numSVDDims = ", self._vt.shape[0])
print("******************************************************************")
self.numSVDDims = self._vt.shape[0]
self._vt = self._vt[:self.numSVDDims]
# Added when svd is not able to decompose vectors - uses raw spare vectors
if len(self._vt) == 0:
return
self._Memory = numpy.zeros((self._numPatterns,self.numSVDDims))
self._M = self._Memory
self.useSparseMemory = False
for i in range(self._numPatterns):
self._Memory[i] = numpy.dot(self._vt, self._a[i])
self._a = None
def remapCategories(self, mapping):
"""Change the category indices.
Used by the Network Builder to keep the category indices in sync with the
ImageSensor categoryInfo when the user renames or removes categories.
:param mapping: List of new category indices. For example, mapping=[2,0,1]
would change all vectors of category 0 to be category 2, category 1 to
0, and category 2 to 1
"""
categoryArray = numpy.array(self._categoryList)
newCategoryArray = numpy.zeros(categoryArray.shape[0])
newCategoryArray.fill(-1)
for i in range(len(mapping)):
newCategoryArray[categoryArray==i] = mapping[i]
self._categoryList = list(newCategoryArray)
def setCategoryOfVectors(self, vectorIndices, categoryIndices):
"""Change the category associated with this vector(s).
Used by the Network Builder to move vectors between categories, to enable
categories, and to invalidate vectors by setting the category to -1.
:param vectorIndices: Single index or list of indices
:param categoryIndices: Single index or list of indices. Can also be a
single index when vectorIndices is a list, in which case the same
category will be used for all vectors
"""
if not hasattr(vectorIndices, "__iter__"):
vectorIndices = [vectorIndices]
categoryIndices = [categoryIndices]
elif not hasattr(categoryIndices, "__iter__"):
categoryIndices = [categoryIndices] * len(vectorIndices)
for i in range(len(vectorIndices)):
vectorIndex = vectorIndices[i]
categoryIndex = categoryIndices[i]
# Out-of-bounds is not an error, because the KNN may not have seen the
# vector yet
if vectorIndex < len(self._categoryList):
self._categoryList[vectorIndex] = categoryIndex
@staticmethod
def getSchema():
return KNNClassifierProto
@classmethod
def read(cls, proto):
if proto.version != KNNCLASSIFIER_VERSION:
raise RuntimeError("Invalid KNNClassifier Version")
knn = object.__new__(cls)
knn.version = proto.version
knn.k = proto.k
knn.exact = proto.exact
knn.distanceNorm = proto.distanceNorm
knn.distanceMethod = proto.distanceMethod
knn.distThreshold = proto.distThreshold
knn.doBinarization = proto.doBinarization
knn.binarizationThreshold = proto.binarizationThreshold
knn.useSparseMemory = proto.useSparseMemory
knn.sparseThreshold = proto.sparseThreshold
knn.relativeThreshold = proto.relativeThreshold
knn.numWinners = proto.numWinners
knn.numSVDSamples = proto.numSVDSamples
knn.numSVDDims = proto.numSVDDims
knn.fractionOfMax = proto.fractionOfMax
knn.verbosity = proto.verbosity
knn.maxStoredPatterns = proto.maxStoredPatterns
knn.replaceDuplicates = proto.replaceDuplicates
knn.cellsPerCol = proto.cellsPerCol
knn.minSparsity = proto.minSparsity
if knn.numSVDDims == "adaptive":
knn._adaptiveSVDDims = True
else:
knn._adaptiveSVDDims = False
# Read private state
knn.clear()
if proto.memory is not None:
which = proto.memory.which()
if which == "ndarray":
knn._Memory = numpy.array(proto.memory.ndarray, dtype=numpy.float64)
elif which == "nearestNeighbor":
knn._Memory = NearestNeighbor()
knn._Memory.read(proto.memory.nearestNeighbor)
knn._numPatterns = proto.numPatterns
if proto.m is not None:
knn._M = numpy.array(proto.m, dtype=numpy.float64)
if proto.categoryList is not None:
knn._categoryList = list(proto.categoryList)
if proto.partitionIdList is not None:
knn._partitionIdList = list(proto.partitionIdList)
knn._rebuildPartitionIdMap(knn._partitionIdList)
knn._iterationIdx = proto.iterationIdx
knn._finishedLearning = proto.finishedLearning
if proto.s is not None:
knn._s = numpy.array(proto.s, dtype=numpy.float32)
if proto.vt is not None:
knn._vt = numpy.array(proto.vt, dtype=numpy.float32)
if proto.mean is not None:
knn._mean = numpy.array(proto.mean, dtype=numpy.float32)
return knn
def write(self, proto):
proto.version = self.version
proto.k = self.k
proto.exact = bool(self.exact)
proto.distanceNorm = self.distanceNorm
proto.distanceMethod = self.distanceMethod
proto.distThreshold = self.distThreshold
proto.doBinarization = bool(self.doBinarization)
proto.binarizationThreshold = self.binarizationThreshold
proto.useSparseMemory = bool(self.useSparseMemory)
proto.sparseThreshold = self.sparseThreshold
proto.relativeThreshold = bool(self.relativeThreshold)
proto.numWinners = self.numWinners
proto.verbosity = self.verbosity
proto.maxStoredPatterns = self.maxStoredPatterns
proto.replaceDuplicates = bool(self.replaceDuplicates)
proto.cellsPerCol = self.cellsPerCol
proto.minSparsity = self.minSparsity
# Write private state
if self._Memory is not None:
if isinstance(self._Memory, numpy.ndarray):
proto.memory.ndarray = self._Memory.tolist()
else:
proto.memory.init("nearestNeighbor")
self._Memory.write(proto.memory.nearestNeighbor)
proto.numPatterns = self._numPatterns
if self._M is not None:
proto.m = self._M.tolist()
if self._categoryList is not None:
proto.categoryList = self._categoryList
if self._partitionIdList is not None:
proto.partitionIdList = self._partitionIdList
proto.finishedLearning = bool(self._finishedLearning)
proto.iterationIdx = self._iterationIdx
if self._s is not None:
proto.s = self._s.tolist()
if self._vt is not None:
proto.vt = self._vt.tolist()
if self._mean is not None:
proto.mean = self._mean.tolist()
def __getstate__(self):
"""Return serializable state.
This function will return a version of the __dict__.
"""
state = self.__dict__.copy()
return state
def __setstate__(self, state):
"""Set the state of this object from a serialized state."""
if "version" not in state:
pass
elif state["version"] == 1:
pass
elif state["version"] == 2:
raise RuntimeError("Invalid deserialization of invalid KNNClassifier"
"Version")
# Backward compatibility
if "_partitionIdArray" in state:
state.pop("_partitionIdArray")
if "minSparsity" not in state:
state["minSparsity"] = 0.0
self.__dict__.update(state)
# Backward compatibility
if "_partitionIdMap" not in state:
self._rebuildPartitionIdMap(self._partitionIdList)
# Set to new version
self.version = KNNCLASSIFIER_VERSION
class KNNClassifier(Serializable):
"""
This class implements NuPIC's k Nearest Neighbor Classifier. KNN is very
useful as a basic classifier for many situations. This implementation contains
many enhancements that are useful for HTM experiments. These enhancements
include an optimized C++ class for sparse vectors, support for continuous
online learning, support for various distance methods (including Lp-norm and
raw overlap), support for performing SVD on the input vectors (very useful for
large vectors), support for a fixed-size KNN, and a mechanism to store custom
ID's for each vector.
:param k: (int) The number of nearest neighbors used in the classification
of patterns. Must be odd.
:param exact: (boolean) If true, patterns must match exactly when assigning
class labels
:param distanceNorm: (int) When distance method is "norm", this specifies
the p value of the Lp-norm
:param distanceMethod: (string) The method used to compute distance between
input patterns and prototype patterns. The possible options are:
- ``norm``: When distanceNorm is 2, this is the euclidean distance,
When distanceNorm is 1, this is the manhattan distance
In general: sum(abs(x-proto) ^ distanceNorm) ^ (1/distanceNorm)
The distances are normalized such that farthest prototype from
a given input is 1.0.
- ``rawOverlap``: Only appropriate when inputs are binary. This computes:
(width of the input) - (# bits of overlap between input
and prototype).
- ``pctOverlapOfInput``: Only appropriate for binary inputs. This computes
1.0 - (# bits overlap between input and prototype) /
(# ON bits in input)
- ``pctOverlapOfProto``: Only appropriate for binary inputs. This computes
1.0 - (# bits overlap between input and prototype) /
(# ON bits in prototype)
- ``pctOverlapOfLarger``: Only appropriate for binary inputs. This computes
1.0 - (# bits overlap between input and prototype) /
max(# ON bits in input, # ON bits in prototype)
:param distThreshold: (float) A threshold on the distance between learned
patterns and a new pattern proposed to be learned. The distance must be
greater than this threshold in order for the new pattern to be added to
the classifier's memory.
:param doBinarization: (boolean) If True, then scalar inputs will be
binarized.
:param binarizationThreshold: (float) If doBinarization is True, this
specifies the threshold for the binarization of inputs
:param useSparseMemory: (boolean) If True, classifier will use a sparse
memory matrix
:param sparseThreshold: (float) If useSparseMemory is True, input variables
whose absolute values are less than this threshold will be stored as
zero
:param relativeThreshold: (boolean) Flag specifying whether to multiply
sparseThreshold by max value in input
:param numWinners: (int) Number of elements of the input that are stored. If
0, all elements are stored
:param numSVDSamples: (int) Number of samples the must occur before a SVD
(Singular Value Decomposition) transformation will be performed. If 0,
the transformation will never be performed
:param numSVDDims: (string) Controls dimensions kept after SVD
transformation. If "adaptive", the number is chosen automatically
:param fractionOfMax: (float) If numSVDDims is "adaptive", this controls the
smallest singular value that is retained as a fraction of the largest
singular value
:param verbosity: (int) Console verbosity level where 0 is no output and
larger integers provide increasing levels of verbosity
:param maxStoredPatterns: (int) Limits the maximum number of the training
patterns stored. When KNN learns in a fixed capacity mode, the unused
patterns are deleted once the number of stored patterns is greater than
maxStoredPatterns. A value of -1 is no limit
:param replaceDuplicates: (bool) A boolean flag that determines whether,
during learning, the classifier replaces duplicates that match exactly,
even if distThreshold is 0. Should be True for online learning
:param cellsPerCol: (int) If >= 1, input is assumed to be organized into
columns, in the same manner as the temporal memory AND whenever a new
prototype is stored, only the start cell (first cell) is stored in any
bursting column
:param minSparsity: (float) If useSparseMemory is set, only vectors with
sparsity >= minSparsity will be stored during learning. A value of 0.0
implies all vectors will be stored. A value of 0.1 implies only vectors
with at least 10% sparsity will be stored
"""
def __init__(self, k=1,
exact=False,
distanceNorm=2.0,
distanceMethod="norm",
distThreshold=0,
doBinarization=False,
binarizationThreshold=0.5,
useSparseMemory=True,
sparseThreshold=0.1,
relativeThreshold=False,
numWinners=0,
numSVDSamples=None,
numSVDDims=None,
fractionOfMax=None,
verbosity=0,
maxStoredPatterns=-1,
replaceDuplicates=False,
cellsPerCol=0,
minSparsity=0.0):
self.version = KNNCLASSIFIER_VERSION
self.k = k
self.exact = exact
self.distanceNorm = distanceNorm
assert (distanceMethod in ("norm", "rawOverlap", "pctOverlapOfLarger",
"pctOverlapOfProto", "pctOverlapOfInput"))
self.distanceMethod = distanceMethod
self.distThreshold = distThreshold
self.doBinarization = doBinarization
self.binarizationThreshold = binarizationThreshold
self.useSparseMemory = useSparseMemory
self.sparseThreshold = sparseThreshold
self.relativeThreshold = relativeThreshold
self.numWinners = numWinners
self.numSVDSamples = numSVDSamples
self.numSVDDims = numSVDDims
self.fractionOfMax = fractionOfMax
if self.numSVDDims=="adaptive":
self._adaptiveSVDDims = True
else:
self._adaptiveSVDDims = False
self.verbosity = verbosity
self.replaceDuplicates = replaceDuplicates
self.cellsPerCol = cellsPerCol
self.maxStoredPatterns = maxStoredPatterns
self.minSparsity = minSparsity
self.clear()
def clear(self):
"""Clears the state of the KNNClassifier."""
self._Memory = None
self._numPatterns = 0
self._M = None
self._categoryList = []
self._partitionIdList = []
self._partitionIdMap = {}
self._finishedLearning = False
self._iterationIdx = -1
# Fixed capacity KNN
if self.maxStoredPatterns > 0:
assert self.useSparseMemory, ("Fixed capacity KNN is implemented only "
"in the sparse memory mode")
self.fixedCapacity = True
self._categoryRecencyList = []
else:
self.fixedCapacity = False
# Cached value of the store prototype sizes
self._protoSizes = None
# Used by PCA
self._s = None
self._vt = None
self._nc = None
self._mean = None
# Used by Network Builder
self._specificIndexTraining = False
self._nextTrainingIndices = None
def _doubleMemoryNumRows(self):
m = 2 * self._Memory.shape[0]
n = self._Memory.shape[1]
self._Memory = numpy.resize(self._Memory,(m,n))
self._M = self._Memory[:self._numPatterns]
def _sparsifyVector(self, inputPattern, doWinners=False):
# Do sparsification, using a relative or absolute threshold
if not self.relativeThreshold:
inputPattern = inputPattern*(abs(inputPattern) > self.sparseThreshold)
elif self.sparseThreshold > 0:
inputPattern = inputPattern * \
(abs(inputPattern) > (self.sparseThreshold * abs(inputPattern).max()))
# Do winner-take-all
if doWinners:
if (self.numWinners>0) and (self.numWinners < (inputPattern > 0).sum()):
sparseInput = numpy.zeros(inputPattern.shape)
# Don't consider strongly negative numbers as winners.
sorted = inputPattern.argsort()[0:self.numWinners]
sparseInput[sorted] += inputPattern[sorted]
inputPattern = sparseInput
# Do binarization
if self.doBinarization:
# Don't binarize negative numbers to positive 1.
inputPattern = (inputPattern > self.binarizationThreshold).astype(float)
return inputPattern
def prototypeSetCategory(self, idToCategorize, newCategory):
"""
Allows ids to be assigned a category and subsequently enables users to use:
- :meth:`~.KNNClassifier.KNNClassifier.removeCategory`
- :meth:`~.KNNClassifier.KNNClassifier.closestTrainingPattern`
- :meth:`~.KNNClassifier.KNNClassifier.closestOtherTrainingPattern`
"""
if idToCategorize not in self._categoryRecencyList:
return
recordIndex = self._categoryRecencyList.index(idToCategorize)
self._categoryList[recordIndex] = newCategory
def removeIds(self, idsToRemove):
"""
There are two caveats. First, this is a potentially slow operation. Second,
pattern indices will shift if patterns before them are removed.
:param idsToRemove: A list of row indices to remove.
"""
# Form a list of all categories to remove
rowsToRemove = [k for k, rowID in enumerate(self._categoryRecencyList) \
if rowID in idsToRemove]
# Remove rows from the classifier
self._removeRows(rowsToRemove)
def removeCategory(self, categoryToRemove):
"""
There are two caveats. First, this is a potentially slow operation. Second,
pattern indices will shift if patterns before them are removed.
:param categoryToRemove: Category label to remove
"""
removedRows = 0
if self._Memory is None:
return removedRows
# The internal category indices are stored in float
# format, so we should compare with a float
catToRemove = float(categoryToRemove)
# Form a list of all categories to remove
rowsToRemove = [k for k, catID in enumerate(self._categoryList) \
if catID == catToRemove]
# Remove rows from the classifier
self._removeRows(rowsToRemove)
assert catToRemove not in self._categoryList
def _removeRows(self, rowsToRemove):
"""
A list of row indices to remove. There are two caveats. First, this is
a potentially slow operation. Second, pattern indices will shift if
patterns before them are removed.
"""
# Form a numpy array of row indices to be removed
removalArray = numpy.array(rowsToRemove)
# Remove categories
self._categoryList = numpy.delete(numpy.array(self._categoryList),
removalArray).tolist()
if self.fixedCapacity:
self._categoryRecencyList = numpy.delete(
numpy.array(self._categoryRecencyList), removalArray).tolist()
# Remove the partition ID, if any for these rows and rebuild the id map.
for row in reversed(rowsToRemove): # Go backwards
# Remove these patterns from partitionList
self._partitionIdList.pop(row)
self._rebuildPartitionIdMap(self._partitionIdList)
# Remove actual patterns
if self.useSparseMemory:
# Delete backwards
for rowIndex in rowsToRemove[::-1]:
self._Memory.deleteRow(rowIndex)
else:
self._M = numpy.delete(self._M, removalArray, 0)
numRemoved = len(rowsToRemove)
# Sanity checks
numRowsExpected = self._numPatterns - numRemoved
if self.useSparseMemory:
if self._Memory is not None:
assert self._Memory.nRows() == numRowsExpected
else:
assert self._M.shape[0] == numRowsExpected
assert len(self._categoryList) == numRowsExpected
self._numPatterns -= numRemoved
return numRemoved
def doIteration(self):
"""
Utility method to increment the iteration index. Intended for models that
don't learn each timestep.
"""
self._iterationIdx += 1
def learn(self, inputPattern, inputCategory, partitionId=None, isSparse=0,
rowID=None):
"""
Train the classifier to associate specified input pattern with a
particular category.
:param inputPattern: (list) The pattern to be assigned a category. If
isSparse is 0, this should be a dense array (both ON and OFF bits
present). Otherwise, if isSparse > 0, this should be a list of the
indices of the non-zero bits in sorted order
:param inputCategory: (int) The category to be associated to the training
pattern
:param partitionId: (int) partitionID allows you to associate an id with each
input vector. It can be used to associate input patterns stored in the
classifier with an external id. This can be useful for debugging or
visualizing. Another use case is to ignore vectors with a specific id
during inference (see description of infer() for details). There can be
at most one partitionId per stored pattern (i.e. if two patterns are
within distThreshold, only the first partitionId will be stored). This
is an optional parameter.
:param isSparse: (int) 0 if the input pattern is a dense representation.
When the input pattern is a list of non-zero indices, then isSparse
is the number of total bits (n). E.g. for the dense array
[0, 1, 1, 0, 0, 1], isSparse should be `0`. For the equivalent sparse
representation [1, 2, 5] (which specifies the indices of active bits),
isSparse should be `6`, which is the total number of bits in the input
space.
:param rowID: (int) UNKNOWN
:returns: The number of patterns currently stored in the classifier
"""
if self.verbosity >= 1:
print "%s learn:" % g_debugPrefix
print " category:", int(inputCategory)
print " active inputs:", _labeledInput(inputPattern,
cellsPerCol=self.cellsPerCol)
if isSparse > 0:
assert all(inputPattern[i] <= inputPattern[i+1]
for i in xrange(len(inputPattern)-1)), \
"Sparse inputPattern must be sorted."
assert all(bit < isSparse for bit in inputPattern), \
("Sparse inputPattern must not index outside the dense "
"representation's bounds.")
if rowID is None:
rowID = self._iterationIdx
# Dense vectors
if not self.useSparseMemory:
# Not supported
assert self.cellsPerCol == 0, "not implemented for dense vectors"
# If the input was given in sparse form, convert it to dense
if isSparse > 0:
denseInput = numpy.zeros(isSparse)
denseInput[inputPattern] = 1.0
inputPattern = denseInput
if self._specificIndexTraining and not self._nextTrainingIndices:
# Specific index mode without any index provided - skip training
return self._numPatterns
if self._Memory is None:
# Initialize memory with 100 rows and numPatterns = 0
inputWidth = len(inputPattern)
self._Memory = numpy.zeros((100,inputWidth))
self._numPatterns = 0
self._M = self._Memory[:self._numPatterns]
addRow = True
if self._vt is not None:
# Compute projection
inputPattern = numpy.dot(self._vt, inputPattern - self._mean)
if self.distThreshold > 0:
# Check if input is too close to an existing input to be accepted
dist = self._calcDistance(inputPattern)
minDist = dist.min()
addRow = (minDist >= self.distThreshold)
if addRow:
self._protoSizes = None # need to re-compute
if self._numPatterns == self._Memory.shape[0]:
# Double the size of the memory
self._doubleMemoryNumRows()
if not self._specificIndexTraining:
# Normal learning - append the new input vector
self._Memory[self._numPatterns] = inputPattern
self._numPatterns += 1
self._categoryList.append(int(inputCategory))
else:
# Specific index training mode - insert vector in specified slot
vectorIndex = self._nextTrainingIndices.pop(0)
while vectorIndex >= self._Memory.shape[0]:
self._doubleMemoryNumRows()
self._Memory[vectorIndex] = inputPattern
self._numPatterns = max(self._numPatterns, vectorIndex + 1)
if vectorIndex >= len(self._categoryList):
self._categoryList += [-1] * (vectorIndex -
len(self._categoryList) + 1)
self._categoryList[vectorIndex] = int(inputCategory)
# Set _M to the "active" part of _Memory
self._M = self._Memory[0:self._numPatterns]
self._addPartitionId(self._numPatterns-1, partitionId)
# Sparse vectors
else:
# If the input was given in sparse form, convert it to dense if necessary
if isSparse > 0 and (self._vt is not None or self.distThreshold > 0 \
or self.numSVDDims is not None or self.numSVDSamples is not None \
or self.numWinners > 0):
denseInput = numpy.zeros(isSparse)
denseInput[inputPattern] = 1.0
inputPattern = denseInput
isSparse = 0
# Get the input width
if isSparse > 0:
inputWidth = isSparse
else:
inputWidth = len(inputPattern)
# Allocate storage if this is the first training vector
if self._Memory is None:
self._Memory = NearestNeighbor(0, inputWidth)
# Support SVD if it is on
if self._vt is not None:
inputPattern = numpy.dot(self._vt, inputPattern - self._mean)
# Threshold the input, zeroing out entries that are too close to 0.
# This is only done if we are given a dense input.
if isSparse == 0:
thresholdedInput = self._sparsifyVector(inputPattern, True)
addRow = True
# If given the layout of the cells, then turn on the logic that stores
# only the start cell for bursting columns.
if self.cellsPerCol >= 1:
burstingCols = thresholdedInput.reshape(-1,
self.cellsPerCol).min(axis=1).nonzero()[0]
for col in burstingCols:
thresholdedInput[(col * self.cellsPerCol) + 1 :
(col * self.cellsPerCol) + self.cellsPerCol] = 0
# Don't learn entries that are too close to existing entries.
if self._Memory.nRows() > 0:
dist = None
# if this vector is a perfect match for one we already learned, then
# replace the category - it may have changed with online learning on.
if self.replaceDuplicates:
dist = self._calcDistance(thresholdedInput, distanceNorm=1)
if dist.min() == 0:
rowIdx = dist.argmin()
self._categoryList[rowIdx] = int(inputCategory)
if self.fixedCapacity:
self._categoryRecencyList[rowIdx] = rowID
addRow = False
# Don't add this vector if it matches closely with another we already
# added
if self.distThreshold > 0:
if dist is None or self.distanceNorm != 1:
dist = self._calcDistance(thresholdedInput)
minDist = dist.min()
addRow = (minDist >= self.distThreshold)
if not addRow:
if self.fixedCapacity:
rowIdx = dist.argmin()
self._categoryRecencyList[rowIdx] = rowID
# If sparsity is too low, we do not want to add this vector
if addRow and self.minSparsity > 0.0:
if isSparse==0:
sparsity = ( float(len(thresholdedInput.nonzero()[0])) /
len(thresholdedInput) )
else:
sparsity = float(len(inputPattern)) / isSparse
if sparsity < self.minSparsity:
addRow = False
# Add the new sparse vector to our storage
if addRow:
self._protoSizes = None # need to re-compute
if isSparse == 0:
self._Memory.addRow(thresholdedInput)
else:
self._Memory.addRowNZ(inputPattern, [1]*len(inputPattern))
self._numPatterns += 1
self._categoryList.append(int(inputCategory))
self._addPartitionId(self._numPatterns-1, partitionId)
if self.fixedCapacity:
self._categoryRecencyList.append(rowID)
if self._numPatterns > self.maxStoredPatterns and \
self.maxStoredPatterns > 0:
leastRecentlyUsedPattern = numpy.argmin(self._categoryRecencyList)
self._Memory.deleteRow(leastRecentlyUsedPattern)
self._categoryList.pop(leastRecentlyUsedPattern)
self._categoryRecencyList.pop(leastRecentlyUsedPattern)
self._numPatterns -= 1
if self.numSVDDims is not None and self.numSVDSamples is not None \
and self._numPatterns == self.numSVDSamples:
self.computeSVD()
return self._numPatterns
def getOverlaps(self, inputPattern):
"""
Return the degree of overlap between an input pattern and each category
stored in the classifier. The overlap is computed by computing:
.. code-block:: python
logical_and(inputPattern != 0, trainingPattern != 0).sum()
:param inputPattern: pattern to check overlap of
:returns: (overlaps, categories) Two numpy arrays of the same length, where:
* overlaps: an integer overlap amount for each category
* categories: category index for each element of overlaps
"""
assert self.useSparseMemory, "Not implemented yet for dense storage"
overlaps = self._Memory.rightVecSumAtNZ(inputPattern)
return (overlaps, self._categoryList)
def getDistances(self, inputPattern):
"""Return the distances between the input pattern and all other
stored patterns.
:param inputPattern: pattern to check distance with
:returns: (distances, categories) numpy arrays of the same length.
- overlaps: an integer overlap amount for each category
- categories: category index for each element of distances
"""
dist = self._getDistances(inputPattern)
return (dist, self._categoryList)
def infer(self, inputPattern, computeScores=True, overCategories=True,
partitionId=None):
"""Finds the category that best matches the input pattern. Returns the
winning category index as well as a distribution over all categories.
:param inputPattern: (list or array) The pattern to be classified. This
must be a dense representation of the array (e.g. [0, 0, 1, 1, 0, 1]).
:param computeScores: NO EFFECT
:param overCategories: NO EFFECT
:param partitionId: (int) If provided, all training vectors with partitionId
equal to that of the input pattern are ignored.
For example, this may be used to perform k-fold cross validation
without repopulating the classifier. First partition all the data into
k equal partitions numbered 0, 1, 2, ... and then call learn() for each
vector passing in its partitionId. Then, during inference, by passing
in the partition ID in the call to infer(), all other vectors with the
same partitionId are ignored simulating the effect of repopulating the
classifier while ommitting the training vectors in the same partition.
:returns: 4-tuple with these keys:
- ``winner``: The category with the greatest number of nearest neighbors
within the kth nearest neighbors. If the inferenceResult contains no
neighbors, the value of winner is None. This can happen, for example,
in cases of exact matching, if there are no stored vectors, or if
minSparsity is not met.
- ``inferenceResult``: A list of length numCategories, each entry contains
the number of neighbors within the top k neighbors that are in that
category.
- ``dist``: A list of length numPrototypes. Each entry is the distance
from the unknown to that prototype. All distances are between 0.0 and
1.0.
- ``categoryDist``: A list of length numCategories. Each entry is the
distance from the unknown to the nearest prototype of
that category. All distances are between 0 and 1.0.
"""
# Calculate sparsity. If sparsity is too low, we do not want to run
# inference with this vector
sparsity = 0.0
if self.minSparsity > 0.0:
sparsity = ( float(len(inputPattern.nonzero()[0])) /
len(inputPattern) )
if len(self._categoryList) == 0 or sparsity < self.minSparsity:
# No categories learned yet; i.e. first inference w/ online learning or
# insufficient sparsity
winner = None
inferenceResult = numpy.zeros(1)
dist = numpy.ones(1)
categoryDist = numpy.ones(1)
else:
maxCategoryIdx = max(self._categoryList)
inferenceResult = numpy.zeros(maxCategoryIdx+1)
dist = self._getDistances(inputPattern, partitionId=partitionId)
validVectorCount = len(self._categoryList) - self._categoryList.count(-1)
# Loop through the indices of the nearest neighbors.
if self.exact:
# Is there an exact match in the distances?
exactMatches = numpy.where(dist<0.00001)[0]
if len(exactMatches) > 0:
for i in exactMatches[:min(self.k, validVectorCount)]:
inferenceResult[self._categoryList[i]] += 1.0
else:
sorted = dist.argsort()
for j in sorted[:min(self.k, validVectorCount)]:
inferenceResult[self._categoryList[j]] += 1.0
# Prepare inference results.
if inferenceResult.any():
winner = inferenceResult.argmax()
inferenceResult /= inferenceResult.sum()
else:
winner = None
categoryDist = min_score_per_category(maxCategoryIdx,
self._categoryList, dist)
categoryDist.clip(0, 1.0, categoryDist)
if self.verbosity >= 1:
print "%s infer:" % (g_debugPrefix)
print " active inputs:", _labeledInput(inputPattern,
cellsPerCol=self.cellsPerCol)
print " winner category:", winner
print " pct neighbors of each category:", inferenceResult
print " dist of each prototype:", dist
print " dist of each category:", categoryDist
result = (winner, inferenceResult, dist, categoryDist)
return result
def getClosest(self, inputPattern, topKCategories=3):
"""Returns the index of the pattern that is closest to inputPattern,
the distances of all patterns to inputPattern, and the indices of the k
closest categories.
"""
inferenceResult = numpy.zeros(max(self._categoryList)+1)
dist = self._getDistances(inputPattern)
sorted = dist.argsort()
validVectorCount = len(self._categoryList) - self._categoryList.count(-1)
for j in sorted[:min(self.k, validVectorCount)]:
inferenceResult[self._categoryList[j]] += 1.0
winner = inferenceResult.argmax()
topNCats = []
for i in range(topKCategories):
topNCats.append((self._categoryList[sorted[i]], dist[sorted[i]] ))
return winner, dist, topNCats
def closestTrainingPattern(self, inputPattern, cat):
"""Returns the closest training pattern to inputPattern that belongs to
category "cat".
:param inputPattern: The pattern whose closest neighbor is sought
:param cat: The required category of closest neighbor
:returns: A dense version of the closest training pattern, or None if no
such patterns exist
"""
dist = self._getDistances(inputPattern)
sorted = dist.argsort()
for patIdx in sorted:
patternCat = self._categoryList[patIdx]
# If closest pattern belongs to desired category, return it
if patternCat == cat:
if self.useSparseMemory:
closestPattern = self._Memory.getRow(int(patIdx))
else:
closestPattern = self._M[patIdx]
return closestPattern
# No patterns were found!
return None
def closestOtherTrainingPattern(self, inputPattern, cat):
"""Return the closest training pattern that is *not* of the given
category "cat".
:param inputPattern: The pattern whose closest neighbor is sought
:param cat: Training patterns of this category will be ignored no matter
their distance to inputPattern
:returns: A dense version of the closest training pattern, or None if no
such patterns exist
"""
dist = self._getDistances(inputPattern)
sorted = dist.argsort()
for patIdx in sorted:
patternCat = self._categoryList[patIdx]
# If closest pattern does not belong to specified category, return it
if patternCat != cat:
if self.useSparseMemory:
closestPattern = self._Memory.getRow(int(patIdx))
else:
closestPattern = self._M[patIdx]
return closestPattern
# No patterns were found!
return None
def getPattern(self, idx, sparseBinaryForm=False, cat=None):
"""Gets a training pattern either by index or category number.
:param idx: Index of the training pattern
:param sparseBinaryForm: If true, returns a list of the indices of the
non-zero bits in the training pattern
:param cat: If not None, get the first pattern belonging to category cat. If
this is specified, idx must be None.
:returns: The training pattern with specified index
"""
if cat is not None:
assert idx is None
idx = self._categoryList.index(cat)
if not self.useSparseMemory:
pattern = self._Memory[idx]
if sparseBinaryForm:
pattern = pattern.nonzero()[0]
else:
(nz, values) = self._Memory.rowNonZeros(idx)
if not sparseBinaryForm:
pattern = numpy.zeros(self._Memory.nCols())
numpy.put(pattern, nz, 1)
else:
pattern = nz
return pattern
def getPartitionId(self, i):
"""
Gets the partition id given an index.
:param i: index of partition
:returns: the partition id associated with pattern i. Returns None if no id
is associated with it.
"""
if (i < 0) or (i >= self._numPatterns):
raise RuntimeError("index out of bounds")
partitionId = self._partitionIdList[i]
if partitionId == numpy.inf:
return None
else:
return partitionId
def getPartitionIdList(self):
"""
:returns: a list of complete partition id objects
"""
return self._partitionIdList
def getNumPartitionIds(self):
"""
:returns: the number of unique partition Ids stored.
"""
return len(self._partitionIdMap)
def getPartitionIdKeys(self):
"""
:returns: a list containing unique (non-None) partition Ids (just the keys)
"""
return self._partitionIdMap.keys()
def getPatternIndicesWithPartitionId(self, partitionId):
"""
:returns: a list of pattern indices corresponding to this partitionId.
Return an empty list if there are none.
"""
return self._partitionIdMap.get(partitionId, [])
def _addPartitionId(self, index, partitionId=None):
"""
Adds partition id for pattern index
"""
if partitionId is None:
self._partitionIdList.append(numpy.inf)
else:
self._partitionIdList.append(partitionId)
indices = self._partitionIdMap.get(partitionId, [])
indices.append(index)
self._partitionIdMap[partitionId] = indices
def _rebuildPartitionIdMap(self, partitionIdList):
"""
Rebuilds the partition Id map using the given partitionIdList
"""
self._partitionIdMap = {}
for row, partitionId in enumerate(partitionIdList):
indices = self._partitionIdMap.get(partitionId, [])
indices.append(row)
self._partitionIdMap[partitionId] = indices
def _calcDistance(self, inputPattern, distanceNorm=None):
"""Calculate the distances from inputPattern to all stored patterns. All
distances are between 0.0 and 1.0
:param inputPattern The pattern from which distances to all other patterns
are calculated
:param distanceNorm Degree of the distance norm
"""
if distanceNorm is None:
distanceNorm = self.distanceNorm
# Sparse memory
if self.useSparseMemory:
if self._protoSizes is None:
self._protoSizes = self._Memory.rowSums()
overlapsWithProtos = self._Memory.rightVecSumAtNZ(inputPattern)
inputPatternSum = inputPattern.sum()
if self.distanceMethod == "rawOverlap":
dist = inputPattern.sum() - overlapsWithProtos
elif self.distanceMethod == "pctOverlapOfInput":
dist = inputPatternSum - overlapsWithProtos
if inputPatternSum > 0:
dist /= inputPatternSum
elif self.distanceMethod == "pctOverlapOfProto":
overlapsWithProtos /= self._protoSizes
dist = 1.0 - overlapsWithProtos
elif self.distanceMethod == "pctOverlapOfLarger":
maxVal = numpy.maximum(self._protoSizes, inputPatternSum)
if maxVal.all() > 0:
overlapsWithProtos /= maxVal
dist = 1.0 - overlapsWithProtos
elif self.distanceMethod == "norm":
dist = self._Memory.vecLpDist(self.distanceNorm, inputPattern)
distMax = dist.max()
if distMax > 0:
dist /= distMax
else:
raise RuntimeError("Unimplemented distance method %s" %
self.distanceMethod)
# Dense memory
else:
if self.distanceMethod == "norm":
dist = numpy.power(numpy.abs(self._M - inputPattern), self.distanceNorm)
dist = dist.sum(1)
dist = numpy.power(dist, 1.0/self.distanceNorm)
dist /= dist.max()
else:
raise RuntimeError ("Not implemented yet for dense storage....")
return dist
def _getDistances(self, inputPattern, partitionId=None):
"""Return the distances from inputPattern to all stored patterns.
:param inputPattern The pattern from which distances to all other patterns
are returned
:param partitionId If provided, ignore all training vectors with this
partitionId.
"""
if not self._finishedLearning:
self.finishLearning()
self._finishedLearning = True
if self._vt is not None and len(self._vt) > 0:
inputPattern = numpy.dot(self._vt, inputPattern - self._mean)
sparseInput = self._sparsifyVector(inputPattern)
# Compute distances
dist = self._calcDistance(sparseInput)
# Invalidate results where category is -1
if self._specificIndexTraining:
dist[numpy.array(self._categoryList) == -1] = numpy.inf
# Ignore vectors with this partition id by setting their distances to inf
if partitionId is not None:
dist[self._partitionIdMap.get(partitionId, [])] = numpy.inf
return dist
def finishLearning(self):
"""
Used for batch scenarios. This method needs to be called between learning
and inference.
"""
if self.numSVDDims is not None and self._vt is None:
self.computeSVD()
def computeSVD(self, numSVDSamples=None, finalize=True):
"""
Compute the singular value decomposition (SVD). The SVD is a factorization
of a real or complex matrix. It factors the matrix `a` as
`u * np.diag(s) * v`, where `u` and `v` are unitary and `s` is a 1-d array
of `a`'s singular values.
**Reason for computing the SVD:**
There are cases where you want to feed a lot of vectors to the
KNNClassifier. However, this can be slow. You can speed up training by (1)
computing the SVD of the input patterns which will give you the
eigenvectors, (2) only keeping a fraction of the eigenvectors, and (3)
projecting the input patterns onto the remaining eigenvectors.
Note that all input patterns are projected onto the eigenvectors in the same
fashion. Keeping only the highest eigenvectors increases training
performance since it reduces the dimensionality of the input.
:param numSVDSamples: (int) the number of samples to use for the SVD
computation.
:param finalize: (bool) whether to apply SVD to the input patterns.
:returns: (array) The singular values for every matrix, sorted in
descending order.
"""
if numSVDSamples is None:
numSVDSamples = self._numPatterns
if not self.useSparseMemory:
self._a = self._Memory[:self._numPatterns]
else:
self._a = self._Memory.toDense()[:self._numPatterns]
self._mean = numpy.mean(self._a, axis=0)
self._a -= self._mean
u,self._s,self._vt = numpy.linalg.svd(self._a[:numSVDSamples])
if finalize:
self._finalizeSVD()
return self._s
def getAdaptiveSVDDims(self, singularValues, fractionOfMax=0.001):
"""
Compute the number of eigenvectors (singularValues) to keep.
:param singularValues:
:param fractionOfMax:
:return:
"""
v = singularValues/singularValues[0]
idx = numpy.where(v<fractionOfMax)[0]
if len(idx):
print "Number of PCA dimensions chosen: ", idx[0], "out of ", len(v)
return idx[0]
else:
print "Number of PCA dimensions chosen: ", len(v)-1, "out of ", len(v)
return len(v)-1
def _finalizeSVD(self, numSVDDims=None):
"""
Called by finalizeLearning(). This will project all the patterns onto the
SVD eigenvectors.
:param numSVDDims: (int) number of egeinvectors used for projection.
:return:
"""
if numSVDDims is not None:
self.numSVDDims = numSVDDims
if self.numSVDDims=="adaptive":
if self.fractionOfMax is not None:
self.numSVDDims = self.getAdaptiveSVDDims(self._s, self.fractionOfMax)
else:
self.numSVDDims = self.getAdaptiveSVDDims(self._s)
if self._vt.shape[0] < self.numSVDDims:
print "******************************************************************"
print ("Warning: The requested number of PCA dimensions is more than "
"the number of pattern dimensions.")
print "Setting numSVDDims = ", self._vt.shape[0]
print "******************************************************************"
self.numSVDDims = self._vt.shape[0]
self._vt = self._vt[:self.numSVDDims]
# Added when svd is not able to decompose vectors - uses raw spare vectors
if len(self._vt) == 0:
return
self._Memory = numpy.zeros((self._numPatterns,self.numSVDDims))
self._M = self._Memory
self.useSparseMemory = False
for i in range(self._numPatterns):
self._Memory[i] = numpy.dot(self._vt, self._a[i])
self._a = None
def remapCategories(self, mapping):
"""Change the category indices.
Used by the Network Builder to keep the category indices in sync with the
ImageSensor categoryInfo when the user renames or removes categories.
:param mapping: List of new category indices. For example, mapping=[2,0,1]
would change all vectors of category 0 to be category 2, category 1 to
0, and category 2 to 1
"""
categoryArray = numpy.array(self._categoryList)
newCategoryArray = numpy.zeros(categoryArray.shape[0])
newCategoryArray.fill(-1)
for i in xrange(len(mapping)):
newCategoryArray[categoryArray==i] = mapping[i]
self._categoryList = list(newCategoryArray)
def setCategoryOfVectors(self, vectorIndices, categoryIndices):
"""Change the category associated with this vector(s).
Used by the Network Builder to move vectors between categories, to enable
categories, and to invalidate vectors by setting the category to -1.
:param vectorIndices: Single index or list of indices
:param categoryIndices: Single index or list of indices. Can also be a
single index when vectorIndices is a list, in which case the same
category will be used for all vectors
"""
if not hasattr(vectorIndices, "__iter__"):
vectorIndices = [vectorIndices]
categoryIndices = [categoryIndices]
elif not hasattr(categoryIndices, "__iter__"):
categoryIndices = [categoryIndices] * len(vectorIndices)
for i in xrange(len(vectorIndices)):
vectorIndex = vectorIndices[i]
categoryIndex = categoryIndices[i]
# Out-of-bounds is not an error, because the KNN may not have seen the
# vector yet
if vectorIndex < len(self._categoryList):
self._categoryList[vectorIndex] = categoryIndex
@staticmethod
def getSchema():
return KNNClassifierProto
@classmethod
def read(cls, proto):
if proto.version != KNNCLASSIFIER_VERSION:
raise RuntimeError("Invalid KNNClassifier Version")
knn = object.__new__(cls)
knn.version = proto.version
knn.k = proto.k
knn.exact = proto.exact
knn.distanceNorm = proto.distanceNorm
knn.distanceMethod = proto.distanceMethod
knn.distThreshold = proto.distThreshold
knn.doBinarization = proto.doBinarization
knn.binarizationThreshold = proto.binarizationThreshold
knn.useSparseMemory = proto.useSparseMemory
knn.sparseThreshold = proto.sparseThreshold
knn.relativeThreshold = proto.relativeThreshold
knn.numWinners = proto.numWinners
knn.numSVDSamples = proto.numSVDSamples
knn.numSVDDims = proto.numSVDDims
knn.fractionOfMax = proto.fractionOfMax
knn.verbosity = proto.verbosity
knn.maxStoredPatterns = proto.maxStoredPatterns
knn.replaceDuplicates = proto.replaceDuplicates
knn.cellsPerCol = proto.cellsPerCol
knn.minSparsity = proto.minSparsity
if knn.numSVDDims == "adaptive":
knn._adaptiveSVDDims = True
else:
knn._adaptiveSVDDims = False
# Read private state
knn.clear()
if proto.memory is not None:
which = proto.memory.which()
if which == "ndarray":
knn._Memory = numpy.array(proto.memory.ndarray, dtype=numpy.float64)
elif which == "nearestNeighbor":
knn._Memory = NearestNeighbor()
knn._Memory.read(proto.memory.nearestNeighbor)
knn._numPatterns = proto.numPatterns
if proto.m is not None:
knn._M = numpy.array(proto.m, dtype=numpy.float64)
if proto.categoryList is not None:
knn._categoryList = list(proto.categoryList)
if proto.partitionIdList is not None:
knn._partitionIdList = list(proto.partitionIdList)
knn._rebuildPartitionIdMap(knn._partitionIdList)
knn._iterationIdx = proto.iterationIdx
knn._finishedLearning = proto.finishedLearning
if proto.s is not None:
knn._s = numpy.array(proto.s, dtype=numpy.float32)
if proto.vt is not None:
knn._vt = numpy.array(proto.vt, dtype=numpy.float32)
if proto.mean is not None:
knn._mean = numpy.array(proto.mean, dtype=numpy.float32)
return knn
def write(self, proto):
proto.version = self.version
proto.k = self.k
proto.exact = bool(self.exact)
proto.distanceNorm = self.distanceNorm
proto.distanceMethod = self.distanceMethod
proto.distThreshold = self.distThreshold
proto.doBinarization = bool(self.doBinarization)
proto.binarizationThreshold = self.binarizationThreshold
proto.useSparseMemory = bool(self.useSparseMemory)
proto.sparseThreshold = self.sparseThreshold
proto.relativeThreshold = bool(self.relativeThreshold)
proto.numWinners = self.numWinners
proto.verbosity = self.verbosity
proto.maxStoredPatterns = self.maxStoredPatterns
proto.replaceDuplicates = bool(self.replaceDuplicates)
proto.cellsPerCol = self.cellsPerCol
proto.minSparsity = self.minSparsity
# Write private state
if self._Memory is not None:
if isinstance(self._Memory, numpy.ndarray):
proto.memory.ndarray = self._Memory.tolist()
else:
proto.memory.init("nearestNeighbor")
self._Memory.write(proto.memory.nearestNeighbor)
proto.numPatterns = self._numPatterns
if self._M is not None:
proto.m = self._M.tolist()
if self._categoryList is not None:
proto.categoryList = self._categoryList
if self._partitionIdList is not None:
proto.partitionIdList = self._partitionIdList
proto.finishedLearning = bool(self._finishedLearning)
proto.iterationIdx = self._iterationIdx
if self._s is not None:
proto.s = self._s.tolist()
if self._vt is not None:
proto.vt = self._vt.tolist()
if self._mean is not None:
proto.mean = self._mean.tolist()
def __getstate__(self):
"""Return serializable state.
This function will return a version of the __dict__.
"""
state = self.__dict__.copy()
return state
def __setstate__(self, state):
"""Set the state of this object from a serialized state."""
if "version" not in state:
pass
elif state["version"] == 1:
pass
elif state["version"] == 2:
raise RuntimeError("Invalid deserialization of invalid KNNClassifier"
"Version")
# Backward compatibility
if "_partitionIdArray" in state:
state.pop("_partitionIdArray")
if "minSparsity" not in state:
state["minSparsity"] = 0.0
self.__dict__.update(state)
# Backward compatibility
if "_partitionIdMap" not in state:
self._rebuildPartitionIdMap(self._partitionIdList)
# Set to new version
self.version = KNNCLASSIFIER_VERSION
