def infer(self, inputPattern, computeScores=True,
overCategories=True, partitionId=None):
"""Find the category that best matches the input pattern. Returns the
winning category index plus a distribution over all categories.
This method returns a 4 item tuple:
(winner, inferenceResult, dist, categoryDist)
winner: The category with the greatest number of nearest neighbors within
the kth nearest neighbors
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 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.
"""
# If no categories learned yet (applies for first inference with
# online learning)
if len(self._categoryList) == 0:
winner = 0
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)
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()
inferenceResult /= inferenceResult.sum()
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:", int(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 infer(self,
inputPattern,
computeScores=True,
overCategories=True,
partitionId=None):
"""Find the category that best matches the input pattern. Returns the
winning category index plus a distribution over all categories.
This method returns a 4 item tuple:
(winner, inferenceResult, dist, categoryDist)
winner: The category with the greatest number of nearest neighbors within
the kth nearest neighbors
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 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.
"""
# If no categories learned yet (applies for first inference with
# online learning)
if len(self._categoryList) == 0:
winner = 0
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)
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()
inferenceResult /= inferenceResult.sum()
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:", int(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 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.
This method returns a 4-tuple: (winner, inferenceResult, dist, categoryDist)
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 applies to the case of exact
matching.
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.
"""
if len(self._categoryList) == 0:
# No categories learned yet; i.e. first inference w/ online learning.
winner = 0
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 infer(self, inputPattern, computeScores=True,
overCategories=True, partitionId=None):
"""Find the category that best matches the input pattern. Returns the
winning category index plus a distribution over all categories.
This method returns a 4 item tuple:
(winner, inferenceResult, dist, categoryDist)
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 applies to the case
of exact matching.
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 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.
"""
if len(self._categoryList) == 0:
# No categories learned yet; i.e. first inference w/ online learning.
winner = 0
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 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 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.
This method returns a 4-tuple: (winner, inferenceResult, dist, categoryDist)
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 applies to the case of exact
matching.
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.
"""
if len(self._categoryList) == 0:
# No categories learned yet; i.e. first inference w/ online learning.
winner = 0
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 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
