def run(self, inputRecord):
"""Run one iteration of this model.
Args:
inputRecord: A record object formatted according to
nupic.data.FileSource.getNext() result format.
Returns:
A ModelResult named tuple (see opf_utils.py). The contents of
ModelResult.inferences depends on the specific inference type of this
model, which can be queried by getInferenceType().
TODO: Implement getInferenceType()?
"""
# set the results. note that there is no translation to sensorInput
results = super(PreviousValueModel, self).run(inputRecord)
results.sensorInput = opf_utils.SensorInput(dataRow= \
[inputRecord[fn] for fn in self._fieldNames])
# select the current value for the prediction with probablity of 1
results.inferences = {opf_utils.InferenceElement.multiStepBestPredictions : \
dict((steps, inputRecord[self._predictedField]) \
for steps in self._predictionSteps),
opf_utils.InferenceElement.multiStepPredictions : \
dict((steps, {inputRecord[self._predictedField] : 1}) \
for steps in self._predictionSteps)
}
# set the next step prediction if step of 1 is selected
if 1 in self._predictionSteps:
results.inferences[opf_utils.InferenceElement.prediction] = \
inputRecord[self._predictedField]
return results
def run(self, inputRecord):
results = super(TwoGramModel, self).run(inputRecord)
# Set up the lists of values, defaults, and encoded values.
values = [inputRecord[k] for k in self._fieldNames]
defaults = ['' if type(v) == str else 0 for v in values]
inputFieldEncodings = self._encoder.encodeEachField(inputRecord)
inputBuckets = self._encoder.getBucketIndices(inputRecord)
results.sensorInput = opf_utils.SensorInput(
dataRow=values,
dataEncodings=inputFieldEncodings,
sequenceReset=int(self._reset))
# Keep track of the last value associated with each encoded value for that
# predictions can be returned in the original value format.
for value, bucket in zip(values, inputBuckets):
self._hashToValueDict[bucket] = value
# Update the two-gram dict if learning is enabled.
for bucket, prevValue, twoGramDict in zip(inputBuckets,
self._prevValues,
self._twoGramDicts):
if self._learningEnabled and not self._reset:
if prevValue not in twoGramDict:
twoGramDict[prevValue] = collections.defaultdict(int)
twoGramDict[prevValue][bucket] += 1
# Populate the results.inferences dict with the predictions and encoded
# predictions.
predictions = []
encodedPredictions = []
for bucket, twoGramDict, default, fieldName in (zip(
inputBuckets, self._twoGramDicts, defaults, self._fieldNames)):
if bucket in twoGramDict:
probabilities = list(twoGramDict[bucket].items())
prediction = self._hashToValueDict[max(probabilities,
key=lambda x: x[1])[0]]
predictions.append(prediction)
encodedPredictions.append(
self._encoder.encodeField(fieldName, prediction))
else:
predictions.append(default)
encodedPredictions.append(
self._encoder.encodeField(fieldName, default))
results.inferences = dict()
results.inferences[opf_utils.InferenceElement.prediction] = predictions
results.inferences[
opf_utils.InferenceElement.encodings] = encodedPredictions
self._prevValues = inputBuckets
self._reset = False
return results
def run(self, inputRecord):
# set the results. note that there is no translation to sensorInput
results = super(PreviousValueModel, self).run(inputRecord)
results.sensorInput = opf_utils.SensorInput(dataRow= \
[inputRecord[fn] for fn in self._fieldNames])
# select the current value for the prediction with probablity of 1
results.inferences = {opf_utils.InferenceElement.multiStepBestPredictions : \
dict((steps, inputRecord[self._predictedField]) \
for steps in self._predictionSteps),
opf_utils.InferenceElement.multiStepPredictions : \
dict((steps, {inputRecord[self._predictedField] : 1}) \
for steps in self._predictionSteps)
}
# set the next step prediction if step of 1 is selected
if 1 in self._predictionSteps:
results.inferences[opf_utils.InferenceElement.prediction] = \
inputRecord[self._predictedField]
return results
def run(self, inputRecord):
"""Run one iteration of this model.
Args:
inputRecord: A record object formatted according to
nupic.data.FileSource.getNext() result format.
Returns:
A ModelResult named tuple (see opf_utils.py). The contents of
ModelResult.inferences depends on the specific inference type of this
model, which can be queried by getInferenceType().
TODO: Implement getInferenceType()?
"""
results = super(TwoGramModel, self).run(inputRecord)
# Set up the lists of values, defaults, and encoded values.
values = [inputRecord[k] for k in self._fieldNames]
defaults = ['' if type(v) == str else 0 for v in values]
inputFieldEncodings = self._encoder.encodeEachField(inputRecord)
inputBuckets = self._encoder.getBucketIndices(inputRecord)
results.sensorInput = opf_utils.SensorInput(
dataRow=values, dataEncodings=inputFieldEncodings,
sequenceReset=int(self._reset))
# Keep track of the last value associated with each encoded value for that
# predictions can be returned in the original value format.
for value, bucket in itertools.izip(values, inputBuckets):
self._hashToValueDict[bucket] = value
# Update the two-gram dict if learning is enabled.
for bucket, prevValue, twoGramDict in itertools.izip(
inputBuckets, self._prevValues, self._twoGramDicts):
if self._learningEnabled and not self._reset:
if prevValue not in twoGramDict:
twoGramDict[prevValue] = collections.defaultdict(int)
twoGramDict[prevValue][bucket] += 1
# Populate the results.inferences dict with the predictions and encoded
# predictions.
predictions = []
encodedPredictions = []
for bucket, twoGramDict, default, fieldName in (
itertools.izip(inputBuckets, self._twoGramDicts, defaults,
self._fieldNames)):
if bucket in twoGramDict:
probabilities = twoGramDict[bucket].items()
prediction = self._hashToValueDict[
max(probabilities, key=lambda x: x[1])[0]]
predictions.append(prediction)
encodedPredictions.append(self._encoder.encodeField(fieldName,
prediction))
else:
predictions.append(default)
encodedPredictions.append(self._encoder.encodeField(fieldName,
default))
results.inferences = dict()
results.inferences[opf_utils.InferenceElement.prediction] = predictions
results.inferences[opf_utils.InferenceElement.encodings] = encodedPredictions
self._prevValues = inputBuckets
self._reset = False
return results
