def __init__(self,
verbosity=1,
numLabels=3,
modelDir="ClassificationModelFingerprint",
fingerprintType=EncoderTypes.word,
unionSparsity=20.0):
super(ClassificationModelFingerprint,
self).__init__(verbosity=verbosity,
numLabels=numLabels,
modelDir=modelDir)
# Init kNN classifier and Cortical.io encoder; need valid API key (see
# CioEncoder init for details).
self.classifier = KNNClassifier(k=numLabels,
distanceMethod='rawOverlap',
exact=False,
verbosity=verbosity - 1)
if fingerprintType is (not EncoderTypes.document
or not EncoderTypes.word):
raise ValueError("Invaid type of fingerprint encoding; see the "
"EncoderTypes class for eligble types.")
self.encoder = CioEncoder(cacheDir="./fluent/experiments/cioCache",
fingerprintType=fingerprintType,
unionSparsity=unionSparsity)
self.n = self.encoder.n
self.w = int((self.encoder.targetSparsity / 100) * self.n)
def __init__(self, inputFilePath, verbosity=1, numLabels=3, spTrainingSize=0,
tmTrainingSize=0, clsTrainingSize=0, classifierType="KNN"):
"""
@param inputFilePath (str) Path to data formatted for network
API
@param spTrainingSize (int) Number of samples the network has to
be trained on before training the
spatial pooler
@param tmTrainingSize (int) Number of samples the network has to
be trained on before training the
temporal memory
@param clsTrainingSize (int) Number of samples the network has to
be trained on before training the
classifier
@param classifierType (str) Either "KNN" or "CLA"
See ClassificationModel for remaining parameters
"""
self.spTrainingSize = spTrainingSize
self.tmTrainingSize = tmTrainingSize
self.clsTrainingSize = clsTrainingSize
super(ClassificationModelHTM, self).__init__(verbosity=verbosity,
numLabels=numLabels)
# Initialize Network
self.classifierType = classifierType
self.recordStream = FileRecordStream(streamID=inputFilePath)
self.encoder = CioEncoder(cacheDir="./experiments/cache")
self._initModel()
def initModel(self):
"""
Initialize the network; self.networdDataPath must already be set.
"""
recordStream = FileRecordStream(streamID=self.networkDataPath)
encoder = CioEncoder(cacheDir="./experiments/cache")
return configureNetwork(recordStream, self.networkConfig, encoder)
def testWordFingerprint(self):
"""Test the Cortical.io term (word-lelevl) encoding."""
cio = CioEncoder(fingerprintType=EncoderTypes.word)
response = cio.encode(self.text)
self.assertFingerprintFields(response)
encodingDict = getTestData("cio_encoding_word.json")
self.assertEqual(encodingDict["fingerprint"]["positions"],
response["fingerprint"]["positions"], "Cio bitmap is not as expected.")
def __init__(self, verbosity=1, numLabels=1):
"""
Initialize the CorticalClient and CioEncoder. Requires a valid API key
"""
super(ClassificationModelContext, self).__init__(verbosity)
self.encoder = CioEncoder(cacheDir="./experiments/cache")
self.client = CorticalClient(self.encoder.apiKey)
self.n = self.encoder.n
self.w = int((self.encoder.targetSparsity / 100) * self.n)
self.categoryBitmaps = {}
self.numLabels = numLabels
def __init__(self, verbosity=1, numLabels=3):
"""
Initialize the encoder as CioEncoder; requires a valid API key.
"""
super(ClassificationModelEndpoint, self).__init__(verbosity, numLabels)
self.encoder = CioEncoder(cacheDir="./experiments/cache")
self.compareEncoder = LanguageEncoder()
self.n = self.encoder.n
self.w = int((self.encoder.targetSparsity / 100) * self.n)
self.categoryBitmaps = {}
self.negatives = defaultdict(list)
self.positives = defaultdict(list)
