def __init__(self,
features = None,
discount = 0.99,
name = 'dynamic',
w=1e7):
self.n = len(features)
self.d = len(features[0])
if self.hasMissingData(features):
raise NameError("The current version does not support missing data" +
"in the features.")
self.features = tl.duplicateList(features)
self.componentType = 'dynamic'
self.name = name
self.discount = np.ones(self.d) * discount
# Initialize all basic quantities
self.evaluation = None
self.transition = None
self.covPrior = None
self.meanPrior = None
# create all basic quantities
self.createEvaluation(0)
self.createTransition()
self.createCovPrior(scale=w)
self.createMeanPrior()
# record current step in case of lost
self.step = 0
def __init__(self,
features = None,
discount = 0.99,
name = 'dynamic',
w=1e7):
self.n = len(features)
self.d = len(features[0])
self.features = tl.duplicateList(features)
self.componentType = 'dynamic'
self.name = name
self.discount = np.ones(self.d) * discount
# Initialize all basic quantities
self.evaluation = None
self.transition = None
self.covPrior = None
self.meanPrior = None
# create all basic quantities
self.createEvaluation(0)
self.createTransition()
self.createCovPrior(scale=w)
self.createMeanPrior()
# record current step in case of lost
self.step = 0
def appendNewData(self, newData):
""" For updating feature matrix when new data is added.
Args:
newData: is a list of list. The inner list is the feature vector. The outer
list may contain multiple feature vectors.
"""
self.features.extend(tl.duplicateList(newData))
self.n = len(self.features)
def appendNewData(self, newData):
""" For updating feature matrix when new data is added.
Args:
newData: is a list of list. The inner list is the feature vector. The outer
list may contain multiple feature vectors.
"""
if self.hasMissingData(newData):
raise NameError("The current version does not support missing data" +
"in the features.")
self.features.extend(tl.duplicateList(newData))
self.n = len(self.features)
def appendNewData(self, newData):
""" For updating feature matrix when new data is added.
Args:
newData: is a list of list. The inner list is the feature vector. The outer
list may contain multiple feature vectors.
"""
if self.hasMissingData(newData):
raise NameError("The current version does not support missing data" +
"in the features.")
self.features.extend(tl.duplicateList(newData))
self.n = len(self.features)