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)