def train(self, dataset, trainbags):
self.bagnames = dataset.getBagNames()
self.state = self.initialize(dataset, trainbags)
gd = GradientDescent(self.lrate,
self.logPosterior, self.grad_logPosterior,
self.state.vectorize(), (dataset, trainbags),
self.max_iter, sys.stdout)
state_vec = gd.minimize()
self.state = self.state.devectorize(state_vec)
if self.islearnthres == 1:
self.learnThres(dataset, trainbags)
def train(self,dataset,trainbags):
self.bagnames = dataset.getBagNames()
self.state = self.initialize(dataset,trainbags)
gd = GradientDescent(self.lrate,self.logPosterior,self.grad_logPosterior,self.state.vectorize(),(dataset,trainbags),self.max_iter, sys.stdout)
state_vec = gd.minimize()
self.state = self.state.devectorize(state_vec)
if self.islearnthres==1:
self.learnThres(dataset,trainbags)
def __init__(self,learningrate, func, dfunc, Xinit, args, maxiter,outfilename):
GradientDescent.__init__(self,learningrate, func, dfunc, Xinit, args, maxiter,outfilename)
def __init__(self, learningrate, func, dfunc, Xinit, args, maxiter,
outfilename):
GradientDescent.__init__(self, learningrate, func, dfunc, Xinit, args,
maxiter, outfilename)