class Parameters:
def __init__(self, config, ys, n):
self.config = config
self.d = config['h_states']
ones = [1 for i in xrange(self.d)]
self.y = ys
self.n = n
self.minYk = min(ys)
self.maxYk = max(ys)
self.epsilon = (self.minYk + self.maxYk) / 2.0
self.r = self.maxYk - self.minYk
self.k = 1.0 / self.r**2
self.alpha = 2.0
self.g = 0.2
self.h = 10.0 / self.r**2
self.init_priors = Dirichlet(ones)
self.init_beta = Gamma(self.g, self.h)
self.init_root_sigma = Gamma(self.alpha, self.init_beta.sample())
self.init_means = Normal(self.epsilon, self.k**-1)
self.init_trans = [Dirichlet(ones) for i in xrange(self.d)]
self.x = [choice(xrange(config['h_states'])) for i in xrange(self.n)]
def set_initial_params(self):
self.priors = self.init_priors.sample()
self.beta = self.init_beta.sample()
self.sigma = self.init_root_sigma.sample() ** (-1.0/2)
# print "starting stds = " + str(self.sigma)
self.means = [self.init_means.sample() for i in xrange(self.d)]
self.trans = [self.init_trans[i].sample() for i in xrange(self.d)]
def __init__(self, config, ys, n):
self.config = config
self.d = config['h_states']
ones = [1 for i in xrange(self.d)]
self.y = ys
self.n = n
self.minYk = min(ys)
self.maxYk = max(ys)
self.epsilon = (self.minYk + self.maxYk) / 2.0
self.r = self.maxYk - self.minYk
self.k = 1.0 / self.r**2
self.alpha = 2.0
self.g = 0.2
self.h = 10.0 / self.r**2
self.init_priors = Dirichlet(ones)
self.init_beta = Gamma(self.g, self.h)
self.init_root_sigma = Gamma(self.alpha, self.init_beta.sample())
self.init_means = Normal(self.epsilon, self.k**-1)
self.init_trans = [Dirichlet(ones) for i in xrange(self.d)]
self.x = [choice(xrange(config['h_states'])) for i in xrange(self.n)]
