def __init__(self,
p_x_y_,
card_T_,
posterior_mean,
posterior_var,
x_vec,
sigma_x,
symmetric_init=True,
nror=50,
KL_gmm=False):
GenericIB.__init__(self, p_x_y_, card_T_, np.inf, [], nror)
self.name = 'KL means IB'
self.cardinality_X = p_x_y_.shape[1]
self.cardinality_Y = p_x_y_.shape[0]
self.p_y = self.p_x_y.sum(1)
self.p_t = np.zeros(self.cardinality_T)
self.x_vec = x_vec
self.sigma_x = sigma_x
self.symmetric_init = symmetric_init
self.KL_gmm = KL_gmm
# compute the posterior mean and variance of observation
self.posterior_mean = posterior_mean
self.posterior_var = posterior_var
def __init__(self, p_x_y_, card_T_):
GenericIB.__init__(self, p_x_y_, card_T_, np.inf, [], 5)
self.name = 'agglomerative IB'
self.cardinality_X = p_x_y_.shape[1]
self.cardinality_Y = p_x_y_.shape[0]
self.cost_mat = np.zeros(
(self.cardinality_Y, self.cardinality_Y)) + np.inf
self.merge_costs = dict()
self.index_list = np.arange(self.cardinality_Y).tolist()
self.index_vec = np.arange(self.cardinality_Y)
def __init__(self, p_x_y_, card_T_,symmetric_init = False ,nror=50):
GenericIB.__init__(self, p_x_y_, card_T_, np.inf, [], nror)
self.name = 'KL means IB'
self.cardinality_X=p_x_y_.shape[1]
self.cardinality_Y=p_x_y_.shape[0]
#self.cost_mat= np.zeros((self.cardinality_Y,self.cardinality_Y))+np.inf
self.symmetric_init = symmetric_init
self.p_y = self.p_x_y.sum(1)
self.p_t = np.zeros(self.cardinality_T)
# calculate p(x|y)
self.p_x_given_y = self.p_x_y / self.p_y[:, np.newaxis]
def __init__(self, p_x_y_, card_T_, nror_):
GenericIB.__init__(self, p_x_y_, card_T_, np.inf, [], nror_)
self.name = 'symmetric sIB'
def __init__(self, p_x_y_, card_T_, nror_):
GenericIB.__init__(self, p_x_y_, card_T_, np.inf, [], nror_)
self.name = 'sequential IB'
def __init__(self, p_x_y_, card_T_, nror_):
GenericIB.__init__(self, p_x_y_, card_T_, np.inf, [], nror_)
self.name = 'modified sIB'
def __init__(self, p_x_y_, card_T_, nror_):
GenericIB.__init__(self, p_x_y_, card_T_, np.inf, [], nror_)
self.name = 'linearized symmetric sIB'
self.p_x_and_tilde_y = []
self.sort_indices = []
self.reconstruct_ind = []