def __init__(self, num_enbs, num_states, num_obs, A, B, V, W, P=None):
super(EnsembleKalmanFilter, self).__init__(num_states, num_obs,
A, B, V, W, P)
self.weights = init_weights(num_enbs)
self.num_enbs = num_enbs
self.cov_type = None
def __init__(self, num_enbs, params):
self.num_enbs = num_enbs
super(EnsembleSIR, self).__init__(params)
del self.alpha
del self.beta
self.current_Is = uniform(0, self.i * 2, num_enbs)
self.current_Ss = ones(num_enbs) - self.current_Is
self.alphas = uniform(0., 1, num_enbs)
self.betas = uniform(0., 1, num_enbs)
self.weights = [init_weights(num_enbs)] # matrix-like
for i in range(num_enbs):
if self.alphas[i] < self.betas[i]:
self.alphas[i], self.betas[i] = self.betas[i], self.alphas[i]
self.Is = [self.current_Is.tolist()]
self.Ss = [self.current_Ss.tolist()]
def __init__(self, num_enbs, params):
self.num_enbs = num_enbs
super(ParticleSIR, self).__init__(params)
del self.alpha
del self.beta
self.current_Is = uniform(0, self.i * 2, num_enbs)
self.current_Ss = ones(num_enbs) - self.current_Is
self.alphas = uniform(0., 1, num_enbs)
self.betas = uniform(0., 1, num_enbs)
self.weights = [init_weights(num_enbs)] # matrix-like
for i in range(num_enbs):
if self.alphas[i] < self.betas[i]:
self.alphas[i], self.betas[i] = self.betas[i], self.alphas[i]
self.Is = [self.current_Is.tolist()]
self.Ss = [self.current_Ss.tolist()]
def __init__(self, num_enbs, params):
self.num_enbs = num_enbs
super(EnsembleAdjustmentSIR, self).__init__(params)
del self.alpha
del self.beta
self.inflate = 1.001
self.current_Is = uniform(0, self.i * 2, num_enbs)
self.current_Ss = ones(num_enbs) - self.current_Is
self.alphas = uniform(0, .5, num_enbs)
self.betas = uniform(0, .5, num_enbs)
self.weights = [init_weights(num_enbs)] # matrix-like
for i in range(num_enbs):
if self.alphas[i] < self.betas[i]:
self.alphas[i], self.betas[i] = self.betas[i], self.alphas[i]
self.Is = [self.current_Is.tolist()]
self.Ss = [self.current_Ss.tolist()]
def __init__(self, num_part, params={}):
self.num_part = num_part
self.weights = init_weights(num_part)
self.x_prior = None
def __init__(self, num_part, params={}):
self.num_part = num_part
self.weights = init_weights(num_part)
self.x_prior = None
