def __init__( self, var_v, p_s ):
self.A = np.matrix([[1,0,1,0],[0,1,0,1],[0,0,1,0],[0,0,0,1]])
self.Q = var_v * np.matrix([ [ 1.0/3, 0 ,1.0/2,0 ]
,[ 0 , 1.0/3, 0 ,1.0/2 ]
,[ 1.0/2, 0 , 1 ,0 ]
,[ 0 , 1.0/2, 0 ,1 ] ])
self.rv = mv_normal( np.array([0,0,0,0]), self.Q)
self.p_s = p_s
def __init__(self, system, var_z, p_d):
self.u_z = np.array([0, 0])
self.C_z = var_z * np.identity(2)
self.r_z = mv_normal(self.u_z, self.C_z)
self.r_d = uniform()
self.system = system
#detection probability
self.p_d = p_d
def __init__(self, var_v, p_s):
self.A = np.matrix([[1, 0, 1, 0], [0, 1, 0, 1], [0, 0, 1, 0],
[0, 0, 0, 1]])
self.Q = var_v * np.matrix([[1.0 / 3, 0, 1.0 / 2, 0],
[0, 1.0 / 3, 0, 1.0 / 2],
[1.0 / 2, 0, 1, 0], [0, 1.0 / 2, 0, 1]])
self.rv = mv_normal(np.array([0, 0, 0, 0]), self.Q)
self.p_s = p_s
def __init__( self, system, var_z, p_d ):
self.u_z = np.array( [ 0, 0 ] )
self.C_z = var_z * np.identity( 2 )
self.r_z = mv_normal( self.u_z, self.C_z )
self.r_d = uniform()
self.system = system
#detection probability
self.p_d = p_d
def compute_loss(self, X):
m = X.shape[0]
self.loss = np.zeros((m, self.no_clusters))
for c in range(self.no_clusters):
dist = mv_normal(self.mu[c], self.cv[c], allow_singular=True)
self.loss[:, c] = self.probs[:, c] * (np.log(self.ak[c] + 1e-8) +
dist.logpdf(X) -
np.log(self.probs[:, c] + 1e-8))
self.loss = np.sum(self.loss)
