def __init__(self,
num_dimensions,
user_mat,
first_mat,
gparams,
variance_prior=None,
offset_prior=None,
active_dims=None):
super(MyKernel, self).__init__(active_dims=active_dims)
self.user_mat = user_mat
self.first_mat = first_mat
self.psi_dim_one = gparams.psi_indices[0]
self.psi_dim_two = gparams.psi_indices[1]
self.psi_indices = gparams.psi_indices
#print(self.psi_dim_one)
#print(self.psi_dim_two)
self.init_u1 = gparams.sigma_u[0][0]
#init_u1 = gparams.u1
self.init_u2 = gparams.sigma_u[1][1]
#init_u2 = gparams.u2
#self.register_parameter(name="u1", parameter=torch.nn.Parameter(init_u1*torch.tensor(1.0)))
self.register_parameter(name="raw_u1",
parameter=torch.nn.Parameter(
self.init_u1 * torch.tensor(1.0)))
#self.register_parameter(name="u2", parameter=torch.nn.Parameter(init_u2*torch.tensor(1.0)))
self.register_parameter(name="raw_u2",
parameter=torch.nn.Parameter(
self.init_u2 * torch.tensor(1.0)))
t = gparams.sigma_u[0][0]**.5 * gparams.sigma_u[1][1]**.5
self.r = (gparams.sigma_u[0][1] + t) / t
#r = gparams.rho_term
#self.register_parameter(name="rho", parameter=torch.nn.Parameter(r*torch.tensor(1.0)))
self.register_parameter(name="raw_rho",
parameter=torch.nn.Parameter(
self.r * torch.tensor(1.0)))
#initial_value=init_u2*torch.tensor(1.0)
#initial_value=init_u1*torch.tensor(1.0)
self.register_constraint(
"raw_u1",
constraint=constraints.Positive(initial_value=self.init_u1 *
torch.tensor(1.0)))
#initial_value=init_u2*torch.tensor(1.0)
self.register_constraint(
"raw_u2",
constraint=constraints.Positive(initial_value=self.init_u2 *
torch.tensor(1.0)))
self.register_constraint("raw_rho",
constraint=constraints.Interval(0.0, 2.0))
def __init__(self, num_dimensions,user_mat, first_mat,gparams, variance_prior=None, offset_prior=None, active_dims=None):
super(MyKernel, self).__init__(active_dims=active_dims)
self.user_mat = user_mat
self.first_mat = first_mat
self.action_indices = [8,13]
self.psi_dim_one = gparams.psi_indices[0]
self.psi_dim_two = gparams.psi_indices[1]
self.psi_indices =gparams.psi_indices
print(self.psi_indices)
self.g_indices = [i for i in range(8)]
self.action_indices_one = [i for i in range(8,8+5)]
self.action_indices_two = [i for i in range(8+5,18)]
self.init_u1 = gparams.sigma_u[0][0]
self.init_u2 = gparams.sigma_u[1][1]
self.register_parameter(name="raw_u1", parameter=torch.nn.Parameter(1.0*torch.tensor(1.0)))
self.register_parameter(name="test", parameter=torch.nn.Parameter(1.0*torch.tensor(1.0)))
self.register_parameter(name="raw_u2", parameter=torch.nn.Parameter(1.0*torch.tensor(1.0)))
t =gparams.sigma_u[0][0]**.5 * gparams.sigma_u[1][1]**.5
self.r = (gparams.sigma_u[0][1]+t)/t
self.register_parameter(name="raw_rho", parameter=torch.nn.Parameter(1.0*torch.tensor(1.0)))
self.register_constraint("raw_u1",constraint= constraints.Positive())
self.register_constraint("raw_u2",constraint= constraints.Positive())
self.register_constraint("raw_rho",constraint= constraints.Interval(0.0,2.0))
self.u1 = self.init_u1
self.u2 = self.init_u2
self.rho = self.r
def __init__(self,
num_dimensions,
user_mat,
first_mat,
gparams,
variance_prior=None,
offset_prior=None,
active_dims=None):
super(MyKernel, self).__init__(active_dims=active_dims)
self.user_mat = user_mat
self.first_mat = first_mat
self.psi_dim_one = gparams.psi_indices[0]
self.psi_dim_two = gparams.psi_indices[1]
self.psi_indices = gparams.psi_indices
self.action_indices_one = gparams.action_indices_one
self.action_indices_two = gparams.action_indices_two
self.g_indices = gparams.g_indices
#print(self.psi_dim_one)
#print(self.psi_dim_two)
#init_u1 = gparams.sigma_u[0][0]
print(gparams.u1)
print(gparams.u2)
self.init_u1 = gparams.u1 * torch.tensor(1.0)
#init_u2 = gparams.sigma_u[1][1]
self.init_u2 = gparams.u2 * torch.tensor(1.0)
self.init_u3 = gparams.u3 * torch.tensor(1.0)
self.init_u4 = gparams.u4 * torch.tensor(1.0)
self.r12 = gparams.r12 * torch.tensor(1.0)
self.r13 = gparams.r13 * torch.tensor(1.0)
self.r14 = gparams.r14 * torch.tensor(1.0)
self.r23 = gparams.r23 * torch.tensor(1.0)
self.r24 = gparams.r24 * torch.tensor(1.0)
self.r34 = gparams.r34 * torch.tensor(1.0)
#self.register_parameter(name="u1", parameter=torch.nn.Parameter(init_u1*torch.tensor(1.0)))
self.register_parameter(name="raw_u1",
parameter=torch.nn.Parameter(
self.init_u1 * torch.tensor(1.0)))
#self.register_parameter(name="u2", parameter=torch.nn.Parameter(init_u2*torch.tensor(1.0)))
self.register_parameter(name="raw_u2",
parameter=torch.nn.Parameter(
self.init_u2 * torch.tensor(1.0)))
#self.register_parameter(name="u3", parameter=torch.nn.Parameter(init_u3*torch.tensor(1.0)))
self.register_parameter(name="raw_u3",
parameter=torch.nn.Parameter(
self.init_u3 * torch.tensor(1.0)))
#self.register_parameter(name="u4", parameter=torch.nn.Parameter(init_u4*torch.tensor(1.0)))
self.register_parameter(name="raw_u4",
parameter=torch.nn.Parameter(
self.init_u4 * torch.tensor(1.0)))
#t =gparams.sigma_u[0][0]**.5 * gparams.sigma_u[1][1]**.5
#r = (gparams.sigma_u[0][1]+t)/t
#sr = gparams.rho_term
#self.register_parameter(name="rho_12", parameter=torch.nn.Parameter(r12*torch.tensor(1.0)))
self.register_parameter(name="raw_rho_12",
parameter=torch.nn.Parameter(
self.r12 * torch.tensor(1.0)))
#self.register_parameter(name="rho_13", parameter=torch.nn.Parameter(r13*torch.tensor(1.0)))
self.register_parameter(name="raw_rho_13",
parameter=torch.nn.Parameter(
self.r13 * torch.tensor(1.0)))
#self.register_parameter(name="rho_14", parameter=torch.nn.Parameter(r14*torch.tensor(1.0)))
self.register_parameter(name="raw_rho_14",
parameter=torch.nn.Parameter(
self.r14 * torch.tensor(1.0)))
#self.register_parameter(name="rho_23", parameter=torch.nn.Parameter(r23*torch.tensor(1.0)))
self.register_parameter(name="raw_rho_23",
parameter=torch.nn.Parameter(
self.r23 * torch.tensor(1.0)))
#self.register_parameter(name="rho_24", parameter=torch.nn.Parameter(r24*torch.tensor(1.0)))
self.register_parameter(name="raw_rho_24",
parameter=torch.nn.Parameter(
self.r24 * torch.tensor(1.0)))
#self.register_parameter(name="rho_34", parameter=torch.nn.Parameter(r34*torch.tensor(1.0)))
self.register_parameter(name="raw_rho_34",
parameter=torch.nn.Parameter(
self.r34 * torch.tensor(1.0)))
self.register_constraint("raw_u1", constraint=constraints.Positive())
self.register_constraint("raw_u2", constraint=constraints.Positive())
self.register_constraint("raw_u3", constraint=constraints.Positive())
self.register_constraint("raw_u4", constraint=constraints.Positive())
self.register_constraint("raw_rho_12",
constraint=constraints.Interval(0.0, 2.0))
self.register_constraint("raw_rho_13",
constraint=constraints.Interval(0.0, 2.0))
self.register_constraint("raw_rho_14",
constraint=constraints.Interval(0.0, 2.0))
self.register_constraint("raw_rho_23",
constraint=constraints.Interval(0.0, 2.0))
self.register_constraint("raw_rho_24",
constraint=constraints.Interval(0.0, 2.0))
self.register_constraint("raw_rho_34",
constraint=constraints.Interval(0.0, 2.0))
#print('got here')
self.u1 = gparams.u1
#print('initialized')
#init_u2 = gparams.sigma_u[1][1]
self.u2 = gparams.u2
#print('initialized')
self.u3 = gparams.u3
self.u4 = gparams.u4
self.rho_12 = gparams.r12
self.rho_13 = gparams.r13
self.rho_14 = gparams.r14
self.rho_23 = gparams.r23
self.rho_24 = gparams.r24
self.rho_34 = gparams.r34
