def param_control_2(variables3):
ll_vars = NullVariables()
@jax.jit
def e(p, t):
q = p[1]
return jnp.array([[1., 0.], [q, 0.]])
@jax.jit
def a(p, t):
q = p[1]
return jnp.array([[-1., 0.], [-q, 1.]])
@jax.jit
def b(p, t):
q = p[1]
return jnp.array([[1.], [q]])
@jax.jit
def c(p, t):
return jnp.identity(2)
@jax.jit
def d(p, t):
return np.array([[0.]])
@jax.jit
def f(p, t):
return np.array([0., 0.])
return LinearParameterControlSystem(ll_vars, *variables3, e, a, b, c, d, f)
def param_control_3(variables3, request):
fix_p = request.param
ll_vars = NullVariables()
@jax.jit
def e(p, t):
q = p[1]
return jnp.array([[1., 0.], [q, 0.]])
@jax.jit
def a(p, t):
q = p[1]
return jnp.array([[-1., 0.], [-q, 1.]])
@jax.jit
def b(p, t):
q = p[1]
return jnp.array([[1.], [q]])
@jax.jit
def c(p, t):
return jnp.identity(2)
@jax.jit
def d(p, t):
return jnp.array([[0.]])
@jax.jit
def f(p, t):
q_0 = p[0]
q = p[1]
return jnp.array([1., q + fix_p + q_0])
return LinearParameterControlSystem(ll_vars, *variables3, e, a, b, c, d, f)
def param_control(variables2):
ll_vars = NullVariables()
def e(p, t):
return np.array([[1.]])
def a(p, t):
return np.array([[-1.]])
def b(p, t):
return np.array([[1.]])
def c(p, t):
return np.array([[1.]])
def d(p, t):
return np.array([[0.]])
def f(p, t):
return np.array([0.])
return LinearParameterControlSystem(ll_vars, *variables2, e, a, b, c, d, f)
def param_vars():
states = StateVariablesContainer(2)
domain = RNDomain(1)
parameters = ParameterContainer(1, domain)
null_vars = NullVariables()
return null_vars, parameters, states
def variables():
domain = RNDomain(1)
parameters = ParameterContainer(1, domain)
state_input = InputStateVariables(n_states=2, m_inputs=1)
null_vars = NullVariables()
return null_vars, parameters, state_input
def nlsq_2(nlsq_obj_2, variables3):
variables = (variables3[1], NullVariables(), variables3[0])
return NonLinearLeastSquares(nlsq_obj_2, *variables)
def nlsq_3(nlsq_obj_3, variables2):
variables = (variables2[1], NullVariables(), variables2[0])
return NonLinearLeastSquares(nlsq_obj_3, *variables)
def nlsq_obj_3(f_nlsq_3, variables2):
variables = (variables2[1], NullVariables(), variables2[0])
return optimization.objectives.NonLinearLeastSquares(*variables, f_nlsq_3)
def nlsq_obj_2(f_nlsq_2, variables3):
variables = (variables3[1], NullVariables(), variables3[0])
return optimization.objectives.NonLinearLeastSquares(*variables, f_nlsq_2)
def mloc_object(self, opt):
optimizations = [opt[0], opt[0]]
variableslist = [NullVariables(), NullVariables()]
mloc = MultiLevelOptimalControl(optimizations, variableslist)
return mloc