def id_from_inputs(inputs):
if isinstance(inputs, (dict, OrderedDict)):
inputs = inputs.items()
if not inputs:
return '()'
return ','.join(k + ''.join(map(str, d.shape)) for k, d in inputs)
@dispatch(object, object, Variadic[float])
def allclose(a, b, rtol=1e-05, atol=1e-08):
if type(a) != type(b):
return False
return ops.abs(a - b) < rtol + atol * ops.abs(b)
dispatch(np.ndarray, np.ndarray, Variadic[float])(np.allclose)
@dispatch(Tensor, Tensor, Variadic[float])
def allclose(a, b, rtol=1e-05, atol=1e-08):
if a.inputs != b.inputs or a.output != b.output:
return False
return allclose(a.data, b.data, rtol=rtol, atol=atol)
def is_array(x):
# XXX: in some JAX version, name of device array is DeviceArray,
# while in some version, it is _DeviceArray
return isinstance(
x, (np.ndarray, np.generic)) or "DeviceArray" in type(x).__name__
elif isinstance(self.exo_grid, EmptyGrid):
out = self.eval_s(s)
elif isinstance(self.exo_grid, CartesianGrid):
m = self.dprocess.inode(i, j)[None, :].repeat(s.shape[0], axis=0)
out = self.eval_ms(m, s)
else:
raise Exception("Not Implemented.")
return out
# this is *not* meant to be used by users
from multipledispatch import dispatch
namespace = dict()
multimethod = dispatch(namespace=namespace)
# Cartesian x Cartesian x Linear
@multimethod
def get_coefficients(itp: object, exo_grid: CartesianGrid,
endo_grid: CartesianGrid, interp_type: Linear, x: object):
grid = exo_grid + endo_grid
xx = x.reshape(tuple(grid.n) + (-1, ))
return xx.copy()
@multimethod
def eval_ms(itp: object, exo_grid: CartesianGrid, endo_grid: CartesianGrid,
interp_type: Linear, m: object, s: object):