def _abs_taylor_rule(x, series_in, **params): x, = x zero = lax.full_like(x, 0, shape=()) primal_out = lax.abs_p.bind(x, **params) negs = lax.select(lax.lt(x, zero), lax.full_like(x, -1), lax.full_like(x, 1.0)) fix_sign = lambda y: negs * y series_out = [fix_sign(*terms_in, **params) for terms_in in zip(*series_in)] return primal_out, series_out
def _where(condition, x=None, y=None):
if x is None or y is None:
raise ValueError("Either both or neither of the x and y arguments should "
"be provided to jax.numpy.where, got {} and {}."
.format(x, y))
if not np.issubdtype(_dtype(condition), np.bool_):
condition = lax.ne(condition, lax_internal._zero(condition))
x, y = _promote_dtypes(x, y)
condition, x, y = _broadcast_arrays(condition, x, y)
try: is_always_empty = core.is_empty_shape(np.shape(x))
except: is_always_empty = False # can fail with dynamic shapes
return lax.select(condition, x, y) if not is_always_empty else x
def _lax_min_taylor_rule(primal_in, series_in):
x, y = primal_in
xgy = x < y # less than mask
xey = x == y # equal to mask
primal_out = lax.select(xgy, x, y)
def select_min_and_avg_eq(x_i, y_i):
"""Select x where x>y or average when x==y"""
min_i = lax.select(xgy, x_i, y_i)
min_i = lax.select(xey, (x_i + y_i)/2, min_i)
return min_i
series_out = [select_min_and_avg_eq(*terms_in) for terms_in in zip(*series_in)]
return primal_out, series_out
def select_min_and_avg_eq(x_i, y_i):
"""Select x where x>y or average when x==y"""
min_i = lax.select(xgy, x_i, y_i)
min_i = lax.select(xey, (x_i + y_i) / 2, min_i)
return min_i
def _select_taylor_rule(primal_in, series_in, **params):
b, x, y = primal_in
primal_out = lax.select_p.bind(b, x, y, **params)
sel = lambda _, x, y: lax.select(b, x, y)
series_out = [sel(*terms_in, **params) for terms_in in zip(*series_in)]
return primal_out, series_out
def reducer(x, y):
kx, vx = x
ky, vy = y
which = select_prim.bind(kx, ky)
return (lax.select(which, kx, ky), lax.select(which, vx, vy))
def assert_func(error: Error, pred: Bool, msg: str) -> Error:
code = next_code()
out_err = error.err | jnp.logical_not(pred)
out_code = lax.select(error.err, error.code, code)
return Error(out_err, out_code, {code: msg, **error.msgs})
def assert_discharge_rule(error, pred, code, *, msgs):
out_err = error.err | jnp.logical_not(pred)
out_code = lax.select(error.err, error.code, code)
return [], Error(out_err, out_code, {**error.msgs, **msgs})
def _bcast_select(pred, on_true, on_false):
if np.ndim(pred) != np.ndim(on_true):
idx = list(range(np.ndim(pred)))
pred = lax.broadcast_in_dim(pred, np.shape(on_true), idx)
return lax.select(pred, on_true, on_false)
