def f1(x):
return np.sin(np.sin(np.sin(x)))
def f(x):
_, y = lax.while_loop(lambda s: s[0] < 0., lambda s:
(jnp.sin(s[0]), jnp.cos(s[1])), (x, x))
return y + 1.
def simple_fun_fanout(x, y):
return jnp.sin(x * y) * x
def test_check_jaxpr_correct(self):
jaxpr = make_jaxpr(lambda x: jnp.sin(x) + jnp.cos(x))(1.).jaxpr
core.check_jaxpr(jaxpr)
def f(x):
return jnp.sin(x) + jnp.cos(x)
def test_nested_jit(self): f_jax = jax.jit(lambda x: jnp.sin(jax.jit(jnp.cos)(x))) f_tf = jax2tf.convert(f_jax) np.testing.assert_allclose(f_jax(0.7), f_tf(0.7))
def product_io_fun(x, y):
xa = x['a']
xb = x['b']
y1, (y2, y3) = y
return jnp.sin(xa + y2), [xb, (y1, y3)]
def foo(x):
return np.sin(x)
def foo(x):
return np.sin(2. * x)
def test_complex_output_jacrev_raises_error(self):
self.assertRaises(TypeError, lambda: jacrev(lambda x: np.sin(x))
(1 + 2j))
def test_complex_input_jacfwd_raises_error(self):
self.assertRaises(TypeError, lambda: jacfwd(lambda x: np.sin(x))
(1 + 2j))
def test_holomorphic_grad(self):
out = grad(lambda x: np.sin(x), holomorphic=True)(1 + 2j)
expected = 2.0327230070196656 - 3.0518977991518j
self.assertAllClose(out, expected, check_dtypes=False)
def test_complex_grad_raises_error(self):
self.assertRaises(TypeError, lambda: grad(lambda x: np.sin(x))(1 + 2j))
def f1(x, y):
return np.sin(x) * np.cos(y) * np.sin(x) * np.cos(y)
def test_variable_input(self): f_jax = lambda x: jnp.sin(jnp.cos(x)) f_tf = jax2tf.convert(f_jax) v = tf.Variable(0.7, dtype=dtypes.canonicalize_dtype(jnp.float_)) self.assertIsInstance(f_tf(v), tf.Tensor) self.assertAllClose(f_jax(0.7), f_tf(v))
def foo(x, y):
return np.sin(x * y)
def test_jit(self): f_jax = jax.jit(lambda x: jnp.sin(jnp.cos(x))) self.ConvertAndCompare(f_jax, jnp.float_(0.7))
def h(x):
return np.sin(np.cos(x))
def foo(y, z):
return (x * x) * jnp.sin(y) * z
def pol2cart(theta, rho):
x = (rho * np.cos(theta)).reshape(-1, 1)
y = (rho * np.sin(theta)).reshape(-1, 1)
return np.concatenate([x, y], axis=1)
def bar(y):
return jnp.sin(x * y)
def test_Cdo_timeseries(plot=False):
if plot:
import pylab as pl
x = np.linspace(0, 40, 400).reshape((-1, 1))
y = np.sin(x) + randn(len(x)).reshape((-1, 1)) * 0.2
proc_data = np.hstack([x, y])
if plot:
pl.plot(x.flatten(), y.flatten())
invec = FiniteVec(GaussianKernel(0.5),
np.array([y.squeeze()[i:i + 10] for i in range(190)]))
outvec = FiniteVec(GaussianKernel(0.5), y[10:200])
refervec = FiniteVec(
outvec.k,
np.linspace(y[:-201].min() - 2, y[:-201].max() + 2, 5000)[:, None])
cd = Cdo(invec, outvec, refervec, 0.1)
cd = Cmo(invec, outvec, 0.1)
sol2 = np.array([
multiply(cd, FiniteVec(invec.k,
y[end - 10:end].T)).normalized().get_mean_var()
for end in range(200, 400)
])
if plot:
pl.plot(x[200:].flatten(), sol2.T[0].flatten())
invec = CombVec(
FiniteVec(PeriodicKernel(np.pi, 5), x[:200, :]),
SpVec(SplitDimsKernel([0, 1, 2],
[PeriodicKernel(np.pi, 5),
GaussianKernel(0.1)]),
proc_data[:200, :],
np.array([200]),
use_subtrajectories=True), np.multiply)
outvec = FiniteVec(GaussianKernel(0.5), y[1:-199])
#cd = Cdo(invec, outvec, refervec, 0.1)
cd = Cmo(invec, outvec, 0.1)
#sol = (cd.inp_feat.inner(SpVec(invec.k, proc_data[:230], np.array([230]), use_subtrajectories=True)))
#sol = [(cd.inp_feat.inner(SiEdSpVec(invec.k_obs, y[:end], np.array([end]), invec.k_idx, use_subtrajectories=False ))) for end in range(200,400) ]
#pl.plot(np.array([sol[i][-1] for i in range(len(sol))]))
#sol = np.array([multiply (cd, SpVec(invec.k, proc_data[:end], np.array([end]), use_subtrajectories=False)).normalized().get_mean_var() for end in range(200,400) ])
sol = multiply(
cd,
CombVec(
FiniteVec(invec.v1.k, x),
SpVec(invec.v2.k,
proc_data[:400],
np.array([400]),
use_subtrajectories=True),
np.multiply)).normalized().get_mean_var()
print(sol)
return sol2.T[0], sol.T[0][200:], y[200:]
(true_x1, est_x1, este_x1, true_x2, est_x2, este_x2) = [
lambda samps: true_dens(
np.hstack([np.repeat(x1, len(samps), 0), samps])),
lambda samps: np.squeeze(
inner(
multiply(cd, FiniteVec.construct_RKHS_Elem(invec.k, x1)).
normalized().unsigned_projection().normalized(),
FiniteVec(refervec.k, samps, prefactors=np.ones(len(samps))))),
lambda samps: np.squeeze(
inner(
multiply(cm, FiniteVec.construct_RKHS_Elem(invec.k, x1)).
normalized().unsigned_projection().normalized(),
FiniteVec(refervec.k, samps, prefactors=np.ones(len(samps))))),
lambda samps: true_dens(
np.hstack([np.repeat(x2, len(samps), 0), samps])),
lambda samps: np.squeeze(
inner(
multiply(cd, FiniteVec.construct_RKHS_Elem(invec.k, x2)).
normalized().unsigned_projection().normalized(),
FiniteVec(refervec.k, samps, prefactors=np.ones(len(samps))))),
lambda samps: np.squeeze(
inner(
multiply(cm, FiniteVec.construct_RKHS_Elem(invec.k, x2)).
normalized().unsigned_projection().normalized(),
FiniteVec(refervec.k, samps, prefactors=np.ones(len(samps)))))
]
t = np.array(
(true_x1(refervec.inspace_points), true_x2(refervec.inspace_points)))
e = np.array(
(est_x1(refervec.inspace_points), est_x2(refervec.inspace_points)))
if plot:
import pylab as pl
(fig, ax) = pl.subplots(1, 3, False, False)
ax[0].plot(refervec.inspace_points, t[0])
ax[0].plot(refervec.inspace_points, e[0], "--", label="dens")
ax[0].plot(refervec.inspace_points,
este_x1(refervec.inspace_points),
"-.",
label="emb")
ax[1].plot(refervec.inspace_points, t[1])
ax[1].plot(refervec.inspace_points, e[1], "--", label="dens")
ax[1].plot(refervec.inspace_points,
este_x2(refervec.inspace_points),
"-.",
label="emb")
ax[2].scatter(*rvs.T)
fig.legend()
fig.show()
assert (np.allclose(e, t, atol=0.5))
def f(c, x):
b = jnp.cos(jnp.sum(jnp.sin(x)) + jnp.sum(jnp.cos(c)))
c = jnp.sin(c * b)
return c, b
def test_basics(self): f_jax = lambda x: jnp.sin(jnp.cos(x)) _, res_tf = self.ConvertAndCompare(f_jax, jnp.float_(0.7))
def simple_fun(x, y):
return jnp.sin(x * y)
def f(x1): x2 = jnp.sin(x1) x3 = jnp.sin(x2) x4 = jnp.sin(x3) return jnp.sum(x4)
def f(x):
_, y = lax.cond(x < 0., lambda x: (jnp.sin(x), x + 1.), lambda x:
(jnp.cos(x), x + 2.), x)
return y
def f_jax(): return jnp.sin(1.)
def f(x): return np.sin(x)
def f(x):
g, aux = grad(lambda x: (x**3, [x**3]), has_aux=True)(x)
return aux[0] * np.sin(x)
