Fixed Polar GP example

docs/examples/constructing_new_kernels.py

@@ -218,27 +218,21 @@ class Polar(gpx.kernels.AbstractKernel):
     period: float = static_field(2 * jnp.pi)
     tau: float = param_field(jnp.array([4.0]), bijector=bij)
 
-    def __post_init__(self):
-        self.c = self.period / 2.0
-
     def __call__(
         self, x: Float[Array, "1 D"], y: Float[Array, "1 D"]
     ) -> Float[Array, "1"]:
-        t = angular_distance(x, y, self.c)
-        K = (1 + self.tau * t / self.c) * jnp.clip(
-            1 - t / self.c, 0, jnp.inf
-        ) ** self.tau
+        c = self.period / 2.0
+        t = angular_distance(x, y, c)
+        K = (1 + self.tau * t / c) * jnp.clip(1 - t / c, 0, jnp.inf) ** self.tau
         return K.squeeze()
 
 
 # %% [markdown]
 # We unpack this now to make better sense of it. In the kernel's initialiser
 # we specify the length of a single period. As the underlying
-# domain is a circle, this is $2\pi$. Next, we define
-# the Kernel's half-period parameter. As the kernel is a `dataclass` and `c` is
-# function of `period`, we must define it in the `__post_init__` method.
-# Finally, we define the kernel's `__call__`
-# function which is a direct implementation of Equation (1).
+# domain is a circle, this is $2\pi$. We then define the kernel's `__call__`
+# function which is a direct implementation of Equation (1) where we define `c`
+# as half the value of `period`.
 #
 # To constrain $\tau$ to be greater than 4, we use a `Softplus` bijector with a
 # clipped lower bound of 4.0. This is done by specifying the `bijector` argument
@@ -267,11 +261,11 @@ def __call__(
 PKern = Polar()
 meanf = gpx.mean_functions.Zero()
 likelihood = gpx.Gaussian(num_datapoints=n)
-circlular_posterior = gpx.Prior(mean_function=meanf, kernel=PKern) * likelihood
+circular_posterior = gpx.Prior(mean_function=meanf, kernel=PKern) * likelihood
 
 # Optimise GP's marginal log-likelihood using Adam
 opt_posterior, history = gpx.fit(
-    model=circlular_posterior,
+    model=circular_posterior,
     objective=jit(gpx.ConjugateMLL(negative=True)),
     train_data=D,
     optim=ox.adamw(learning_rate=0.05),
@@ -302,6 +296,7 @@ def __call__(
     alpha=0.3,
     label=r"1 Posterior s.d.",
     color=cols[1],
+    lw=0,
 )
 ax.fill_between(
     angles.squeeze(),
@@ -310,6 +305,7 @@ def __call__(
     alpha=0.15,
     label=r"3 Posterior s.d.",
     color=cols[1],
+    lw=0,
 )
 ax.plot(angles, mu, label="Posterior mean")
 ax.scatter(D.X, D.y, alpha=1, label="Observations")