Fix potentials bug in independence sampler

Author: alexgdebeer

deep_tensor/debiasing/mcmc.py

@@ -305,17 +305,19 @@ def run_independence_sampler(
     """
 
     n, d = xs.shape
-    potentials = neglogfxs_exact - neglogfxs_irt
 
     chain = MarkovChain(n, d)
-    chain.add_new_state(xs[0], potentials[0])
+    chain.add_new_state(xs[0], neglogfxs_exact[0])
+    i_cur = 0
 
     for i in range(n-1):
-        
-        alpha = chain.current_potential - potentials[i+1]
+
+        alpha = (neglogfxs_exact[i_cur] + neglogfxs_irt[i+1]
+                 - neglogfxs_exact[i+1] - neglogfxs_irt[i_cur])
 
         if alpha.exp() > torch.rand(1):
-            chain.add_new_state(xs[i+1], potentials[i+1])
+            chain.add_new_state(xs[i+1], neglogfxs_exact[i+1])
+            i_cur = i+1
         else:
             chain.add_current_state()
 

deep_tensor/polynomials/spectral/spectral_cdf.py

@@ -163,6 +163,7 @@ def newton(
         self,
         coefs: Tensor, 
         cdf_poly_base: Tensor, 
+        cdf_poly_norm,
         zs_unnorm: Tensor,
         l0s: Tensor,
         l1s: Tensor
@@ -177,7 +178,7 @@ def newton(
         for _ in range(self.num_newton):  
             zs, dzs = self.eval_int_newton(coefs, cdf_poly_base, zs_unnorm, ls)
             ls, dls = self._newton_step(ls, zs, dzs, l0s, l1s)
-            if self.converged(zs, dls):
+            if self.converged(zs / cdf_poly_norm, dls / cdf_poly_norm):
                 return ls
 
         # self.print_unconverged(zs, dls, "Newton's method")
@@ -232,5 +233,5 @@ def invert_cdf(self, ps: Tensor, zs: Tensor) -> Tensor:
         l0s = self.sampling_nodes[left_inds]
         l1s = self.sampling_nodes[left_inds+1]
 
-        ls = self.newton(coefs, cdf_poly_base, zs_cdf, l0s, l1s)
+        ls = self.newton(coefs, cdf_poly_base, cdf_poly_norm, zs_cdf, l0s, l1s)
         return ls

docs/_freeze/examples/heat/execute-results/html.json

@@ -142,7 +142,7 @@ d_obs += noise
 
 fig, ax = plt.subplots(figsize=(6.0, 2.0))
 cbar_label = r"$u(\bm{x}, 10)$"
-plot_dl_function(fig, ax, model_full.vec2func(u_true[:, -1]), cbar_label, vmin=-0.15, vmax=0.1)
+plot_dl_function(fig, ax, model_full.vec2func(u_true[:, -1]), cbar_label, vmin=-0.15, vmax=0.10)
 ax.scatter(*model_full.xs_obs.T, s=16, c="k", marker=".")
 ax.set_xlabel(r"$x_{0}$")
 ax.set_ylabel(r"$x_{1}$")