From bb08e1f364cda6a4938cf00517cc6a96c324a3b8 Mon Sep 17 00:00:00 2001
From: Gilad Turok <36947659+gil2rok@users.noreply.github.com>
Date: Mon, 19 Aug 2024 03:58:35 +0000
Subject: [PATCH] Doc: formatting

---
 blackjax/vi/fullrank_vi.py | 33 +++++++++++++++++----------------
 1 file changed, 17 insertions(+), 16 deletions(-)

diff --git a/blackjax/vi/fullrank_vi.py b/blackjax/vi/fullrank_vi.py
index 009de960e..4ae17fcf6 100644
--- a/blackjax/vi/fullrank_vi.py
+++ b/blackjax/vi/fullrank_vi.py
@@ -39,12 +39,12 @@ class FRVIState(NamedTuple):
         Mean of the Gaussian approximation.
     chol_params:
         Flattened Cholesky factor of the Gaussian approximation, used to parameterize
-        the full-rank covariance matrix. A vector of length d(d+1)/2 for a 
-        d-dimensional Gaussian, containing d diagonal elements (in log space) followed 
+        the full-rank covariance matrix. A vector of length d(d+1)/2 for a
+        d-dimensional Gaussian, containing d diagonal elements (in log space) followed
         by lower triangular elements in row-major order.
     opt_state:
         Optax optimizer state.
-    
+
     """
 
     mu: ArrayTree
@@ -54,11 +54,12 @@ class FRVIState(NamedTuple):
 
 class FRVIInfo(NamedTuple):
     """Extra information of the full-rank VI algorithm.
-    
+
     elbo:
         ELBO of approximation wrt target distribution.
 
     """
+
     elbo: float
 
 
@@ -168,42 +169,42 @@ def sample_fn(rng_key: PRNGKey, state: FRVIState, num_samples: int):
 def _unflatten_cholesky(chol_params):
     """Construct the Cholesky factor from a flattened vector of Cholesky parameters.
 
-    The Cholesky factor (L) is a lower triangular matrix with positive diagonal 
-    elements used to parameterize the (full-rank) covariance matrix of the Gaussian 
+    The Cholesky factor (L) is a lower triangular matrix with positive diagonal
+    elements used to parameterize the (full-rank) covariance matrix of the Gaussian
     approximation as Sigma = LL^T.
-    
+
     This parameterization allows for (1) efficient sampling and log density evaluation,
-    and (2) ensuring the covariance matrix is symmetric and positive definite during 
+    and (2) ensuring the covariance matrix is symmetric and positive definite during
     (unconconstrained) optimization.
-    
+
     Transforms a flattened vector representation of the Cholesky factor (`chol_params`)
-    into its proper lower triangular matrix form (`chol_factor`). It specifically 
-    reshapes the input vector `chol_params` into a lower triangular matrix with zeros 
+    into its proper lower triangular matrix form (`chol_factor`). It specifically
+    reshapes the input vector `chol_params` into a lower triangular matrix with zeros
     above the diagonal and exponentiates the diagonal elements to ensure positivity.
 
     Parameters
     ----------
     chol_params
         Flattened Cholesky factor of the full-rank covariance matrix.
-    
+
     Returns
     -------
     chol_factor
         Cholesky factor of the full-rank covariance matrix.
     """
-    
+
     n = chol_params.size
     dim = int(jnp.sqrt(1 + 8 * n) - 1) // 2
     tril = jnp.zeros((dim, dim))
     tril = tril.at[jnp.tril_indices(dim, k=-1)].set(chol_params[dim:])
-    diag = jnp.exp(chol_params[:dim]) # TODO: replace with softplus?
+    diag = jnp.exp(chol_params[:dim])  # TODO: replace with softplus?
     chol_factor = tril + jnp.diag(diag)
     return chol_factor
 
 
 def _sample(rng_key, mu, chol_params, num_samples):
     """Sample from the full-rank Gaussian approximation of the target distribution.
-    
+
     Parameters
     ----------
     rng_key
@@ -214,7 +215,7 @@ def _sample(rng_key, mu, chol_params, num_samples):
         Flattened Cholesky factor of the Gaussian approximation.
     num_samples
         Number of samples to draw.
-    
+
     Returns
     -------
     Samples drawn from the full-rank Gaussian approximation.