docstring fixes. rho and sigma parameter description still missing in optimize_layout_inverse. Flagged some potential issues with FIXME tag
@@ -141,6 +141,8 @@ def _optimize_layout_euclidean_single_epoch(
grad_d = clip(grad_coeff * (current[d] - other[d]))
if densmap_flag:
+ # FIXME: grad_cor_coeff might be referenced before assignment
+
grad_d += clip(2 * grad_cor_coeff * (current[d] - other[d]))
current[d] += grad_d * alpha
@@ -257,7 +259,7 @@ def optimize_layout_euclidean(
The number of training epochs to use in optimization.
n_vertices: int
The number of vertices (0-simplices) in the dataset.
- epochs_per_samples: array of shape (n_1_simplices)
+ epochs_per_sample: array of shape (n_1_simplices)
A float value of the number of epochs per 1-simplex. 1-simplices with
weaker membership strength will have more epochs between being sampled.
a: float
@@ -336,6 +338,8 @@ def optimize_layout_euclidean(
)
if densmap_flag:
+ # FIXME: dens_init_fn might be referenced before assignment
+
dens_init_fn(
head_embedding,
tail_embedding,
@@ -347,6 +351,7 @@ def optimize_layout_euclidean(
dens_phi_sum,
)
+ # FIXME: dens_var_shift might be referenced before assignment
dens_re_std = np.sqrt(np.var(dens_re_sum) + dens_var_shift)
dens_re_mean = np.mean(dens_re_sum)
dens_re_cov = np.dot(dens_re_sum, dens_R) / (n_vertices - 1)
@@ -514,9 +519,6 @@ def optimize_layout_generic(
tail: array of shape (n_1_simplices)
The indices of the tails of 1-simplices with non-zero membership.
- weight: array of shape (n_1_simplices)
- The membership weights of the 1-simplices.
-
n_epochs: int
The number of training epochs to use in optimization.
@@ -720,6 +722,10 @@ def optimize_layout_inverse(
weight: array of shape (n_1_simplices)
The membership weights of the 1-simplices.
+ sigmas:
+
+ rhos:
+
n_epochs: int
The number of training epochs to use in optimization.
