Add suggestions from code review

lib/scholar/cluster/affinity_propagation.ex

@@ -59,7 +59,7 @@ defmodule Scholar.Cluster.AffinityPropagation do
       If `true`, the learning loop is unrolled.
       """
     ],
-    convergence_iter: [
+    converge_after: [
       type: :pos_integer,
       default: 15,
       doc: ~S"""
@@ -127,6 +127,7 @@ defmodule Scholar.Cluster.AffinityPropagation do
     iterations = opts[:iterations]
     damping_factor = opts[:damping_factor]
     self_preference = opts[:self_preference]
+    converge_after = opts[:converge_after]
     num_samples = Nx.axis_size(data, 0)
 
     {initial_a, initial_r, s, affinity_matrix} =
@@ -143,9 +144,7 @@ defmodule Scholar.Cluster.AffinityPropagation do
 
     range = Nx.iota({n})
 
-    Nx.broadcast(Nx.tensor(0, type: Nx.type(data)), {n, opts[:convergence_iter]})
-
-    e = Nx.broadcast(Nx.s64(0), {num_samples, opts[:convergence_iter]})
+    e = Nx.broadcast(Nx.s64(0), {num_samples, converge_after})
     stop = Nx.u8(0)
 
     {{a, r, it}, _} =
@@ -178,12 +177,12 @@ defmodule Scholar.Cluster.AffinityPropagation do
 
         curr_e = Nx.take_diagonal(a) + Nx.take_diagonal(r) > 0
         curr_e_slice = Nx.reshape(curr_e, {:auto, 1})
-        e = Nx.put_slice(e, [0, Nx.remainder(i, opts[:convergence_iter])], curr_e_slice)
+        e = Nx.put_slice(e, [0, Nx.remainder(i, converge_after)], curr_e_slice)
         k = Nx.sum(curr_e, axes: [0])
 
-        stop = if i >= opts[:convergence_iter] do
+        stop = if i >= converge_after do
           se = Nx.sum(e, axes: [1])
-          unconverged = Nx.sum((se == 0) + (se == opts[:convergence_iter])) != num_samples
+          unconverged = Nx.sum((se == 0) + (se == converge_after)) != num_samples
           if (not unconverged and k > 0) or i == iterations do
             Nx.u8(1)
           else