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Author: ksew1

lib/scholar/naive_bayes/categorical.ex

@@ -476,8 +476,8 @@ defmodule Scholar.NaiveBayes.Categorical do
           },
           x
         ) do
-    {_, _, _, jll} =
-      while {i = 0, feature_log_probability, x, jll = Nx.broadcast(0.0, Nx.shape(x))},
+    {_, jll} =
+      while {{i = 0, feature_log_probability, x}, jll = Nx.broadcast(0.0, Nx.shape(x))},
             i < Nx.axis_size(x, 1) do
         indices = Nx.slice_along_axis(x, i, 1, axis: 1) |> Nx.squeeze(axes: [1])
 
@@ -488,33 +488,32 @@ defmodule Scholar.NaiveBayes.Categorical do
           |> Nx.transpose()
           |> Nx.add(jll)
 
-        {i + 1, feature_log_probability, x, jll}
+        {{i + 1, feature_log_probability, x}, jll}
       end
 
-    total_jll = jll + class_log_priors
-    total_jll
+    jll + class_log_priors
   end
 
   defnp count(x, y_weighted, num_features, num_classes, num_categories, num_samples) do
     class_count = Nx.sum(y_weighted, axes: [0])
 
     feature_count = Nx.broadcast(0.0, {num_features, num_classes, num_categories})
 
-    {_, _, _, _, feature_count} =
-      while {i = 0, x, y_weighted, num_features, feature_count}, i < num_samples do
-        {_, _, _, _, _, feature_count} =
-          while {j = 0, x, y_weighted, i, num_features, feature_count}, j < num_features do
+    {_, feature_count} =
+      while {{i = 0, x, y_weighted, num_features}, feature_count}, i < num_samples do
+        {_, feature_count} =
+          while {{j = 0, x, y_weighted, i, num_features}, feature_count}, j < num_features do
             category_value = x[i][j]
             class_label = Nx.argmax(y_weighted[i])
 
             index = Nx.stack([j, class_label, category_value])
 
             feature_count = Nx.indexed_add(feature_count, index, y_weighted[i][class_label])
 
-            {j + 1, x, y_weighted, i, num_features, feature_count}
+            {{j + 1, x, y_weighted, i, num_features}, feature_count}
           end
 
-        {i + 1, x, y_weighted, num_features, feature_count}
+        {{i + 1, x, y_weighted, num_features}, feature_count}
       end
 
     {class_count, feature_count}
@@ -523,8 +522,8 @@ defmodule Scholar.NaiveBayes.Categorical do
   defnp compute_feature_log_probability(feature_count, alpha, min_categories) do
     feature_log_probability = Nx.broadcast(0.0, Nx.shape(feature_count))
 
-    {_, _, _, _, feature_log_probability} =
-      while {i = 0, feature_count, alpha, min_categories, feature_log_probability},
+    {_, feature_log_probability} =
+      while {{i = 0, feature_count, alpha, min_categories}, feature_log_probability},
             i < Nx.axis_size(feature_count, 0) do
         smoothed_class_count =
           Nx.sum(feature_count[i], axes: [1])
@@ -548,7 +547,7 @@ defmodule Scholar.NaiveBayes.Categorical do
         feature_log_probability =
           Nx.put_slice(feature_log_probability, [i, 0, 0], smoothed_cat_count)
 
-        {i + 1, feature_count, alpha, min_categories, feature_log_probability}
+        {{i + 1, feature_count, alpha, min_categories}, feature_log_probability}
       end
 
     feature_log_probability