feat(nisps-core,wasm): add per-sample weights to MLP training

Add optional sample_weights parameter to MLP::Train() and the WASM
nisps_mlp_train binding. When provided, weights replace the uniform
1/N scaling per sample — enabling recency, spatial, or any custom
importance weighting without changing the training interface.

nisps-core/include/nisps/mlp.hpp

@@ -130,7 +130,8 @@ public:
                 float learning_rate,
                 int max_iterations = 5000,
                 float min_error_cost = 0.001,
-                bool output_log = true);
+                bool output_log = true,
+                const std::vector<T>* sample_weights = nullptr);
 
     /**
      * @brief Training with batch support

nisps-core/include/nisps/mlp_impl.hpp

@@ -516,39 +516,33 @@ T MLP<T>::Train(const training_pair_t& training_sample_set_with_bias,
     float learning_rate,
     int max_iterations,
     float min_error_cost,
-    bool) {
+    bool,
+    const std::vector<T>* sample_weights) {
 
     int i = 0;
     T current_iteration_cost_function = 0.f;
 
-    T sampleSizeReciprocal = 1.f / training_sample_set_with_bias.first.size();
+    const size_t n_samples = training_sample_set_with_bias.first.size();
+    T sampleSizeReciprocal = 1.f / n_samples;
 
     for (i = 0; i < max_iterations; i++) {
         current_iteration_cost_function = 0.f;
 
         auto training_features = training_sample_set_with_bias.first;
         auto training_labels = training_sample_set_with_bias.second;
-        auto t_feat = training_features.begin();
-        auto t_label = training_labels.begin();
 
-        while (t_feat != training_features.end() || t_label != training_labels.end()) {
+        for (size_t s = 0; s < n_samples; s++) {
+            T weight = sample_weights ? (*sample_weights)[s] : sampleSizeReciprocal;
 
-            // Payload
             current_iteration_cost_function +=
-                _TrainOnExample(*t_feat, *t_label, learning_rate, sampleSizeReciprocal);
+                _TrainOnExample(training_features[s], training_labels[s], learning_rate, weight);
-
-            // \Payload
-            if (t_feat != training_features.end())
-            {
-                ++t_feat;
-            }
-            if (t_label != training_labels.end())
-            {
-                ++t_label;
-            }
         }
 
-        current_iteration_cost_function *= sampleSizeReciprocal;
+        // When using custom weights (already normalized to sum to 1), loss is already scaled.
+        // With uniform weights, multiply by sampleSizeReciprocal for backward compat.
+        if (!sample_weights) {
+            current_iteration_cost_function *= sampleSizeReciprocal;
+        }
 
         ReportProgress(true, 100, i, current_iteration_cost_function);
 

playground/wasm/nisps_bindings.cpp

@@ -90,6 +90,7 @@ EMSCRIPTEN_KEEPALIVE
 float nisps_mlp_train(void* ptr,
     float* features_flat, int n_samples, int feature_dim,
     float* labels_flat, int label_dim,
+    float* sample_weights,
     float learning_rate, int max_iterations, float min_error) {
 
     auto* mlp = static_cast<nisps::MLP<float>*>(ptr);
@@ -104,8 +105,16 @@ float nisps_mlp_train(void* ptr,
                          labels_flat + (i + 1) * label_dim);
     }
 
+    // Build weights vector if provided (non-null pointer)
+    std::vector<float> weights_vec;
+    const std::vector<float>* weights_ptr = nullptr;
+    if (sample_weights) {
+        weights_vec.assign(sample_weights, sample_weights + n_samples);
+        weights_ptr = &weights_vec;
+    }
+
     nisps::MLP<float>::training_pair_t data(features, labels);
-    return mlp->Train(data, learning_rate, max_iterations, min_error, false);
+    return mlp->Train(data, learning_rate, max_iterations, min_error, false, weights_ptr);
 }
 
 // ---- Weight manipulation with spread ----