grid don't work

CMakeLists.txt

@@ -1,5 +1,5 @@
 cmake_minimum_required(VERSION 3.25)
-project(COSC-4P80-Assignment-3 VERSION 0.0.18)
+project(COSC-4P80-Assignment-3 VERSION 0.0.19)
 include(FetchContent)
 
 option(ENABLE_ADDRSAN "Enable the address sanitizer" OFF)

include/assign3/array.h

@@ -29,12 +29,24 @@ namespace assign3
     class array_t
     {
         public:
-            explicit array_t(blt::size_t dimensions, blt::size_t width, blt::size_t height):
+            explicit array_t(blt::size_t dimensions, blt::size_t width, blt::size_t height, shape_t shape):
                     width(static_cast<blt::i64>(width)), height(static_cast<blt::i64>(height))
             {
-                for (blt::size_t i = 0; i < width; i++)
+                switch (shape)
+                {
+                    case shape_t::GRID:
+                    case shape_t::GRID_WRAP:
                         for (blt::size_t j = 0; j < height; j++)
+                            for (blt::size_t i = 0; i < width; i++)
+                                map.emplace_back(dimensions, i, j);
+                        break;
+                    case shape_t::GRID_OFFSET:
+                    case shape_t::GRID_OFFSET_WRAP:
+                        for (blt::size_t j = 0; j < height; j++)
+                            for (blt::size_t i = 0; i < width; i++)
                                 map.emplace_back(dimensions, (j % 2 == 0 ? static_cast<Scalar>(i) : static_cast<Scalar>(i) + 0.5f), j);
+                        break;
+                }
             }
             
             [[nodiscard]] blt::vec2ul from_index(blt::size_t index) const

include/assign3/fwdecl.h

@@ -30,15 +30,19 @@ namespace assign3
     inline constexpr blt::i32 RENDER_2D = 0x0;
     inline constexpr blt::i32 RENDER_3D = 0x1;
     
-    enum class shape : blt::i32
+    enum class shape_t : blt::i32
     {
-        GRID = RENDER_2D,
+        GRID,
-        GRID_WRAP = RENDER_2D,
+        GRID_WRAP,
-        GRID_OFFSET = RENDER_2D,
+        GRID_OFFSET,
-        GRID_OFFSET_WRAP = RENDER_2D,
+        GRID_OFFSET_WRAP
-        GAUSSIAN_DIST = RENDER_2D,
+    };
-        TOROIDAL = RENDER_3D,
+    
-        CYLINDER = RENDER_3D
+    inline std::array<std::string, 4> shape_names{
+        "Grid",
+        "Edge Wrapped Grid",
+        "Honey Comb Grid",
+        "Edge Wrapped Honey Comb"
     };
     
     enum class debug_type

include/assign3/manager.h

@@ -92,31 +92,43 @@ namespace assign3
             
             void render();
             
-            void update_graphics();
-            
             void regenerate_network()
             {
-                som = std::make_unique<som_t>(motor_data.files[currently_selected_network].normalize(), som_width, som_height, max_epochs);
+                switch (static_cast<shape_t>(selected_som_mode))
+                {
+                    case shape_t::GRID:
+                    case shape_t::GRID_OFFSET:
+                        distance_function = std::make_unique<euclidean_distance_function_t>();
+                        break;
+                    case shape_t::GRID_OFFSET_WRAP:
+                    case shape_t::GRID_WRAP:
+                        distance_function = std::make_unique<toroidal_euclidean_distance_function_t>(som_width, som_height);
+                        break;
+                }
+                error_plotting.clear();
+                som = std::make_unique<som_t>(motor_data.files[currently_selected_network], som_width, som_height, max_epochs,
+                                              distance_function.get(), static_cast<shape_t>(selected_som_mode));
+                error_plotting.push_back(som->topological_error(motor_data.files[currently_selected_network]));
             }
         
         private:
             motor_data_t& motor_data;
             std::unique_ptr<som_t> som;
             std::unique_ptr<topology_function_t> topology_function;
+            std::unique_ptr<distance_function_t> distance_function;
+            
+            std::vector<Scalar> error_plotting;
             
             blt::gfx::font_renderer_t fr2d{};
             blt::gfx::batch_renderer_2d br2d;
             
-            float draw_width = 0;
-            float draw_height = 0;
-            float neuron_scale = 35;
-            
             blt::i32 som_width = 5;
             blt::i32 som_height = 5;
-            blt::i32 max_epochs = 100;
+            blt::i32 max_epochs = 2000;
-            Scalar initial_learn_rate = 0.1;
+            Scalar initial_learn_rate = 1;
             
             int currently_selected_network = 0;
+            int selected_som_mode = 0;
             bool debug_mode = false;
             bool draw_colors = true;
             bool draw_data_lines = false;

include/assign3/neuron.h

@@ -42,17 +42,30 @@ namespace assign3
             
             [[nodiscard]] Scalar dist(const std::vector<Scalar>& X) const;
             
-            [[nodiscard]] inline const std::vector<Scalar>& get_data() const
+            neuron_t& set_activation(Scalar act)
+            {
+                activation = act;
+                return *this;
+            }
+            
+            void activate(Scalar act)
+            { activation += act; }
+            
+            [[nodiscard]] const std::vector<Scalar>& get_data() const
             { return data; }
             
-            [[nodiscard]] inline Scalar get_x() const
+            [[nodiscard]] Scalar get_x() const
             { return x_pos; }
             
-            [[nodiscard]] inline Scalar get_y() const
+            [[nodiscard]] Scalar get_y() const
             { return y_pos; }
             
+            [[nodiscard]] Scalar get_activation() const
+            { return activation; }
+        
         private:
             Scalar x_pos, y_pos;
+            Scalar activation = 0;
             std::vector<Scalar> data;
     };
     

include/assign3/som.h

@@ -29,7 +29,7 @@ namespace assign3
     class som_t
     {
         public:
-            som_t(const data_file_t& file, blt::size_t width, blt::size_t height, blt::size_t max_epochs);
+            som_t(const data_file_t& file, blt::size_t width, blt::size_t height, blt::size_t max_epochs, distance_function_t* dist_func, shape_t shape);
             
             blt::size_t get_closest_neuron(const std::vector<Scalar>& data);
             
@@ -39,6 +39,8 @@ namespace assign3
             
             blt::vec2 get_topological_position(const std::vector<Scalar>& data);
             
+            Scalar topological_error(const data_file_t& data);
+            
             [[nodiscard]] const array_t& get_array() const
             { return array; }
             
@@ -53,6 +55,7 @@ namespace assign3
             data_file_t file;
             blt::size_t current_epoch = 0;
             blt::size_t max_epochs;
+            distance_function_t* dist_func;
     };
     
 }

src/manager.cpp

@@ -97,7 +97,6 @@ namespace assign3
         topology_function = std::make_unique<gaussian_function_t>();
         
         regenerate_network();
-        update_graphics();
     }
     
     void renderer_t::cleanup()
@@ -142,8 +141,13 @@ namespace assign3
                 ImGui::Checkbox("Run to completion", &running);
                 ImGui::Text("Epoch %ld / %ld", som->get_current_epoch(), som->get_max_epochs());
             }
+            ImGui::SetNextItemOpen(true, ImGuiCond_Appearing);
             if (ImGui::CollapsingHeader("SOM Settings"))
             {
+                ImGui::Text("Network Shape");
+                if (ImGui::ListBox("##NetworkShape", &selected_som_mode, get_selection_string, shape_names.data(),
+                                   static_cast<int>(shape_names.size())))
+                    regenerate_network();
                 if (ImGui::InputInt("SOM Width", &som_width) || ImGui::InputInt("SOM Height", &som_height) ||
                     ImGui::InputInt("Max Epochs", &max_epochs))
                     regenerate_network();
@@ -195,13 +199,22 @@ namespace assign3
                                     ImPlotPoint(som_width, som_height), ImPlotHeatmapFlags_ColMajor);
                 ImPlot::EndPlot();
             }
+            ImPlot::SetNextAxesLimits(0, max_epochs, 0, 1, ImPlotCond_Once);
+            if (ImPlot::BeginPlot("Error"))
+            {
+                ImPlot::PlotLine("##error", error_plotting.data(), static_cast<int>(error_plotting.size()));
+                ImPlot::EndPlot();
+            }
         }
         ImGui::End();
         
         if (running)
         {
             if (som->get_current_epoch() < som->get_max_epochs())
+            {
                 som->train_epoch(initial_learn_rate, topology_function.get());
+                error_plotting.push_back(som->topological_error(current_data_file));
+            }
         }
         
         
@@ -220,24 +233,6 @@ namespace assign3
         fr2d.render();
     }
     
-    void renderer_t::update_graphics()
-    {
-        // find the min x / y for the currently drawn som as positions may depend on type.
-        const auto x_comparator = [](const auto& a, const auto& b) {
-            return a.get_x() < b.get_x();
-        };
-        const auto y_comparator = [](const auto& a, const auto& b) {
-            return a.get_y() < b.get_y();
-        };
-        const auto& som_neurons = som->get_array().get_map();
-        auto min_x = std::min_element(som_neurons.begin(), som_neurons.end(), x_comparator)->get_x();
-        auto max_x = std::max_element(som_neurons.begin(), som_neurons.end(), x_comparator)->get_x();
-        auto min_y = std::min_element(som_neurons.begin(), som_neurons.end(), y_comparator)->get_y();
-        auto max_y = std::max_element(som_neurons.begin(), som_neurons.end(), y_comparator)->get_y();
-        draw_width = (max_x - min_x);
-        draw_height = (max_y - min_y);
-    }
-    
     std::vector<float> renderer_t::get_neuron_activations(const data_file_t& file)
     {
         static std::vector<float> closest_type;
@@ -248,6 +243,9 @@ namespace assign3
         for (auto [i, v] : blt::enumerate(som->get_array().get_map()))
         {
             auto half = som->find_closest_neighbour_distance(i) / at_distance_measurement;
+//            auto sigma = std::sqrt(-(half * half) / (2 * std::log(requested_activation)));
+//            auto r = 1 / (2 * sigma * sigma);
+//
             auto scale = topology_function->scale(half, requested_activation);
             for (const auto& data : file.data_points)
             {

src/som.cpp

@@ -26,8 +26,9 @@
 namespace assign3
 {
     
-    som_t::som_t(const data_file_t& file, blt::size_t width, blt::size_t height, blt::size_t max_epochs):
+    som_t::som_t(const data_file_t& file, blt::size_t width, blt::size_t height, blt::size_t max_epochs, distance_function_t* dist_func,
-            array(file.data_points.begin()->bins.size(), width, height), file(file), max_epochs(max_epochs)
+                 shape_t shape):
+            array(file.data_points.begin()->bins.size(), width, height, shape), file(file), max_epochs(max_epochs), dist_func(dist_func)
     {
         for (auto& v : array.get_map())
             v.randomize(std::random_device{}());
@@ -57,8 +58,7 @@ namespace assign3
             {
                 if (i == v0_idx)
                     continue;
-                toroidal_euclidean_distance_function_t dist_func{static_cast<blt::i32>(array.get_width()), static_cast<blt::i32>(array.get_height())};
+                auto dist = basis_func->call(neuron_t::distance(dist_func, v0, n), time_ratio * scale);
-                auto dist = basis_func->call(neuron_t::distance(&dist_func, v0, n), time_ratio * scale);
                 n.update(current_data.bins, dist, eta);
             }
         }
@@ -84,12 +84,11 @@ namespace assign3
     
     Scalar som_t::find_closest_neighbour_distance(blt::size_t v0)
     {
-        toroidal_euclidean_distance_function_t dist_func{static_cast<blt::i32>(array.get_width()), static_cast<blt::i32>(array.get_height())};
         Scalar distance_min = std::numeric_limits<Scalar>::max();
         for (const auto& [i, n] : blt::enumerate(array.get_map()))
         {
             if (i != v0)
-                distance_min = std::min(distance_min, neuron_t::distance(&dist_func, array.get_map()[v0], n));
+                distance_min = std::min(distance_min, neuron_t::distance(dist_func, array.get_map()[v0], n));
         }
         return distance_min;
     }
@@ -140,5 +139,40 @@ namespace assign3
         return (dp1 * p_1) + (dp2 * p_2) + (dp3 * p_3);
     }
     
+    Scalar som_t::topological_error(const data_file_t& data)
+    {
+        Scalar total = 0;
+        std::vector<std::pair<blt::size_t, Scalar>> distances;
+        
+        for (const auto& x : data.data_points)
+        {
+            distances.clear();
+            for (const auto& [i, n] : blt::enumerate(array.get_map()))
+                distances.emplace_back(i, n.dist(x.bins));
+            
+            std::pair<blt::size_t, Scalar> min1 = {0, std::numeric_limits<Scalar>::max()};
+            std::pair<blt::size_t, Scalar> min2 = {0, std::numeric_limits<Scalar>::max()};
+            
+            for (const auto& elem : distances)
+            {
+                if (elem.second < min1.second)
+                {
+                    min2 = min1;
+                    min1 = elem;
+                } else if (elem.second < min2.second)
+                    min2 = elem;
+            }
+            
+            // we can assert the neurons are neighbours if the distance between the BMUs and the nearest neighbour are equal.
+            auto min_distances = neuron_t::distance(dist_func, array.get_map()[min1.first], array.get_map()[min2.first]);
+            auto neighbour_distances = find_closest_neighbour_distance(min1.first);
+            
+            if (!blt::f_equal(min_distances, neighbour_distances))
+                total += 1;
+        }
+        
+        return total / static_cast<Scalar>(data.data_points.size());
+    }
+    
     
 }