CMakeLists.txt

@@ -1,5 +1,5 @@
 cmake_minimum_required(VERSION 3.25)
-project(COSC-4P80-Assignment-3 VERSION 0.0.24)
+project(COSC-4P80-Assignment-3 VERSION 0.0.22)
 include(FetchContent)
 
 option(ENABLE_ADDRSAN "Enable the address sanitizer" OFF)
@@ -14,6 +14,12 @@ FetchContent_Declare(implot
         FIND_PACKAGE_ARGS)
 FetchContent_MakeAvailable(implot)
 
+FetchContent_Declare(matplotplusplus
+        GIT_REPOSITORY https://github.com/alandefreitas/matplotplusplus
+        GIT_TAG v1.2.1
+        FIND_PACKAGE_ARGS)
+FetchContent_MakeAvailable(matplotplusplus)
+
 add_subdirectory(lib/blt-with-graphics)
 
 include_directories(include/)
@@ -26,7 +32,7 @@ add_executable(COSC-4P80-Assignment-3 ${PROJECT_BUILD_FILES} ${IM_PLOT_FILES})
 target_compile_options(COSC-4P80-Assignment-3 PRIVATE -Wall -Wextra -Wpedantic -Wno-comment)
 target_link_options(COSC-4P80-Assignment-3 PRIVATE -Wall -Wextra -Wpedantic -Wno-comment)
 
-target_link_libraries(COSC-4P80-Assignment-3 PRIVATE BLT_WITH_GRAPHICS)
+target_link_libraries(COSC-4P80-Assignment-3 PRIVATE BLT_WITH_GRAPHICS matplot)
 
 if (${ENABLE_ADDRSAN} MATCHES ON)
     target_compile_options(COSC-4P80-Assignment-3 PRIVATE -fsanitize=address)

include/assign3/manager.h

@@ -94,6 +94,7 @@ namespace assign3
                 som = std::make_unique<som_t>(motor_data.files[currently_selected_network], som_width, som_height, max_epochs,
                                               distance_function.get(), topology_function.get(), static_cast<shape_t>(selected_som_mode),
                                               static_cast<init_t>(selected_init_type), normalize_init);
+                som->compute_neuron_activations();
             }
         
         private:

include/assign3/som.h

@@ -44,8 +44,6 @@ namespace assign3
             
             Scalar quantization_error();
             
-            void compute_errors();
-            
             void compute_neuron_activations(Scalar distance = 2, Scalar activation = 0.5);
             
             void write_activations(std::ostream& out);

plot_heatmap.py

@@ -1,30 +0,0 @@
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import matplotlib
-import matplotlib as mpl
-import sys
-
-filename = sys.argv[1]
-size = sys.argv[2]
-
-df = pd.read_csv(filename, header=None)
-
-height, width = df.shape
-
-data = df.to_numpy()
-
-plt.imshow(data, cmap='coolwarm_r', interpolation='nearest')
-
-plt.xticks(np.arange(width), np.arange(width))
-plt.yticks(np.arange(height), np.arange(height))
-
-plt.xlabel('X Pos')
-plt.ylabel('Y Pos')
-plt.title('Heatmap of Motor Data (Bins: {})'.format(size))
-
-plt.gca().invert_yaxis()
-
-plt.colorbar()
-
-plt.savefig("heatmap.png")

plot_line_graph.py

@@ -1,61 +0,0 @@
-import pandas as pd
-import matplotlib.pyplot as plt
-import numpy as np
-import sys
-
-file1 = sys.argv[1]
-file2 = sys.argv[2]
-bins = sys.argv[3]
-split = sys.argv[4]
-
-df1 = pd.read_csv(file1)
-df2 = pd.read_csv(file2)
-
-data1 = df1.to_numpy()
-data2 = df2.to_numpy()
-
-y_min = np.min(data2)
-y_max = np.max(data2)
-
-if split.lower() == "false":
-    fig, ax1 = plt.subplots()
-
-    ax1.plot(data1, color='b', label='Topological Error')
-    ax1.set_xlabel('Epoch')
-    ax1.set_ylabel('Error %', color='b')
-    ax1.tick_params(axis='y', labelcolor='b')
-    ax1.set_ylim(0, 1)
-    #ax1.set_xlim(0, data1.size)
-
-    ax2 = ax1.twinx()
-
-    ax2.plot(data2, color='r', label='Quantization Error')
-    ax2.set_ylabel('Incorrect BMU', color='r')
-    ax2.tick_params(axis='y', labelcolor='r')
-    ax2.set_ylim(y_min, y_max)
-
-    ax1.set_title('Topological and Quantization Error (Bins: {})'.format(bins))
-
-    plt.savefig("errors{}.png".format(bins))
-else:
-    plt.plot(data1, color='b', label='Topological Error')
-    plt.xlabel('Epoch')
-    plt.ylabel('Error %', color='b')
-    plt.tick_params(axis='y', labelcolor='b')
-    plt.ylim(0, 1)
-    
-    plt.title("Topological Error (Bins: {})".format(bins))
-    
-    plt.savefig("errors-topological{}.png".format(bins))
-    
-    plt.plot(data2, color='b', label='Quantization Error')
-    plt.xlabel('Epoch')
-    plt.ylabel('Incorrect BMU', color='b')
-    plt.tick_params(axis='y', labelcolor='b')
-    plt.ylim(y_min, y_max)
-    
-    plt.title("Quantization Error (Bins: {})".format(bins))
-    
-    plt.savefig("errors-quantization{}.png".format(bins))
-
-

src/main.cpp

@@ -10,6 +10,7 @@
 #include <mutex>
 #include <fstream>
 #include <filesystem>
+#include <matplot/matplot.h>
 
 using namespace assign3;
 
@@ -77,6 +78,11 @@ void action_start_graphics(const std::vector<std::string>& argv_vector)
     blt::gfx::init(blt::gfx::window_data{"My Sexy Window", init, update, destroy}.setSyncInterval(1).setMaximized(true));
 }
 
+struct activation
+{
+    Scalar x, y, act;
+};
+
 struct task_t // NOLINT
 {
     data_file_t* file;
@@ -95,7 +101,7 @@ struct task_t // NOLINT
     gaussian_function_t topology_func{};
     std::vector<std::vector<Scalar>> topological_errors{};
     std::vector<std::vector<Scalar>> quantization_errors{};
-    std::vector<std::vector<Scalar>> activations{};
+    std::vector<std::vector<activation>> activations{};
 };
 
 void action_test(const std::vector<std::string>& argv_vector)
@@ -119,7 +125,7 @@ void action_test(const std::vector<std::string>& argv_vector)
     
     static blt::size_t runs = 30;
     
-    for (blt::size_t _ = 0; _ < std::thread::hardware_concurrency(); _++)
+    for (blt::size_t i = 0; i < std::thread::hardware_concurrency(); i++)
     {
         threads.emplace_back([&task_mutex, &tasks]() {
             do
@@ -133,7 +139,7 @@ void action_test(const std::vector<std::string>& argv_vector)
                     tasks.pop_back();
                 }
                 
-                for (blt::size_t run = 0; run < runs; run++)
+                for (blt::size_t i = 0; i < runs; i++)
                 {
                     gaussian_function_t func{};
                     auto dist = distance_function_t::from_shape(task.shape, task.width, task.height);
@@ -141,13 +147,13 @@ void action_test(const std::vector<std::string>& argv_vector)
                                                                          &task.topology_func, task.shape, task.init, false);
                     while (som->get_current_epoch() < som->get_max_epochs())
                         som->train_epoch(task.initial_learn_rate);
+                    som->compute_neuron_activations();
                     
                     task.topological_errors.push_back(som->get_topological_errors());
                     task.quantization_errors.push_back(som->get_quantization_errors());
-                    
+                    std::vector<activation> acts;
-                    std::vector<Scalar> acts;
+                    for (const auto& neuron : som->get_array().get_map())
-                    for (const auto& v : som->get_array().get_map())
+                        acts.push_back({neuron.get_x(), neuron.get_y(), neuron.get_activation()});
-                        acts.push_back(v.get_activation());
                     task.activations.emplace_back(std::move(acts));
                 }
                 std::stringstream paths;
@@ -164,7 +170,7 @@ void action_test(const std::vector<std::string>& argv_vector)
                 
                 std::vector<Scalar> average_topological_errors;
                 std::vector<Scalar> average_quantization_errors;
-                std::vector<Scalar> average_activations;
+                std::vector<activation> average_activations;
                 
                 average_topological_errors.resize(task.topological_errors.begin()->size());
                 average_quantization_errors.resize(task.quantization_errors.begin()->size());
@@ -178,30 +184,30 @@ void action_test(const std::vector<std::string>& argv_vector)
                         average_quantization_errors[index] += v;
                 for (const auto& vec : task.activations)
                     for (auto [index, v] : blt::enumerate(vec))
-                        average_activations[index] += v;
+                        average_activations[index].act += v.act;
                 
-                std::ofstream topological{path + "topological_avg.csv"};
+                for (auto& v : average_topological_errors)
-                std::ofstream quantization{path + "quantization_avg.csv"};
+                    v /= static_cast<Scalar>(runs);
-                std::ofstream activations{path + "activations_avg.csv"};
+                for (auto& v : average_quantization_errors)
+                    v /= static_cast<Scalar>(runs);
+                for (auto& v : average_activations)
+                    v.act /= static_cast<Scalar>(runs);
                 
-                topological << "error\n";
+                auto f = matplot::figure();
-                quantization << "error\n";
+                f->tiledlayout(2, 1);
-                for (auto [i, v] : blt::enumerate(average_topological_errors))
+                auto axis = f->add_axes();
-                {
+                axis->hold(true);
-                    topological << v / static_cast<Scalar>(runs) << '\n';
+                axis->plot(matplot::linspace(0, static_cast<double>(task.max_epochs)), average_topological_errors)->display_name("Topological Error");
-                }
+                axis->title("Error");
-                for (auto [i, v] : blt::enumerate(average_quantization_errors))
+                axis->xlabel("Epoch");
-                {
+                axis->ylabel("Error");
-                    quantization << v / static_cast<Scalar>(runs) << '\n';
+                axis->grid(true);
-                }
+                axis->plot(matplot::linspace(0, static_cast<double>(task.max_epochs)), average_quantization_errors)->display_name("Quantization Error");
-                for (auto [i, v] : blt::enumerate(average_activations))
+                f->title("Topological and Quantization Errors, " + std::to_string(runs) + " Runs");
-                {
+                
-                    activations << v / static_cast<Scalar>(runs);
+                f->save((path + "errors_plot.eps"), "postscript");
-                    if (i % task.width == task.width-1)
+                f->save((path + "errors_plot.tex"), "epslatex");
-                        activations << '\n';
+                f->save((path + "errors_plot.png"), "png");
-                    else
-                        activations << ',';
-                }
                 
                 BLT_INFO("Task '%s' Complete", path.c_str());
                 

src/manager.cpp

@@ -242,7 +242,10 @@ namespace assign3
         if (running)
         {
             if (som->get_current_epoch() < som->get_max_epochs())
+            {
                 som->train_epoch(initial_learn_rate);
+                som->compute_neuron_activations();
+            }
         }
         
         

src/som.cpp

@@ -34,7 +34,8 @@ namespace assign3
     {
         for (auto& v : array.get_map())
             v.randomize(std::random_device{}(), init, normalize, file);
-        compute_errors();
+        topological_errors.push_back(topological_error());
+        quantization_errors.push_back(quantization_error());
     }
     
     void som_t::train_epoch(Scalar initial_learn_rate)
@@ -66,7 +67,8 @@ namespace assign3
             }
         }
         current_epoch++;
-        compute_errors();
+        topological_errors.push_back(topological_error());
+        quantization_errors.push_back(quantization_error());
     }
     
     blt::size_t som_t::get_closest_neuron(const std::vector<Scalar>& data)
@@ -267,12 +269,5 @@ namespace assign3
         return incorrect;
     }
     
-    void som_t::compute_errors()
-    {
-        compute_neuron_activations();
-        topological_errors.push_back(topological_error());
-        quantization_errors.push_back(quantization_error());
-    }
-    
     
 }