about to start convert
parent
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@ -1,5 +1,5 @@
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cmake_minimum_required(VERSION 3.25)
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project(COSC-4P80-Assignment-3 VERSION 0.0.20)
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project(COSC-4P80-Assignment-3 VERSION 0.0.21)
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include(FetchContent)
|
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|
||||
option(ENABLE_ADDRSAN "Enable the address sanitizer" OFF)
|
||||
|
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|||
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@ -44,8 +44,8 @@ namespace assign3
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case shape_t::GRID_OFFSET_WRAP:
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for (blt::size_t j = 0; j < height; j++)
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for (blt::size_t i = 0; i < width; i++)
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map.emplace_back(dimensions, (j % 2 == 0 ? static_cast<Scalar>(i) : static_cast<Scalar>(i) + 0.5f),
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static_cast<Scalar>(static_cast<double>(j) * (std::sqrt(3) / 2.0)));
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map.emplace_back(dimensions, (j % 2 == 0 ? static_cast<Scalar>(i) : static_cast<Scalar>(i) + 0.5f), j);
|
||||
// static_cast<Scalar>(static_cast<double>(j) * (std::sqrt(3) / 2.0))
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||||
break;
|
||||
}
|
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}
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||||
|
|
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|||
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@ -77,6 +77,23 @@ namespace assign3
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blt::i32 width, height;
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||||
};
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||||
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||||
struct axial_distance_function_t : public distance_function_t
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||||
{
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||||
[[nodiscard]] Scalar distance(blt::span<const Scalar> x, blt::span<const Scalar> y) const final;
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||||
};
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struct toroidal_axial_distance_function_t : public distance_function_t
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{
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||||
public:
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toroidal_axial_distance_function_t(blt::i32 width, blt::i32 height): width(width), height(height)
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||||
{}
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||||
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[[nodiscard]] Scalar distance(blt::span<const Scalar> x, blt::span<const Scalar> y) const final;
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private:
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blt::i32 width, height;
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};
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||||
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}
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||||
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||||
#endif //COSC_4P80_ASSIGNMENT_3_FUNCTIONS_H
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|||
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@ -82,10 +82,6 @@ namespace assign3
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void cleanup();
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std::vector<float> get_neuron_activations(const data_file_t& file);
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static std::vector<float> normalize_data(const std::vector<float>& data);
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||||
void draw_som(neuron_render_info_t info, const std::function<blt::vec4(render_data_t)>& color_func);
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void draw_debug(const data_file_t& file);
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||||
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@ -97,19 +93,22 @@ namespace assign3
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switch (static_cast<shape_t>(selected_som_mode))
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{
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case shape_t::GRID:
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case shape_t::GRID_OFFSET:
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distance_function = std::make_unique<euclidean_distance_function_t>();
|
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break;
|
||||
case shape_t::GRID_OFFSET_WRAP:
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||||
case shape_t::GRID_WRAP:
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||||
distance_function = std::make_unique<toroidal_euclidean_distance_function_t>(som_width, som_height);
|
||||
break;
|
||||
case shape_t::GRID_OFFSET:
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||||
distance_function = std::make_unique<axial_distance_function_t>();
|
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break;
|
||||
case shape_t::GRID_OFFSET_WRAP:
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||||
distance_function = std::make_unique<toroidal_axial_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,
|
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distance_function.get(), static_cast<shape_t>(selected_som_mode),
|
||||
distance_function.get(), topology_function.get(), static_cast<shape_t>(selected_som_mode),
|
||||
static_cast<init_t>(selected_init_type), normalize_init);
|
||||
error_plotting.push_back(som->topological_error(motor_data.files[currently_selected_network]));
|
||||
som->compute_neuron_activations();
|
||||
}
|
||||
|
||||
private:
|
||||
|
|
@ -118,8 +117,6 @@ namespace assign3
|
|||
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;
|
||||
|
||||
|
|
|
|||
|
|
@ -30,17 +30,23 @@ namespace assign3
|
|||
{
|
||||
public:
|
||||
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, init_t init, bool normalize);
|
||||
topology_function_t* topology_function, shape_t shape, init_t init, bool normalize);
|
||||
|
||||
blt::size_t get_closest_neuron(const std::vector<Scalar>& data);
|
||||
|
||||
Scalar find_closest_neighbour_distance(blt::size_t v0);
|
||||
|
||||
void train_epoch(Scalar initial_learn_rate, topology_function_t* basis_func);
|
||||
void train_epoch(Scalar initial_learn_rate);
|
||||
|
||||
blt::vec2 get_topological_position(const std::vector<Scalar>& data);
|
||||
|
||||
Scalar topological_error(const data_file_t& data);
|
||||
Scalar topological_error();
|
||||
|
||||
void compute_neuron_activations(Scalar distance = 2, Scalar activation = 0.5);
|
||||
|
||||
void write_activations(std::ostream& out);
|
||||
|
||||
void write_topology_errors(std::ostream& out);
|
||||
|
||||
[[nodiscard]] const array_t& get_array() const
|
||||
{ return array; }
|
||||
|
|
@ -51,12 +57,18 @@ namespace assign3
|
|||
[[nodiscard]] blt::size_t get_max_epochs() const
|
||||
{ return max_epochs; }
|
||||
|
||||
[[nodiscard]] const std::vector<Scalar>& get_topological_errors() const
|
||||
{ return topological_errors; }
|
||||
|
||||
private:
|
||||
array_t array;
|
||||
data_file_t file;
|
||||
blt::size_t current_epoch = 0;
|
||||
blt::size_t max_epochs;
|
||||
distance_function_t* dist_func;
|
||||
topology_function_t* topology_function;
|
||||
|
||||
std::vector<Scalar> topological_errors;
|
||||
};
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -54,4 +54,38 @@ namespace assign3
|
|||
Scalar y_min = std::min(y_diff, static_cast<Scalar>(height) - y_diff);
|
||||
return std::sqrt(x_min * x_min + y_min * y_min);
|
||||
}
|
||||
|
||||
Scalar axial_distance_function_t::distance(blt::span<const Scalar> x, blt::span<const Scalar> y) const
|
||||
{
|
||||
static thread_local std::vector<Scalar> distances;
|
||||
distances.clear();
|
||||
Scalar total = 0;
|
||||
for (auto [q, r] : blt::in_pairs(x, y))
|
||||
{
|
||||
distances.push_back(std::abs(q - r));
|
||||
total += distances.back();
|
||||
}
|
||||
|
||||
Scalar min = distances.front();
|
||||
for (auto v : distances)
|
||||
min = std::min(min, v);
|
||||
|
||||
return total - min;
|
||||
}
|
||||
|
||||
Scalar axial_distance(Scalar q1, Scalar r1, Scalar q2, Scalar r2) {
|
||||
return (std::abs(q1 - q2) + std::abs(r1 - r2) + std::abs((q1 + r1) - (q2 + r2))) / 2;
|
||||
}
|
||||
|
||||
Scalar toroidal_axial_distance_function_t::distance(blt::span<const Scalar> x, blt::span<const Scalar> y) const
|
||||
{
|
||||
BLT_ASSERT(x.size() == 2 && y.size() == 2);
|
||||
|
||||
Scalar x_diff = std::abs(x[0] - y[0]);
|
||||
Scalar y_diff = std::abs(x[1] - y[1]);
|
||||
Scalar x_min = std::min(x_diff, static_cast<Scalar>(width) - x_diff);
|
||||
Scalar y_min = std::min(y_diff, static_cast<Scalar>(height) - y_diff);
|
||||
Scalar total = x_min + y_min;
|
||||
return total - std::min(x_min, y_min);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,125 @@
|
|||
#include <blt/std/logging.h>
|
||||
#include <blt/parse/argparse.h>
|
||||
#include <blt/gfx/window.h>
|
||||
#include "blt/gfx/renderer/resource_manager.h"
|
||||
#include "blt/gfx/renderer/camera.h"
|
||||
#include "implot.h"
|
||||
#include <assign3/file.h>
|
||||
#include <assign3/manager.h>
|
||||
|
||||
using namespace assign3;
|
||||
|
||||
blt::gfx::matrix_state_manager global_matrices;
|
||||
blt::gfx::resource_manager resources;
|
||||
blt::gfx::first_person_camera_2d camera;
|
||||
assign3::motor_data_t data{};
|
||||
assign3::renderer_t renderer{data, resources, global_matrices};
|
||||
|
||||
void init(const blt::gfx::window_data&)
|
||||
{
|
||||
using namespace blt::gfx;
|
||||
BLT_INFO("Hello World!");
|
||||
|
||||
global_matrices.create_internals();
|
||||
resources.load_resources();
|
||||
renderer.create();
|
||||
|
||||
ImPlot::CreateContext();
|
||||
}
|
||||
|
||||
void update(const blt::gfx::window_data& window_data)
|
||||
{
|
||||
using namespace blt::gfx;
|
||||
global_matrices.update_perspectives(window_data.width, window_data.height, 90, 0.1, 2000);
|
||||
|
||||
camera.update();
|
||||
camera.update_view(global_matrices);
|
||||
global_matrices.update();
|
||||
|
||||
renderer.render();
|
||||
}
|
||||
|
||||
void destroy(const blt::gfx::window_data&)
|
||||
{
|
||||
global_matrices.cleanup();
|
||||
resources.cleanup();
|
||||
renderer.cleanup();
|
||||
ImPlot::DestroyContext();
|
||||
blt::gfx::cleanup();
|
||||
BLT_INFO("Goodbye World!");
|
||||
}
|
||||
|
||||
void load_data_files(const std::string& str)
|
||||
{
|
||||
data.files = assign3::data_file_t::load_data_files_from_path(str);
|
||||
for (auto& v : data.files)
|
||||
v = v.normalize();
|
||||
data.update();
|
||||
}
|
||||
|
||||
void action_start_graphics(const std::vector<std::string>& argv_vector)
|
||||
{
|
||||
blt::arg_parse parser{};
|
||||
|
||||
parser.addArgument(blt::arg_builder{"--file", "-f"}
|
||||
.setDefault("../data")
|
||||
.setHelp("Path to data files").build());
|
||||
|
||||
auto args = parser.parse_args(argv_vector);
|
||||
|
||||
load_data_files(args.get<std::string>("file"));
|
||||
|
||||
blt::gfx::init(blt::gfx::window_data{"My Sexy Window", init, update, destroy}.setSyncInterval(1).setMaximized(true));
|
||||
}
|
||||
|
||||
void action_test(const std::vector<std::string>& argv_vector)
|
||||
{
|
||||
blt::arg_parse parser{};
|
||||
|
||||
parser.addArgument(blt::arg_builder{"--file", "-f"}
|
||||
.setDefault("../data")
|
||||
.setHelp("Path to data files").build());
|
||||
|
||||
auto args = parser.parse_args(argv_vector);
|
||||
|
||||
load_data_files(args.get<std::string>("file"));
|
||||
|
||||
|
||||
}
|
||||
|
||||
void action_convert(const std::vector<std::string>& argv_vector)
|
||||
{
|
||||
|
||||
}
|
||||
|
||||
int main(int argc, const char** argv)
|
||||
{
|
||||
std::vector<std::string> argv_vector;
|
||||
for (int i = 0; i < argc; i++)
|
||||
argv_vector.emplace_back(argv[i]);
|
||||
|
||||
blt::arg_parse parser{};
|
||||
|
||||
parser.addArgument(blt::arg_builder{"action"}
|
||||
.setAction(blt::arg_action_t::SUBCOMMAND)
|
||||
.setHelp("Action to run. Can be: [graphics, test, convert]").build());
|
||||
|
||||
auto args = parser.parse_args(argv_vector);
|
||||
|
||||
if (!args.contains("action"))
|
||||
{
|
||||
BLT_ERROR("Please provide an action");
|
||||
return 0;
|
||||
}
|
||||
|
||||
argv_vector.erase(argv_vector.begin() + 1);
|
||||
|
||||
auto action = blt::string::toLowerCase(args.get<std::string>("action"));
|
||||
if (action == "graphics")
|
||||
action_start_graphics(argv_vector);
|
||||
else if (action == "test")
|
||||
action_test(argv_vector);
|
||||
else if (action == "convert")
|
||||
action_convert(argv_vector);
|
||||
|
||||
}
|
||||
80
src/main.cpp
80
src/main.cpp
|
|
@ -49,18 +49,82 @@ void destroy(const blt::gfx::window_data&)
|
|||
BLT_INFO("Goodbye World!");
|
||||
}
|
||||
|
||||
int main(int argc, const char** argv)
|
||||
void load_data_files(const std::string& str)
|
||||
{
|
||||
blt::arg_parse parser{};
|
||||
|
||||
parser.addArgument(blt::arg_builder{"--file", "-f"}.setDefault("../data").setHelp("Path to data files").build());
|
||||
|
||||
auto args = parser.parse_args(argc, argv);
|
||||
|
||||
data.files = assign3::data_file_t::load_data_files_from_path(args.get<std::string>("file"));
|
||||
data.files = assign3::data_file_t::load_data_files_from_path(str);
|
||||
for (auto& v : data.files)
|
||||
v = v.normalize();
|
||||
data.update();
|
||||
}
|
||||
|
||||
void action_start_graphics(const std::vector<std::string>& argv_vector)
|
||||
{
|
||||
blt::arg_parse parser{};
|
||||
parser.setHelpExtras("graphics");
|
||||
|
||||
parser.addArgument(blt::arg_builder{"--file", "-f"}
|
||||
.setDefault("../data")
|
||||
.setHelp("Path to data files").build());
|
||||
|
||||
auto args = parser.parse_args(argv_vector);
|
||||
|
||||
load_data_files(args.get<std::string>("file"));
|
||||
|
||||
blt::gfx::init(blt::gfx::window_data{"My Sexy Window", init, update, destroy}.setSyncInterval(1).setMaximized(true));
|
||||
}
|
||||
|
||||
void action_test(const std::vector<std::string>& argv_vector)
|
||||
{
|
||||
blt::arg_parse parser{};
|
||||
parser.setHelpExtras("test");
|
||||
|
||||
parser.addArgument(blt::arg_builder{"--file", "-f"}
|
||||
.setDefault("../data")
|
||||
.setHelp("Path to data files").build());
|
||||
|
||||
auto args = parser.parse_args(argv_vector);
|
||||
|
||||
load_data_files(args.get<std::string>("file"));
|
||||
|
||||
|
||||
}
|
||||
|
||||
void action_convert(const std::vector<std::string>& argv_vector)
|
||||
{
|
||||
blt::arg_parse parser{};
|
||||
parser.setHelpExtras("convert");
|
||||
|
||||
auto args = parser.parse_args(argv_vector);
|
||||
}
|
||||
|
||||
int main(int argc, const char** argv)
|
||||
{
|
||||
std::vector<std::string> argv_vector;
|
||||
for (int i = 0; i < argc; i++)
|
||||
argv_vector.emplace_back(argv[i]);
|
||||
|
||||
blt::arg_parse parser{};
|
||||
|
||||
parser.addArgument(blt::arg_builder{"action"}
|
||||
.setAction(blt::arg_action_t::SUBCOMMAND)
|
||||
.setHelp("Action to run. Can be: [graphics, test, convert]").build());
|
||||
|
||||
auto args = parser.parse_args(argv_vector);
|
||||
|
||||
if (!args.contains("action"))
|
||||
{
|
||||
BLT_ERROR("Please provide an action");
|
||||
return 0;
|
||||
}
|
||||
|
||||
// argv_vector.erase(argv_vector.begin() + 1);
|
||||
|
||||
auto action = blt::string::toLowerCase(args.get<std::string>("action"));
|
||||
if (action == "graphics")
|
||||
action_start_graphics(argv_vector);
|
||||
else if (action == "test")
|
||||
action_test(argv_vector);
|
||||
else if (action == "convert")
|
||||
action_convert(argv_vector);
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -123,8 +123,8 @@ namespace assign3
|
|||
{
|
||||
using namespace blt::gfx;
|
||||
|
||||
ImGui::ShowDemoWindow();
|
||||
ImPlot::ShowDemoWindow();
|
||||
// ImGui::ShowDemoWindow();
|
||||
// ImPlot::ShowDemoWindow();
|
||||
|
||||
if (ImGui::Begin("Controls"))
|
||||
{
|
||||
|
|
@ -137,7 +137,10 @@ namespace assign3
|
|||
regenerate_network();
|
||||
|
||||
if (ImGui::Button("Run Epoch"))
|
||||
som->train_epoch(initial_learn_rate, topology_function.get());
|
||||
{
|
||||
som->train_epoch(initial_learn_rate);
|
||||
som->compute_neuron_activations();
|
||||
}
|
||||
ImGui::Checkbox("Run to completion", &running);
|
||||
ImGui::Text("Epoch %ld / %ld", som->get_current_epoch(), som->get_max_epochs());
|
||||
}
|
||||
|
|
@ -152,8 +155,9 @@ namespace assign3
|
|||
if (ImGui::ListBox("##InitType", &selected_init_type, get_selection_string, init_names.data(), static_cast<int>(init_names.size())))
|
||||
regenerate_network();
|
||||
ImGui::TextWrapped("Help: %s", init_helps[selected_init_type].c_str());
|
||||
ImGui::Separator();
|
||||
ImGui::Checkbox("Normalize Init Data", &normalize_init);
|
||||
if (ImGui::Checkbox("Normalize Init Data", &normalize_init))
|
||||
regenerate_network();
|
||||
ImGui::SeparatorText("Som Specifics");
|
||||
if (ImGui::InputInt("SOM Width", &som_width) || ImGui::InputInt("SOM Height", &som_height) ||
|
||||
ImGui::InputInt("Max Epochs", &max_epochs))
|
||||
regenerate_network();
|
||||
|
|
@ -187,7 +191,11 @@ namespace assign3
|
|||
static std::vector<std::string> names;
|
||||
names.clear();
|
||||
for (blt::size_t i = 0; i < som->get_array().get_map().size(); i++)
|
||||
names.push_back("Neuron " + std::to_string(i));
|
||||
{
|
||||
auto pos = som->get_array().from_index(i);
|
||||
names.push_back("Neuron " + std::to_string(i) +
|
||||
" (" + std::to_string(pos.x()) + ", " + std::to_string(pos.y()) + ")");
|
||||
}
|
||||
ImGui::Text("Select Neuron");
|
||||
ImGui::ListBox("##SelectNeuron", &selected_neuron, get_selection_string, names.data(), static_cast<int>(names.size()));
|
||||
}
|
||||
|
|
@ -199,14 +207,17 @@ namespace assign3
|
|||
ImGui::End();
|
||||
|
||||
auto current_data_file = motor_data.files[currently_selected_network];
|
||||
auto closest_type = get_neuron_activations(current_data_file);
|
||||
|
||||
if (ImGui::Begin("Plots & Data"))
|
||||
{
|
||||
ImPlot::SetNextAxesLimits(0, som_width, 0, som_height, ImPlotCond_Always);
|
||||
if (ImPlot::BeginPlot("Activations", ImVec2(-1, 0), ImPlotFlags_NoInputs))
|
||||
{
|
||||
auto rev = rotate90Clockwise(closest_type, som_width, som_height);
|
||||
static std::vector<float> activations;
|
||||
activations.clear();
|
||||
for (const auto& n : som->get_array().get_map())
|
||||
activations.push_back(n.get_activation());
|
||||
auto rev = rotate90Clockwise(activations, som_width, som_height);
|
||||
// auto rev = closest_type;
|
||||
// std::reverse(rev.begin(), rev.end());
|
||||
ImPlot::PlotHeatmap("##data_map", rev.data(), som_height, som_width, 0, 0, "%.1f", ImPlotPoint(0, 0),
|
||||
|
|
@ -216,7 +227,7 @@ namespace assign3
|
|||
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::PlotLine("##error", som->get_topological_errors().data(), static_cast<int>(som->get_topological_errors().size()));
|
||||
ImPlot::EndPlot();
|
||||
}
|
||||
}
|
||||
|
|
@ -226,18 +237,17 @@ namespace assign3
|
|||
{
|
||||
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));
|
||||
som->train_epoch(initial_learn_rate);
|
||||
som->compute_neuron_activations();
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
if (!debug_mode)
|
||||
{
|
||||
closest_type = normalize_data(closest_type);
|
||||
draw_som(neuron_render_info_t{}.set_base_pos({370, 145}).set_neuron_scale(120).set_neuron_padding({5, 5}),
|
||||
[&closest_type](render_data_t context) {
|
||||
auto type = closest_type[context.index];
|
||||
[](render_data_t context) {
|
||||
auto type = context.neuron.get_activation();
|
||||
return type >= 0 ? blt::make_color(0, type, 0) : blt::make_color(-type, 0, 0);
|
||||
});
|
||||
} else
|
||||
|
|
@ -247,33 +257,6 @@ namespace assign3
|
|||
fr2d.render();
|
||||
}
|
||||
|
||||
std::vector<float> renderer_t::get_neuron_activations(const data_file_t& file)
|
||||
{
|
||||
static std::vector<float> closest_type;
|
||||
closest_type.clear();
|
||||
closest_type.resize(som->get_array().get_map().size());
|
||||
|
||||
|
||||
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)
|
||||
{
|
||||
auto ds = topology_function->call(v.dist(data.bins), scale);
|
||||
if (data.is_bad)
|
||||
closest_type[i] -= ds;
|
||||
else
|
||||
closest_type[i] += ds;
|
||||
}
|
||||
}
|
||||
|
||||
return closest_type;
|
||||
}
|
||||
|
||||
void renderer_t::draw_debug(const data_file_t& file)
|
||||
{
|
||||
switch (static_cast<debug_t>(debug_state))
|
||||
|
|
@ -307,9 +290,8 @@ namespace assign3
|
|||
}
|
||||
|
||||
const auto& data_point = file.data_points[selected_data_point];
|
||||
auto closest_type = normalize_data(get_neuron_activations(file));
|
||||
draw_som(neuron_render_info_t{}.set_base_pos({370, 145}).set_neuron_scale(120).set_neuron_padding({0, 0}),
|
||||
[this, &neuron_positions, &data_point, &closest_type](render_data_t context) {
|
||||
[this, &neuron_positions, &data_point](render_data_t context) {
|
||||
auto half = som->find_closest_neighbour_distance(context.index) / at_distance_measurement;
|
||||
auto scale = topology_function->scale(half, requested_activation);
|
||||
auto ds = topology_function->call(context.neuron.dist(data_point.bins), scale);
|
||||
|
|
@ -321,7 +303,7 @@ namespace assign3
|
|||
auto text_height = text.getAssociatedText().getTextHeight();
|
||||
text.setPosition(context.neuron_padded - blt::vec2{text_width / 2.0f, text_height / 2.0f}).setZIndex(1);
|
||||
|
||||
auto type = closest_type[context.index];
|
||||
auto type = context.neuron.get_activation();
|
||||
return type >= 0 ? blt::make_color(0, type, 0) : blt::make_color(-type, 0, 0);
|
||||
});
|
||||
|
||||
|
|
@ -334,7 +316,6 @@ namespace assign3
|
|||
break;
|
||||
case debug_t::DISTANCE:
|
||||
{
|
||||
auto closest_type = normalize_data(get_neuron_activations(file));
|
||||
auto& selected_neuron_ref = som->get_array().get_map()[selected_neuron];
|
||||
static std::vector<Scalar> distances_2d;
|
||||
static std::vector<Scalar> distances_nd;
|
||||
|
|
@ -348,7 +329,7 @@ namespace assign3
|
|||
}
|
||||
|
||||
draw_som(neuron_render_info_t{}.set_base_pos({370, 145}).set_neuron_scale(120).set_neuron_padding({0, 0}),
|
||||
[this, &closest_type](render_data_t context) {
|
||||
[this](render_data_t context) {
|
||||
auto& text = fr2d.render_text(
|
||||
"2D: " + std::to_string(distances_2d[context.index]) + "\nND: " +
|
||||
std::to_string(distances_nd[context.index]), 18).setColor(0.2, 0.2, 0.8);
|
||||
|
|
@ -357,24 +338,11 @@ namespace assign3
|
|||
|
||||
if (static_cast<blt::size_t>(selected_neuron) == context.index)
|
||||
return blt::make_color(0, 0, 1);
|
||||
auto type = closest_type[context.index];
|
||||
auto type = context.neuron.get_activation();
|
||||
return type >= 0 ? blt::make_color(0, type, 0) : blt::make_color(-type, 0, 0);
|
||||
});
|
||||
}
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
std::vector<float> renderer_t::normalize_data(const std::vector<float>& data)
|
||||
{
|
||||
std::vector<float> return_data;
|
||||
auto min_act = *std::min_element(data.begin(), data.end());
|
||||
auto max_act = *std::max_element(data.begin(), data.end());
|
||||
for (auto& v : data)
|
||||
{
|
||||
auto n = 2 * (v - min_act) / (max_act - min_act) - 1;
|
||||
return_data.push_back(n);
|
||||
}
|
||||
return return_data;
|
||||
}
|
||||
}
|
||||
66
src/som.cpp
66
src/som.cpp
|
|
@ -27,14 +27,16 @@ namespace assign3
|
|||
{
|
||||
|
||||
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,
|
||||
shape_t shape, init_t init, bool normalize):
|
||||
array(file.data_points.begin()->bins.size(), width, height, shape), file(file), max_epochs(max_epochs), dist_func(dist_func)
|
||||
topology_function_t* topology_function, shape_t shape, init_t init, bool normalize):
|
||||
array(file.data_points.begin()->bins.size(), width, height, shape), file(file), max_epochs(max_epochs), dist_func(dist_func),
|
||||
topology_function(topology_function)
|
||||
{
|
||||
for (auto& v : array.get_map())
|
||||
v.randomize(std::random_device{}(), init, normalize, file);
|
||||
topological_errors.push_back(topological_error());
|
||||
}
|
||||
|
||||
void som_t::train_epoch(Scalar initial_learn_rate, topology_function_t* basis_func)
|
||||
void som_t::train_epoch(Scalar initial_learn_rate)
|
||||
{
|
||||
blt::random::random_t rand{std::random_device{}()};
|
||||
std::shuffle(file.data_points.begin(), file.data_points.end(), rand);
|
||||
|
|
@ -52,18 +54,18 @@ namespace assign3
|
|||
auto distance_min = find_closest_neighbour_distance(v0_idx);
|
||||
// this will find the required scaling factor to make a point in the middle between v0 and its closest neighbour activate 50%
|
||||
// from the perspective of the gaussian function
|
||||
auto scale = basis_func->scale(distance_min * 0.5f, 0.5);
|
||||
auto scale = topology_function->scale(distance_min * 0.5f, 0.5);
|
||||
|
||||
for (auto [i, n] : blt::enumerate(array.get_map()))
|
||||
{
|
||||
if (i == v0_idx)
|
||||
continue;
|
||||
auto dist = basis_func->call(neuron_t::distance(dist_func, v0, n), time_ratio * scale);
|
||||
auto dist = topology_function->call(neuron_t::distance(dist_func, v0, n), time_ratio * scale);
|
||||
n.update(current_data.bins, dist, eta);
|
||||
}
|
||||
}
|
||||
|
||||
current_epoch++;
|
||||
topological_errors.push_back(topological_error());
|
||||
}
|
||||
|
||||
blt::size_t som_t::get_closest_neuron(const std::vector<Scalar>& data)
|
||||
|
|
@ -139,12 +141,12 @@ namespace assign3
|
|||
return (dp1 * p_1) + (dp2 * p_2) + (dp3 * p_3);
|
||||
}
|
||||
|
||||
Scalar som_t::topological_error(const data_file_t& data)
|
||||
Scalar som_t::topological_error()
|
||||
{
|
||||
Scalar total = 0;
|
||||
std::vector<std::pair<blt::size_t, Scalar>> distances;
|
||||
|
||||
for (const auto& x : data.data_points)
|
||||
for (const auto& x : file.data_points)
|
||||
{
|
||||
distances.clear();
|
||||
for (const auto& [i, n] : blt::enumerate(array.get_map()))
|
||||
|
|
@ -171,7 +173,53 @@ namespace assign3
|
|||
total += 1;
|
||||
}
|
||||
|
||||
return total / static_cast<Scalar>(data.data_points.size());
|
||||
return total / static_cast<Scalar>(file.data_points.size());
|
||||
}
|
||||
|
||||
void som_t::compute_neuron_activations(Scalar distance, Scalar activation)
|
||||
{
|
||||
for (auto& n : array.get_map())
|
||||
n.set_activation(0);
|
||||
|
||||
Scalar min = std::numeric_limits<Scalar>::max();
|
||||
Scalar max = std::numeric_limits<Scalar>::min();
|
||||
|
||||
for (auto [i, v] : blt::enumerate(array.get_map()))
|
||||
{
|
||||
auto half = find_closest_neighbour_distance(i) / distance;
|
||||
// auto sigma = std::sqrt(-(half * half) / (2 * std::log(requested_activation)));
|
||||
// auto r = 1 / (2 * sigma * sigma);
|
||||
//
|
||||
auto scale = topology_function->scale(half, activation);
|
||||
for (const auto& data : file.data_points)
|
||||
{
|
||||
auto ds = topology_function->call(v.dist(data.bins), scale);
|
||||
if (data.is_bad)
|
||||
v.activate(-ds);
|
||||
else
|
||||
v.activate(ds);
|
||||
}
|
||||
|
||||
min = std::min(min, v.get_activation());
|
||||
max = std::max(max, v.get_activation());
|
||||
}
|
||||
|
||||
for (auto& n : array.get_map())
|
||||
n.set_activation(2 * (n.get_activation() - min) / (max - min) - 1);
|
||||
}
|
||||
|
||||
void som_t::write_activations(std::ostream& out)
|
||||
{
|
||||
out << "x,y,activation\n";
|
||||
for (const auto& v : array.get_map())
|
||||
out << v.get_x() << ',' << v.get_y() << ',' << v.get_activation() << '\n';
|
||||
}
|
||||
|
||||
void som_t::write_topology_errors(std::ostream& out)
|
||||
{
|
||||
out << "epoch,error\n";
|
||||
for (auto [i, v] : blt::enumerate(topological_errors))
|
||||
out << i << ',' << v << '\n';
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue