saving neurons
parent
88e28c05b5
commit
7179be26e3
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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 0.0.15)
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project(COSC-4P80-Assignment-3 VERSION 0.0.16)
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include(FetchContent)
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option(ENABLE_ADDRSAN "Enable the address sanitizer" OFF)
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@ -21,6 +21,7 @@
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#include <assign3/fwdecl.h>
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#include <assign3/neuron.h>
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#include <blt/math/vectors.h>
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namespace assign3
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{
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map.emplace_back(dimensions, (j % 2 == 0 ? static_cast<Scalar>(i) : static_cast<Scalar>(i) + 0.5f), j);
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}
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[[nodiscard]] std::pair<blt::size_t, blt::size_t> from_index(blt::size_t index) const
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[[nodiscard]] blt::vec2ul from_index(blt::size_t index) const
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{
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return {index % width, index / width};
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}
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@ -20,6 +20,8 @@
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#define COSC_4P80_ASSIGNMENT_3_FWDECL_H
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#include <blt/std/types.h>
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#include <blt/std/hashmap.h>
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#include <array>
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namespace assign3
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{
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TOROIDAL = RENDER_3D,
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CYLINDER = RENDER_3D
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};
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enum class debug_type
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{
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DATA_POINT,
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DISTANCE
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};
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inline std::array<std::string, 2> debug_names {
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"Distance to Datapoint",
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"Distance to Neighbours"
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};
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}
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#endif //COSC_4P80_ASSIGNMENT_3_FWDECL_H
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{
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class renderer_t;
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struct render_info_t
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struct neuron_render_info_t
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{
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blt::vec2 base_pos;
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blt::vec2 neuron_padding;
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float neuron_scale = 0;
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static render_info_t fill_screen(renderer_t& renderer, float neuron_scale = 35, float x_padding = 250, float y_padding = 50, float w_padding = 50, float h_padding = 50);
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static neuron_render_info_t fill_screen(renderer_t& renderer, float neuron_scale = 35, float x_padding = 250, float y_padding = 50,
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float w_padding = 50, float h_padding = 50);
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BLT_LVALUE_SETTER(blt::vec2, base_pos);
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BLT_LVALUE_SETTER(blt::vec2, neuron_padding);
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BLT_PRVALUE_SETTER(float, neuron_scale);
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};
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@ -52,11 +55,24 @@ namespace assign3
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void update();
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};
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struct render_data_t
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{
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blt::size_t index;
|
||||
const neuron_t& neuron;
|
||||
blt::vec2 neuron_scaled;
|
||||
blt::vec2 neuron_offset;
|
||||
blt::vec2 neuron_padded;
|
||||
|
||||
render_data_t(size_t index, const neuron_t& neuron, const blt::vec2& neuronScaled, const blt::vec2& neuronOffset,
|
||||
const blt::vec2& neuronPadded):
|
||||
index(index), neuron(neuron), neuron_scaled(neuronScaled), neuron_offset(neuronOffset), neuron_padded(neuronPadded)
|
||||
{}
|
||||
};
|
||||
|
||||
class renderer_t
|
||||
{
|
||||
friend motor_data_t;
|
||||
friend render_info_t;
|
||||
friend neuron_render_info_t;
|
||||
public:
|
||||
explicit renderer_t(motor_data_t& data, blt::gfx::resource_manager& resources, blt::gfx::matrix_state_manager& state):
|
||||
motor_data(data), br2d{resources, state}
|
||||
|
|
@ -66,7 +82,11 @@ namespace assign3
|
|||
|
||||
void cleanup();
|
||||
|
||||
void draw_som(const std::function<blt::vec4(neuron_t&)>& color_func, bool debug);
|
||||
std::vector<float> get_neuron_activations(const data_file_t& file);
|
||||
|
||||
void draw_som(neuron_render_info_t info, const std::function<blt::vec4(render_data_t)>& color_func);
|
||||
|
||||
void draw_debug(const data_file_t& file);
|
||||
|
||||
void render();
|
||||
|
||||
|
|
@ -95,6 +115,10 @@ namespace assign3
|
|||
Scalar initial_learn_rate = 0.1;
|
||||
|
||||
int currently_selected_network = 0;
|
||||
bool debug_mode = false;
|
||||
bool running = false;
|
||||
int debug_state = 0;
|
||||
int selected_data_point = 0;
|
||||
};
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
Subproject commit 0a0b87fc5566211cb7375d9c1b361ba3e91a3545
|
||||
Subproject commit 361083780325a2a73c9467320dcd5c7ced51e754
|
||||
|
|
@ -3,6 +3,7 @@
|
|||
#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>
|
||||
|
||||
|
|
@ -22,6 +23,8 @@ void init(const blt::gfx::window_data&)
|
|||
global_matrices.create_internals();
|
||||
resources.load_resources();
|
||||
renderer.create();
|
||||
|
||||
ImPlot::CreateContext();
|
||||
}
|
||||
|
||||
void update(const blt::gfx::window_data& window_data)
|
||||
|
|
@ -41,6 +44,7 @@ void destroy(const blt::gfx::window_data&)
|
|||
global_matrices.cleanup();
|
||||
resources.cleanup();
|
||||
renderer.cleanup();
|
||||
ImPlot::DestroyContext();
|
||||
blt::gfx::cleanup();
|
||||
BLT_INFO("Goodbye World!");
|
||||
}
|
||||
|
|
|
|||
281
src/manager.cpp
281
src/manager.cpp
|
|
@ -17,21 +17,35 @@
|
|||
*/
|
||||
#include <assign3/manager.h>
|
||||
#include <blt/gfx/window.h>
|
||||
#include <blt/math/log_util.h>
|
||||
#include <imgui.h>
|
||||
#include <implot.h>
|
||||
|
||||
static void HelpMarker(const std::string& desc)
|
||||
{
|
||||
ImGui::TextDisabled("(?)");
|
||||
if (ImGui::BeginItemTooltip())
|
||||
{
|
||||
ImGui::PushTextWrapPos(ImGui::GetFontSize() * 35.0f);
|
||||
ImGui::TextUnformatted(desc.c_str());
|
||||
ImGui::PopTextWrapPos();
|
||||
ImGui::EndTooltip();
|
||||
}
|
||||
}
|
||||
|
||||
const char* get_selection_string(void* user_data, int selection)
|
||||
{
|
||||
return (*reinterpret_cast<std::vector<std::string>*>(user_data))[selection].c_str();
|
||||
return (reinterpret_cast<std::string*>(user_data))[selection].c_str();
|
||||
}
|
||||
|
||||
namespace assign3
|
||||
{
|
||||
|
||||
render_info_t render_info_t::fill_screen(renderer_t& renderer, float neuron_scale, float x_padding, float y_padding, float w_padding,
|
||||
float h_padding)
|
||||
neuron_render_info_t neuron_render_info_t::fill_screen(renderer_t& renderer, float neuron_scale, float x_padding, float y_padding,
|
||||
float w_padding,
|
||||
float h_padding)
|
||||
{
|
||||
render_info_t info;
|
||||
neuron_render_info_t info;
|
||||
info.set_neuron_scale(neuron_scale);
|
||||
info.set_base_pos({x_padding, y_padding});
|
||||
|
||||
|
|
@ -44,7 +58,9 @@ namespace assign3
|
|||
float remain_width = screen_width - neuron_width;
|
||||
float remain_height = screen_height - neuron_height;
|
||||
|
||||
info.set_neuron_padding({remain_width / static_cast<float>(renderer.som_width), remain_height / static_cast<float>(renderer.som_height)});
|
||||
float remain = std::min(remain_width, remain_height);
|
||||
|
||||
info.set_neuron_padding({remain / static_cast<float>(renderer.som_width), remain / static_cast<float>(renderer.som_height)});
|
||||
|
||||
return info;
|
||||
}
|
||||
|
|
@ -57,9 +73,11 @@ namespace assign3
|
|||
|
||||
void renderer_t::create()
|
||||
{
|
||||
fr2d.create(250, 2048);
|
||||
fr2d.create_default(250, 2048);
|
||||
br2d.create();
|
||||
|
||||
topology_function = std::make_unique<gaussian_function_t>();
|
||||
|
||||
generate_network(currently_selected_network);
|
||||
update_graphics();
|
||||
}
|
||||
|
|
@ -70,150 +88,90 @@ namespace assign3
|
|||
br2d.cleanup();
|
||||
}
|
||||
|
||||
void renderer_t::draw_som(const std::function<blt::vec4(neuron_t&)>& color_func, bool debug)
|
||||
void renderer_t::draw_som(neuron_render_info_t info, const std::function<blt::vec4(render_data_t)>& color_func)
|
||||
{
|
||||
for (const auto& [i, neuron] : blt::enumerate(som->get_array().get_map()))
|
||||
{
|
||||
blt::vec2 neuron_pos = {neuron.get_x(), neuron.get_y()};
|
||||
auto neuron_scaled = neuron_pos * info.neuron_scale;
|
||||
auto neuron_offset = neuron_scaled + info.base_pos;
|
||||
auto neuron_padded = neuron_offset + neuron_pos * info.neuron_padding;
|
||||
|
||||
auto color = color_func({i, neuron, neuron_scaled, neuron_offset, neuron_padded});
|
||||
br2d.drawPointInternal(color, blt::gfx::point2d_t{neuron_padded, info.neuron_scale});
|
||||
}
|
||||
}
|
||||
|
||||
void renderer_t::render()
|
||||
{
|
||||
using namespace blt::gfx;
|
||||
|
||||
ImGui::ShowDemoWindow();
|
||||
ImPlot::ShowDemoWindow();
|
||||
|
||||
if (ImGui::Begin("Controls"))
|
||||
{
|
||||
ImGui::Text("Network Select");
|
||||
if (ImGui::ListBox("##Network Select", ¤tly_selected_network, get_selection_string, &motor_data.map_files_names,
|
||||
static_cast<int>(motor_data.map_files_names.size())))
|
||||
generate_network(currently_selected_network);
|
||||
ImGui::SetNextItemOpen(true, ImGuiCond_Appearing);
|
||||
if (ImGui::CollapsingHeader("SOM Control"))
|
||||
{
|
||||
ImGui::Text("Network Select");
|
||||
if (ImGui::ListBox("##Network Select", ¤tly_selected_network, get_selection_string, motor_data.map_files_names.data(),
|
||||
static_cast<int>(motor_data.map_files_names.size())))
|
||||
generate_network(currently_selected_network);
|
||||
|
||||
if (ImGui::Button("Run Epoch"))
|
||||
{
|
||||
som->train_epoch(initial_learn_rate, topology_function.get());
|
||||
}
|
||||
static bool run;
|
||||
ImGui::Checkbox("Run to completion", &run);
|
||||
if (run)
|
||||
{
|
||||
if (som->get_current_epoch() < som->get_max_epochs())
|
||||
if (ImGui::Button("Run Epoch"))
|
||||
som->train_epoch(initial_learn_rate, topology_function.get());
|
||||
ImGui::Checkbox("Run to completion", &running);
|
||||
ImGui::Text("Epoch %ld / %ld", som->get_current_epoch(), som->get_max_epochs());
|
||||
}
|
||||
if (ImGui::CollapsingHeader("Debug"))
|
||||
{
|
||||
ImGui::Checkbox("Debug Visuals", &debug_mode);
|
||||
if (debug_mode)
|
||||
{
|
||||
ImGui::ListBox("##DebugStateSelect", &debug_state, get_selection_string, debug_names.data(), debug_names.size());
|
||||
switch (static_cast<debug_type>(debug_state))
|
||||
{
|
||||
case debug_type::DATA_POINT:
|
||||
{
|
||||
auto current_data_file = motor_data.files[currently_selected_network].normalize();
|
||||
std::vector<std::string> names;
|
||||
for (const auto& [i, v] : blt::enumerate(current_data_file.data_points))
|
||||
names.push_back("#" + std::to_string(i) + " (" + (v.is_bad ? "Bad)" : "Good)"));
|
||||
ImGui::Text("Select Data Point");
|
||||
ImGui::ListBox("##SelectDataPoint", &selected_data_point, get_selection_string, names.data(),
|
||||
static_cast<int>(names.size()));
|
||||
}
|
||||
break;
|
||||
case debug_type::DISTANCE:
|
||||
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
ImGui::Text("Epoch %ld / %ld", som->get_current_epoch(), som->get_max_epochs());
|
||||
}
|
||||
ImGui::End();
|
||||
|
||||
static std::vector<blt::i64> activations;
|
||||
|
||||
activations.clear();
|
||||
activations.resize(som->get_array().get_map().size());
|
||||
if (running)
|
||||
{
|
||||
if (som->get_current_epoch() < som->get_max_epochs())
|
||||
som->train_epoch(initial_learn_rate, topology_function.get());
|
||||
}
|
||||
|
||||
auto current_data_file = motor_data.files[currently_selected_network].normalize();
|
||||
for (auto& v : current_data_file.data_points)
|
||||
|
||||
if (!debug_mode)
|
||||
{
|
||||
const auto nearest = som->get_closest_neuron(v.bins);
|
||||
activations[nearest] += v.is_bad ? -1 : 1;
|
||||
}
|
||||
auto closest_type = get_neuron_activations(current_data_file);
|
||||
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];
|
||||
return type >= 0 ? blt::make_color(0, type, 0) : blt::make_color(-type, 0, 0);
|
||||
});
|
||||
} else
|
||||
draw_debug(current_data_file);
|
||||
|
||||
const blt::i64 max = *std::max_element(activations.begin(), activations.end());
|
||||
const blt::i64 min = *std::min_element(activations.begin(), activations.end());
|
||||
|
||||
for (auto [i, v] : blt::enumerate(som->get_array().get_map()))
|
||||
{
|
||||
const auto activation = activations[i];
|
||||
|
||||
blt::vec4 color = blt::make_color(1, 1, 1);
|
||||
if (activation > 0)
|
||||
color = blt::make_color(0, static_cast<Scalar>(activation) / static_cast<Scalar>(max), 0);
|
||||
else if (activation < 0)
|
||||
color = blt::make_color(std::abs(static_cast<Scalar>(activation) / static_cast<Scalar>(min)), 0, 0);
|
||||
|
||||
br2d.drawPointInternal(color, point2d_t{v.get_x() * neuron_scale + neuron_scale, v.get_y() * neuron_scale + neuron_scale, neuron_scale});
|
||||
}
|
||||
|
||||
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()))
|
||||
{
|
||||
Scalar lowest_distance = std::numeric_limits<Scalar>::max();
|
||||
bool is_bad = false;
|
||||
for (const auto& [is_bins_bad, bins] : current_data_file.data_points)
|
||||
{
|
||||
if (const auto dist = v.dist(bins); dist < lowest_distance)
|
||||
{
|
||||
lowest_distance = dist;
|
||||
is_bad = is_bins_bad;
|
||||
}
|
||||
}
|
||||
// BLT_TRACE(is_bad ? -lowest_distance : lowest_distance);
|
||||
closest_type[i] = is_bad ? -lowest_distance : lowest_distance;
|
||||
}
|
||||
|
||||
auto min_dist = *std::min_element(closest_type.begin(), closest_type.end());
|
||||
auto max_dist = *std::max_element(closest_type.begin(), closest_type.end());
|
||||
|
||||
for (auto [i, v] : blt::enumerate(som->get_array().get_map()))
|
||||
{
|
||||
auto type = closest_type[i];
|
||||
|
||||
blt::vec4 color = blt::make_color(1, 1, 1);
|
||||
if (type >= 0)
|
||||
color = blt::make_color(0, 1 - (type / max_dist) + 0.1f, 0);
|
||||
else if (type < 0)
|
||||
color = blt::make_color(1 - (type / min_dist) + 0.1f, 0, 0);
|
||||
|
||||
br2d.drawPointInternal(color,
|
||||
point2d_t{
|
||||
draw_width + neuron_scale * 2 + v.get_x() * neuron_scale + neuron_scale,
|
||||
v.get_y() * neuron_scale + neuron_scale, neuron_scale
|
||||
});
|
||||
}
|
||||
|
||||
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);
|
||||
auto scale = topology_function->scale(half * 0.5f, 0.5);
|
||||
for (const auto& data : current_data_file.data_points)
|
||||
{
|
||||
auto dist = v.dist(data.bins);
|
||||
auto ds = topology_function->call(dist, scale);
|
||||
// BLT_TRACE("%f, %f, %f", ds, dist, scale);
|
||||
if (data.is_bad)
|
||||
closest_type[i] -= ds;
|
||||
else
|
||||
closest_type[i] += ds;
|
||||
}
|
||||
}
|
||||
auto min_act = *std::min_element(closest_type.begin(), closest_type.end());
|
||||
auto max_act = *std::max_element(closest_type.begin(), closest_type.end());
|
||||
for (auto& v : closest_type)
|
||||
{
|
||||
auto n = 2 * (v - min_act) / (max_act - min_act) - 1;
|
||||
v = n;
|
||||
}
|
||||
// BLT_TRACE("Min %f Max %f", min_act, max_act);
|
||||
|
||||
for (auto [i, v] : blt::enumerate(som->get_array().get_map()))
|
||||
{
|
||||
auto type = closest_type[i];
|
||||
|
||||
blt::vec4 color;
|
||||
if (type >= 0)
|
||||
color = blt::make_color(0, type, 0);
|
||||
else
|
||||
color = blt::make_color(-type, 0, 0);
|
||||
|
||||
br2d.drawPointInternal(color,
|
||||
point2d_t{
|
||||
draw_width + neuron_scale * 2 + v.get_x() * neuron_scale + neuron_scale,
|
||||
draw_height + neuron_scale * 2 + v.get_y() * neuron_scale + neuron_scale, neuron_scale
|
||||
});
|
||||
}
|
||||
|
||||
|
||||
br2d.render(0,0);
|
||||
br2d.render(0, 0);
|
||||
fr2d.render();
|
||||
}
|
||||
|
||||
|
|
@ -231,9 +189,66 @@ namespace assign3
|
|||
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) * neuron_scale;
|
||||
draw_height = (max_y - min_y) * neuron_scale;
|
||||
draw_width = (max_x - min_x);
|
||||
draw_height = (max_y - min_y);
|
||||
}
|
||||
|
||||
topology_function = std::make_unique<gaussian_function_t>();
|
||||
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);
|
||||
auto scale = topology_function->scale(half * 0.5f, 0.5);
|
||||
for (const auto& data : file.data_points)
|
||||
{
|
||||
auto dist = v.dist(data.bins);
|
||||
auto ds = topology_function->call(dist, scale);
|
||||
if (data.is_bad)
|
||||
closest_type[i] -= ds;
|
||||
else
|
||||
closest_type[i] += ds;
|
||||
}
|
||||
}
|
||||
auto min_act = *std::min_element(closest_type.begin(), closest_type.end());
|
||||
auto max_act = *std::max_element(closest_type.begin(), closest_type.end());
|
||||
for (auto& v : closest_type)
|
||||
{
|
||||
auto n = 2 * (v - min_act) / (max_act - min_act) - 1;
|
||||
v = n;
|
||||
}
|
||||
|
||||
return closest_type;
|
||||
}
|
||||
|
||||
void renderer_t::draw_debug(const data_file_t& file)
|
||||
{
|
||||
switch (static_cast<debug_type>(debug_state))
|
||||
{
|
||||
case debug_type::DATA_POINT:
|
||||
{
|
||||
const auto& data_point = file.data_points[selected_data_point];
|
||||
auto closest_type = get_neuron_activations(file);
|
||||
draw_som(neuron_render_info_t{}.set_base_pos({370, 145}).set_neuron_scale(120).set_neuron_padding({50, 50}),
|
||||
[this, &closest_type](render_data_t context) {
|
||||
auto& text = fr2d.render_text(std::to_string(closest_type[context.index]), 13);
|
||||
auto text_width = text.getAssociatedText().getTextWidth();
|
||||
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];
|
||||
return type >= 0 ? blt::make_color(0, type, 0) : blt::make_color(-type, 0, 0);
|
||||
});
|
||||
}
|
||||
break;
|
||||
case debug_type::DISTANCE:
|
||||
{
|
||||
|
||||
}
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
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Reference in New Issue