cancerious copy consturctors and fun little tests

include/config.h

@@ -21,12 +21,13 @@
 
 #include <blt/std/types.h>
 
+inline constexpr blt::i32 MIN_DEPTH = 3;
 inline constexpr blt::i32 MAX_DEPTH = 12;
 inline constexpr blt::i32 width = 256, height = 256;
 inline constexpr blt::i32 POPULATION_SIZE = 20;
 inline constexpr blt::i32 GEN_COUNT = 20;
 inline constexpr blt::i32 TOURNAMENT_SIZE = 3;
 inline constexpr float CROSSOVER_RATE = 0.9;
-inline constexpr float MUTATION_RATE = 0.9;
+inline constexpr float MUTATION_RATE = 0.1;
 
 #endif //GP_IMAGE_TEST_CONFIG_H

include/functions.h

@@ -244,8 +244,7 @@ inline static allowed_funcs<function_t> intersection_comp(const allowed_funcs<fu
 
 // distribution from normality (DFN)
 float eval_DNF_SW(const image& img);
-
+float eval_DNF_SW_1(const image& img);
-float eval_DNF_KS(const image& img);
 
 //template<typename F>
 //bool isNan(F f)

include/gp.h

@@ -65,6 +65,8 @@ struct node
             }
         }
         
+        node(const node& copy);
+        
         static node* construct_random_tree(blt::size_t max_depth = MAX_DEPTH);
         
         void evaluate();

src/extern.cpp

@@ -46,8 +46,11 @@ const std::vector<double> nearly_c {
         -0.44, -0.31, -0.25, -0.21, -0.18, -0.15, -0.12, -0.10, -0.08, -0.06, -0.04, -0.02,
         0.0, 0.02, 0.04, 0.06, 0.08, 0.10, 0.12, 0.15, 0.18, 0.21, 0.25, 0.31, 0.44};
 
+const auto runs = 500000;
+
 #undef BLT_DEBUG
 #define BLT_DEBUG(...)
+
 void shapiro_test_local()
 {
     auto normally = normally_c;
@@ -58,6 +61,9 @@ void shapiro_test_local()
     std::sort(nearly.begin(), nearly.end());
     std::sort(large.begin(), large.end());
     
+    BLT_START_INTERVAL("shapiro", "local_sorted");
+    for (size_t i = 0; i < runs; i++)
+    {
         willbruh w1(normally);
         BLT_ASSERT(w1->estimate == 0.9999999999999999);
         BLT_ASSERT(w1->p_value == 1.0);
@@ -73,6 +79,8 @@ void shapiro_test_local()
         BLT_ASSERT(w3->p_value == 0.0009134904817755807);
         BLT_DEBUG("%f // %f", w3->estimate, w3->p_value);
     }
+    BLT_END_INTERVAL("shapiro", "local_sorted");
+}
 
 void shapiro_test_unsorted()
 {
@@ -80,6 +88,9 @@ void shapiro_test_unsorted()
     auto nearly = nearly_c;
     auto large = large_c;
     
+    BLT_START_INTERVAL("shapiro", "unsorted_mut");
+    for (int i = 0; i < runs; i++)
+    {
         willbruh w1(willbert_unsorted(normally.data(), normally.size()));
         BLT_ASSERT(w1->estimate == 0.9999999999999999);
         BLT_ASSERT(w1->p_value == 1.0);
@@ -95,6 +106,8 @@ void shapiro_test_unsorted()
         BLT_ASSERT(w3->p_value == 0.0009134904817755807);
         BLT_DEBUG("%f // %f", w3->estimate, w3->p_value);
     }
+    BLT_END_INTERVAL("shapiro", "unsorted_mut");
+}
 
 void shapiro_test_copy()
 {
@@ -102,6 +115,9 @@ void shapiro_test_copy()
     auto nearly = nearly_c;
     auto large = large_c;
     
+    BLT_START_INTERVAL("shapiro", "copy");
+    for (int i = 0; i < runs; i++)
+    {
         willbruh w1(willbert_unsorted_copy(normally.data(), normally.size()));
         BLT_ASSERT(w1->estimate == 0.9999999999999999);
         BLT_ASSERT(w1->p_value == 1.0);
@@ -117,21 +133,14 @@ void shapiro_test_copy()
         BLT_ASSERT(w3->p_value == 0.0009134904817755807);
         BLT_DEBUG("%f // %f", w3->estimate, w3->p_value);
     }
+    BLT_END_INTERVAL("shapiro", "copy");
+}
 
 void shapiro_test_run()
 {
-    const auto runs = 500000;
+    
-    BLT_START_INTERVAL("shapiro", "copy");
-    for (size_t i = 0; i < runs; i++)
     shapiro_test_copy();
-    BLT_END_INTERVAL("shapiro", "copy");
-    BLT_START_INTERVAL("shapiro", "unsorted_mut");
-    for (size_t i = 0; i < runs; i++)
     shapiro_test_unsorted();
-    BLT_END_INTERVAL("shapiro", "unsorted_mut");
-    BLT_START_INTERVAL("shapiro", "local_sorted");
-    for (size_t i = 0; i < runs; i++)
     shapiro_test_local();
-    BLT_END_INTERVAL("shapiro", "local_sorted");
     BLT_PRINT_PROFILE("shapiro");
 }

src/functions.cpp

@@ -281,8 +281,30 @@ float eval_DNF_SW(const image& img)
     return static_cast<float>(total);
 }
 
-float eval_DNF_KS(const image& img)
+float eval_DNF_SW_1(const image& img)
 {
-    return 0;
+    std::vector<double> order(width * height);
+    
+    for (const auto& v : img.getData())
+        order.push_back(v.magnitude());
+    
+    std::sort(order.begin(), order.end());
+    
+    blt::size_t len = width / 4;
+    blt::size_t current_pos = 0;
+    double total = 0;
+    while (true)
+    {
+        if (order.size() - current_pos < len)
+        {
+            len = order.size() - current_pos;
+            if (len <= 0)
+                break;
+        }
+        total += on_data(order.data() + current_pos, len);
+        current_pos += len;
+    }
+    
+    return static_cast<float>(total);
 }
 

src/gp.cpp

@@ -148,7 +148,7 @@ node* node::construct_random_tree(blt::size_t max_depth)
         if (front.second != current_depth)
             current_depth++;
         
-        if (choice(engine))
+        if (choice(engine) && current_depth >= MIN_DEPTH)
             front.first->grow(front.second >= max_depth);
         else
             front.first->full(front.second >= max_depth);
@@ -192,3 +192,17 @@ void node::evaluate()
     }
 #undef FUNC_DEFINE
 }
+
+node::node(const node& copy)
+{
+    argc = copy.argc;
+    type = copy.type;
+    static_assert(sizeof(data_t) == sizeof(float) * 3 && "Uhhh something is wrong here!");
+    std::memcpy(data.data(), copy.data.data(), sizeof(data_t));
+    for (blt::size_t i = 0; i < argc; i++)
+    {
+        sub_nodes[i] = node_allocator.allocate(1);
+        // tee hee
+        ::new(&sub_nodes[i]) node(*copy.sub_nodes[i]);
+    }
+}

src/main.cpp

@@ -109,6 +109,7 @@ class tree
                 auto top = stack.top();
                 auto* node = top.second;
                 auto depth = top.first;
+                stack.pop();
                 max_depth = std::max(max_depth, depth);
                 for (blt::size_t i = 0; i < node->argc; i++)
                 {
@@ -117,7 +118,6 @@ class tree
                         stack.emplace(depth + 1, node->sub_nodes[i]);
                     }
                 }
-                stack.pop();
             }
             
             return max_depth;
@@ -125,9 +125,14 @@ class tree
         
         static bool crossover(tree* p1, tree* p2)
         {
+            if (p1 == nullptr || p2 == nullptr)
+                return false;
             auto n1 = p1->select_random_child();
             auto n2 = p2->select_random_child();
             
+            if (n1.parent == nullptr || n2.parent == nullptr)
+                return false;
+            
             const auto& p1_allowed = function_arg_allowed_set_map[to_underlying(n1.parent->type)][n1.index];
             const auto& p2_allowed = function_arg_allowed_set_map[to_underlying(n2.parent->type)][n2.index];
             
@@ -142,7 +147,7 @@ class tree
             return true;
         }
         
-        static void mutate(tree* p)
+        static bool mutate(tree* p)
         {
             static std::random_device dev;
             static std::mt19937_64 engine{dev()};
@@ -150,11 +155,15 @@ class tree
             
             auto n = p->select_random_child();
             
+            if (n.parent == nullptr)
+                return false;
+            
             auto d = depth(n.child);
             
             node* new_subtree = node::construct_random_tree(d + 1);
             n.parent->sub_nodes[n.index] = new_subtree;
             destroyNode(n.child);
+            return true;
         }
         
         void evaluate()
@@ -180,7 +189,8 @@ class tree
         
         float fitness()
         {
-            return eval_DNF_SW(root->getImage());
+            auto& img = root->getImage();
+            return eval_DNF_SW(img) * eval_DNF_SW_1(img);
         }
         
         void printTree()
@@ -197,6 +207,7 @@ class gp_population
             std::unique_ptr<tree> t = nullptr;
             float fitness = 0;
         };
+        blt::size_t best = -1;
     private:
         std::array<gp_i, POPULATION_SIZE> pop;
     public:
@@ -218,17 +229,20 @@ class gp_population
             evaluate_population();
         }
         
-        tree* select()
+        tree* select(blt::size_t* out = nullptr)
         {
             tree* n = nullptr;
             float fitness = 0;
             for (int i = 0; i < TOURNAMENT_SIZE; i++)
             {
-                auto& v = pop[choice()];
+                auto index = choice();
+                auto& v = pop[index];
                 if (n != v.t.get() && v.fitness > fitness)
                 {
                     n = v.t.get();
                     fitness = v.fitness;
+                    if (out != nullptr)
+                        *out = index;
                 }
             }
             return n;
@@ -240,35 +254,53 @@ class gp_population
             static std::mt19937_64 engine{dev()};
             static std::uniform_real_distribution rand(0.0, 1.0);
             
+            blt::size_t crossover_count = 0;
+            blt::size_t mutation_count = 0;
+            BLT_TRACE("Running Crossover");
             for (blt::size_t i = 0; i < POPULATION_SIZE; i++)
             {
                 if (rand(engine) < CROSSOVER_RATE)
                 {
-                    auto* p1 = select();
+                    blt::size_t i1 = -1, i2 = -1;
-                    auto* p2 = select();
+                    auto* p1 = select(&i1);
+                    auto* p2 = select(&i2);
                     
-                    tree::crossover(p1, p2);
+                    // do not crossover the elite
+                    if (i1 == best || i2 == best)
+                        continue;
+                    
+                    if (tree::crossover(p1, p2))
+                        crossover_count++;
                 }
             }
             
+            BLT_TRACE("Running Mutation");
             for (blt::size_t i = 0; i < POPULATION_SIZE; i++)
             {
                 if (rand(engine) < MUTATION_RATE)
                 {
-                    auto* p1 = select();
+                    blt::size_t i1 = 0;
+                    auto* p1 = select(&i1);
                     
-                    tree::mutate(p1);
+                    if (i1 == best)
+                        continue;
+                    
+                    if(tree::mutate(p1))
+                        mutation_count++;
                 }
             }
+            BLT_TRACE("ran %d crossovers and %d mutations", crossover_count, mutation_count);
         }
         
         void evaluate_population()
         {
+            BLT_TRACE("Running Eval");
             for (auto& v : pop)
             {
                 v.t->evaluate();
                 v.fitness = v.t->fitness();
             }
+            BLT_TRACE("Complete");
         }
         
         image& display(blt::size_t i)
@@ -288,6 +320,7 @@ class gp_population
                     fitness = pop[j].fitness;
                 }
             }
+            best = i;
             return {i, fitness};
         }
         
@@ -341,6 +374,7 @@ void update(std::int32_t w, std::int32_t h)
     if (ImGui::Button("Regenerate"))
     {
         BLT_INFO("Regen tree");
+        pop = {};
         pop.init();
         best = pop.best_fitness();
     }
@@ -358,10 +392,6 @@ void update(std::int32_t w, std::int32_t h)
     {
         BLT_INFO("Uploading");
         texture->upload((void*) pop.display_best().getData().data(), GL_RGB, 0, 0, 0, -1, -1, GL_FLOAT);
-        
-        ;
-//        if (test_tree->hasTree())
-//            test_tree->printTree();
     }
     ImGui::Text("Best Fitness: %f", best);