work

.idea/copyright/profiles_settings.xml

@@ -0,0 +1,7 @@
+<component name="CopyrightManager">
+  <settings default="GPL3">
+    <module2copyright>
+      <element module="All" copyright="GPL3" />
+    </module2copyright>
+  </settings>
+</component>

CMakeLists.txt

@@ -1,10 +1,10 @@
 cmake_minimum_required(VERSION 3.25)
-project(COSC-4P80-Assignment-2 VERSION 0.0.2)
+project(COSC-4P80-Assignment-2 VERSION 0.0.3)
 
 option(ENABLE_ADDRSAN "Enable the address sanitizer" OFF)
 option(ENABLE_UBSAN "Enable the ub sanitizer" OFF)
 option(ENABLE_TSAN "Enable the thread data race sanitizer" OFF)
-option(EIGEN_TEST_CXX11 "Enable testing with C++11 and C++11 features (e.g. Tensor module)." ON)
+#option(EIGEN_TEST_CXX11 "Enable testing with C++11 and C++11 features (e.g. Tensor module)." ON)
 
 set(CMAKE_CXX_STANDARD 17)
 
@@ -14,7 +14,7 @@ endif()
 
 add_subdirectory(lib/blt)
 
-add_subdirectory(lib/eigen-3.4.0)
+#add_subdirectory(lib/eigen-3.4.0)
 
 include_directories(include/)
 file(GLOB_RECURSE PROJECT_BUILD_FILES "${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp")
@@ -24,7 +24,8 @@ add_executable(COSC-4P80-Assignment-2 ${PROJECT_BUILD_FILES})
 target_compile_options(COSC-4P80-Assignment-2 PRIVATE -Wall -Wextra -Wpedantic -Wno-comment)
 target_link_options(COSC-4P80-Assignment-2 PRIVATE -Wall -Wextra -Wpedantic -Wno-comment)
 
-target_link_libraries(COSC-4P80-Assignment-2 PRIVATE BLT Eigen3::Eigen)
+#target_link_libraries(COSC-4P80-Assignment-2 PRIVATE BLT Eigen3::Eigen)
+target_link_libraries(COSC-4P80-Assignment-2 PRIVATE BLT)
 
 if (${ENABLE_ADDRSAN} MATCHES ON)
     target_compile_options(COSC-4P80-Assignment-2 PRIVATE -fsanitize=address)

include/assign2/common.h

@@ -19,14 +19,15 @@
 #ifndef COSC_4P80_ASSIGNMENT_2_COMMON_H
 #define COSC_4P80_ASSIGNMENT_2_COMMON_H
 
-#include <Eigen/Dense>
 
 namespace assign2
 {
+    using Scalar = float;
+    
     struct data_t
     {
         bool is_bad = false;
-        std::vector<float> bins;
+        std::vector<Scalar> bins;
     };
     
     struct data_file_t
@@ -37,9 +38,57 @@ namespace assign2
     class layer_t;
     class network_t;
     
-    using Scalar = float;
+    struct weight_view
-    using matrix_t = Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic>;
+    {
-    using vector_t = Eigen::Matrix<Scalar, Eigen::Dynamic, 1>;
+        public:
+            weight_view(double* data, blt::size_t size): m_data(data), m_size(size)
+            {}
+            
+            inline double& operator[](blt::size_t index) const
+            {
+#if BLT_DEBUG_LEVEL > 0
+                if (index >= size)
+                    throw std::runtime_error("Index is out of bounds!");
+#endif
+                return m_data[index];
+            }
+            
+            [[nodiscard]] inline blt::size_t size() const
+            {
+                return m_size;
+            }
+            
+            [[nodiscard]] auto begin() const
+            {
+                return m_data;
+            }
+            
+            [[nodiscard]] auto end() const
+            {
+                return m_data + m_size;
+            }
+        
+        private:
+            double* m_data;
+            blt::size_t m_size;
+    };
+    
+    /**
+     * this class exists purely as an optimization
+     */
+    class weight_t
+    {
+        public:
+            weight_view allocate_view(blt::size_t count)
+            {
+                auto size = data.size();
+                data.resize(size + count);
+                return {&data[size], count};
+            }
+        
+        private:
+            std::vector<double> data;
+    };
 }
 
 #endif //COSC_4P80_ASSIGNMENT_2_COMMON_H

include/assign2/functions.h

@@ -35,13 +35,6 @@ namespace assign2
         {
             return call(s) * (1 - call(s));
         }
-        
-        vector_t operator()(vector_t out) const
-        {
-            for (auto& v : out)
-                v = call(v);
-            return out;
-        }
     };
     
     struct linear_function

include/assign2/initializers.h

@@ -27,50 +27,35 @@ namespace assign2
 {
     struct empty_init
     {
-        template<int rows, int columns>
+        inline Scalar operator()(blt::i32) const
-        inline void operator()(Eigen::Matrix<Scalar, rows, columns>& matrix) const
         {
-            for (auto r : matrix.rowwise())
+            return 0;
-            {
-                for (auto& v : r)
-                    v = 0;
-            }
         }
     };
     
     struct half_init
     {
-        template<int rows, int columns>
+        inline Scalar operator()(blt::i32) const
-        inline void operator()(Eigen::Matrix<Scalar, rows, columns>& matrix) const
         {
-            for (auto r : matrix.rowwise())
+            return 0;
-            {
-                for (auto& v : r)
-                    v = 0.5f;
-            }
         }
     };
     
     struct random_init
     {
         public:
-            explicit random_init(blt::size_t seed, float min = 0.5 - 0.125, float max = 0.5 + 0.125): seed(seed), min(min), max(max)
+            explicit random_init(blt::size_t seed, Scalar min = -0.5, Scalar max = 0.5): random(seed), seed(seed), min(min), max(max)
             {}
             
-            template<int rows, int columns>
+            inline Scalar operator()(blt::i32)
-            inline void operator()(Eigen::Matrix<Scalar, rows, columns>& matrix) const
             {
-                blt::random::random_t random(seed);
+                return static_cast<Scalar>(random.get_double(min, max));
-                for (auto r : matrix.rowwise())
-                {
-                    for (auto& v : r)
-                        v = random.get_float(min, max);
-                }
             }
         
         private:
+            blt::random::random_t random;
             blt::size_t seed;
-            float min, max;
+            Scalar min, max;
     };
     
 }

include/assign2/layer.h

@@ -21,41 +21,109 @@
 
 #include <blt/std/types.h>
 #include <assign2/initializers.h>
+#include "blt/iterator/zip.h"
+#include "blt/iterator/iterator.h"
 
 namespace assign2
 {
-    class layer_t
+    class neuron_t
     {
         public:
-            layer_t(const blt::i32 in, const blt::i32 out): in_size(in), out_size(out)
+            // empty neuron for loading from a stream
+            explicit neuron_t(weight_view weights): weights(weights)
             {}
             
-            template<typename WeightsFunc = empty_init, typename BiasFunc = empty_init>
+            // neuron with bias
-            void init(WeightsFunc weightFunc = empty_init{}, BiasFunc biasFunc = empty_init{})
+            explicit neuron_t(weight_view weights, Scalar bias): bias(bias), weights(weights)
+            {}
+            
+            template<typename ActFunc>
+            Scalar activate(const Scalar* inputs, ActFunc func) const
             {
-                weights.resize(in_size, out_size);
+                auto sum = bias;
-                bias.resize(out_size);
+                for (auto [x, w] : blt::zip_iterator_container({inputs, inputs + weights.size()}, {weights.begin(), weights.end()}))
-                
+                    sum += x * w;
-                weightFunc(weights);
+                return func.call(sum);
-                biasFunc(bias);
             }
             
-            template<typename ActFunction>
+            template<typename OStream>
-            vector_t call(const vector_t& in, ActFunction func = ActFunction{})
+            OStream& serialize(OStream& stream)
             {
-                vector_t out;
+                stream << bias;
-                out.resize(out_size, Eigen::NoChange_t{});
+                for (auto d : weights)
-                out.noalias() = weights.transpose() * in;
+                    stream << d;
-                out.colwise() += bias;
+            }
-                return func(std::move(out));
+            
+            template<typename IStream>
+            IStream& deserialize(IStream& stream)
+            {
+                for (auto& d : blt::iterate(weights).rev())
+                    stream >> d;
+                stream >> bias;
             }
         
+        private:
+            Scalar bias = 0;
+            weight_view weights;
+    };
+    
+    class layer_t
+    {
+        public:
+            template<typename WeightFunc, typename BiasFunc>
+            layer_t(const blt::i32 in, const blt::i32 out, WeightFunc w, BiasFunc b): in_size(in), out_size(out)
+            {
+                neurons.reserve(out_size);
+                for (blt::i32 i = 0; i < out_size; i++)
+                {
+                    auto weight = weights.allocate_view(in_size);
+                    for (auto& v : weight)
+                        v = w(i);
+                    neurons.push_back(neuron_t{weight, b(i)});
+                }
+            }
+            
+            template<typename ActFunction>
+            std::vector<Scalar> call(const std::vector<Scalar>& in, ActFunction func = ActFunction{})
+            {
+                std::vector<Scalar> out;
+                out.reserve(out_size);
+#if BLT_DEBUG_LEVEL > 0
+                if (in.size() != in_size)
+                    throw std::runtime_exception("Input vector doesn't match expected input size!");
+#endif
+                for (auto& n : neurons)
+                    out.push_back(n.activate(in.data(), func));
+                return out;
+            }
+            
+            template<typename OStream>
+            OStream& serialize(OStream& stream)
+            {
+                for (auto d : neurons)
+                    stream << d;
+            }
+            
+            template<typename IStream>
+            IStream& deserialize(IStream& stream)
+            {
+                for (auto& d : blt::iterate(neurons).rev())
+                    stream >> d;
+            }
+            
+            [[nodiscard]] inline blt::i32 get_in_size() const
+            {
+                return in_size;
+            }
+            
+            [[nodiscard]] inline blt::i32 get_out_size() const
+            {
+                return out_size;
+            }
         private:
             const blt::i32 in_size, out_size;
-            matrix_t weights{};
+            weight_t weights;
-            matrix_t dweights{}; // derivative of weights
+            std::vector<neuron_t> neurons;
-            vector_t bias{};
-            vector_t dbias{}; // derivative of bias
     };
 }
 

include/assign2/network.h

@@ -27,17 +27,98 @@ namespace assign2
     class network_t
     {
         public:
-            network_t(blt::i32 input_size, blt::i32 output_size, blt::i32 hidden_count, blt::i32 hidden_size):
+            template<typename WeightFunc, typename BiasFunc>
-                    input_size(input_size), output_size(output_size), hidden_count(hidden_count), hidden_size(hidden_size)
+            network_t(blt::i32 input_size, blt::i32 output_size, blt::i32 layer_count, blt::i32 hidden_size, WeightFunc w, BiasFunc b):
+                    input_size(input_size), output_size(output_size), hidden_count(layer_count), hidden_size(hidden_size)
             {
-                if (hidden_count > 0)
+                if (layer_count > 0)
                 {
-                    layers.push_back(layer_t{input_size, hidden_size});
+                    for (blt::i32 i = 0; i < layer_count; i++)
-                    for (blt::i32 i = 1; i < hidden_count; i++)
+                    {
-                        layers.push_back(layer_t{hidden_size, hidden_size});
+                        if (i == 0)
-                    layers.push_back(layer_t{hidden_size, output_size});
+                            layers.push_back(layer_t{input_size, hidden_size, w, b});
+                        else
+                            layers.push_back(layer_t{hidden_size, hidden_size, w, b});
+                    }
+                    layers.push_back(layer_t{hidden_size, output_size, w, b});
                 } else
-                    layers.push_back(layer_t{input_size, output_size});
+                {
+                    layers.push_back(layer_t{input_size, output_size, w, b});
+                }
+            }
+            
+            template<typename WeightFunc, typename BiasFunc, typename OutputWeightFunc, typename OutputBiasFunc>
+            network_t(blt::i32 input_size, blt::i32 output_size, blt::i32 layer_count, blt::i32 hidden_size,
+                      WeightFunc w, BiasFunc b, OutputWeightFunc ow, OutputBiasFunc ob):
+                    input_size(input_size), output_size(output_size), hidden_count(layer_count), hidden_size(hidden_size)
+            {
+                if (layer_count > 0)
+                {
+                    for (blt::i32 i = 0; i < layer_count; i++)
+                    {
+                        if (i == 0)
+                            layers.push_back(layer_t{input_size, hidden_size, w, b});
+                        else
+                            layers.push_back(layer_t{hidden_size, hidden_size, w, b});
+                    }
+                    layers.push_back(layer_t{hidden_size, output_size, ow, ob});
+                } else
+                {
+                    layers.push_back(layer_t{input_size, output_size, ow, ob});
+                }
+            }
+            
+            explicit network_t(std::vector<layer_t> layers):
+                    input_size(layers.begin()->get_in_size()), output_size(layers.end()->get_out_size()),
+                    hidden_count(static_cast<blt::i32>(layers.size()) - 1), hidden_size(layers.end()->get_in_size()), layers(std::move(layers))
+            {}
+            
+            network_t() = default;
+            
+            template<typename ActFunc, typename ActFuncOut>
+            std::vector<Scalar> execute(const std::vector<Scalar>& input, ActFunc func, ActFuncOut outFunc)
+            {
+                std::vector<Scalar> previous_output;
+                std::vector<Scalar> current_output;
+                
+                for (auto [i, v] : blt::enumerate(layers))
+                {
+                    previous_output = current_output;
+                    if (i == 0)
+                        current_output = v.call(input, func);
+                    else if (i == layers.size() - 1)
+                        current_output = v.call(previous_output, outFunc);
+                    else
+                        current_output = v.call(previous_output, func);
+                }
+                
+                return current_output;
+            }
+            
+            Scalar train(const data_file_t& example)
+            {
+                const Scalar learn_rate = 0.1;
+                
+                Scalar total_error = 0;
+                for (const auto& x : example.data_points)
+                {
+                    auto o = execute(x.bins, sigmoid_function{}, sigmoid_function{});
+                    auto y = x.is_bad ? 1.0f : 0.0f;
+                    
+                    Scalar is_bad = 0;
+                    if (o[0] >= 1)
+                        is_bad = 0;
+                    else if (o[1] >= 1)
+                        is_bad = 1;
+                    
+                    auto error = y - is_bad;
+                    if (o[0] >= 1 && o[1] >= 1)
+                        error += 1;
+                    
+                    total_error += error;
+                    
+                }
+                return total_error;
             }
         
         private:

src/main.cpp

@@ -6,6 +6,7 @@
 #include "blt/iterator/enumerate.h"
 #include <assign2/layer.h>
 #include <assign2/functions.h>
+#include <assign2/network.h>
 
 using namespace assign2;
 
@@ -67,6 +68,18 @@ std::vector<data_file_t> load_data_files(const std::vector<std::string>& files)
     return loaded_data;
 }
 
+template<typename T>
+decltype(std::cout)& print_vec(const std::vector<T>& vec)
+{
+    for (auto [i, v] : blt::enumerate(vec))
+    {
+        std::cout << v;
+        if (i != vec.size() - 1)
+            std::cout << ", ";
+    }
+    return std::cout;
+}
+
 int main(int argc, const char** argv)
 {
     blt::arg_parse parser;
@@ -77,35 +90,29 @@ int main(int argc, const char** argv)
     
     auto data_files = load_data_files(get_data_files(data_directory));
     
-    vector_t input;
+    std::vector<Scalar> input;
     input.resize(16);
     for (auto f : data_files)
     {
         if (f.data_points.begin()->bins.size() == 16)
         {
             for (auto [i, b] : blt::enumerate(f.data_points.begin()->bins))
-                input(static_cast<Eigen::Index>(i)) = b;
+                input[i] = b;
         }
     }
     
     random_init randomizer{619};
     sigmoid_function sig;
     
-    layer_t layer1{16, 4};
+    layer_t layer1{16, 4, randomizer, empty_init{}};
-    layer_t layer2{4, 4};
+    layer_t layer2{4, 4, randomizer, empty_init{}};
-    layer_t layer3{4, 4};
+    layer_t layer3{4, 4, randomizer, empty_init{}};
-    layer_t layer_output{4, 1};
+    layer_t layer_output{4, 1, randomizer, empty_init{}};
-    layer1.init(randomizer, empty_init{});
-    layer2.init(randomizer, empty_init{});
-    layer3.init(randomizer, empty_init{});
-    layer_output.init(randomizer, empty_init{});
     
-    auto output = layer1.call(input, sig);
+    network_t network{{layer1, layer2, layer3, layer_output}};
-    output = layer2.call(output, sig);
-    output = layer3.call(output, sig);
-    output = layer_output.call(output, sig);
     
-    std::cout << output << std::endl;
+    auto output = network.execute(input, sig, sig);
+    print_vec(output) << std::endl;
 
 //    for (auto d : data_files)
 //    {