Update coderabbitai.cpp

@coderabbitai create a docstring to combine, refactor and enhance this in your own PR .

Signed-off-by: J. K. Edwards <joed6834@colorado.edu>

.engine/coderabbitai.cpp

@@ -440,3 +440,377 @@ double evaluate_code_style(const char *code) {
     // Placeholder for actual style evaluation
     return 0.75;
 }
+
+#include "codingrabbitaibrain.h"
+#include <iostream>
+#include <memory>
+#include <vector>
+#include <string>
+#include <chrono>
+#include <random>
+#include <cmath>
+#include <algorithm>
+#include <exception>
+
+// Constants using constexpr
+constexpr int CODE_PATTERN_LIMIT = 10000;
+constexpr double CODE_COMPLEXITY_FACTOR = 1.0;
+constexpr double LTM_THRESHOLD = 0.5;
+constexpr int NN_INPUT_SIZE = 1000;
+constexpr int NN_HIDDEN_SIZE = 500;
+constexpr int NN_OUTPUT_SIZE = 10;
+
+// Memory hierarchy structures with modern C++ constructs
+struct CacheEntry {
+    std::vector<char> data;  
+    size_t size;
+    uint64_t timestamp;
+};
+
+struct STM_Cache {
+    std::vector<CacheEntry> entries;
+    size_t capacity;
+    size_t used;
+};
+
+struct SerializedTopic {
+    std::vector<char> data;
+    size_t size;
+    std::string topic_id;
+};
+
+struct LTM_Gradient {
+    double relevance;
+    double permanence;
+};
+
+struct LTM_JudgeNode {
+    LTM_Gradient true_gradient;
+    LTM_Gradient false_gradient;
+};
+
+struct CodePattern {
+    std::string snippet;
+    std::string language;
+    double complexity;
+};
+
+struct CodeMemory {
+    std::vector<std::unique_ptr<CodePattern>> patterns;
+};
+
+struct CodeWorkbench {
+    std::string code_request;
+    std::string current_code;
+    std::string suggested_code;
+};
+
+struct EmotionalNode {
+    std::string emotion;
+    double intensity;
+    double reward_value;
+};
+
+struct EmotionalGraph {
+    std::vector<EmotionalNode> nodes;
+};
+
+struct NeuralNetwork {
+    std::vector<std::vector<double>> weights;
+    std::vector<double> bias;
+    std::vector<std::vector<double>> output_weights;
+    std::vector<double> output_bias;
+};
+
+// Helper functions for time and ID generation
+uint64_t get_current_timestamp() {
+    using namespace std::chrono;
+    return duration_cast<milliseconds>(system_clock::now().time_since_epoch()).count();
+}
+
+std::string generate_unique_id() {
+    std::random_device rd;
+    std::mt19937 gen(rd());
+    std::uniform_int_distribution<> dis(0, 15);
+    std::uniform_int_distribution<> dis2(8, 11);
+
+    std::stringstream ss;
+    ss << std::hex;
+    for (int i = 0; i < 8; i++) {
+        ss << dis(gen);
+    }
+    ss << "-";
+    for (int i = 0; i < 3; i++) {
+        ss << dis2(gen) << dis(gen);
+    }
+    ss << "-";
+    for (int i = 0; i < 12; i++) {
+        ss << dis(gen);
+    }
+    return ss.str();
+}
+
+// Core Function Implementations
+
+STM_Cache init_stm_cache(size_t capacity) {
+    return {std::vector<CacheEntry>(capacity), capacity, 0};
+}
+
+SerializedTopic serialize_novel_topic(const std::string& topic_data) {
+    SerializedTopic serialized;
+    serialized.data.assign(topic_data.begin(), topic_data.end());
+    serialized.size = topic_data.size();
+    serialized.topic_id = generate_unique_id();
+    return serialized;
+}
+
+std::unique_ptr<CodePattern> create_code_pattern(const std::string& snippet, const std::string& language, double complexity) {
+    return std::make_unique<CodePattern>(CodePattern{snippet, language, complexity});
+}
+
+void add_pattern_to_memory(CodeMemory& memory, std::unique_ptr<CodePattern>&& pattern) {
+    if (memory.patterns.size() == CODE_PATTERN_LIMIT) {
+        memory.patterns.erase(memory.patterns.begin()); // Remove oldest pattern
+    }
+    memory.patterns.push_back(std::move(pattern));
+}
+
+void analyze_code_complexity(CodePattern& pattern) {
+    pattern.complexity += CODE_COMPLEXITY_FACTOR;
+}
+
+void update_code_suggestion(CodeWorkbench& workbench, const std::string& new_suggestion) {
+    workbench.suggested_code = new_suggestion;
+}
+
+// Logic Loop Implementations
+
+void pmll_logic_loop(CodeWorkbench& workbench, const CodeMemory& memory) {
+    for (const auto& pattern : memory.patterns) {
+        if (pattern->snippet.find(workbench.code_request) != std::string::npos) {
+            std::cout << "Pattern matched: " << pattern->snippet << "\n";
+            break; // Match only one pattern for simplicity
+        }
+    }
+}
+
+void arll_logic_loop(CodeWorkbench& workbench) {
+    if (!workbench.suggested_code.empty()) {
+        std::cout << "Analyzing code structure...\n";
+        // Placeholder for actual analysis
+    }
+}
+
+void efll_logic_loop(CodeWorkbench& workbench) {
+    if (!workbench.suggested_code.empty()) {
+        double style_score = evaluate_code_style(workbench.suggested_code.c_str()); // Placeholder
+        std::cout << "Code style score: " << style_score << "\n";
+    }
+}
+
+void judge_and_consolidate(LTM_JudgeNode& judge_node, const SerializedTopic& topic) {
+    std::random_device rd;
+    std::mt19937 gen(rd());
+    std::uniform_real_distribution<> dis(0.0, 1.0);
+
+    double relevance = dis(gen);
+    double permanence = dis(gen);
+
+    if (relevance > LTM_THRESHOLD) {
+        judge_node.true_gradient.relevance = relevance;
+        judge_node.true_gradient.permanence = permanence;
+        std::cout << "Consolidating topic into LTM with relevance " << relevance << " and permanence " << permanence << "\n";
+    } else {
+        judge_node.false_gradient.relevance = relevance;
+        judge_node.false_gradient.permanence = permanence;
+        std::cout << "Rejecting topic for LTM with relevance " << relevance << "\n";
+    }
+}
+
+// Neural Network Initialization
+void init_neural_network(NeuralNetwork& nn) {
+    auto rand_double = [](std::mt19937& gen) {
+        std::uniform_real_distribution<> dis(-1.0, 1.0);
+        return dis(gen);
+    };
+
+    std::random_device rd;
+    std::mt19937 gen(rd());
+
+    nn.weights = std::vector<std::vector<double>>(NN_HIDDEN_SIZE, std::vector<double>(NN_INPUT_SIZE, 0.0));
+    nn.bias = std::vector<double>(NN_HIDDEN_SIZE, 0.0);
+    nn.output_weights = std::vector<std::vector<double>>(NN_OUTPUT_SIZE, std::vector<double>(NN_HIDDEN_SIZE, 0.0));
+    nn.output_bias = std::vector<double>(NN_OUTPUT_SIZE, 0.0);
+
+    for (auto& layer : nn.weights) {
+        std::generate(layer.begin(), layer.end(), [&]() { return rand_double(gen); });
+    }
+    std::generate(nn.bias.begin(), nn.bias.end(), [&]() { return rand_double(gen); });
+
+    for (auto& layer : nn.output_weights) {
+        std::generate(layer.begin(), layer.end(), [&]() { return rand_double(gen); });
+    }
+    std::generate(nn.output_bias.begin(), nn.output_bias.end(), [&]() { return rand_double(gen); });
+}
+
+double evaluate_code_with_nn(const NeuralNetwork& nn, const std::string& code) {
+    std::vector<double> input(NN_INPUT_SIZE, 0.0);
+    for (size_t i = 0; i < std::min(NN_INPUT_SIZE, code.size()); ++i) {
+        input[i] = static_cast<double>(code[i]) / 255.0;
+    }
+
+    std::vector<double> hidden(NN_HIDDEN_SIZE);
+    for (int h = 0; h < NN_HIDDEN_SIZE; ++h) {
+        hidden[h] = nn.bias[h];
+        for (int i = 0; i < NN_INPUT_SIZE; ++i) {
+            hidden[h] += input[i] * nn.weights[h][i];
+        }
+        hidden[h] = 1.0 / (1.0 + std::exp(-hidden[h])); // Sigmoid activation
+    }
+
+    std::vector<double> output(NN_OUTPUT_SIZE);
+    for (int o = 0; o < NN_OUTPUT_SIZE; ++o) {
+        output[o] = nn.output_bias[o];
+        for (int h = 0; h < NN_HIDDEN_SIZE; ++h) {
+            output[o] += hidden[h] * nn.output_weights[o][h];
+        }
+        output[o] = 1.0 / (1.0 + std::exp(-output[o])); // Sigmoid for output
+    }
+
+    return std::accumulate(output.begin(), output.end(), 0.0) / NN_OUTPUT_SIZE;
+}
+
+// Emotional Graph Functions
+void add_emotional_node(EmotionalGraph& eg, const std::string& emotion, double intensity, double reward_value) {
+    eg.nodes.emplace_back(emotion, intensity, reward_value);
+}
+
+void reward_good_practice(EmotionalGraph& eg, const std::string& code, const std::string& documentation) {
+    NeuralNetwork nn; // Placeholder for actual NN initialization
+    init_neural_network(nn);
+    double code_quality = evaluate_code_with_nn(nn, code);
+    double doc_quality = evaluate_code_with_nn(nn, documentation);
+
+    if (code_quality > 0.7) {
+        add_emotional_node(eg, "Satisfaction", 0.8, 1.0);
+    } else if (code_quality > 0.5) {
+        add_emotional_node(eg, "Content", 0.5, 0.5);
+    } else {
+        add_emotional_node(eg, "Frustration", 0.6, -0.5);
+    }
+
+    if (doc_quality > 0.7) {
+        add_emotional_node(eg, "Pride", 0.7, 1.0);
+    } else if (doc_quality > 0.3) {
+        add_emotional_node(eg, "Neutral", 0.3, 0);
+    } else {
+        add_emotional_node(eg, "Disappointment", 0.6, -0.5);
+    }
+
+    std::cout << "Emotional response to code and documentation:\n";
+    for (const auto& node : eg.nodes) {
+        std::cout << "- " << node.emotion << ": Intensity " << node.intensity << ", Reward " << node.reward_value << "\n";
+    }
+}
+
+// AI Processing Functions
+
+void process_code_request(CodeWorkbench& workbench, CodeMemory& memory, EmotionalGraph& eg) {
+    if (workbench.code_request.empty()) {
+        throw std::runtime_error("No code request provided.");
+    }
+
+    try {
+        pmll_logic_loop(workbench, memory);
+        arll_logic_loop(workbench);
+        efll_logic_loop(workbench);
+        generate_code_suggestion(workbench, memory);
+        reward_good_practice(eg, workbench.suggested_code, workbench.code_request);
+    } catch (const std::exception& e) {
+        std::cerr << "Error during code request processing: " << e.what() << "\n";
+    }
+}
+
+void refine_code_suggestion(CodeWorkbench& workbench, CodeMemory& memory, STM_Cache& stm_cache, LTM_JudgeNode& judge_node) {
+    if (workbench.suggested_code.empty()) return;
+
+    SerializedTopic serialized = serialize_novel_topic(workbench.suggested_code);
+    if (stm_cache.used < stm_cache.capacity) {
+        stm_cache.entries[stm_cache.used++] = {serialized.data,
+
+        serialized.size, get_current_timestamp()};
+    } else {
+        for (auto& entry : stm_cache.entries) {
+            SerializedTopic topic = {entry.data, entry.size, ""}; // Reconstruct serialized topic
+            judge_and_consolidate(judge_node, topic);
+        }
+        stm_cache.used = 0; // Reset STM cache after evaluation
+    }
+
+    try {
+        std::string refined = "Refined code suggestion:\n" + workbench.suggested_code;
+        update_code_suggestion(workbench, refined);
+    } catch (const std::exception& e) {
+        std::cerr << "Error refining code suggestion: " << e.what() << "\n";
+    }
+}
+
+// Main Orchestration
+
+void orchestrate_coding_session(CodeWorkbench& workbench, CodeMemory& memory, EmotionalGraph& eg) {
+    if (workbench.code_request.empty()) {
+        throw std::runtime_error("No code request to process.");
+    }
+
+    std::cout << "Starting coding session for request: " << workbench.code_request << "\n";
+
+    try {
+        process_code_request(workbench, memory, eg);
+        std::cout << "Initial suggestion:\n" << workbench.suggested_code << "\n";
+
+        static STM_Cache stm_cache = init_stm_cache(1024);
+        static LTM_JudgeNode judge_node;
+
+        refine_code_suggestion(workbench, memory, stm_cache, judge_node);
+        std::cout << "Refined suggestion:\n" << workbench.suggested_code << "\n";
+
+        commit_code_to_blockchain(workbench.suggested_code.c_str(), "Final Suggestion");
+        std::cout << "Coding session completed.\n";
+    } catch (const std::exception& e) {
+        std::cerr << "Error in coding session: " << e.what() << "\n";
+    }
+}
+
+// Main function with error handling
+int main() {
+    try {
+        CodeMemory memory;
+        EmotionalGraph eg;
+        CodeWorkbench workbench{"Create a function to sort an array"};
+
+        NeuralNetwork nn;
+        init_neural_network(nn);
+
+        orchestrate_coding_session(workbench, memory, eg);
+    } catch (const std::exception& e) {
+        std::cerr << "An error occurred: " << e.what() << "\n";
+        return 1;
+    }
+    return 0;
+}
+
+// Placeholder functions for undefined methods
+
+void generate_code_suggestion(CodeWorkbench& workbench, const CodeMemory& memory) {
+    workbench.suggested_code = "/* Generated code suggestion */\n"
+                                "// This could be based on patterns in memory.";
+}
+
+void commit_code_to_blockchain(const char* code, const char* description) {
+    std::cout << "Committing code to blockchain: " << description << "\n";
+}
+
+double evaluate_code_style(const char* code) {
+    // Placeholder for actual style evaluation
+    return 0.75;
+}