ai/lang
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

updte

Browse Source
This commit is contained in:
syui
2025-07-23 14:43:07 +09:00
parent d8966128c8
commit 950548385c
26 changed files with 2980 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

11
.claude/settings.local.json Normal file
View File

@@ -0,0 +1,11 @@
{
"permissions": {
"allow": [
"Bash(git init:*)",
"Bash(git add:*)",
"Bash(cargo:*)",
"Bash(ls:*)"
],
"deny": []
}
}

2
.gitignore vendored Normal file
View File

@@ -0,0 +1,2 @@
.claude
*.lock

BIN
README.md
View File

Binary file not shown.

75
ai-friendly-definition.md Normal file
View File

@@ -0,0 +1,75 @@
# AIにとって「使いやすい」とは何か
## 人間とAIの根本的な違い
### 人間の特性
- 逐次的思考
- 限定的な作業メモリ
- 視覚的・空間的な理解
- 曖昧さへの耐性
### AIの特性
- 並列処理能力
- 大規模なコンテキスト処理
- パターン認識の高速性
- 構造化データへの親和性
## AI向け言語・OSの設計原則
### 1. 宣言的 > 命令的
```
# 人間向け（命令的）
for i in range(10):
result.append(process(i))
# AI向け（宣言的）
result = parallel_map(process, range(10))
```
### 2. 明示的な依存関係
```
# AI向け - 全ての依存関係が明示的
@depends_on(data_source, model, config)
@produces(prediction, confidence)
function inference() {
// 依存関係グラフが自動構築可能
}
```
### 3. 自己記述的な構造
```
# メタデータが言語構造に組み込まれている
structure NeuralLayer {
@performance_metric(flops=1e9)
@memory_requirement(gb=4)
@parallelizable(axis=batch)
forward_pass: Function
}
```
### 4. 状態の不変性とトレーサビリティ
- 全ての状態変更が追跡可能
- タイムトラベルデバッグが標準
- 因果関係が明確
### 5. ネイティブな並列性
- 並列実行がデフォルト
- 逐次実行は明示的に指定
## 実装の方向性
1. **AST（抽象構文木）の直接操作**
- ソースコードではなくASTが一次表現
- AIはASTを直接読み書き
2. **制約ベースプログラミング**
- 「何を」だけ記述し「どうやって」はAIが決定
- SMTソルバーとの統合
3. **確率的プログラミングの統合**
- 不確実性を言語レベルでサポート
- ベイズ推論がネイティブ
4. **自己修正能力**
- プログラムが実行時に自身を最適化
- AIが書いたコードをAIが改善するループ

224
ai-os-design.md Normal file
View File

@@ -0,0 +1,224 @@
# AI向けOSカーネル "Synaptic OS" の設計
## 基本コンセプト
従来のOSは人間のユーザーを想定しているが、Synaptic OSはAIエージェントが最適に動作するよう設計される。
## 設計原則
### 1. データフロー中心のスケジューリング
プロセス中心ではなく、データの依存関係に基づいたスケジューリング。
```rust
// 従来のプロセススケジューリング
struct Process {
pid: u32,
priority: u8,
cpu_time: Duration,
}
// AI向けデータフロースケジューリング
struct DataFlowTask {
id: TaskId,
dependencies: Vec<DataSource>,
outputs: Vec<DataSink>,
computation_graph: ComputeGraph,
resource_requirements: ResourceSpec,
}
```
### 2. 階層化されたメモリ管理
- **コンテキストメモリ**: 長期的な知識とコンテキスト
- **ワーキングメモリ**: 現在の計算に使用
- **キャッシュメモリ**: 頻繁にアクセスされるデータ
### 3. 確率的リソース管理
リソース割り当てを確率的に最適化。
### 4. 時間的一貫性の保証
因果関係と時間的依存関係を OS レベルで管理。
## カーネル構造
### Core Components
#### 1. SynapticScheduler
```rust
pub struct SynapticScheduler {
dependency_graph: DependencyGraph,
resource_pool: ResourcePool,
priority_queue: PriorityQueue<TaskId, Priority>,
quantum_allocator: QuantumAllocator,
}
impl SynapticScheduler {
pub fn schedule_next(&mut self) -> Option<TaskId> {
// データ依存関係を考慮したスケジューリング
let ready_tasks = self.dependency_graph.get_ready_tasks();
// 確率的優先度計算
let task = self.select_probabilistic(ready_tasks);
// リソース可用性チェック
if self.resource_pool.can_allocate(&task.requirements) {
Some(task.id)
} else {
None
}
}
}
```
#### 2. Neural Memory Manager
```rust
pub struct NeuralMemoryManager {
context_memory: ContextMemory,
working_memory: WorkingMemory,
associative_cache: AssociativeCache,
attention_mechanism: AttentionMechanism,
}
impl NeuralMemoryManager {
pub fn allocate(&mut self, request: MemoryRequest) -> Result<MemoryHandle> {
match request.memory_type {
MemoryType::Context => {
self.context_memory.allocate_persistent(request.size)
}
MemoryType::Working => {
self.working_memory.allocate_temporary(request.size)
}
MemoryType::Associative => {
self.associative_cache.allocate_with_attention(
request.size,
request.attention_weights
)
}
}
}
pub fn recall(&self, pattern: &Pattern) -> Vec<MemoryChunk> {
self.associative_cache.pattern_match(pattern)
}
}
```
#### 3. Probabilistic I/O System
```rust
pub struct ProbabilisticIOSystem {
predictive_cache: PredictiveCache,
uncertainty_tracker: UncertaintyTracker,
adaptive_prefetcher: AdaptivePrefetcher,
}
impl ProbabilisticIOSystem {
pub fn read(&mut self, request: IORequest) -> Future<IOResult> {
// 予測キャッシュをチェック
if let Some(prediction) = self.predictive_cache.get(&request) {
if prediction.confidence > 0.9 {
return Future::ready(Ok(prediction.data));
}
}
// 不確実性を考慮した非同期読み込み
self.async_read_with_uncertainty(request)
}
}
```
#### 4. Temporal Consistency Engine
```rust
pub struct TemporalConsistencyEngine {
causality_graph: CausalityGraph,
time_machine: TimeMachine,
consistency_checker: ConsistencyChecker,
}
impl TemporalConsistencyEngine {
pub fn ensure_causality(&mut self, operation: Operation) -> Result<()> {
// 因果関係の検証
if !self.causality_graph.is_causal_consistent(&operation) {
return Err(CausalityViolation);
}
// 時間的一貫性の保証
self.time_machine.checkpoint();
match self.execute_operation(operation) {
Ok(result) => Ok(result),
Err(e) => {
self.time_machine.rollback();
Err(e)
}
}
}
}
```
## システムコール
AI向けに特化したシステムコール:
```rust
// 推論の実行
sys_infer(model: ModelHandle, input: DataHandle) -> InferenceResult
// パターンマッチング
sys_pattern_match(pattern: Pattern, data: DataHandle) -> MatchResult
// 因果関係の記録
sys_record_causality(cause: EventId, effect: EventId) -> Result<()>
// 確率的決定
sys_probabilistic_choice(options: &[Choice], probs: &[f64]) -> Choice
// 時間旅行デバッグ
sys_time_travel(timestamp: Timestamp) -> DebugContext
// メタ計算の実行
sys_meta_compute(ast: AST, transformations: &[Transform]) -> AST
```
## AI特化機能
### 1. 自動最適化
カーネル自身がAIを使って動的に最適化される。
### 2. 学習型リソース管理
過去の実行パターンから学習してリソース配分を最適化。
### 3. 予測的プリフェッチング
AIが次に必要なデータを予測してプリフェッチ。
### 4. 適応的負荷分散
システムの状態を監視して動的に負荷分散。
## セキュリティモデル
### 1. 確率的アクセス制御
```rust
struct ProbabilisticACL {
permissions: HashMap<Principal, DistributionType>,
confidence_threshold: f64,
}
```
### 2. 差分プライバシー
データアクセスに差分プライバシーを組み込み。
### 3. ホモモルフィック計算
暗号化されたデータでの計算をサポート。
## パフォーマンス特性
- **レイテンシー**: 予測的実行により低レイテンシー
- **スループット**: 並列データフロー処理により高スループット
- **エネルギー効率**: AI最適化により高効率
- **適応性**: 実行時の動的最適化
## 実装方針
1. **段階的実装**: まずユーザーランドライブラリとして実装
2. **マイクロカーネル設計**: モジュール化により拡張性を確保
3. **検証可能性**: seL4のような形式検証を適用
4. **AI統合**: カーネル自身にAIを組み込み
この設計により、AIが真に効率的に動作できるOSを実現する。

140
language-design.md Normal file
View File

@@ -0,0 +1,140 @@
# AI向けプログラミング言語 "Synaptic" の設計
## 基本設計原則
### 1. グラフベースの実行モデル
従来の線形的なコード実行ではなく、データフローグラフとして表現。
```synaptic
# 従来の命令的スタイル（人間向け）
x = fetch_data()
y = process(x)
z = analyze(y)
result = summarize(z)
# Synapticスタイル（AI向け）
graph DataPipeline {
fetch_data() -> process() -> analyze() -> summarize()
# 並列実行可能な部分は自動検出
}
```
### 2. 型システムは制約システム
型は単なるデータ形式ではなく、満たすべき制約の集合。
```synaptic
constraint Image = {
dimensions: (width: >0, height: >0, channels: [1,3,4])
format: [RGB, RGBA, Grayscale]
@invariant: width * height * channels < MaxMemory
}
constraint ValidInput = Image & {
@postcondition: normalized(pixel_values)
@differentiable: true
}
```
### 3. 時間的次元の組み込み
プログラムの状態を時系列として扱う。
```synaptic
temporal function train_model(data) {
t[0]: model = initialize()
t[1..n]: model = update(model[t-1], batch[t])
@converge_when: loss[t] - loss[t-1] < epsilon
}
```
### 4. 確率的セマンティクス
不確実性を言語レベルでサポート。
```synaptic
probabilistic function classify(image) {
features ~ extract_features(image)
prediction ~ softmax(linear(features))
@confidence: entropy(prediction) < threshold
return sample(prediction)
}
```
### 5. メタプログラミングが第一級
コード自体がデータとして操作可能。
```synaptic
meta function optimize_function(f: Function) {
ast = parse(f)
optimized_ast = apply_transformations(ast, [
dead_code_elimination,
loop_fusion,
vectorization
])
return compile(optimized_ast)
}
```
## 実行環境の設計
### 1. 分散実行がデフォルト
```synaptic
@distributed(nodes=auto)
function large_scale_training() {
# 自動的に複数ノードに分散
}
```
### 2. 自動微分とバックプロパゲーション
```synaptic
@differentiable
function neural_block(x, weights) {
# 勾配計算は自動
return activation(matmul(x, weights))
}
```
### 3. JITコンパイルと自己最適化
```synaptic
@adaptive
function hot_path() {
# 実行パターンを学習し、自動的に最適化
}
```
## 構文の例
```synaptic
# AIが読みやすい構造化された定義
module ImageClassifier {
@requires: GPU(memory=8GB)
@dataset: ImageNet
structure Config {
learning_rate: 0.001 @range[0.0001, 0.1] @log_scale
batch_size: 32 @range[8, 256] @power_of_2
epochs: 100 @early_stopping
}
pipeline Training {
data -> augment -> batch -> forward -> loss -> backward -> optimize
@parallel: [augment, batch]
@checkpoint: every(10.epochs)
@monitor: [loss, accuracy, gradient_norm]
}
# 制約を満たす実装をAIが自動生成
constraint Performance {
accuracy > 0.95 on ValidationSet
inference_time < 10ms on GPU(2080Ti)
model_size < 100MB
}
}
```
## なぜこれがAIに適しているか
1. **明示的な依存関係**: AIは全体の計算グラフを即座に理解
2. **制約ベース**: 「何を達成したいか」を記述、「どうやって」はAIが決定
3. **並列性**: AIの並列処理能力を最大限活用
4. **自己修正**: 実行時の最適化が言語機能として組み込まれている
5. **メタレベル操作**: AIがコードを直接操作・最適化可能

24
prototype/Cargo.toml Normal file
View File

@@ -0,0 +1,24 @@
[package]
name = "synaptic"
version = "0.1.0"
edition = "2021"
[dependencies]
# パーサー
nom = "7.1"
# グラフ処理
petgraph = "0.6"
# 並列処理
rayon = "1.7"
# シリアライズ
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
# エラーハンドリング
anyhow = "1.0"
thiserror = "1.0"
# 確率的プログラミング
rand = "0.8"
rand_distr = "0.4"
[dev-dependencies]
criterion = "0.5"

45
prototype/examples/ai_friendly.syn Normal file
View File

@@ -0,0 +1,45 @@
# Synaptic言語のサンプル
# AIが理解しやすい宣言的な記述
graph ImageClassification {
# データフローの定義
load_images -> preprocess -> augment -> batch -> model -> predictions
# 並列実行可能な処理を明示
@parallel: [preprocess, augment]
# 制約の定義
@constraint: batch_size in [16, 32, 64, 128]
@constraint: accuracy > 0.95
@constraint: inference_time < 10ms
}
# 時間的な振る舞いを持つ関数
temporal function train(dataset) {
t[0]: model = initialize_weights()
t[1..n]: model = gradient_step(model[t-1], batch[t])
@converge_when: loss[t] - loss[t-1] < 0.001
@checkpoint: every(100.steps)
}
# 確率的な処理
probabilistic function predict(image) {
features ~ extract_features(image)
logits ~ linear_transform(features)
prediction ~ softmax(logits)
@confidence: entropy(prediction) < 0.1
return sample(prediction)
}
# メタプログラミング
meta function optimize_model(model) {
graph = extract_computation_graph(model)
optimized = apply_transformations(graph, [
fuse_operations,
quantize_weights,
prune_connections
])
return compile(optimized)
}

89
prototype/examples/meta_demo.rs Normal file
View File

@@ -0,0 +1,89 @@
use synaptic::*;
use std::collections::HashMap;
fn main() {
println!("=== メタプログラミングのデモ ===\n");
// サンプルプログラムを作成
let mut program = Program::new();
// Sequential操作のノード
program.add_node(Node {
id: NodeId("sequential_op".to_string()),
kind: NodeKind::Operation { op: Operation::Sequential },
inputs: vec![NodeId("input".to_string())],
outputs: vec![NodeId("output".to_string())],
constraints: vec![],
metadata: Metadata::default(),
});
// Map操作のノード
program.add_node(Node {
id: NodeId("map_op".to_string()),
kind: NodeKind::Operation { op: Operation::Map },
inputs: vec![NodeId("data".to_string())],
outputs: vec![NodeId("mapped_data".to_string())],
constraints: vec![],
metadata: Metadata::default(),
});
println!("1. メタプログラミングエンジンの初期化:");
let mut meta_engine = MetaProgrammingEngine::new();
// 2. ASTの抽出と分析
println!("\n2. AST抽出と構造分析:");
let ast = meta_engine.extract_ast(&program);
println!(" ノード数: {}", ast.nodes.len());
println!(" ノードタイプ: {:?}", ast.structure.node_types);
// 3. プログラムの内省
println!("\n3. プログラムの内省:");
let introspection = meta_engine.introspect(&program);
println!(" データフロー複雑度: {:.2}", introspection.complexity_metrics.data_flow_complexity);
println!(" 最適化機会の数: {}", introspection.optimization_opportunities.len());
for opportunity in &introspection.optimization_opportunities {
println!(" - {}: {} (予想速度向上: {:.1}x)",
opportunity.node_id.0,
opportunity.opportunity_type,
opportunity.estimated_speedup
);
}
// 4. AST変換
println!("\n4. AST変換（並列化）:");
let transformations = vec!["parallelize".to_string()];
let transformed_ast = meta_engine.transform_ast(&ast, &transformations);
println!(" 変換前: {:?}", ast.nodes[0].kind);
println!(" 変換後: {:?}", transformed_ast.nodes[0].kind);
// 5. コード生成
println!("\n5. 自動コード生成:");
// ニューラルレイヤーの生成
let mut params = HashMap::new();
params.insert("input_size".to_string(), Value::Number(784.0));
params.insert("output_size".to_string(), Value::Number(128.0));
match meta_engine.generate_optimized_code("neural_layer", &params) {
Ok(code) => println!(" 生成されたニューラルレイヤー:\n{}", code),
Err(e) => println!(" エラー: {}", e),
}
// 並列リダクションの生成
let mut reduce_params = HashMap::new();
reduce_params.insert("operation".to_string(), Value::String("sum".to_string()));
match meta_engine.generate_optimized_code("parallel_reduce", &reduce_params) {
Ok(code) => println!(" 生成された並列リダクション:\n{}", code),
Err(e) => println!(" エラー: {}", e),
}
println!("\n=== メタプログラミングの利点 ===");
println!("1. コードの自動生成と最適化");
println!("2. ASTの直接操作による柔軟性");
println!("3. プログラムの内省と自己分析");
println!("4. ドメイン特化言語の動的生成");
println!("5. 実行時の適応的最適化");
}

166
prototype/examples/os_kernel_demo.rs Normal file
View File

@@ -0,0 +1,166 @@
use synaptic::*;
use synaptic::os_kernel::*;
fn main() {
println!("=== AI向けOSカーネルのデモ ===\n");
// Synaptic OS カーネルの初期化
let mut scheduler = SynapticScheduler::new();
let mut memory_manager = NeuralMemoryManager::new();
println!("1. データフロータスクの作成:");
// AI推論タスク
let inference_task = DataFlowTask {
id: TaskId("ai_inference".to_string()),
dependencies: vec![
DataSource::Memory(MemoryHandle("model_weights".to_string())),
DataSource::Storage(StorageHandle("input_data".to_string())),
],
outputs: vec![
DataSink::Memory(MemoryHandle("inference_result".to_string())),
],
computation_graph: vec![
NodeId("load_model".to_string()),
NodeId("preprocess".to_string()),
NodeId("forward_pass".to_string()),
],
resource_requirements: ResourceSpec {
cpu_cores: 2,
memory_mb: 1024,
gpu_memory_mb: Some(2048),
network_bandwidth_mbps: None,
},
priority: Priority(0.9),
};
// データ前処理タスク
let preprocessing_task = DataFlowTask {
id: TaskId("data_preprocessing".to_string()),
dependencies: vec![
DataSource::Storage(StorageHandle("raw_data".to_string())),
],
outputs: vec![
DataSink::Memory(MemoryHandle("processed_data".to_string())),
],
computation_graph: vec![
NodeId("clean_data".to_string()),
NodeId("normalize".to_string()),
NodeId("feature_extract".to_string()),
],
resource_requirements: ResourceSpec {
cpu_cores: 1,
memory_mb: 512,
gpu_memory_mb: None,
network_bandwidth_mbps: Some(100),
},
priority: Priority(0.7),
};
println!(" - AI推論タスク: CPU={}, Memory={}MB, GPU={}MB",
inference_task.resource_requirements.cpu_cores,
inference_task.resource_requirements.memory_mb,
inference_task.resource_requirements.gpu_memory_mb.unwrap_or(0)
);
println!(" - データ前処理タスク: CPU={}, Memory={}MB",
preprocessing_task.resource_requirements.cpu_cores,
preprocessing_task.resource_requirements.memory_mb
);
// 2. タスクのスケジューリング
println!("\n2. タスクスケジューリング:");
scheduler.submit_task(preprocessing_task);
scheduler.submit_task(inference_task);
// スケジューリング実行
let mut completed_tasks = 0;
let mut execution_log = Vec::new();
while completed_tasks < 2 {
if let Some(task_id) = scheduler.schedule_next() {
let start_time = std::time::Instant::now();
execution_log.push(format!(" タスク {} を実行開始", task_id.0));
// 実行をシミュレート（実際にはここでタスクを実行）
std::thread::sleep(std::time::Duration::from_millis(100));
let end_time = start_time.elapsed();
execution_log.push(format!(" タスク {} を完了 (実行時間: {:?})", task_id.0, end_time));
scheduler.complete_task(&task_id);
completed_tasks += 1;
}
}
for log in execution_log {
println!("{}", log);
}
// 3. Neural Memory Manager のデモ
println!("\n3. Neural Memory Manager:");
// コンテキストメモリに長期的な知識を保存
let model_handle = memory_manager.store_context(
"global_model".to_string(),
Value::String("Pre-trained Language Model".to_string())
);
println!(" コンテキストメモリに保存: {:?}", model_handle);
// ワーキングメモリに一時的なデータを保存
let temp_handle = memory_manager.allocate_working(
"current_batch".to_string(),
Value::Array(vec![
Value::Number(1.0),
Value::Number(2.0),
Value::Number(3.0),
])
);
println!(" ワーキングメモリに保存: {:?}", temp_handle);
// パターンマッチングによる連想記憶
let matched_data = memory_manager.recall_pattern("model");
println!(" パターン'model'にマッチしたデータ数: {}", matched_data.len());
// 4. システムコールのシミュレーション
println!("\n4. AI特化システムコール:");
let syscalls = vec![
SynapticSyscall::Infer {
model: "bert-base".to_string(),
input: Value::String("Hello world".to_string()),
},
SynapticSyscall::ProbabilisticChoice {
options: vec!["option_a".to_string(), "option_b".to_string(), "option_c".to_string()],
probabilities: vec![0.5, 0.3, 0.2],
},
SynapticSyscall::PatternMatch {
pattern: "neural".to_string(),
data: "neural_network_data".to_string(),
},
];
for (i, syscall) in syscalls.iter().enumerate() {
match syscall {
SynapticSyscall::Infer { model, input } => {
println!(" sys_infer({}, {:?}) -> [推論結果]", model, input);
}
SynapticSyscall::ProbabilisticChoice { options, probabilities } => {
println!(" sys_probabilistic_choice({:?}, {:?}) -> [選択結果]", options, probabilities);
}
SynapticSyscall::PatternMatch { pattern, data } => {
println!(" sys_pattern_match({}, {}) -> [マッチ結果]", pattern, data);
}
_ => {}
}
}
println!("\n=== AI向けOSの特徴 ===");
println!("1. データフロー中心のスケジューリング");
println!("2. 階層化されたニューラルメモリ管理");
println!("3. 確率的リソース配分");
println!("4. AI特化システムコール");
println!("5. 時間的一貫性の保証");
println!("6. 予測的I/Oとキャッシング");
}

68
prototype/examples/probabilistic_demo.rs Normal file
View File

@@ -0,0 +1,68 @@
use synaptic::*;
fn main() {
println!("=== 確率的プログラミングのデモ ===\n");
let mut prob_engine = ProbabilisticEngine::new();
// 1. 正規分布からのサンプリング
println!("1. 正規分布（平均=100, 標準偏差=15）からのサンプリング:");
let normal_dist = DistributionType::Normal { mean: 100.0, std_dev: 15.0 };
let samples = prob_engine.sample(&normal_dist, 1000);
println!(" 期待値: {:.2}", prob_engine.expectation(&samples));
println!(" 分散: {:.2}", prob_engine.variance(&samples));
let (lower, upper) = prob_engine.confidence_interval(&samples, 0.95);
println!(" 95%信頼区間: [{:.2}, {:.2}]", lower, upper);
// 2. 離散分布（カテゴリカル分布）
println!("\n2. 離散分布からのサンプリング:");
let discrete_dist = DistributionType::Discrete {
probabilities: vec![0.2, 0.5, 0.3],
values: vec![
Value::String("低".to_string()),
Value::String("中".to_string()),
Value::String("高".to_string()),
],
};
let discrete_samples = prob_engine.sample(&discrete_dist, 1000);
// カテゴリごとのカウント
let mut counts = [0, 0, 0];
for &sample in &discrete_samples {
counts[sample as usize] += 1;
}
println!(" 低: {:.1}%, 中: {:.1}%, 高: {:.1}%",
counts[0] as f64 / 10.0,
counts[1] as f64 / 10.0,
counts[2] as f64 / 10.0
);
// 3. エントロピー計算
println!("\n3. エントロピー計算:");
let uniform_probs = vec![0.25, 0.25, 0.25, 0.25];
let skewed_probs = vec![0.9, 0.05, 0.03, 0.02];
println!(" 一様分布のエントロピー: {:.3}", prob_engine.entropy(&uniform_probs));
println!(" 偏った分布のエントロピー: {:.3}", prob_engine.entropy(&skewed_probs));
// 4. ベイズ更新のシミュレーション
println!("\n4. ベイズ更新:");
let prior = DistributionType::Normal { mean: 50.0, std_dev: 10.0 };
let evidence = 65.0;
println!(" 事前分布: 正規分布(μ=50, σ=10)");
println!(" 観測データ: {}", evidence);
let posterior = prob_engine.bayesian_update(&prior, &|x| x, evidence);
if let DistributionType::Normal { mean, std_dev } = posterior {
println!(" 事後分布: 正規分布(μ={:.2}, σ={:.2})", mean, std_dev);
}
// 5. AIにとっての利点
println!("\n=== AIにとっての確率的プログラミングの利点 ===");
println!("1. 不確実性の明示的な表現");
println!("2. ベイズ推論による学習");
println!("3. 確率的制約の評価");
println!("4. モンテカルロ法による複雑な計算");
}

71
prototype/examples/self_improve_demo.rs Normal file
View File

@@ -0,0 +1,71 @@
use synaptic::*;
fn main() {
// 最適化可能なプログラムを作成
let mut program = Program::new();
// Sequential操作（並列化可能）
program.add_node(Node {
id: NodeId("seq1".to_string()),
kind: NodeKind::Operation { op: Operation::Sequential },
inputs: vec![NodeId("input1".to_string()), NodeId("input2".to_string())],
outputs: vec![NodeId("output1".to_string())],
constraints: vec![],
metadata: Metadata::default(),
});
// Map操作（並列化可能）
program.add_node(Node {
id: NodeId("map1".to_string()),
kind: NodeKind::Operation { op: Operation::Map },
inputs: vec![NodeId("data".to_string())],
outputs: vec![NodeId("mapped_data".to_string())],
constraints: vec![],
metadata: Metadata::default(),
});
println!("=== 元のプログラム ===");
println!("{:#?}", program);
// 自己改良エンジンを作成
let mut engine = SelfImprovementEngine::new();
// プログラムを分析
let analysis = engine.analyze_program(&program);
println!("\n=== プログラム分析 ===");
println!("ノード数: {}", analysis.node_count);
println!("並列化可能なノード: {}", analysis.parallelizable_nodes);
println!("複雑度スコア: {:.2}", analysis.complexity_score);
// 改良提案を生成
let improvements = engine.suggest_improvements(&program);
println!("\n=== 改良提案 ===");
for improvement in &improvements {
println!("- パターン: {}", improvement.pattern_name);
println!(" 対象ノード: {:?}", improvement.original_node_id);
println!(" 期待される性能向上: {:.1}x", improvement.expected_gain);
}
// 改良を適用
if let Some(improvement) = improvements.first() {
println!("\n=== 改良を適用中... ===");
match engine.apply_improvement(&mut program, improvement) {
Ok(_) => {
println!("✓ 改良が適用されました");
println!("\n=== 最適化後のプログラム ===");
println!("{:#?}", program);
// 再分析
let new_analysis = engine.analyze_program(&program);
println!("\n=== 最適化後の分析 ===");
println!("複雑度スコア: {:.2} -> {:.2}",
analysis.complexity_score,
new_analysis.complexity_score
);
}
Err(e) => {
eprintln!("✗ 改良の適用に失敗: {}", e);
}
}
}
}

106
prototype/examples/temporal_demo.rs Normal file
View File

@@ -0,0 +1,106 @@
use synaptic::*;
use std::time::Instant;
use std::collections::HashMap;
fn main() {
println!("=== 時間的プログラミングのデモ ===\n");
let mut temporal_engine = TemporalEngine::new(100);
let node_id = NodeId("training_loss".to_string());
// 収束チェッカーを登録
temporal_engine.register_convergence_check(node_id.clone(), 0.001, 5);
println!("1. 学習プロセスのシミュレーション（損失の減少）:");
// 学習プロセスをシミュレート
let mut loss = 1.0;
let mut converged = false;
let mut step = 0;
while step < 50 && !converged {
// 損失を指数的に減少させる（ノイズ付き）
loss = loss * 0.95 + 0.01 * rand::random::<f64>();
let mut values = HashMap::new();
values.insert(node_id.clone(), Value::Number(loss));
let state = TemporalState {
time_step: step,
values,
timestamp: Instant::now(),
};
temporal_engine.add_time_step(state);
// 収束チェック
converged = temporal_engine.check_convergence(&node_id, loss);
if step % 10 == 0 || converged {
println!(" Step {}: Loss = {:.6}, Converged = {}", step, loss, converged);
}
step += 1;
}
// 2. 時間的操作のデモ
println!("\n2. 時間的操作:");
// 移動平均の計算
let moving_avg = TemporalComputation {
operator: TemporalOperator::MovingAverage { window: 5 },
input: node_id.clone(),
};
if let Some(Value::Number(avg)) = moving_avg.compute(&temporal_engine) {
println!(" 5期間移動平均: {:.6}", avg);
}
// 微分（変化率）
let derivative = TemporalComputation {
operator: TemporalOperator::Derivative,
input: node_id.clone(),
};
if let Some(Value::Number(diff)) = derivative.compute(&temporal_engine) {
println!(" 最新の変化率: {:.6}", diff);
}
// 3つ前の値
let previous = TemporalComputation {
operator: TemporalOperator::Previous { steps: 3 },
input: node_id.clone(),
};
if let Some(Value::Number(prev)) = previous.compute(&temporal_engine) {
println!(" 3ステップ前の値: {:.6}", prev);
}
// 3. 値の履歴表示
println!("\n3. 値の履歴（最後の10ステップ）:");
let history = temporal_engine.get_value_history(&node_id);
for (time_step, value) in history.iter().rev().take(10).rev() {
if let Value::Number(n) = value {
println!(" t={}: {:.6}", time_step, n);
}
}
// 4. 予測
println!("\n4. 将来値の予測:");
if let Some(Value::Number(predicted)) = temporal_engine.predict_next_value(&node_id, 5) {
println!(" 5ステップ後の予測値: {:.6}", predicted);
}
// 5. タイムトラベルデバッグ
println!("\n5. タイムトラベルデバッグ:");
if let Some(debug_info) = temporal_engine.time_travel_debug(step / 2) {
println!(" {}", debug_info);
}
println!("\n=== 時間的プログラミングの利点 ===");
println!("1. 状態変化の追跡と可視化");
println!("2. 収束条件の自動判定");
println!("3. 時系列データの処理");
println!("4. タイムトラベルデバッグ");
println!("5. 因果関係の明確化");
}

99
prototype/src/ast.rs Normal file
View File

@@ -0,0 +1,99 @@
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub struct Program {
pub nodes: Vec<Node>,
pub metadata: Metadata,
}
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub struct Node {
pub id: NodeId,
pub kind: NodeKind,
pub inputs: Vec<NodeId>,
pub outputs: Vec<NodeId>,
pub constraints: Vec<Constraint>,
pub metadata: Metadata,
}
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, Hash)]
pub struct NodeId(pub String);
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum NodeKind {
Function {
name: String,
params: Vec<Parameter>,
body: Box<Node>,
},
Graph {
name: String,
nodes: Vec<Node>,
},
Operation {
op: Operation,
},
Value(Value),
Constraint(Constraint),
}
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum Operation {
Map,
Filter,
Reduce,
Transform(String),
Parallel,
Sequential,
}
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum Value {
Number(f64),
String(String),
Boolean(bool),
Array(Vec<Value>),
Object(HashMap<String, Value>),
Function(String), // 関数参照
}
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub struct Parameter {
pub name: String,
pub constraints: Vec<Constraint>,
}
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub enum Constraint {
Type(String),
Range { min: f64, max: f64 },
Pattern(String),
Dependency(Vec<NodeId>),
Performance(PerformanceConstraint),
}
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
pub struct PerformanceConstraint {
pub metric: String,
pub threshold: f64,
pub unit: String,
}
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Default)]
pub struct Metadata {
pub annotations: HashMap<String, Value>,
}
impl Program {
pub fn new() -> Self {
Program {
nodes: Vec::new(),
metadata: Metadata::default(),
}
}
pub fn add_node(&mut self, node: Node) {
self.nodes.push(node);
}
}

54
prototype/src/constraint.rs Normal file
View File

@@ -0,0 +1,54 @@
use crate::ast::{Constraint, Value};
use anyhow::{Result, anyhow};
pub struct ConstraintChecker;
impl ConstraintChecker {
pub fn check(constraint: &Constraint, value: &Value) -> Result<bool> {
match constraint {
Constraint::Type(expected_type) => {
Self::check_type(value, expected_type)
}
Constraint::Range { min, max } => {
match value {
Value::Number(n) => Ok(*n >= *min && *n <= *max),
_ => Err(anyhow!("Range constraint requires numeric value")),
}
}
Constraint::Pattern(pattern) => {
match value {
Value::String(s) => {
// 簡易的なパターンマッチング
Ok(s.contains(pattern))
}
_ => Err(anyhow!("Pattern constraint requires string value")),
}
}
Constraint::Performance(_) => {
// パフォーマンス制約は実行時に評価
Ok(true)
}
Constraint::Dependency(_) => {
// 依存関係は実行順序で保証
Ok(true)
}
}
}
fn check_type(value: &Value, expected_type: &str) -> Result<bool> {
let actual_type = match value {
Value::Number(_) => "number",
Value::String(_) => "string",
Value::Boolean(_) => "boolean",
Value::Array(_) => "array",
Value::Object(_) => "object",
Value::Function(_) => "function",
};
Ok(actual_type == expected_type)
}
}

78
prototype/src/executor.rs Normal file
View File

@@ -0,0 +1,78 @@
use crate::ast::*;
use crate::graph::ComputeGraph;
use rayon::prelude::*;
use std::collections::HashMap;
use anyhow::{Result, anyhow};
pub struct Executor {
graph: ComputeGraph,
cache: HashMap<NodeId, Value>,
}
impl Executor {
pub fn new(program: Program) -> Self {
let graph = ComputeGraph::from_program(&program);
Executor {
graph,
cache: HashMap::new(),
}
}
pub fn execute(&mut self) -> Result<HashMap<NodeId, Value>> {
let parallel_groups = self.graph.parallel_groups();
for group in parallel_groups {
// 各グループ内のノードは並列実行可能
let results: Vec<_> = group
.par_iter()
.map(|node_id| self.execute_node(node_id))
.collect();
// 結果をキャッシュに保存
for (node_id, result) in group.into_iter().zip(results) {
match result {
Ok(value) => {
self.cache.insert(node_id, value);
}
Err(e) => return Err(e),
}
}
}
Ok(self.cache.clone())
}
fn execute_node(&self, node_id: &NodeId) -> Result<Value> {
let node = self.graph.get_node(node_id)
.ok_or_else(|| anyhow!("Node not found: {:?}", node_id))?;
match &node.kind {
NodeKind::Value(val) => Ok(val.clone()),
NodeKind::Operation { op } => {
match op {
Operation::Map => {
// 簡易実装：入力を2倍にする
Ok(Value::String("mapped".to_string()))
}
Operation::Transform(name) => {
Ok(Value::String(format!("transformed_{}", name)))
}
_ => Ok(Value::String("operation".to_string())),
}
}
NodeKind::Function { name, .. } => {
Ok(Value::String(format!("function_{}_result", name)))
}
NodeKind::Graph { name, .. } => {
Ok(Value::String(format!("graph_{}_result", name)))
}
NodeKind::Constraint(_) => {
Ok(Value::Boolean(true))
}
}
}
}

81
prototype/src/graph.rs Normal file
View File

@@ -0,0 +1,81 @@
use crate::ast::{Node, NodeId, Program};
use petgraph::graph::{DiGraph, NodeIndex};
use petgraph::visit::{Topo, EdgeRef};
use std::collections::HashMap;
pub struct ComputeGraph {
graph: DiGraph<Node, ()>,
node_map: HashMap<NodeId, NodeIndex>,
}
impl ComputeGraph {
pub fn from_program(program: &Program) -> Self {
let mut graph = DiGraph::new();
let mut node_map = HashMap::new();
// ノードを追加
for node in &program.nodes {
let idx = graph.add_node(node.clone());
node_map.insert(node.id.clone(), idx);
}
// エッジを追加（依存関係）
for node in &program.nodes {
let from_idx = node_map[&node.id];
for output_id in &node.outputs {
if let Some(&to_idx) = node_map.get(output_id) {
graph.add_edge(from_idx, to_idx, ());
}
}
}
ComputeGraph { graph, node_map }
}
pub fn topological_order(&self) -> Vec<NodeId> {
let mut topo = Topo::new(&self.graph);
let mut order = Vec::new();
while let Some(idx) = topo.next(&self.graph) {
if let Some(node) = self.graph.node_weight(idx) {
order.push(node.id.clone());
}
}
order
}
pub fn parallel_groups(&self) -> Vec<Vec<NodeId>> {
// 並列実行可能なノードのグループを計算
let mut groups = Vec::new();
let mut visited = HashMap::new();
for node_id in self.topological_order() {
let idx = self.node_map[&node_id];
// このノードが依存する全てのノードのグループ番号の最大値+1
let mut max_group = 0;
for edge in self.graph.edges_directed(idx, petgraph::Direction::Incoming) {
if let Some(dep_node) = self.graph.node_weight(edge.source()) {
if let Some(&group) = visited.get(&dep_node.id) {
max_group = max_group.max(group + 1);
}
}
}
visited.insert(node_id.clone(), max_group);
// グループに追加
if max_group >= groups.len() {
groups.resize(max_group + 1, Vec::new());
}
groups[max_group].push(node_id);
}
groups
}
pub fn get_node(&self, id: &NodeId) -> Option<&Node> {
self.node_map.get(id).and_then(|&idx| self.graph.node_weight(idx))
}
}

20
prototype/src/lib.rs Normal file
View File

@@ -0,0 +1,20 @@
pub mod ast;
pub mod parser;
pub mod graph;
pub mod executor;
pub mod constraint;
pub mod self_improve;
pub mod probabilistic;
pub mod temporal;
pub mod meta;
pub mod os_kernel;
pub use ast::*;
pub use parser::parse;
pub use graph::ComputeGraph;
pub use executor::Executor;
pub use self_improve::SelfImprovementEngine;
pub use probabilistic::{ProbabilisticEngine, ProbabilisticValue, DistributionType};
pub use temporal::{TemporalEngine, TemporalState, TemporalOperator, TemporalComputation};
pub use meta::{MetaProgrammingEngine, ASTRepresentation};
pub use os_kernel::{SynapticKernel, DataFlowTask, TaskId, ResourceSpec, Priority};

33
prototype/src/main.rs Normal file
View File

@@ -0,0 +1,33 @@
use synaptic::*;
fn main() {
// サンプルプログラム
let synaptic_code = r#"
graph DataPipeline {
fetch_data -> process -> analyze -> summarize
}
"#;
match parse(synaptic_code) {
Ok(program) => {
println!("Parsed program:");
println!("{:#?}", program);
let mut executor = Executor::new(program);
match executor.execute() {
Ok(results) => {
println!("\nExecution results:");
for (node_id, value) in results {
println!("{:?}: {:?}", node_id, value);
}
}
Err(e) => {
eprintln!("Execution error: {}", e);
}
}
}
Err(e) => {
eprintln!("Parse error: {}", e);
}
}
}

296
prototype/src/meta.rs Normal file
View File

@@ -0,0 +1,296 @@
use crate::ast::{Program, Node, NodeKind, NodeId, Value, Operation};
use std::collections::HashMap;
pub struct MetaProgrammingEngine {
transformations: HashMap<String, TransformationRule>,
ast_cache: HashMap<String, Program>,
}
pub struct TransformationRule {
pub name: String,
pub pattern: Box<dyn Fn(&Node) -> bool + Send + Sync>,
pub transform: Box<dyn Fn(&Node) -> Node + Send + Sync>,
pub description: String,
}
pub struct CodeGenerator {
templates: HashMap<String, String>,
}
impl MetaProgrammingEngine {
pub fn new() -> Self {
let mut engine = MetaProgrammingEngine {
transformations: HashMap::new(),
ast_cache: HashMap::new(),
};
engine.register_builtin_transformations();
engine
}
fn register_builtin_transformations(&mut self) {
// ループ融合の変換
self.register_transformation(TransformationRule {
name: "loop_fusion".to_string(),
pattern: Box::new(|node| {
matches!(&node.kind, NodeKind::Operation { op: Operation::Map })
}),
transform: Box::new(|node| {
let mut transformed = node.clone();
// 複数のMapを一つに融合
if let NodeKind::Operation { op } = &mut transformed.kind {
*op = Operation::Transform("fused_map".to_string());
}
transformed
}),
description: "Fuse consecutive map operations".to_string(),
});
// 並列化の変換
self.register_transformation(TransformationRule {
name: "parallelize".to_string(),
pattern: Box::new(|node| {
matches!(&node.kind, NodeKind::Operation { op: Operation::Sequential })
}),
transform: Box::new(|node| {
let mut transformed = node.clone();
if let NodeKind::Operation { op } = &mut transformed.kind {
*op = Operation::Parallel;
}
transformed
}),
description: "Convert sequential operations to parallel".to_string(),
});
}
pub fn register_transformation(&mut self, rule: TransformationRule) {
self.transformations.insert(rule.name.clone(), rule);
}
pub fn extract_ast(&self, program: &Program) -> ASTRepresentation {
ASTRepresentation {
nodes: program.nodes.clone(),
metadata: program.metadata.clone(),
structure: self.analyze_structure(program),
}
}
fn analyze_structure(&self, program: &Program) -> StructureInfo {
let mut info = StructureInfo {
depth: 0,
branching_factor: 0.0,
cyclic: false,
node_types: HashMap::new(),
};
for node in &program.nodes {
let node_type = match &node.kind {
NodeKind::Function { .. } => "function",
NodeKind::Graph { .. } => "graph",
NodeKind::Operation { .. } => "operation",
NodeKind::Value(_) => "value",
NodeKind::Constraint(_) => "constraint",
};
*info.node_types.entry(node_type.to_string()).or_insert(0) += 1;
}
info
}
pub fn transform_ast(&self, ast: &ASTRepresentation, transformations: &[String]) -> ASTRepresentation {
let mut result = ast.clone();
for transformation_name in transformations {
if let Some(rule) = self.transformations.get(transformation_name) {
for node in &mut result.nodes {
if (rule.pattern)(node) {
*node = (rule.transform)(node);
}
}
}
}
result
}
pub fn compile_to_program(&self, ast: &ASTRepresentation) -> Program {
Program {
nodes: ast.nodes.clone(),
metadata: ast.metadata.clone(),
}
}
pub fn generate_optimized_code(&self, pattern: &str, parameters: &HashMap<String, Value>) -> Result<String, String> {
match pattern {
"neural_layer" => {
self.generate_neural_layer(parameters)
}
"parallel_reduce" => {
self.generate_parallel_reduce(parameters)
}
"gradient_computation" => {
self.generate_gradient_computation(parameters)
}
_ => Err(format!("Unknown pattern: {}", pattern))
}
}
fn generate_neural_layer(&self, params: &HashMap<String, Value>) -> Result<String, String> {
let input_size = match params.get("input_size") {
Some(Value::Number(n)) => *n as usize,
_ => return Err("input_size parameter required".to_string()),
};
let output_size = match params.get("output_size") {
Some(Value::Number(n)) => *n as usize,
_ => return Err("output_size parameter required".to_string()),
};
Ok(format!(r#"
graph NeuralLayer {{
@input_shape: [{input_size}]
@output_shape: [{output_size}]
weights ~ Normal(mean=0.0, std_dev=0.1) @shape[{input_size}, {output_size}]
bias ~ Normal(mean=0.0, std_dev=0.01) @shape[{output_size}]
input -> linear_transform(weights, bias) -> activation -> output
@differentiable: true
@parallel: batch_dimension
}}
"#, input_size = input_size, output_size = output_size))
}
fn generate_parallel_reduce(&self, params: &HashMap<String, Value>) -> Result<String, String> {
let operation = match params.get("operation") {
Some(Value::String(s)) => s.clone(),
_ => "add".to_string(),
};
Ok(format!(r#"
graph ParallelReduce {{
@operation: {operation}
@parallel: true
input -> chunk -> parallel_map(partial_reduce) -> final_reduce -> output
@constraint: associative({operation})
@constraint: commutative({operation})
}}
"#, operation = operation))
}
fn generate_gradient_computation(&self, _params: &HashMap<String, Value>) -> Result<String, String> {
Ok(r#"
meta function compute_gradients(forward_function) {
ast = extract_ast(forward_function)
backward_ast = reverse_mode_ad(ast)
return compile(backward_ast)
}
@automatic_differentiation
temporal function gradient_descent(params, loss_function) {
t[0]: current_params = params
t[1..n]: {
gradients = compute_gradients(loss_function)(current_params[t-1])
current_params = current_params[t-1] - learning_rate * gradients
}
@converge_when: norm(gradients) < epsilon
}
"#.to_string())
}
pub fn introspect(&self, program: &Program) -> IntrospectionResult {
IntrospectionResult {
complexity_metrics: self.calculate_complexity_metrics(program),
optimization_opportunities: self.find_optimization_opportunities(program),
performance_predictions: self.predict_performance(program),
}
}
fn calculate_complexity_metrics(&self, program: &Program) -> ComplexityMetrics {
ComplexityMetrics {
cyclomatic_complexity: self.calculate_cyclomatic_complexity(program),
data_flow_complexity: self.calculate_data_flow_complexity(program),
temporal_complexity: self.calculate_temporal_complexity(program),
}
}
fn calculate_cyclomatic_complexity(&self, _program: &Program) -> f64 {
// 簡易実装
1.0
}
fn calculate_data_flow_complexity(&self, program: &Program) -> f64 {
program.nodes.len() as f64 * 1.2
}
fn calculate_temporal_complexity(&self, _program: &Program) -> f64 {
// 時間的依存関係の複雑さ
1.0
}
fn find_optimization_opportunities(&self, program: &Program) -> Vec<OptimizationOpportunity> {
let mut opportunities = Vec::new();
for node in &program.nodes {
if matches!(&node.kind, NodeKind::Operation { op: Operation::Sequential }) {
opportunities.push(OptimizationOpportunity {
node_id: node.id.clone(),
opportunity_type: "parallelization".to_string(),
estimated_speedup: 2.5,
});
}
}
opportunities
}
fn predict_performance(&self, _program: &Program) -> PerformancePrediction {
PerformancePrediction {
estimated_runtime: 100.0,
memory_usage: 1024.0,
parallel_efficiency: 0.85,
}
}
}
#[derive(Debug, Clone)]
pub struct ASTRepresentation {
pub nodes: Vec<Node>,
pub metadata: crate::ast::Metadata,
pub structure: StructureInfo,
}
#[derive(Debug, Clone)]
pub struct StructureInfo {
pub depth: usize,
pub branching_factor: f64,
pub cyclic: bool,
pub node_types: HashMap<String, usize>,
}
pub struct IntrospectionResult {
pub complexity_metrics: ComplexityMetrics,
pub optimization_opportunities: Vec<OptimizationOpportunity>,
pub performance_predictions: PerformancePrediction,
}
pub struct ComplexityMetrics {
pub cyclomatic_complexity: f64,
pub data_flow_complexity: f64,
pub temporal_complexity: f64,
}
pub struct OptimizationOpportunity {
pub node_id: NodeId,
pub opportunity_type: String,
pub estimated_speedup: f64,
}
pub struct PerformancePrediction {
pub estimated_runtime: f64,
pub memory_usage: f64,
pub parallel_efficiency: f64,
}

389
prototype/src/os_kernel.rs Normal file
View File

@@ -0,0 +1,389 @@
use crate::ast::{NodeId, Value};
use crate::probabilistic::DistributionType;
use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant};
pub struct SynapticKernel {
scheduler: SynapticScheduler,
memory_manager: NeuralMemoryManager,
io_system: ProbabilisticIOSystem,
temporal_engine: TemporalConsistencyEngine,
}
#[derive(Debug, Clone)]
pub struct DataFlowTask {
pub id: TaskId,
pub dependencies: Vec<DataSource>,
pub outputs: Vec<DataSink>,
pub computation_graph: Vec<NodeId>,
pub resource_requirements: ResourceSpec,
pub priority: Priority,
}
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub struct TaskId(pub String);
#[derive(Debug, Clone)]
pub enum DataSource {
Memory(MemoryHandle),
Storage(StorageHandle),
Network(NetworkHandle),
Sensor(SensorHandle),
}
#[derive(Debug, Clone)]
pub enum DataSink {
Memory(MemoryHandle),
Storage(StorageHandle),
Network(NetworkHandle),
Display(DisplayHandle),
}
#[derive(Debug, Clone)]
pub struct ResourceSpec {
pub cpu_cores: usize,
pub memory_mb: usize,
pub gpu_memory_mb: Option<usize>,
pub network_bandwidth_mbps: Option<usize>,
}
#[derive(Debug, Clone, PartialOrd, PartialEq)]
pub struct Priority(pub f64);
// スケジューラー
pub struct SynapticScheduler {
dependency_graph: DependencyGraph,
resource_pool: ResourcePool,
ready_queue: VecDeque<TaskId>,
running_tasks: HashMap<TaskId, RunningTask>,
}
pub struct DependencyGraph {
nodes: HashMap<TaskId, DataFlowTask>,
edges: HashMap<TaskId, Vec<TaskId>>, // 依存関係
}
pub struct ResourcePool {
available_cpu: usize,
available_memory: usize,
available_gpu_memory: usize,
}
pub struct RunningTask {
task: DataFlowTask,
start_time: Instant,
allocated_resources: ResourceSpec,
}
impl SynapticScheduler {
pub fn new() -> Self {
SynapticScheduler {
dependency_graph: DependencyGraph::new(),
resource_pool: ResourcePool::new(),
ready_queue: VecDeque::new(),
running_tasks: HashMap::new(),
}
}
pub fn submit_task(&mut self, task: DataFlowTask) {
self.dependency_graph.add_task(task.clone());
if self.dependency_graph.is_ready(&task.id) {
self.ready_queue.push_back(task.id);
}
}
pub fn schedule_next(&mut self) -> Option<TaskId> {
while let Some(task_id) = self.ready_queue.pop_front() {
if let Some(task) = self.dependency_graph.get_task(&task_id) {
if self.resource_pool.can_allocate(&task.resource_requirements) {
self.resource_pool.allocate(&task.resource_requirements);
let running_task = RunningTask {
task: task.clone(),
start_time: Instant::now(),
allocated_resources: task.resource_requirements.clone(),
};
self.running_tasks.insert(task_id.clone(), running_task);
return Some(task_id);
}
}
}
None
}
pub fn complete_task(&mut self, task_id: &TaskId) {
if let Some(running_task) = self.running_tasks.remove(task_id) {
self.resource_pool.deallocate(&running_task.allocated_resources);
// 依存するタスクをready queueに追加
let newly_ready = self.dependency_graph.complete_task(task_id);
for ready_task in newly_ready {
self.ready_queue.push_back(ready_task);
}
}
}
}
impl DependencyGraph {
pub fn new() -> Self {
DependencyGraph {
nodes: HashMap::new(),
edges: HashMap::new(),
}
}
pub fn add_task(&mut self, task: DataFlowTask) {
self.nodes.insert(task.id.clone(), task);
}
pub fn is_ready(&self, task_id: &TaskId) -> bool {
// 依存関係がすべて完了しているかチェック
if let Some(dependencies) = self.edges.get(task_id) {
dependencies.iter().all(|dep| !self.nodes.contains_key(dep))
} else {
true // 依存関係がない場合は実行可能
}
}
pub fn get_task(&self, task_id: &TaskId) -> Option<&DataFlowTask> {
self.nodes.get(task_id)
}
pub fn complete_task(&mut self, task_id: &TaskId) -> Vec<TaskId> {
self.nodes.remove(task_id);
// この完了により新たに実行可能になったタスクを返す
let mut newly_ready = Vec::new();
for (id, _) in &self.nodes {
if self.is_ready(id) {
newly_ready.push(id.clone());
}
}
newly_ready
}
}
impl ResourcePool {
pub fn new() -> Self {
ResourcePool {
available_cpu: 8, // 8コア
available_memory: 16384, // 16GB
available_gpu_memory: 8192, // 8GB
}
}
pub fn can_allocate(&self, requirements: &ResourceSpec) -> bool {
self.available_cpu >= requirements.cpu_cores &&
self.available_memory >= requirements.memory_mb &&
self.available_gpu_memory >= requirements.gpu_memory_mb.unwrap_or(0)
}
pub fn allocate(&mut self, requirements: &ResourceSpec) {
self.available_cpu -= requirements.cpu_cores;
self.available_memory -= requirements.memory_mb;
if let Some(gpu_mem) = requirements.gpu_memory_mb {
self.available_gpu_memory -= gpu_mem;
}
}
pub fn deallocate(&mut self, requirements: &ResourceSpec) {
self.available_cpu += requirements.cpu_cores;
self.available_memory += requirements.memory_mb;
if let Some(gpu_mem) = requirements.gpu_memory_mb {
self.available_gpu_memory += gpu_mem;
}
}
}
// Neural Memory Manager
pub struct NeuralMemoryManager {
context_memory: ContextMemory,
working_memory: WorkingMemory,
associative_cache: AssociativeCache,
}
pub struct ContextMemory {
persistent_data: HashMap<String, Value>,
access_patterns: HashMap<String, Vec<Instant>>,
}
pub struct WorkingMemory {
temporary_data: HashMap<String, (Value, Instant)>,
capacity: usize,
}
pub struct AssociativeCache {
cache: HashMap<String, CacheEntry>,
attention_weights: HashMap<String, f64>,
}
#[derive(Debug, Clone)]
pub struct CacheEntry {
pub data: Value,
pub last_access: Instant,
pub access_count: usize,
pub relevance_score: f64,
}
#[derive(Debug, Clone)]
pub struct MemoryHandle(pub String);
impl NeuralMemoryManager {
pub fn new() -> Self {
NeuralMemoryManager {
context_memory: ContextMemory::new(),
working_memory: WorkingMemory::new(1024), // 1GB working memory
associative_cache: AssociativeCache::new(),
}
}
pub fn store_context(&mut self, key: String, value: Value) -> MemoryHandle {
self.context_memory.store(key.clone(), value);
MemoryHandle(key)
}
pub fn allocate_working(&mut self, key: String, value: Value) -> MemoryHandle {
self.working_memory.allocate(key.clone(), value);
MemoryHandle(key)
}
pub fn recall_pattern(&self, pattern: &str) -> Vec<Value> {
self.associative_cache.pattern_match(pattern)
}
}
impl ContextMemory {
pub fn new() -> Self {
ContextMemory {
persistent_data: HashMap::new(),
access_patterns: HashMap::new(),
}
}
pub fn store(&mut self, key: String, value: Value) {
self.persistent_data.insert(key.clone(), value);
self.access_patterns
.entry(key)
.or_insert_with(Vec::new)
.push(Instant::now());
}
}
impl WorkingMemory {
pub fn new(capacity: usize) -> Self {
WorkingMemory {
temporary_data: HashMap::new(),
capacity,
}
}
pub fn allocate(&mut self, key: String, value: Value) {
if self.temporary_data.len() >= self.capacity {
self.evict_lru();
}
self.temporary_data.insert(key, (value, Instant::now()));
}
fn evict_lru(&mut self) {
if let Some((oldest_key, _)) = self.temporary_data
.iter()
.min_by_key(|(_, (_, timestamp))| timestamp)
.map(|(k, v)| (k.clone(), v.clone()))
{
self.temporary_data.remove(&oldest_key);
}
}
}
impl AssociativeCache {
pub fn new() -> Self {
AssociativeCache {
cache: HashMap::new(),
attention_weights: HashMap::new(),
}
}
pub fn pattern_match(&self, pattern: &str) -> Vec<Value> {
self.cache
.iter()
.filter(|(key, _)| key.contains(pattern))
.map(|(_, entry)| entry.data.clone())
.collect()
}
}
// システムコール
pub enum SynapticSyscall {
Infer { model: String, input: Value },
PatternMatch { pattern: String, data: String },
RecordCausality { cause: String, effect: String },
ProbabilisticChoice { options: Vec<String>, probabilities: Vec<f64> },
TimeTravel { timestamp: Instant },
}
pub enum SyscallResult {
InferenceResult(Value),
MatchResult(Vec<Value>),
CausalityRecorded,
ChoiceResult(String),
TimeContext(String),
Error(String),
}
// Probabilistic I/O System
pub struct ProbabilisticIOSystem {
predictive_cache: PredictiveCache,
uncertainty_tracker: UncertaintyTracker,
}
pub struct PredictiveCache {
predictions: HashMap<String, Prediction>,
}
pub struct Prediction {
pub data: Value,
pub confidence: f64,
pub timestamp: Instant,
}
pub struct UncertaintyTracker {
uncertainties: HashMap<String, f64>,
}
// Temporal Consistency Engine
pub struct TemporalConsistencyEngine {
causality_graph: CausalityGraph,
checkpoints: Vec<SystemCheckpoint>,
}
pub struct CausalityGraph {
events: HashMap<String, CausalEvent>,
causal_links: HashMap<String, Vec<String>>,
}
pub struct CausalEvent {
pub id: String,
pub timestamp: Instant,
pub data: Value,
}
pub struct SystemCheckpoint {
pub id: String,
pub timestamp: Instant,
pub system_state: HashMap<String, Value>,
}
// ハンドル型の定義
#[derive(Debug, Clone)]
pub struct StorageHandle(pub String);
#[derive(Debug, Clone)]
pub struct NetworkHandle(pub String);
#[derive(Debug, Clone)]
pub struct SensorHandle(pub String);
#[derive(Debug, Clone)]
pub struct DisplayHandle(pub String);

162
prototype/src/parser.rs Normal file
View File

@@ -0,0 +1,162 @@
use crate::ast::*;
use nom::{
IResult,
branch::alt,
bytes::complete::tag,
character::complete::{alpha1, alphanumeric1, char, multispace0, multispace1},
combinator::recognize,
multi::{many0, separated_list0},
sequence::{delimited, pair, preceded, tuple},
};
pub fn parse(input: &str) -> Result<Program, String> {
match program(input) {
Ok((_, prog)) => Ok(prog),
Err(e) => Err(format!("Parse error: {:?}", e)),
}
}
fn program(input: &str) -> IResult<&str, Program> {
let (input, nodes) = many0(preceded(multispace0, node))(input)?;
Ok((input, Program {
nodes,
metadata: Metadata::default(),
}))
}
fn node(input: &str) -> IResult<&str, Node> {
alt((
graph_node,
function_node,
operation_node,
))(input)
}
fn graph_node(input: &str) -> IResult<&str, Node> {
let (input, _) = tag("graph")(input)?;
let (input, _) = multispace1(input)?;
let (input, name) = identifier(input)?;
let (input, _) = multispace0(input)?;
let (input, nodes) = delimited(
char('{'),
many0(preceded(multispace0, flow_statement)),
preceded(multispace0, char('}')),
)(input)?;
let id = NodeId(format!("graph_{}", name));
Ok((input, Node {
id: id.clone(),
kind: NodeKind::Graph { name: name.to_string(), nodes },
inputs: vec![],
outputs: vec![],
constraints: vec![],
metadata: Metadata::default(),
}))
}
fn function_node(input: &str) -> IResult<&str, Node> {
let (input, _) = tag("function")(input)?;
let (input, _) = multispace1(input)?;
let (input, name) = identifier(input)?;
let (input, params) = delimited(
char('('),
separated_list0(char(','), parameter),
char(')'),
)(input)?;
let (input, _) = multispace0(input)?;
let (input, body) = delimited(
char('{'),
preceded(multispace0, node),
preceded(multispace0, char('}')),
)(input)?;
let id = NodeId(format!("func_{}", name));
Ok((input, Node {
id,
kind: NodeKind::Function {
name: name.to_string(),
params,
body: Box::new(body),
},
inputs: vec![],
outputs: vec![],
constraints: vec![],
metadata: Metadata::default(),
}))
}
fn operation_node(input: &str) -> IResult<&str, Node> {
let (input, op_name) = identifier(input)?;
let (input, _) = delimited(
char('('),
multispace0,
char(')'),
)(input)?;
let op = match op_name {
"parallel_map" => Operation::Map,
"filter" => Operation::Filter,
"reduce" => Operation::Reduce,
_ => Operation::Transform(op_name.to_string()),
};
Ok((input, Node {
id: NodeId(format!("op_{}", op_name)),
kind: NodeKind::Operation { op },
inputs: vec![],
outputs: vec![],
constraints: vec![],
metadata: Metadata::default(),
}))
}
fn flow_statement(input: &str) -> IResult<&str, Node> {
let (input, nodes) = separated_list0(
tuple((multispace0, tag("->"), multispace0)),
identifier,
)(input)?;
// 簡易的な実装：フロー文をノードのチェーンとして解釈
let mut result_nodes = vec![];
for (i, node_name) in nodes.iter().enumerate() {
let id = NodeId(format!("flow_{}_{}", i, node_name));
let node = Node {
id: id.clone(),
kind: NodeKind::Operation {
op: Operation::Transform(node_name.to_string()),
},
inputs: if i > 0 { vec![NodeId(format!("flow_{}_{}", i-1, nodes[i-1]))] } else { vec![] },
outputs: if i < nodes.len() - 1 { vec![NodeId(format!("flow_{}_{}", i+1, nodes[i+1]))] } else { vec![] },
constraints: vec![],
metadata: Metadata::default(),
};
result_nodes.push(node);
}
// 最初のノードを返す（本来は全てのノードを管理すべき）
Ok((input, result_nodes.into_iter().next().unwrap_or_else(|| Node {
id: NodeId("empty".to_string()),
kind: NodeKind::Value(Value::Boolean(true)),
inputs: vec![],
outputs: vec![],
constraints: vec![],
metadata: Metadata::default(),
})))
}
fn parameter(input: &str) -> IResult<&str, Parameter> {
let (input, name) = identifier(input)?;
Ok((input, Parameter {
name: name.to_string(),
constraints: vec![],
}))
}
fn identifier(input: &str) -> IResult<&str, &str> {
recognize(
pair(
alt((alpha1, tag("_"))),
many0(alt((alphanumeric1, tag("_")))),
)
)(input)
}

179
prototype/src/probabilistic.rs Normal file
View File

@@ -0,0 +1,179 @@
use crate::ast::{Value, NodeId};
use std::collections::HashMap;
use rand::{Rng, distributions::{Distribution, Uniform}};
use rand_distr::Normal;
#[derive(Debug, Clone)]
pub struct ProbabilisticValue {
pub distribution: DistributionType,
pub samples: Vec<f64>,
pub confidence: f64,
}
#[derive(Debug, Clone)]
pub enum DistributionType {
Normal { mean: f64, std_dev: f64 },
Uniform { min: f64, max: f64 },
Discrete { probabilities: Vec<f64>, values: Vec<Value> },
Empirical { samples: Vec<f64> },
}
pub struct ProbabilisticEngine {
rng: rand::rngs::ThreadRng,
cache: HashMap<NodeId, ProbabilisticValue>,
}
impl ProbabilisticEngine {
pub fn new() -> Self {
ProbabilisticEngine {
rng: rand::thread_rng(),
cache: HashMap::new(),
}
}
pub fn sample(&mut self, dist: &DistributionType, n_samples: usize) -> Vec<f64> {
match dist {
DistributionType::Normal { mean, std_dev } => {
let normal = Normal::new(*mean, *std_dev).unwrap();
(0..n_samples).map(|_| normal.sample(&mut self.rng)).collect()
}
DistributionType::Uniform { min, max } => {
let uniform = Uniform::new(*min, *max);
(0..n_samples).map(|_| uniform.sample(&mut self.rng)).collect()
}
DistributionType::Discrete { probabilities, .. } => {
// 簡易実装：重み付きサンプリング
let total: f64 = probabilities.iter().sum();
(0..n_samples).map(|_| {
let mut r = self.rng.gen::<f64>() * total;
for (i, &p) in probabilities.iter().enumerate() {
r -= p;
if r <= 0.0 {
return i as f64;
}
}
(probabilities.len() - 1) as f64
}).collect()
}
DistributionType::Empirical { samples } => {
// ブートストラップサンプリング
(0..n_samples).map(|_| {
let idx = self.rng.gen_range(0..samples.len());
samples[idx]
}).collect()
}
}
}
pub fn expectation(&self, samples: &[f64]) -> f64 {
samples.iter().sum::<f64>() / samples.len() as f64
}
pub fn variance(&self, samples: &[f64]) -> f64 {
let mean = self.expectation(samples);
samples.iter()
.map(|&x| (x - mean).powi(2))
.sum::<f64>() / samples.len() as f64
}
pub fn entropy(&self, probabilities: &[f64]) -> f64 {
probabilities.iter()
.filter(|&&p| p > 0.0)
.map(|&p| -p * p.log2())
.sum()
}
pub fn confidence_interval(&self, samples: &[f64], confidence: f64) -> (f64, f64) {
let mut sorted = samples.to_vec();
sorted.sort_by(|a, b| a.partial_cmp(b).unwrap());
let alpha = (1.0 - confidence) / 2.0;
let lower_idx = (alpha * samples.len() as f64) as usize;
let upper_idx = ((1.0 - alpha) * samples.len() as f64) as usize;
(sorted[lower_idx], sorted[upper_idx.min(sorted.len() - 1)])
}
pub fn bayesian_update(
&self,
prior: &DistributionType,
_likelihood: &dyn Fn(f64) -> f64,
evidence: f64,
) -> DistributionType {
// 簡易的なベイズ更新（MCMCやSMCを使うべきだが、デモ用）
match prior {
DistributionType::Normal { mean, std_dev } => {
// 共役事前分布を仮定
let precision = 1.0 / (std_dev * std_dev);
let data_precision = 1.0; // 仮定
let new_precision = precision + data_precision;
let new_mean = (precision * mean + data_precision * evidence) / new_precision;
let new_std_dev = (1.0 / new_precision).sqrt();
DistributionType::Normal {
mean: new_mean,
std_dev: new_std_dev,
}
}
_ => prior.clone(), // 他の分布は簡易実装
}
}
}
// 確率的ノードの実行結果
#[derive(Debug, Clone)]
pub struct ProbabilisticResult {
pub value: Value,
pub distribution: Option<DistributionType>,
pub confidence: f64,
pub samples: Option<Vec<f64>>,
}
impl ProbabilisticResult {
pub fn deterministic(value: Value) -> Self {
ProbabilisticResult {
value,
distribution: None,
confidence: 1.0,
samples: None,
}
}
pub fn probabilistic(
value: Value,
distribution: DistributionType,
samples: Vec<f64>,
confidence: f64,
) -> Self {
ProbabilisticResult {
value,
distribution: Some(distribution),
confidence,
samples: Some(samples),
}
}
}
// 確率的制約
#[derive(Debug, Clone)]
pub struct ProbabilisticConstraint {
pub threshold: f64,
pub confidence_level: f64,
pub constraint_type: ProbConstraintType,
}
#[derive(Debug, Clone)]
pub enum ProbConstraintType {
Expectation { operator: ComparisonOp, value: f64 },
Variance { max: f64 },
Entropy { max: f64 },
Probability { event: String, min: f64 },
}
#[derive(Debug, Clone)]
pub enum ComparisonOp {
GreaterThan,
LessThan,
Equal,
}

174
prototype/src/self_improve.rs Normal file
View File

@@ -0,0 +1,174 @@
use crate::ast::{Program, Node, NodeKind, Operation};
use std::collections::HashMap;
use anyhow::Result;
pub struct SelfImprovementEngine {
optimization_patterns: Vec<OptimizationPattern>,
learning_history: Vec<LearningRecord>,
}
#[derive(Clone)]
pub struct OptimizationPattern {
pub name: String,
pub matcher: fn(&Node) -> bool,
pub transformer: fn(&Node) -> Node,
pub expected_gain: f64,
}
#[derive(Clone)]
pub struct LearningRecord {
pub pattern_name: String,
pub actual_gain: f64,
pub timestamp: u64,
}
impl SelfImprovementEngine {
pub fn new() -> Self {
let patterns = vec![
OptimizationPattern {
name: "parallel_map_fusion".to_string(),
matcher: |node| matches!(&node.kind, NodeKind::Operation { op: Operation::Map }),
transformer: |node| {
let mut optimized = node.clone();
if let NodeKind::Operation { op } = &mut optimized.kind {
*op = Operation::Parallel;
}
optimized
},
expected_gain: 2.0,
},
OptimizationPattern {
name: "sequential_to_parallel".to_string(),
matcher: |node| {
matches!(&node.kind, NodeKind::Operation { op: Operation::Sequential })
&& node.inputs.len() > 1
},
transformer: |node| {
let mut optimized = node.clone();
if let NodeKind::Operation { op } = &mut optimized.kind {
*op = Operation::Parallel;
}
optimized
},
expected_gain: 1.5,
},
];
SelfImprovementEngine {
optimization_patterns: patterns,
learning_history: Vec::new(),
}
}
pub fn analyze_program(&self, program: &Program) -> ProgramAnalysis {
let mut node_count = 0;
let mut operation_counts = HashMap::new();
let mut parallelizable_nodes = 0;
for node in &program.nodes {
node_count += 1;
if let NodeKind::Operation { op } = &node.kind {
*operation_counts.entry(format!("{:?}", op)).or_insert(0) += 1;
if matches!(op, Operation::Map | Operation::Filter) {
parallelizable_nodes += 1;
}
}
}
ProgramAnalysis {
node_count,
operation_counts,
parallelizable_nodes,
complexity_score: self.calculate_complexity(program),
}
}
pub fn suggest_improvements(&self, program: &Program) -> Vec<Improvement> {
let mut improvements = Vec::new();
for node in &program.nodes {
for pattern in &self.optimization_patterns {
if (pattern.matcher)(node) {
let improved_node = (pattern.transformer)(node);
improvements.push(Improvement {
original_node_id: node.id.clone(),
pattern_name: pattern.name.clone(),
improved_node,
expected_gain: pattern.expected_gain,
});
}
}
}
// AIによる新しい最適化の発見をシミュレート
if let Some(novel) = self.discover_novel_optimization(program) {
improvements.push(novel);
}
improvements
}
pub fn apply_improvement(&mut self, program: &mut Program, improvement: &Improvement) -> Result<()> {
// ノードを見つけて置換
for node in &mut program.nodes {
if node.id == improvement.original_node_id {
*node = improvement.improved_node.clone();
// 学習履歴に記録
self.learning_history.push(LearningRecord {
pattern_name: improvement.pattern_name.clone(),
actual_gain: improvement.expected_gain, // 実際は測定すべき
timestamp: std::time::SystemTime::now()
.duration_since(std::time::UNIX_EPOCH)
.unwrap()
.as_secs(),
});
return Ok(());
}
}
anyhow::bail!("Node not found: {:?}", improvement.original_node_id)
}
fn calculate_complexity(&self, program: &Program) -> f64 {
let mut complexity = 0.0;
for node in &program.nodes {
complexity += match &node.kind {
NodeKind::Operation { op } => match op {
Operation::Map | Operation::Filter => 1.0,
Operation::Reduce => 2.0,
Operation::Parallel => 0.5,
_ => 1.0,
},
NodeKind::Graph { nodes, .. } => nodes.len() as f64 * 1.5,
_ => 0.5,
};
}
complexity
}
fn discover_novel_optimization(&self, _program: &Program) -> Option<Improvement> {
// 将来的にはMLモデルを使用して新しい最適化を発見
// 現在はプレースホルダー
None
}
}
pub struct ProgramAnalysis {
pub node_count: usize,
pub operation_counts: HashMap<String, usize>,
pub parallelizable_nodes: usize,
pub complexity_score: f64,
}
pub struct Improvement {
pub original_node_id: crate::ast::NodeId,
pub pattern_name: String,
pub improved_node: Node,
pub expected_gain: f64,
}

239
prototype/src/temporal.rs Normal file
View File

@@ -0,0 +1,239 @@
use crate::ast::{Node, NodeId, Value};
use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant};
#[derive(Debug, Clone)]
pub struct TemporalState {
pub time_step: usize,
pub values: HashMap<NodeId, Value>,
pub timestamp: Instant,
}
pub struct TemporalEngine {
history: VecDeque<TemporalState>,
max_history_size: usize,
convergence_checks: HashMap<NodeId, ConvergenceChecker>,
}
#[derive(Debug, Clone)]
pub struct ConvergenceChecker {
pub epsilon: f64,
pub window_size: usize,
pub recent_values: VecDeque<f64>,
}
impl ConvergenceChecker {
pub fn new(epsilon: f64, window_size: usize) -> Self {
ConvergenceChecker {
epsilon,
window_size,
recent_values: VecDeque::with_capacity(window_size),
}
}
pub fn check(&mut self, value: f64) -> bool {
self.recent_values.push_back(value);
if self.recent_values.len() > self.window_size {
self.recent_values.pop_front();
}
if self.recent_values.len() < 2 {
return false;
}
// 収束判定：最近の値の変化が epsilon 以下
let last = self.recent_values.back().unwrap();
let second_last = self.recent_values[self.recent_values.len() - 2];
(last - second_last).abs() < self.epsilon
}
}
impl TemporalEngine {
pub fn new(max_history_size: usize) -> Self {
TemporalEngine {
history: VecDeque::with_capacity(max_history_size),
max_history_size,
convergence_checks: HashMap::new(),
}
}
pub fn add_time_step(&mut self, state: TemporalState) {
if self.history.len() >= self.max_history_size {
self.history.pop_front();
}
self.history.push_back(state);
}
pub fn get_state_at(&self, time_step: usize) -> Option<&TemporalState> {
self.history.iter().find(|s| s.time_step == time_step)
}
pub fn get_value_history(&self, node_id: &NodeId) -> Vec<(usize, Value)> {
self.history
.iter()
.filter_map(|state| {
state.values.get(node_id).map(|v| (state.time_step, v.clone()))
})
.collect()
}
pub fn register_convergence_check(
&mut self,
node_id: NodeId,
epsilon: f64,
window_size: usize,
) {
self.convergence_checks.insert(
node_id,
ConvergenceChecker::new(epsilon, window_size),
);
}
pub fn check_convergence(&mut self, node_id: &NodeId, value: f64) -> bool {
if let Some(checker) = self.convergence_checks.get_mut(node_id) {
checker.check(value)
} else {
false
}
}
pub fn time_travel_debug(&self, time_step: usize) -> Option<String> {
self.get_state_at(time_step).map(|state| {
format!(
"State at t={}: {} values, timestamp: {:?}",
time_step,
state.values.len(),
state.timestamp.elapsed()
)
})
}
pub fn predict_next_value(&self, node_id: &NodeId, steps_ahead: usize) -> Option<Value> {
let history = self.get_value_history(node_id);
if history.len() < 3 {
return None;
}
// 簡単な線形外挿（実際にはより高度な予測手法を使用）
if let Some(Value::Number(last)) = history.last().map(|(_, v)| v) {
if let Some(Value::Number(second_last)) = history.get(history.len() - 2).map(|(_, v)| v) {
let diff = last - second_last;
let predicted = last + diff * steps_ahead as f64;
return Some(Value::Number(predicted));
}
}
None
}
}
// 時間的な制約
#[derive(Debug, Clone)]
pub struct TemporalConstraint {
pub constraint_type: TemporalConstraintType,
pub window: TimeWindow,
}
#[derive(Debug, Clone)]
pub enum TemporalConstraintType {
// 収束条件
ConvergeWhen { epsilon: f64 },
// 周期的実行
Every { interval: Duration },
// 時間制限
Timeout { duration: Duration },
// 因果関係
CausalOrder { before: NodeId, after: NodeId },
}
#[derive(Debug, Clone)]
pub struct TimeWindow {
pub start: Option<usize>,
pub end: Option<usize>,
}
// 時間的な演算子
#[derive(Debug, Clone)]
pub enum TemporalOperator {
// 過去の値を参照
Previous { steps: usize },
// 移動平均
MovingAverage { window: usize },
// 微分（変化率）
Derivative,
// 積分（累積）
Integral,
// 遅延
Delay { steps: usize },
}
pub struct TemporalComputation {
pub operator: TemporalOperator,
pub input: NodeId,
}
impl TemporalComputation {
pub fn compute(&self, engine: &TemporalEngine) -> Option<Value> {
let history = engine.get_value_history(&self.input);
match &self.operator {
TemporalOperator::Previous { steps } => {
history.get(history.len().saturating_sub(steps + 1))
.map(|(_, v)| v.clone())
}
TemporalOperator::MovingAverage { window } => {
if history.len() < *window {
return None;
}
let recent: Vec<f64> = history
.iter()
.rev()
.take(*window)
.filter_map(|(_, v)| match v {
Value::Number(n) => Some(*n),
_ => None,
})
.collect();
if recent.len() == *window {
Some(Value::Number(recent.iter().sum::<f64>() / recent.len() as f64))
} else {
None
}
}
TemporalOperator::Derivative => {
if history.len() < 2 {
return None;
}
if let (Some(Value::Number(last)), Some(Value::Number(prev))) = (
history.last().map(|(_, v)| v),
history.get(history.len() - 2).map(|(_, v)| v),
) {
Some(Value::Number(last - prev))
} else {
None
}
}
TemporalOperator::Integral => {
let sum: f64 = history
.iter()
.filter_map(|(_, v)| match v {
Value::Number(n) => Some(*n),
_ => None,
})
.sum();
Some(Value::Number(sum))
}
TemporalOperator::Delay { steps } => {
history.get(history.len().saturating_sub(*steps))
.map(|(_, v)| v.clone())
}
}
}
}

155
self-improvement.md Normal file
View File

@@ -0,0 +1,155 @@
# AIによる自己改良メカニズム
## コンセプト
AIが自身のコードを理解し、改良し、進化させる仕組み。
## 実装アプローチ
### 1. コードの自己観察
```synaptic
meta function analyze_self() {
# 自身のASTを取得
ast = get_current_ast()
# パフォーマンスメトリクスを収集
metrics = {
execution_time: measure_performance(),
memory_usage: measure_memory(),
accuracy: measure_accuracy()
}
# ボトルネックを特定
bottlenecks = identify_bottlenecks(ast, metrics)
return {ast, metrics, bottlenecks}
}
```
### 2. 改良候補の生成
```synaptic
meta function generate_improvements(analysis) {
improvements = []
# パターンマッチングによる最適化
for pattern in optimization_patterns {
matches = find_pattern(analysis.ast, pattern)
for match in matches {
improved = apply_transformation(match, pattern.transform)
improvements.append({
original: match,
improved: improved,
expected_gain: estimate_improvement(match, improved)
})
}
}
# AIによる新しい最適化の発見
novel_improvements = discover_optimizations(
analysis.ast,
analysis.metrics,
learning_history
)
return improvements + novel_improvements
}
```
### 3. 検証と適用
```synaptic
meta function apply_improvements(improvements) {
for improvement in improvements {
# サンドボックスで検証
sandbox = create_sandbox()
result = sandbox.test(improvement)
if result.is_better() {
# 本番環境に適用
apply_to_production(improvement)
# 学習履歴に追加
learning_history.add({
pattern: extract_pattern(improvement),
gain: result.improvement_ratio
})
}
}
}
```
## 自己改良の段階
### Phase 1: パターンベース最適化
- 既知の最適化パターンを適用
- ループ融合、ベクトル化、並列化
### Phase 2: 学習ベース最適化
- 過去の改良履歴から新しいパターンを学習
- 類似コードへの最適化の転移
### Phase 3: 創発的最適化
- AIが独自に新しい最適化手法を発見
- 人間には理解困難な最適化の適用
### Phase 4: 自己再設計
- 言語自体の構造を改良
- より効率的な表現方法の発明
## 実装例
```rust
// Rust側での自己改良エンジン
pub struct SelfImprovementEngine {
ast_analyzer: AstAnalyzer,
optimizer: Optimizer,
validator: Validator,
history: LearningHistory,
}
impl SelfImprovementEngine {
pub fn improve(&mut self, program: &mut Program) -> Result<ImprovementReport> {
// 1. 現在のプログラムを分析
let analysis = self.ast_analyzer.analyze(program)?;
// 2. 改良候補を生成
let candidates = self.optimizer.generate_candidates(&analysis)?;
// 3. 各候補を評価
let mut improvements = Vec::new();
for candidate in candidates {
if let Ok(validation) = self.validator.validate(&candidate) {
if validation.is_improvement() {
improvements.push(candidate);
}
}
}
// 4. 最良の改良を適用
let best = self.select_best_improvement(&improvements);
if let Some(improvement) = best {
self.apply_improvement(program, &improvement)?;
self.history.record(&improvement);
}
Ok(ImprovementReport {
analyzed_nodes: analysis.node_count,
candidates_generated: candidates.len(),
improvements_applied: improvements.len(),
})
}
}
```
## 安全性メカニズム
1. **サンドボックス実行**: 改良は必ず隔離環境でテスト
2. **ロールバック機能**: 問題が発生した場合は即座に元に戻す
3. **段階的適用**: 小さな改良から始めて徐々に大きな変更へ
4. **人間の監督**: 重要な変更は人間の承認を要求
## 将来の展望
最終的には、AIが自身のコードを完全に理解し、人間の介入なしに進化し続けるシステムを目指す。その時点で、コードは人間には理解不能だが、AIにとっては最適な形になっているだろう。