ruvector_memopt/core/
patterns.rs

1//! Memory pattern representation for neural learning
2
3use chrono::{Datelike, Timelike};
4use crate::windows::memory::MemoryStatus;
5use serde::{Deserialize, Serialize};
6
7/// Memory pattern vector for HNSW indexing
8#[derive(Debug, Clone, Serialize, Deserialize)]
9pub struct MemoryPattern {
10    /// Normalized memory load (0.0-1.0)
11    pub load: f32,
12    /// Rate of memory consumption (MB/sec)
13    pub consumption_rate: f32,
14    /// Available physical memory ratio
15    pub available_ratio: f32,
16    /// Page file usage ratio
17    pub page_file_ratio: f32,
18    /// Number of processes
19    pub process_count: u32,
20    /// Hour of day (0-23) for temporal patterns
21    pub hour: u8,
22    /// Day of week (0-6)
23    pub day_of_week: u8,
24    /// Time since last optimization (seconds)
25    pub time_since_last_opt: f32,
26}
27
28impl MemoryPattern {
29    /// Create pattern from current memory status
30    pub fn from_status(status: &MemoryStatus) -> Self {
31        let now = chrono::Local::now();
32        
33        Self {
34            load: status.memory_load_percent as f32 / 100.0,
35            consumption_rate: 0.0, // Would need historical data
36            available_ratio: (status.available_physical_mb / status.total_physical_mb) as f32,
37            page_file_ratio: 1.0 - (status.available_page_file_mb / status.total_page_file_mb) as f32,
38            process_count: 0, // Would need process enumeration
39            hour: now.hour() as u8,
40            day_of_week: now.weekday().num_days_from_monday() as u8,
41            time_since_last_opt: 0.0,
42        }
43    }
44    
45    /// Convert to vector for HNSW indexing
46    pub fn to_vector(&self) -> Vec<f32> {
47        vec![
48            self.load,
49            self.consumption_rate,
50            self.available_ratio,
51            self.page_file_ratio,
52            self.process_count as f32 / 1000.0, // Normalize
53            self.hour as f32 / 24.0,
54            self.day_of_week as f32 / 7.0,
55            self.time_since_last_opt / 3600.0, // Normalize to hours
56        ]
57    }
58    
59    /// Vector dimension
60    pub const DIM: usize = 8;
61}
62
63/// Labeled pattern for learning
64#[derive(Debug, Clone, Serialize, Deserialize)]
65pub struct LabeledPattern {
66    pub pattern: MemoryPattern,
67    /// Whether optimization was triggered
68    pub optimized: bool,
69    /// Whether it was aggressive
70    pub aggressive: bool,
71    /// Memory freed (MB)
72    pub freed_mb: f32,
73    /// Whether the optimization was considered successful
74    pub success: bool,
75}
ruvector_memopt/core/patterns.rs

ruvector_memopt/core/
patterns.rs
