oxirs_embed/vision_language_graph/
meta_learner.rs

1//! Module for vision-language-graph integration
2
3use super::*;
4use anyhow::Result;
5use scirs2_core::ndarray_ext::{Array1, Array2};
6use scirs2_core::random::{Random, Rng};
7use std::collections::HashMap;
8#[derive(Debug)]
9pub struct MetaLearner {
10    pub config: MetaLearningConfig,
11    /// Meta-parameters
12    pub meta_parameters: HashMap<String, Array2<f32>>,
13    /// Task-specific parameters
14    pub task_parameters: HashMap<String, Array2<f32>>,
15}
16
17impl MetaLearner {
18    pub fn new(config: MetaLearningConfig) -> Self {
19        let mut meta_parameters = HashMap::new();
20        let mut task_parameters = HashMap::new();
21
22        // Initialize meta-learning parameters
23        let mut random = Random::default();
24        meta_parameters.insert(
25            "meta_weights".to_string(),
26            Array2::from_shape_fn((512, 512), |_| (random.random::<f32>() - 0.5) * 0.1),
27        );
28
29        let mut random = Random::default();
30        task_parameters.insert(
31            "adaptation_weights".to_string(),
32            Array2::from_shape_fn((256, 512), |_| (random.random::<f32>() - 0.5) * 0.1),
33        );
34
35        Self {
36            config,
37            meta_parameters,
38            task_parameters,
39        }
40    }
41
42    /// Adapt to new task with few examples
43    pub fn adapt_to_task(
44        &mut self,
45        support_set: &[(Array1<f32>, Array1<f32>)],
46        _query_set: &[(Array1<f32>, Array1<f32>)],
47    ) -> Result<HashMap<String, Array2<f32>>> {
48        match self.config.algorithm {
49            MetaLearningAlgorithm::MAML => self.maml_adaptation(support_set),
50            MetaLearningAlgorithm::ProtoNet => self.prototypical_adaptation(support_set),
51            _ => self.maml_adaptation(support_set),
52        }
53    }
54
55    /// MAML adaptation
56    fn maml_adaptation(
57        &mut self,
58        support_set: &[(Array1<f32>, Array1<f32>)],
59    ) -> Result<HashMap<String, Array2<f32>>> {
60        let mut adapted_params = self.meta_parameters.clone();
61
62        // Perform gradient steps on support set
63        for _step in 0..self.config.adaptation_steps {
64            // Simplified gradient computation
65            for (input, _target) in support_set {
66                if let Some(weights) = adapted_params.get_mut("meta_weights") {
67                    // Compute forward pass
68                    let _output = weights.dot(input);
69
70                    // Simplified gradient update (in real implementation would compute actual gradients)
71                    *weights = &*weights * 0.99; // Simple decay as placeholder
72                }
73            }
74        }
75
76        Ok(adapted_params)
77    }
78
79    /// Prototypical Networks adaptation
80    fn prototypical_adaptation(
81        &self,
82        support_set: &[(Array1<f32>, Array1<f32>)],
83    ) -> Result<HashMap<String, Array2<f32>>> {
84        // Compute prototypes for each class
85        let mut prototypes = HashMap::new();
86        let mut class_counts = HashMap::new();
87
88        for (input, target) in support_set {
89            // Convert target to class ID (simplified)
90            let class_id = target[0] as i32;
91
92            let class_key = class_id.to_string();
93            let prototype = prototypes
94                .entry(class_key.clone())
95                .or_insert(Array1::zeros(input.len()));
96            let count = class_counts.entry(class_key).or_insert(0);
97
98            *prototype = &*prototype + input;
99            *count += 1;
100        }
101
102        // Average prototypes
103        for (class_key, count) in class_counts {
104            if let Some(prototype) = prototypes.get_mut(&class_key) {
105                *prototype /= count as f32;
106            }
107        }
108
109        // Return adapted parameters (simplified)
110        Ok(self.meta_parameters.clone())
111    }
112}
oxirs_embed/vision_language_graph/meta_learner.rs

oxirs_embed/vision_language_graph/
meta_learner.rs
