oxirs-embed 0.2.4

Knowledge graph embeddings with TransE, ComplEx, and custom models
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112

//! Module for vision-language-graph integration

use super::*;
use anyhow::Result;
use scirs2_core::ndarray_ext::{Array1, Array2};
use scirs2_core::random::{Random, RngExt};
use std::collections::HashMap;
#[derive(Debug)]
pub struct MetaLearner {
    pub config: MetaLearningConfig,
    /// Meta-parameters
    pub meta_parameters: HashMap<String, Array2<f32>>,
    /// Task-specific parameters
    pub task_parameters: HashMap<String, Array2<f32>>,
}

impl MetaLearner {
    pub fn new(config: MetaLearningConfig) -> Self {
        let mut meta_parameters = HashMap::new();
        let mut task_parameters = HashMap::new();

        // Initialize meta-learning parameters
        let mut random = Random::default();
        meta_parameters.insert(
            "meta_weights".to_string(),
            Array2::from_shape_fn((512, 512), |_| (random.random::<f32>() - 0.5) * 0.1),
        );

        let mut random = Random::default();
        task_parameters.insert(
            "adaptation_weights".to_string(),
            Array2::from_shape_fn((256, 512), |_| (random.random::<f32>() - 0.5) * 0.1),
        );

        Self {
            config,
            meta_parameters,
            task_parameters,
        }
    }

    /// Adapt to new task with few examples
    pub fn adapt_to_task(
        &mut self,
        support_set: &[(Array1<f32>, Array1<f32>)],
        _query_set: &[(Array1<f32>, Array1<f32>)],
    ) -> Result<HashMap<String, Array2<f32>>> {
        match self.config.algorithm {
            MetaLearningAlgorithm::MAML => self.maml_adaptation(support_set),
            MetaLearningAlgorithm::ProtoNet => self.prototypical_adaptation(support_set),
            _ => self.maml_adaptation(support_set),
        }
    }

    /// MAML adaptation
    fn maml_adaptation(
        &mut self,
        support_set: &[(Array1<f32>, Array1<f32>)],
    ) -> Result<HashMap<String, Array2<f32>>> {
        let mut adapted_params = self.meta_parameters.clone();

        // Perform gradient steps on support set
        for _step in 0..self.config.adaptation_steps {
            // Simplified gradient computation
            for (input, _target) in support_set {
                if let Some(weights) = adapted_params.get_mut("meta_weights") {
                    // Compute forward pass
                    let _output = weights.dot(input);

                    // Simplified gradient update (in real implementation would compute actual gradients)
                    *weights = &*weights * 0.99; // Simple decay as placeholder
                }
            }
        }

        Ok(adapted_params)
    }

    /// Prototypical Networks adaptation
    fn prototypical_adaptation(
        &self,
        support_set: &[(Array1<f32>, Array1<f32>)],
    ) -> Result<HashMap<String, Array2<f32>>> {
        // Compute prototypes for each class
        let mut prototypes = HashMap::new();
        let mut class_counts = HashMap::new();

        for (input, target) in support_set {
            // Convert target to class ID (simplified)
            let class_id = target[0] as i32;

            let class_key = class_id.to_string();
            let prototype = prototypes
                .entry(class_key.clone())
                .or_insert(Array1::zeros(input.len()));
            let count = class_counts.entry(class_key).or_insert(0);

            *prototype = &*prototype + input;
            *count += 1;
        }

        // Average prototypes
        for (class_key, count) in class_counts {
            if let Some(prototype) = prototypes.get_mut(&class_key) {
                *prototype /= count as f32;
            }
        }

        // Return adapted parameters (simplified)
        Ok(self.meta_parameters.clone())
    }
}