107 lines
2.7 KiB
Rust
107 lines
2.7 KiB
Rust
use rtx_automeasure::{AutoMLResult, TaskType};
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use rtx_tensor::{Device, Tensor};
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// Note: This test file is a placeholder for meta-learning tests.
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// Meta-learning has a complex API that requires significant implementation.
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// These tests verify basic concepts.
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#[tokio::test]
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async fn test_placeholder() -> AutoMLResult<()> {
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// Placeholder test to ensure the test file compiles
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let device = Device::cpu();
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let _x = Tensor::randn(&[10, 5], &device)?;
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Ok(())
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}
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#[tokio::test]
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async fn test_meta_learning_concepts() -> AutoMLResult<()> {
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let device = Device::cpu();
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// Meta-learning: learning from multiple tasks
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let n_tasks = 5;
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let n_samples_per_task = 20;
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let n_features = 10;
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// Simulate multiple task datasets
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for _ in 0..n_tasks {
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let _x_task = Tensor::randn(&[n_samples_per_task, n_features], &device)?;
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let _y_task = Tensor::zeros(&[n_samples_per_task], &device)?;
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}
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Ok(())
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}
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#[tokio::test]
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async fn test_maml_concepts() {
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// Test Model-Agnostic Meta-Learning (MAML) concepts
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struct MAMLConfig {
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inner_lr: f64, // Learning rate for task adaptation
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outer_lr: f64, // Learning rate for meta-update
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n_inner_steps: usize,
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}
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let config = MAMLConfig {
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inner_lr: 0.01,
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outer_lr: 0.001,
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n_inner_steps: 5,
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};
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assert!(config.inner_lr > config.outer_lr);
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assert_eq!(config.n_inner_steps, 5);
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}
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#[tokio::test]
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async fn test_task_similarity() {
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// Test measuring task similarity for meta-learning
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struct TaskMetadata {
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n_classes: usize,
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n_features: usize,
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domain: String,
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}
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let task1 = TaskMetadata {
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n_classes: 10,
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n_features: 784,
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domain: "image".to_string(),
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};
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let task2 = TaskMetadata {
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n_classes: 10,
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n_features: 784,
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domain: "image".to_string(),
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};
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// Similarity based on matching characteristics
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let similarity = if task1.n_classes == task2.n_classes
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&& task1.n_features == task2.n_features
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&& task1.domain == task2.domain
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{
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1.0
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} else {
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0.0
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};
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assert_eq!(similarity, 1.0);
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}
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#[tokio::test]
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async fn test_few_shot_learning() -> AutoMLResult<()> {
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let device = Device::cpu();
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// Few-shot learning: N-way K-shot classification
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let n_way = 5; // 5 classes
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let k_shot = 3; // 3 examples per class
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let n_query = 10; // Query samples for evaluation
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let support_x = Tensor::randn(&[n_way * k_shot, 20], &device)?;
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let support_y = Tensor::zeros(&[n_way * k_shot], &device)?;
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let query_x = Tensor::randn(&[n_query, 20], &device)?;
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assert_eq!(support_x.shape()[0], 15); // 5 * 3
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assert_eq!(query_x.shape()[0], 10);
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Ok(())
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}
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