quantrs2-ml 0.1.3

Quantum Machine Learning module for QuantRS2
Documentation
//! Auto-generated module
//!
//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)

use crate::error::{MLError, Result};
use crate::quantum_in_context_learning::types::*;
use scirs2_core::ndarray::{Array1, Array2, Array3, ArrayView1, Axis};
use scirs2_core::random::ChaCha20Rng;
use scirs2_core::random::{Rng, SeedableRng};
use scirs2_core::Complex64;
use std::f64::consts::PI;
#[cfg(test)]
mod tests {
    use super::*;
    #[test]
    fn test_quantum_in_context_learner_creation() {
        let config = QuantumInContextLearningConfig::default();
        let learner = QuantumInContextLearner::new(config);
        assert!(learner.is_ok());
    }
    #[test]
    fn test_context_encoding() {
        let config = QuantumInContextLearningConfig::default();
        let encoder = QuantumContextEncoder::new(&config).expect("should create context encoder");
        let example = ContextExample {
            input: Array1::from_vec(vec![0.1, 0.2, 0.3]),
            output: Array1::from_vec(vec![0.8]),
            metadata: ContextMetadata {
                task_type: "classification".to_string(),
                difficulty_level: 0.5,
                modality: ContextModality::Tabular,
                timestamp: 0,
                importance_weight: 1.0,
            },
            quantum_encoding: QuantumContextState {
                quantum_amplitudes: Array1::zeros(16).mapv(|_: f64| Complex64::new(1.0, 0.0)),
                classical_features: Array1::from_vec(vec![0.1, 0.2, 0.3]),
                entanglement_measure: 0.5,
                coherence_time: 1.0,
                fidelity: 1.0,
                phase_information: Complex64::new(1.0, 0.0),
                context_metadata: ContextMetadata {
                    task_type: "classification".to_string(),
                    difficulty_level: 0.5,
                    modality: ContextModality::Tabular,
                    timestamp: 0,
                    importance_weight: 1.0,
                },
            },
        };
        let encoded = encoder.encode_example(&example);
        assert!(encoded.is_ok());
    }
    #[test]
    fn test_zero_shot_learning() {
        let config = QuantumInContextLearningConfig::default();
        let learner =
            QuantumInContextLearner::new(config.clone()).expect("Failed to create learner");
        let query = Array1::from_vec(vec![0.5, -0.3, 0.8]);
        let result = learner.zero_shot_learning(&query);
        assert!(result.is_ok());
        let prediction = result.expect("Failed to perform zero-shot learning");
        assert_eq!(prediction.len(), config.model_dim);
    }
    #[test]
    fn test_few_shot_learning() {
        let config = QuantumInContextLearningConfig {
            model_dim: 3,
            max_context_examples: 5,
            ..Default::default()
        };
        let mut learner = QuantumInContextLearner::new(config).expect("Failed to create learner");
        let examples = vec![ContextExample {
            input: Array1::from_vec(vec![0.1, 0.2, 0.3]),
            output: Array1::from_vec(vec![0.8]),
            metadata: ContextMetadata {
                task_type: "test".to_string(),
                difficulty_level: 0.5,
                modality: ContextModality::Tabular,
                timestamp: 0,
                importance_weight: 1.0,
            },
            quantum_encoding: QuantumContextState {
                quantum_amplitudes: Array1::zeros(256).mapv(|_: f64| Complex64::new(1.0, 0.0)),
                classical_features: Array1::from_vec(vec![0.1, 0.2, 0.3]),
                entanglement_measure: 0.5,
                coherence_time: 1.0,
                fidelity: 1.0,
                phase_information: Complex64::new(1.0, 0.0),
                context_metadata: ContextMetadata {
                    task_type: "test".to_string(),
                    difficulty_level: 0.5,
                    modality: ContextModality::Tabular,
                    timestamp: 0,
                    importance_weight: 1.0,
                },
            },
        }];
        let query = Array1::from_vec(vec![0.5, -0.3, 0.8]);
        let result = learner.few_shot_learning(&examples, &query, 3);
        assert!(result.is_ok());
    }
    #[test]
    fn test_quantum_memory_operations() {
        let config = QuantumInContextLearningConfig::default();
        let mut memory = QuantumEpisodicMemory::new(&config).expect("Failed to create memory");
        let test_state = QuantumContextState {
            quantum_amplitudes: Array1::zeros(256).mapv(|_: f64| Complex64::new(1.0, 0.0)),
            classical_features: Array1::from_vec(vec![0.1, 0.2, 0.3]),
            entanglement_measure: 0.7,
            coherence_time: 0.9,
            fidelity: 0.95,
            phase_information: Complex64::new(1.0, 0.0),
            context_metadata: ContextMetadata {
                task_type: "memory_test".to_string(),
                difficulty_level: 0.6,
                modality: ContextModality::Tabular,
                timestamp: 0,
                importance_weight: 1.0,
            },
        };
        let result = memory.add_experience(test_state.clone());
        assert!(result.is_ok());
        let retrieved = memory.retrieve_similar_contexts(&test_state, 1);
        assert!(retrieved.is_ok());
        assert_eq!(retrieved.expect("Failed to retrieve contexts").len(), 1);
    }
    #[test]
    fn test_adaptation_strategies() {
        let config = QuantumInContextLearningConfig {
            adaptation_strategy: AdaptationStrategy::QuantumInterference {
                interference_strength: 0.8,
            },
            ..Default::default()
        };
        let learner = QuantumInContextLearner::new(config);
        assert!(learner.is_ok());
    }
    #[test]
    fn test_prototype_bank_operations() {
        let config = QuantumInContextLearningConfig::default();
        let mut bank = PrototypeBank::new(&config).expect("Failed to create prototype bank");
        let test_state = QuantumContextState {
            quantum_amplitudes: Array1::zeros(256).mapv(|_: f64| Complex64::new(1.0, 0.0)),
            classical_features: Array1::from_vec(vec![0.1, 0.2, 0.3]),
            entanglement_measure: 0.5,
            coherence_time: 1.0,
            fidelity: 1.0,
            phase_information: Complex64::new(1.0, 0.0),
            context_metadata: ContextMetadata {
                task_type: "prototype_test".to_string(),
                difficulty_level: 0.5,
                modality: ContextModality::Tabular,
                timestamp: 0,
                importance_weight: 1.0,
            },
        };
        let result = bank.add_prototype(test_state.clone());
        assert!(result.is_ok());
        assert_eq!(bank.get_prototype_count(), 1);
        let found = bank.find_nearest_prototypes(&test_state, 1);
        assert!(found.is_ok());
        assert_eq!(found.expect("Failed to find nearest prototypes").len(), 1);
    }
    #[test]
    fn test_quantum_attention_mechanism() {
        let config = QuantumInContextLearningConfig {
            num_attention_heads: 2,
            ..Default::default()
        };
        let attention =
            QuantumContextAttention::new(&config).expect("Failed to create attention mechanism");
        let query_state = QuantumContextState {
            quantum_amplitudes: Array1::zeros(256).mapv(|_: f64| Complex64::new(1.0, 0.0)),
            classical_features: Array1::from_vec(vec![0.1, 0.2, 0.3]),
            entanglement_measure: 0.5,
            coherence_time: 1.0,
            fidelity: 1.0,
            phase_information: Complex64::new(1.0, 0.0),
            context_metadata: ContextMetadata {
                task_type: "attention_test".to_string(),
                difficulty_level: 0.5,
                modality: ContextModality::Tabular,
                timestamp: 0,
                importance_weight: 1.0,
            },
        };
        let contexts = vec![query_state.clone(), query_state.clone()];
        let weights = attention.compute_attention_weights(&query_state, &contexts);
        assert!(weights.is_ok());
        assert_eq!(
            weights.expect("Failed to compute attention weights").len(),
            2
        );
    }
}