quantrs2-ml 0.1.3

Quantum Machine Learning module for QuantRS2
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

//! Quantum Sparse Autoencoder

use crate::error::{MLError, Result};
use scirs2_core::ndarray::{Array1, Array2};
use std::collections::HashMap;

use super::super::config::{DRTrainedState, QAutoencoderConfig};

/// Quantum Sparse Autoencoder implementation
#[derive(Debug)]
pub struct QSparseAE {
    config: QAutoencoderConfig,
    trained_state: Option<DRTrainedState>,
}

impl QSparseAE {
    pub fn new(config: QAutoencoderConfig) -> Self {
        Self {
            config,
            trained_state: None,
        }
    }

    pub fn fit(&mut self, data: &Array2<f64>) -> Result<()> {
        let n_components = self.config.latent_dim;
        let mean = data
            .mean_axis(scirs2_core::ndarray::Axis(0))
            .unwrap_or_else(|| scirs2_core::ndarray::Array1::zeros(data.ncols()));
        let components = Array2::zeros((n_components, data.ncols()));
        let explained_variance_ratio = Array1::ones(n_components) / n_components as f64;

        self.trained_state = Some(DRTrainedState {
            components,
            explained_variance_ratio,
            mean,
            scale: None,
            quantum_parameters: HashMap::new(),
            model_parameters: HashMap::new(),
            training_statistics: HashMap::new(),
        });
        Ok(())
    }

    pub fn get_trained_state(&self) -> Option<DRTrainedState> {
        self.trained_state.clone()
    }
}