quantrs2-ml 0.1.3

Quantum Machine Learning module for QuantRS2
Documentation 
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//! Quantum Independent Component Analysis

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

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

/// Quantum Independent Component Analysis implementation
#[derive(Debug)]
pub struct QICA {
    config: QICAConfig,
    trained_state: Option<DRTrainedState>,
}

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

    pub fn fit(&mut self, data: &Array2<f64>) -> Result<()> {
        let n_features = data.ncols();
        let n_components = self.config.n_components.min(n_features);
        let mean = data
            .mean_axis(scirs2_core::ndarray::Axis(0))
            .unwrap_or_else(|| scirs2_core::ndarray::Array1::zeros(data.ncols()));
        let components = Array2::eye(n_features)
            .slice(s![..n_components, ..])
            .to_owned();
        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()
    }
}