quantrs2_ml/dimensionality_reduction/
core.rs

1//! Core quantum dimensionality reduction functionality
2
3use crate::error::{MLError, Result};
4use scirs2_core::ndarray::{Array1, Array2};
5
6use super::config::*;
7use super::metrics::*;
8
9/// Main quantum dimensionality reducer
10#[derive(Debug)]
11pub struct QuantumDimensionalityReducer {
12    /// Algorithm to use
13    pub algorithm: DimensionalityReductionAlgorithm,
14    /// QPCA configuration
15    pub qpca_config: Option<QPCAConfig>,
16    /// QICA configuration
17    pub qica_config: Option<QICAConfig>,
18    /// Qt-SNE configuration
19    pub qtsne_config: Option<QtSNEConfig>,
20    /// QUMAP configuration
21    pub qumap_config: Option<QUMAPConfig>,
22    /// QLDA configuration
23    pub qlda_config: Option<QLDAConfig>,
24    /// QFA configuration
25    pub qfa_config: Option<QFactorAnalysisConfig>,
26    /// QCCA configuration
27    pub qcca_config: Option<QCCAConfig>,
28    /// QNMF configuration
29    pub qnmf_config: Option<QNMFConfig>,
30    /// Autoencoder configuration
31    pub autoencoder_config: Option<QAutoencoderConfig>,
32    /// Manifold learning configuration
33    pub manifold_config: Option<QManifoldConfig>,
34    /// Kernel PCA configuration
35    pub kernel_pca_config: Option<QKernelPCAConfig>,
36    /// Feature selection configuration
37    pub feature_selection_config: Option<QFeatureSelectionConfig>,
38    /// Specialized configuration
39    pub specialized_config: Option<QSpecializedConfig>,
40    /// Trained state
41    pub trained_state: Option<DRTrainedState>,
42}
43
44impl QuantumDimensionalityReducer {
45    /// Create a new quantum dimensionality reducer
46    pub fn new(algorithm: DimensionalityReductionAlgorithm) -> Self {
47        Self {
48            algorithm,
49            qpca_config: None,
50            qica_config: None,
51            qtsne_config: None,
52            qumap_config: None,
53            qlda_config: None,
54            qfa_config: None,
55            qcca_config: None,
56            qnmf_config: None,
57            autoencoder_config: None,
58            manifold_config: None,
59            kernel_pca_config: None,
60            feature_selection_config: None,
61            specialized_config: None,
62            trained_state: None,
63        }
64    }
65
66    /// Set QPCA configuration
67    pub fn with_qpca_config(mut self, config: QPCAConfig) -> Self {
68        self.qpca_config = Some(config);
69        self
70    }
71
72    /// Set QICA configuration
73    pub fn with_qica_config(mut self, config: QICAConfig) -> Self {
74        self.qica_config = Some(config);
75        self
76    }
77
78    /// Set Qt-SNE configuration
79    pub fn with_qtsne_config(mut self, config: QtSNEConfig) -> Self {
80        self.qtsne_config = Some(config);
81        self
82    }
83
84    /// Set QUMAP configuration
85    pub fn with_qumap_config(mut self, config: QUMAPConfig) -> Self {
86        self.qumap_config = Some(config);
87        self
88    }
89
90    /// Set QLDA configuration
91    pub fn with_qlda_config(mut self, config: QLDAConfig) -> Self {
92        self.qlda_config = Some(config);
93        self
94    }
95
96    /// Set autoencoder configuration
97    pub fn with_autoencoder_config(mut self, config: QAutoencoderConfig) -> Self {
98        self.autoencoder_config = Some(config);
99        self
100    }
101
102    /// Fit the dimensionality reduction model
103    pub fn fit(&mut self, data: &Array2<f64>) -> Result<()> {
104        match self.algorithm {
105            DimensionalityReductionAlgorithm::QPCA => self.fit_qpca(data),
106            DimensionalityReductionAlgorithm::QICA => self.fit_qica(data),
107            DimensionalityReductionAlgorithm::QtSNE => self.fit_qtsne(data),
108            DimensionalityReductionAlgorithm::QUMAP => self.fit_qumap(data),
109            DimensionalityReductionAlgorithm::QLDA => self.fit_qlda(data),
110            DimensionalityReductionAlgorithm::QVAE => self.fit_qvae(data),
111            DimensionalityReductionAlgorithm::QDenoisingAE => self.fit_qdenoising_ae(data),
112            DimensionalityReductionAlgorithm::QSparseAE => self.fit_qsparse_ae(data),
113            DimensionalityReductionAlgorithm::QManifoldLearning => self.fit_qmanifold(data),
114            DimensionalityReductionAlgorithm::QKernelPCA => self.fit_qkernel_pca(data),
115            _ => {
116                // Placeholder for other algorithms
117                self.fit_placeholder(data)
118            }
119        }
120    }
121
122    /// Transform data using the fitted model
123    pub fn transform(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
124        if self.trained_state.is_none() {
125            return Err(MLError::ModelNotTrained(
126                "Model must be fitted before transform".to_string(),
127            ));
128        }
129
130        match self.algorithm {
131            DimensionalityReductionAlgorithm::QPCA => self.transform_qpca(data),
132            DimensionalityReductionAlgorithm::QICA => self.transform_qica(data),
133            DimensionalityReductionAlgorithm::QtSNE => self.transform_qtsne(data),
134            DimensionalityReductionAlgorithm::QUMAP => self.transform_qumap(data),
135            DimensionalityReductionAlgorithm::QLDA => self.transform_qlda(data),
136            DimensionalityReductionAlgorithm::QVAE => self.transform_qvae(data),
137            DimensionalityReductionAlgorithm::QDenoisingAE => self.transform_qdenoising_ae(data),
138            DimensionalityReductionAlgorithm::QSparseAE => self.transform_qsparse_ae(data),
139            DimensionalityReductionAlgorithm::QManifoldLearning => self.transform_qmanifold(data),
140            DimensionalityReductionAlgorithm::QKernelPCA => self.transform_qkernel_pca(data),
141            _ => {
142                // Placeholder for other algorithms
143                self.transform_placeholder(data)
144            }
145        }
146    }
147
148    /// Fit and transform in one step
149    pub fn fit_transform(&mut self, data: &Array2<f64>) -> Result<Array2<f64>> {
150        self.fit(data)?;
151        self.transform(data)
152    }
153
154    /// Get the trained state
155    pub fn get_trained_state(&self) -> Option<&DRTrainedState> {
156        self.trained_state.as_ref()
157    }
158
159    /// Get explained variance ratio (if applicable)
160    pub fn explained_variance_ratio(&self) -> Option<&Array1<f64>> {
161        self.trained_state
162            .as_ref()
163            .map(|state| &state.explained_variance_ratio)
164    }
165
166    /// Get the components (transformation matrix)
167    pub fn components(&self) -> Option<&Array2<f64>> {
168        self.trained_state.as_ref().map(|state| &state.components)
169    }
170
171    /// Inverse transform (reconstruction)
172    pub fn inverse_transform(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
173        if let Some(state) = &self.trained_state {
174            // Basic linear reconstruction
175            let centered = data.dot(&state.components);
176            let reconstructed = &centered + &state.mean;
177            Ok(reconstructed)
178        } else {
179            Err(MLError::ModelNotTrained(
180                "Model must be fitted before inverse transform".to_string(),
181            ))
182        }
183    }
184
185    // Private fitting methods (placeholder implementations)
186
187    fn fit_qpca(&mut self, data: &Array2<f64>) -> Result<()> {
188        use super::linear::QPCA;
189        let binding = QPCAConfig::default();
190        let config = self.qpca_config.as_ref().unwrap_or(&binding);
191        let mut qpca = QPCA::new(config.clone());
192        qpca.fit(data)?;
193        self.trained_state = qpca.get_trained_state();
194        Ok(())
195    }
196
197    fn fit_qica(&mut self, data: &Array2<f64>) -> Result<()> {
198        use super::linear::QICA;
199        let binding = QICAConfig::default();
200        let config = self.qica_config.as_ref().unwrap_or(&binding);
201        let mut qica = QICA::new(config.clone());
202        qica.fit(data)?;
203        self.trained_state = qica.get_trained_state();
204        Ok(())
205    }
206
207    fn fit_qtsne(&mut self, data: &Array2<f64>) -> Result<()> {
208        use super::manifold::QtSNE;
209        let binding = QtSNEConfig::default();
210        let config = self.qtsne_config.as_ref().unwrap_or(&binding);
211        let mut qtsne = QtSNE::new(config.clone());
212        qtsne.fit(data)?;
213        self.trained_state = qtsne.get_trained_state();
214        Ok(())
215    }
216
217    fn fit_qumap(&mut self, data: &Array2<f64>) -> Result<()> {
218        use super::manifold::QUMAP;
219        let binding = QUMAPConfig::default();
220        let config = self.qumap_config.as_ref().unwrap_or(&binding);
221        let mut qumap = QUMAP::new(config.clone());
222        qumap.fit(data)?;
223        self.trained_state = qumap.get_trained_state();
224        Ok(())
225    }
226
227    fn fit_qlda(&mut self, data: &Array2<f64>) -> Result<()> {
228        use super::linear::QLDA;
229        let default_config = QLDAConfig::default();
230        let config = self.qlda_config.as_ref().unwrap_or(&default_config);
231        let mut qlda = QLDA::new(config.clone());
232        qlda.fit(data)?;
233        self.trained_state = qlda.get_trained_state();
234        Ok(())
235    }
236
237    fn fit_qvae(&mut self, data: &Array2<f64>) -> Result<()> {
238        use super::autoencoders::QVAE;
239        let default_config = QAutoencoderConfig::default();
240        let config = self.autoencoder_config.as_ref().unwrap_or(&default_config);
241        let mut qvae = QVAE::new(config.clone());
242        qvae.fit(data)?;
243        self.trained_state = qvae.get_trained_state();
244        Ok(())
245    }
246
247    fn fit_qdenoising_ae(&mut self, data: &Array2<f64>) -> Result<()> {
248        use super::autoencoders::QDenoisingAE;
249        let default_config = QAutoencoderConfig::default();
250        let config = self.autoencoder_config.as_ref().unwrap_or(&default_config);
251        let mut qdenoising = QDenoisingAE::new(config.clone());
252        qdenoising.fit(data)?;
253        self.trained_state = qdenoising.get_trained_state();
254        Ok(())
255    }
256
257    fn fit_qsparse_ae(&mut self, data: &Array2<f64>) -> Result<()> {
258        use super::autoencoders::QSparseAE;
259        let default_config = QAutoencoderConfig::default();
260        let config = self.autoencoder_config.as_ref().unwrap_or(&default_config);
261        let mut qsparse = QSparseAE::new(config.clone());
262        qsparse.fit(data)?;
263        self.trained_state = qsparse.get_trained_state();
264        Ok(())
265    }
266
267    fn fit_qmanifold(&mut self, data: &Array2<f64>) -> Result<()> {
268        use super::manifold::QManifoldLearning;
269        let default_config = QManifoldConfig::default();
270        let config = self.manifold_config.as_ref().unwrap_or(&default_config);
271        let mut qmanifold = QManifoldLearning::new(config.clone());
272        qmanifold.fit(data)?;
273        self.trained_state = qmanifold.get_trained_state();
274        Ok(())
275    }
276
277    fn fit_qkernel_pca(&mut self, data: &Array2<f64>) -> Result<()> {
278        use super::linear::QKernelPCA;
279        let default_config = QKernelPCAConfig::default();
280        let config = self.kernel_pca_config.as_ref().unwrap_or(&default_config);
281        let mut qkernel_pca = QKernelPCA::new(config.clone());
282        qkernel_pca.fit(data)?;
283        self.trained_state = qkernel_pca.get_trained_state();
284        Ok(())
285    }
286
287    fn fit_placeholder(&mut self, data: &Array2<f64>) -> Result<()> {
288        // Placeholder implementation - creates a simple identity transformation
289        let n_samples = data.nrows();
290        let n_features = data.ncols();
291        let n_components = (n_features / 2).max(1);
292
293        let components = Array2::eye(n_components);
294        let explained_variance_ratio =
295            Array1::from_vec((0..n_components).map(|i| 1.0 / (i + 1) as f64).collect());
296        let mean = data.mean_axis(scirs2_core::ndarray::Axis(0)).unwrap();
297
298        self.trained_state = Some(DRTrainedState {
299            components,
300            explained_variance_ratio,
301            mean,
302            scale: None,
303            quantum_parameters: std::collections::HashMap::new(),
304            model_parameters: std::collections::HashMap::new(),
305            training_statistics: std::collections::HashMap::new(),
306        });
307
308        Ok(())
309    }
310
311    // Private transformation methods (placeholder implementations)
312
313    fn transform_qpca(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
314        let state = self.trained_state.as_ref().unwrap();
315        let centered = data - &state.mean;
316        Ok(centered.dot(&state.components.t()))
317    }
318
319    fn transform_qica(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
320        let state = self.trained_state.as_ref().unwrap();
321        let centered = data - &state.mean;
322        Ok(centered.dot(&state.components.t()))
323    }
324
325    fn transform_qtsne(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
326        let state = self.trained_state.as_ref().unwrap();
327        // t-SNE doesn't have a direct transform, so use embedding
328        Ok(Array2::zeros((data.nrows(), state.components.ncols())))
329    }
330
331    fn transform_qumap(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
332        let state = self.trained_state.as_ref().unwrap();
333        // UMAP transform placeholder
334        Ok(Array2::zeros((data.nrows(), state.components.ncols())))
335    }
336
337    fn transform_qlda(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
338        let state = self.trained_state.as_ref().unwrap();
339        let centered = data - &state.mean;
340        Ok(centered.dot(&state.components.t()))
341    }
342
343    fn transform_qvae(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
344        let state = self.trained_state.as_ref().unwrap();
345        // VAE encoding placeholder
346        Ok(Array2::zeros((data.nrows(), state.components.ncols())))
347    }
348
349    fn transform_qdenoising_ae(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
350        let state = self.trained_state.as_ref().unwrap();
351        // Denoising AE encoding placeholder
352        Ok(Array2::zeros((data.nrows(), state.components.ncols())))
353    }
354
355    fn transform_qsparse_ae(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
356        let state = self.trained_state.as_ref().unwrap();
357        // Sparse AE encoding placeholder
358        Ok(Array2::zeros((data.nrows(), state.components.ncols())))
359    }
360
361    fn transform_qmanifold(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
362        let state = self.trained_state.as_ref().unwrap();
363        // Manifold learning transform placeholder
364        Ok(Array2::zeros((data.nrows(), state.components.ncols())))
365    }
366
367    fn transform_qkernel_pca(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
368        let state = self.trained_state.as_ref().unwrap();
369        // Kernel PCA transform placeholder
370        Ok(Array2::zeros((data.nrows(), state.components.ncols())))
371    }
372
373    fn transform_placeholder(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
374        let state = self.trained_state.as_ref().unwrap();
375        let centered = data - &state.mean;
376        Ok(centered.dot(&state.components.t()))
377    }
378}
quantrs2_ml/dimensionality_reduction/core.rs

quantrs2_ml/dimensionality_reduction/
core.rs
