quantrs2_tytan/
scirs_stub.rs

1//! Stub for SciRS2 integration
2//!
3//! This module provides placeholders for SciRS2 integration.
4//! The actual integration would be more comprehensive once
5//! the SciRS2 API stabilizes.
6
7#![allow(dead_code)]
8
9use ::scirs2_core::ndarray::{Array2, ArrayD};
10use ::scirs2_core::random::prelude::*;
11
12/// Placeholder for enhanced QUBO operations
13pub fn enhance_qubo_matrix(matrix: &Array2<f64>) -> Array2<f64> {
14    // In a real implementation, this would:
15    // - Convert to sparse format
16    // - Apply optimizations
17    // - Use BLAS operations
18    matrix.clone()
19}
20
21/// Placeholder for HOBO tensor operations
22pub fn optimize_hobo_tensor(tensor: &ArrayD<f64>) -> ArrayD<f64> {
23    // In a real implementation, this would:
24    // - Apply tensor decomposition
25    // - Use efficient tensor operations
26    // - Leverage parallelization
27    tensor.clone()
28}
29
30/// Placeholder for parallel sampling
31pub fn parallel_sample_qubo(matrix: &Array2<f64>, num_samples: usize) -> Vec<(Vec<bool>, f64)> {
32    // In a real implementation, this would use parallel processing
33    let n = matrix.shape()[0];
34    let mut results = Vec::with_capacity(num_samples);
35
36    // use rand::{rng, Rng}; // Replaced by scirs2_core::random::prelude::*
37    let mut rng = thread_rng();
38
39    for _ in 0..num_samples {
40        let solution: Vec<bool> = (0..n).map(|_| rng.gen()).collect();
41        let energy = evaluate_qubo(&solution, matrix);
42        results.push((solution, energy));
43    }
44
45    results
46}
47
48fn evaluate_qubo(solution: &[bool], matrix: &Array2<f64>) -> f64 {
49    let mut energy = 0.0;
50    let n = solution.len();
51
52    for i in 0..n {
53        if solution[i] {
54            energy += matrix[[i, i]];
55            for j in (i + 1)..n {
56                if solution[j] {
57                    energy += matrix[[i, j]];
58                }
59            }
60        }
61    }
62
63    energy
64}
65
66/// Marker that SciRS2 integration is available
67pub const SCIRS2_AVAILABLE: bool = cfg!(feature = "scirs");
68
69// When SciRS2 feature is enabled, we still use stubs for now
70// until SciRS2 is fully available
71pub mod scirs2_core {
72    pub use super::scirs2_core_stub::*;
73}
74
75pub mod scirs2_linalg {
76    pub use super::scirs2_linalg_stub::*;
77}
78
79pub mod scirs2_plot {
80    pub use super::scirs2_plot_stub::*;
81}
82
83pub mod scirs2_statistics {
84    pub use super::scirs2_statistics_stub::*;
85}
86
87pub mod scirs2_optimization {
88    pub use super::scirs2_optimization_stub::*;
89}
90
91pub mod scirs2_graphs {
92    pub use super::scirs2_graphs_stub::*;
93}
94
95pub mod scirs2_ml {
96    pub use super::scirs2_ml_stub::*;
97}
98
99// Define stub modules that can be used regardless of feature flags
100mod scirs2_core_stub {
101    use std::error::Error;
102
103    pub fn init_simd() -> Result<(), Box<dyn Error>> {
104        Ok(())
105    }
106
107    pub mod simd {
108        pub trait SimdOps {}
109    }
110
111    pub mod memory {
112        pub fn get_current_usage() -> Result<usize, std::io::Error> {
113            Err(std::io::Error::new(
114                std::io::ErrorKind::NotFound,
115                "Not implemented",
116            ))
117        }
118    }
119
120    pub mod statistics {
121        pub struct OnlineStats {
122            count: usize,
123            mean: f64,
124            m2: f64,
125        }
126
127        impl Default for OnlineStats {
128            fn default() -> Self {
129                Self::new()
130            }
131        }
132
133        impl OnlineStats {
134            pub const fn new() -> Self {
135                Self {
136                    count: 0,
137                    mean: 0.0,
138                    m2: 0.0,
139                }
140            }
141
142            pub fn update(&mut self, value: f64) {
143                self.count += 1;
144                let delta = value - self.mean;
145                self.mean += delta / self.count as f64;
146                let delta2 = value - self.mean;
147                self.m2 += delta * delta2;
148            }
149
150            pub const fn mean(&self) -> f64 {
151                self.mean
152            }
153
154            pub fn variance(&self) -> f64 {
155                if self.count < 2 {
156                    0.0
157                } else {
158                    self.m2 / (self.count - 1) as f64
159                }
160            }
161        }
162
163        pub struct MovingAverage {
164            window_size: usize,
165            values: Vec<f64>,
166        }
167
168        impl MovingAverage {
169            pub const fn new(window_size: usize) -> Self {
170                Self {
171                    window_size,
172                    values: Vec::new(),
173                }
174            }
175
176            pub fn update(&mut self, value: f64) {
177                self.values.push(value);
178                if self.values.len() > self.window_size {
179                    self.values.remove(0);
180                }
181            }
182
183            pub fn mean(&self) -> f64 {
184                if self.values.is_empty() {
185                    0.0
186                } else {
187                    self.values.iter().sum::<f64>() / self.values.len() as f64
188                }
189            }
190        }
191    }
192
193    pub mod gpu {
194        use std::error::Error;
195        use std::time::Duration;
196
197        pub const fn get_device_count() -> usize {
198            0
199        }
200
201        pub struct GpuContext {
202            pub device_id: usize,
203        }
204
205        impl GpuContext {
206            pub fn new(_device_id: usize) -> Result<Self, Box<dyn Error>> {
207                Err("GPU not available".into())
208            }
209
210            pub fn measure_kernel_latency(&self) -> Result<Duration, Box<dyn Error>> {
211                Err("GPU not available".into())
212            }
213
214            pub const fn get_device_info(&self) -> DeviceInfo {
215                DeviceInfo {
216                    memory_mb: 0,
217                    compute_units: 0,
218                    clock_mhz: 0,
219                }
220            }
221        }
222
223        pub struct DeviceInfo {
224            pub memory_mb: usize,
225            pub compute_units: usize,
226            pub clock_mhz: usize,
227        }
228
229        pub struct GpuDevice {
230            pub id: u32,
231        }
232
233        impl GpuDevice {
234            pub fn new(_id: u32) -> Result<Self, Box<dyn Error>> {
235                Err("GPU not available".into())
236            }
237
238            pub fn random_array<T>(
239                &self,
240                _shape: (usize, usize),
241            ) -> Result<GpuArray<T>, Box<dyn Error>> {
242                Err("GPU not available".into())
243            }
244
245            pub fn binarize<T>(
246                &self,
247                _array: &GpuArray<T>,
248                _threshold: f64,
249            ) -> Result<GpuArray<bool>, Box<dyn Error>> {
250                Err("GPU not available".into())
251            }
252
253            pub fn qubo_energy<T>(
254                &self,
255                _states: &GpuArray<bool>,
256                _matrix: &GpuArray<T>,
257            ) -> Result<GpuArray<f64>, Box<dyn Error>> {
258                Err("GPU not available".into())
259            }
260        }
261
262        impl Clone for GpuDevice {
263            fn clone(&self) -> Self {
264                Self { id: self.id }
265            }
266        }
267
268        pub struct GpuArray<T> {
269            _phantom: std::marker::PhantomData<T>,
270        }
271
272        impl<T> GpuArray<T> {
273            pub fn from_ndarray(
274                _device: GpuDevice,
275                _array: &scirs2_core::ndarray::Array2<T>,
276            ) -> Result<Self, Box<dyn Error>> {
277                Err("GPU not available".into())
278            }
279
280            pub fn to_ndarray(&self) -> Result<scirs2_core::ndarray::Array2<T>, Box<dyn Error>>
281            where
282                T: Clone + Default,
283            {
284                Err("GPU not available".into())
285            }
286        }
287    }
288}
289
290mod scirs2_linalg_stub {
291
292    pub mod sparse {
293        use scirs2_core::ndarray::Array2;
294
295        pub struct SparseMatrix;
296
297        impl SparseMatrix {
298            pub const fn from_dense(_matrix: &Array2<f64>) -> Self {
299                Self
300            }
301        }
302    }
303
304    pub mod svd {
305        pub struct SVD;
306    }
307
308    pub mod pca {
309        use scirs2_core::ndarray::Array2;
310        use std::error::Error;
311
312        pub struct PCA {
313            n_components: usize,
314        }
315
316        impl PCA {
317            pub const fn new(n_components: usize) -> Self {
318                Self { n_components }
319            }
320
321            pub fn fit_transform(&self, data: &Array2<f64>) -> Result<Array2<f64>, Box<dyn Error>> {
322                // Simple placeholder: just return first n_components columns
323                let n_samples = data.nrows();
324                let n_features = self.n_components.min(data.ncols());
325                let mut result = Array2::zeros((n_samples, self.n_components));
326
327                for i in 0..n_samples {
328                    for j in 0..n_features {
329                        result[[i, j]] = data[[i, j]];
330                    }
331                }
332
333                Ok(result)
334            }
335        }
336    }
337
338    pub mod norm {
339        use scirs2_core::ndarray::Array1;
340
341        pub trait Norm {
342            fn norm(&self) -> f64;
343        }
344
345        impl Norm for Array1<f64> {
346            fn norm(&self) -> f64 {
347                self.iter().map(|x| x * x).sum::<f64>().sqrt()
348            }
349        }
350    }
351
352    pub mod gpu {
353        use super::*;
354        use scirs2_core::ndarray::Array2;
355        use std::error::Error;
356
357        pub struct GpuMatrix;
358
359        impl GpuMatrix {
360            pub fn from_host(
361                _matrix: &Array2<f64>,
362                _ctx: &crate::scirs_stub::scirs2_core::gpu::GpuContext,
363            ) -> Result<Self, Box<dyn Error>> {
364                Err("GPU not available".into())
365            }
366
367            pub fn to_host(&self) -> Result<Array2<f64>, Box<dyn Error>> {
368                Err("GPU not available".into())
369            }
370        }
371    }
372}
373
374mod scirs2_plot_stub {
375    use std::error::Error;
376
377    pub struct Figure;
378    pub struct Subplot;
379    pub struct Plot2D;
380    pub struct Plot3D;
381    pub struct Heatmap;
382    pub struct ColorMap;
383    pub struct Plot;
384    pub struct Line;
385    pub struct Scatter;
386    pub struct Bar;
387    pub struct NetworkPlot;
388    pub struct MultiPlot;
389    pub struct Annotation;
390    pub struct Violin;
391    pub struct BoxPlot;
392
393    impl Default for Plot {
394        fn default() -> Self {
395            Self::new()
396        }
397    }
398
399    impl Plot {
400        pub const fn new() -> Self {
401            Self
402        }
403        pub fn add_trace(&mut self, _trace: impl Trace) {}
404        pub const fn set_title(&mut self, _title: &str) {}
405        pub const fn set_xlabel(&mut self, _label: &str) {}
406        pub const fn set_ylabel(&mut self, _label: &str) {}
407        pub fn save(&self, _path: &str) -> Result<(), Box<dyn Error>> {
408            Err("Plotting not available".into())
409        }
410    }
411
412    impl Line {
413        pub fn new(_x: Vec<f64>, _y: Vec<f64>) -> Self {
414            Self
415        }
416        pub const fn name(self, _name: &str) -> Self {
417            self
418        }
419    }
420
421    impl Scatter {
422        pub fn new(_x: Vec<f64>, _y: Vec<f64>) -> Self {
423            Self
424        }
425        pub const fn name(self, _name: &str) -> Self {
426            self
427        }
428        pub const fn mode(self, _mode: &str) -> Self {
429            self
430        }
431        pub const fn marker_size(self, _size: u32) -> Self {
432            self
433        }
434        pub fn text(self, _text: Vec<String>) -> Self {
435            self
436        }
437    }
438
439    impl Heatmap {
440        pub fn new(_z: Vec<Vec<f64>>) -> Self {
441            Self
442        }
443        pub fn x(self, _x: Vec<f64>) -> Self {
444            self
445        }
446        pub fn y(self, _y: Vec<f64>) -> Self {
447            self
448        }
449        pub fn x_labels(self, _labels: Vec<String>) -> Self {
450            self
451        }
452        pub fn y_labels(self, _labels: Vec<String>) -> Self {
453            self
454        }
455        pub const fn colorscale(self, _scale: &str) -> Self {
456            self
457        }
458    }
459
460    impl Bar {
461        pub fn new(_x: Vec<String>, _y: Vec<f64>) -> Self {
462            Self
463        }
464        pub const fn name(self, _name: &str) -> Self {
465            self
466        }
467    }
468
469    impl Trace for Line {}
470    impl Trace for Scatter {}
471    impl Trace for Heatmap {}
472    impl Trace for Bar {}
473
474    impl Default for Figure {
475        fn default() -> Self {
476            Self::new()
477        }
478    }
479
480    impl Figure {
481        pub const fn new() -> Self {
482            Self
483        }
484
485        pub fn add_subplot(
486            &mut self,
487            _rows: usize,
488            _cols: usize,
489            _idx: usize,
490        ) -> Result<Subplot, Box<dyn Error>> {
491            Ok(Subplot)
492        }
493
494        pub const fn suptitle(&mut self, _title: &str) {}
495        pub const fn tight_layout(&mut self) {}
496        pub fn show(&self) -> Result<(), Box<dyn Error>> {
497            Err("Plotting not available".into())
498        }
499    }
500
501    impl Subplot {
502        pub const fn bar(&self, _x: &[f64], _y: &[f64]) -> Self {
503            Self
504        }
505        pub const fn scatter(&self, _x: &[f64], _y: &[f64]) -> Self {
506            Self
507        }
508        pub const fn plot(&self, _x: &[f64], _y: &[f64]) -> Self {
509            Self
510        }
511        pub const fn contourf(&self, _x: &[f64], _y: &[f64], _z: &[f64]) -> Self {
512            Self
513        }
514        pub const fn barh(&self, _y: &[f64], _width: &[f64], _left: &[f64], _height: f64) -> Self {
515            Self
516        }
517        pub const fn pie(&self, _sizes: &[f64], _labels: &[String]) -> Self {
518            Self
519        }
520        pub const fn bar_horizontal(&self, _names: &[String], _values: &[f64]) -> Self {
521            Self
522        }
523        pub const fn text(&self, _x: f64, _y: f64, _text: &str) -> Self {
524            Self
525        }
526        pub const fn axvline(&self, _x: f64) -> Self {
527            Self
528        }
529
530        pub const fn set_xlabel(&self, _label: &str) -> &Self {
531            self
532        }
533        pub const fn set_ylabel(&self, _label: &str) -> &Self {
534            self
535        }
536        pub const fn set_title(&self, _title: &str) -> &Self {
537            self
538        }
539        pub const fn set_color(&self, _color: &str) -> &Self {
540            self
541        }
542        pub const fn set_color_data(&self, _data: &[f64]) -> &Self {
543            self
544        }
545        pub const fn set_colormap(&self, _cmap: &str) -> &Self {
546            self
547        }
548        pub const fn set_label(&self, _label: &str) -> &Self {
549            self
550        }
551        pub const fn set_linewidth(&self, _width: f64) -> &Self {
552            self
553        }
554        pub const fn set_linestyle(&self, _style: &str) -> &Self {
555            self
556        }
557        pub const fn set_alpha(&self, _alpha: f64) -> &Self {
558            self
559        }
560        pub const fn set_size(&self, _size: f64) -> &Self {
561            self
562        }
563        pub const fn set_edgecolor(&self, _color: &str) -> &Self {
564            self
565        }
566        pub const fn set_marker(&self, _marker: &str) -> &Self {
567            self
568        }
569        pub const fn set_fontsize(&self, _size: u32) -> &Self {
570            self
571        }
572        pub const fn set_ha(&self, _align: &str) -> &Self {
573            self
574        }
575        pub const fn set_va(&self, _align: &str) -> &Self {
576            self
577        }
578        pub const fn set_verticalalignment(&self, _align: &str) -> &Self {
579            self
580        }
581        pub const fn set_transform(&self, _transform: ()) -> &Self {
582            self
583        }
584        pub const fn set_autopct(&self, _fmt: &str) -> &Self {
585            self
586        }
587        pub const fn set_aspect(&self, _aspect: &str) {}
588        pub const fn set_yscale(&self, _scale: &str) {}
589        pub const fn set_xlim(&self, _min: f64, _max: f64) {}
590        pub const fn set_ylim(&self, _min: f64, _max: f64) {}
591        pub const fn set_axis_off(&self) {}
592        pub const fn set_xticks(&self, _ticks: &[f64]) {}
593        pub const fn set_yticks(&self, _ticks: &[f64]) {}
594        pub const fn set_xticklabels(&self, _labels: &[String]) {}
595        pub const fn set_yticklabels(&self, _labels: &[String]) {}
596        pub fn get_xticklabels(&self) -> Vec<TickLabel> {
597            vec![TickLabel; self.get_xticks().len()]
598        }
599        pub const fn get_xticks(&self) -> Vec<f64> {
600            vec![]
601        }
602        pub const fn axis(&self, _setting: &str) {}
603        pub const fn legend(&self) {}
604        pub const fn legend_unique(&self) {}
605        pub const fn trans_axes(&self) {}
606    }
607
608    #[derive(Clone)]
609    pub struct TickLabel;
610
611    impl TickLabel {
612        pub const fn set_rotation(&self, _angle: u32) {}
613        pub const fn set_ha(&self, _align: &str) {}
614    }
615
616    pub trait Trace {}
617}
618
619mod scirs2_statistics_stub {
620
621    pub mod descriptive {
622        pub const fn mean(_data: &[f64]) -> f64 {
623            0.0
624        }
625        pub const fn std_dev(_data: &[f64]) -> f64 {
626            0.0
627        }
628        pub const fn quantile(_data: &[f64], _q: f64) -> f64 {
629            0.0
630        }
631    }
632
633    pub mod clustering {
634        use scirs2_core::ndarray::Array2;
635        use std::error::Error;
636
637        pub struct KMeans {
638            k: usize,
639        }
640
641        impl KMeans {
642            pub const fn new(k: usize) -> Self {
643                Self { k }
644            }
645
646            pub fn fit_predict(&self, _data: &Array2<f64>) -> Result<Vec<usize>, Box<dyn Error>> {
647                Ok(vec![0; _data.nrows()])
648            }
649        }
650
651        pub struct DBSCAN {
652            eps: f64,
653            min_samples: usize,
654        }
655
656        impl DBSCAN {
657            pub const fn new(eps: f64, min_samples: usize) -> Self {
658                Self { eps, min_samples }
659            }
660
661            pub fn fit_predict(&self, _data: &Array2<f64>) -> Result<Vec<usize>, Box<dyn Error>> {
662                Ok(vec![0; _data.nrows()])
663            }
664        }
665
666        pub fn hierarchical_clustering(
667            _data: &Array2<f64>,
668            _n_clusters: usize,
669            _linkage: &str,
670        ) -> Result<Vec<usize>, Box<dyn Error>> {
671            Ok(vec![0; _data.nrows()])
672        }
673    }
674
675    pub mod kde {
676        use std::error::Error;
677
678        pub struct KernelDensityEstimator;
679
680        impl KernelDensityEstimator {
681            pub fn new(_kernel: &str) -> Result<Self, Box<dyn Error>> {
682                Ok(Self)
683            }
684
685            pub fn estimate_2d(
686                &self,
687                _x: &[f64],
688                _y: &[f64],
689                _xi: f64,
690                _yi: f64,
691            ) -> Result<f64, Box<dyn Error>> {
692                Ok(0.0)
693            }
694        }
695    }
696}
697
698mod scirs2_optimization_stub {
699    use scirs2_core::ndarray::Array1;
700    use std::error::Error;
701
702    pub trait Optimizer: Send {
703        fn minimize(
704            &mut self,
705            objective: &dyn ObjectiveFunction,
706            x0: &Array1<f64>,
707            bounds: &Bounds,
708            max_iter: usize,
709        ) -> Result<OptimizationResult, Box<dyn Error>>;
710    }
711
712    pub trait OptimizationProblem {}
713
714    pub trait ObjectiveFunction {
715        fn evaluate(&self, x: &Array1<f64>) -> f64;
716        fn gradient(&self, x: &Array1<f64>) -> Array1<f64>;
717    }
718
719    pub struct Bounds {
720        lower: Array1<f64>,
721        upper: Array1<f64>,
722    }
723
724    impl Bounds {
725        pub const fn new(lower: Array1<f64>, upper: Array1<f64>) -> Self {
726            Self { lower, upper }
727        }
728    }
729
730    pub struct OptimizationResult {
731        pub x: Array1<f64>,
732        pub f: f64,
733        pub iterations: usize,
734    }
735
736    pub mod gradient {
737        use super::*;
738
739        pub struct LBFGS {
740            dim: usize,
741        }
742
743        impl LBFGS {
744            pub const fn new(dim: usize) -> Self {
745                Self { dim }
746            }
747        }
748
749        impl Optimizer for LBFGS {
750            fn minimize(
751                &mut self,
752                objective: &dyn ObjectiveFunction,
753                x0: &Array1<f64>,
754                _bounds: &Bounds,
755                _max_iter: usize,
756            ) -> Result<OptimizationResult, Box<dyn Error>> {
757                Ok(OptimizationResult {
758                    x: x0.clone(),
759                    f: objective.evaluate(x0),
760                    iterations: 1,
761                })
762            }
763        }
764    }
765
766    pub mod bayesian {
767        use super::*;
768
769        #[derive(Debug, Clone, Copy)]
770        pub enum AcquisitionFunction {
771            ExpectedImprovement,
772            UCB,
773            PI,
774            Thompson,
775        }
776
777        #[derive(Debug, Clone, Copy)]
778        pub enum KernelType {
779            RBF,
780            Matern52,
781            Matern32,
782        }
783
784        pub struct BayesianOptimizer {
785            dim: usize,
786            kernel: KernelType,
787            acquisition: AcquisitionFunction,
788            exploration: f64,
789        }
790
791        impl BayesianOptimizer {
792            pub fn new(
793                dim: usize,
794                kernel: KernelType,
795                acquisition: AcquisitionFunction,
796                exploration: f64,
797            ) -> Result<Self, Box<dyn Error>> {
798                Ok(Self {
799                    dim,
800                    kernel,
801                    acquisition,
802                    exploration,
803                })
804            }
805
806            pub fn update(
807                &mut self,
808                _x_data: &[Array1<f64>],
809                _y_data: &Array1<f64>,
810            ) -> Result<(), Box<dyn Error>> {
811                Ok(())
812            }
813
814            pub fn suggest_next(&self) -> Result<Array1<f64>, Box<dyn Error>> {
815                Ok(Array1::zeros(self.dim))
816            }
817        }
818
819        pub struct GaussianProcess;
820    }
821}
822
823mod scirs2_ml_stub {
824    use scirs2_core::ndarray::Array2;
825    use std::error::Error;
826
827    pub struct RandomForest {
828        n_estimators: usize,
829        max_depth: Option<usize>,
830        min_samples_split: usize,
831    }
832
833    impl Default for RandomForest {
834        fn default() -> Self {
835            Self::new()
836        }
837    }
838
839    impl RandomForest {
840        pub const fn new() -> Self {
841            Self {
842                n_estimators: 100,
843                max_depth: None,
844                min_samples_split: 2,
845            }
846        }
847
848        pub const fn n_estimators(mut self, n: usize) -> Self {
849            self.n_estimators = n;
850            self
851        }
852
853        pub const fn max_depth(mut self, depth: Option<usize>) -> Self {
854            self.max_depth = depth;
855            self
856        }
857
858        pub const fn min_samples_split(mut self, samples: usize) -> Self {
859            self.min_samples_split = samples;
860            self
861        }
862
863        pub fn fit(&mut self, _x: &Vec<Vec<f64>>, _y: &Vec<f64>) -> Result<(), Box<dyn Error>> {
864            Ok(())
865        }
866
867        pub fn predict(&self, _x: &Vec<Vec<f64>>) -> Vec<f64> {
868            vec![0.0; _x.len()]
869        }
870
871        pub fn feature_importances(&self) -> Vec<f64> {
872            vec![0.5; 10] // Placeholder
873        }
874    }
875
876    pub struct GradientBoosting {
877        n_estimators: usize,
878        learning_rate: f64,
879        max_depth: usize,
880    }
881
882    pub struct NeuralNetwork {
883        hidden_layers: Vec<usize>,
884        activation: String,
885        learning_rate: f64,
886    }
887
888    pub struct KMeans {
889        k: usize,
890    }
891
892    impl KMeans {
893        pub const fn new(k: usize) -> Self {
894            Self { k }
895        }
896
897        pub fn fit_predict(&self, _data: &Array2<f64>) -> Result<Vec<usize>, Box<dyn Error>> {
898            Ok(vec![0; _data.nrows()])
899        }
900    }
901
902    pub struct DBSCAN {
903        eps: f64,
904        min_samples: usize,
905    }
906
907    impl DBSCAN {
908        pub const fn new(eps: f64, min_samples: usize) -> Self {
909            Self { eps, min_samples }
910        }
911
912        pub fn fit_predict(&self, _data: &Array2<f64>) -> Result<Vec<usize>, Box<dyn Error>> {
913            Ok(vec![0; _data.nrows()])
914        }
915    }
916
917    pub struct PCA {
918        n_components: usize,
919    }
920
921    impl PCA {
922        pub const fn new(n_components: usize) -> Self {
923            Self { n_components }
924        }
925
926        pub fn fit_transform(&self, data: &Array2<f64>) -> Result<Array2<f64>, Box<dyn Error>> {
927            // Simple placeholder: just return first n_components columns
928            let n_samples = data.nrows();
929            let n_features = self.n_components.min(data.ncols());
930            let mut result = Array2::zeros((n_samples, self.n_components));
931
932            for i in 0..n_samples {
933                for j in 0..n_features {
934                    result[[i, j]] = data[[i, j]];
935                }
936            }
937
938            Ok(result)
939        }
940    }
941
942    pub struct StandardScaler;
943
944    pub struct CrossValidation {
945        n_folds: usize,
946    }
947
948    impl CrossValidation {
949        pub const fn new(n_folds: usize) -> Self {
950            Self { n_folds }
951        }
952
953        pub fn cross_val_score<T>(
954            &self,
955            _model: &T,
956            _x: &Vec<Vec<f64>>,
957            _y: &Vec<f64>,
958        ) -> CVScores {
959            CVScores {
960                scores: vec![0.5; self.n_folds],
961            }
962        }
963    }
964
965    pub struct CVScores {
966        scores: Vec<f64>,
967    }
968
969    impl CVScores {
970        pub fn mean(&self) -> f64 {
971            self.scores.iter().sum::<f64>() / self.scores.len() as f64
972        }
973    }
974
975    pub const fn train_test_split<T>(
976        _x: &[T],
977        _y: &[f64],
978        _test_size: f64,
979    ) -> (Vec<T>, Vec<T>, Vec<f64>, Vec<f64>)
980    where
981        T: Clone,
982    {
983        (vec![], vec![], vec![], vec![])
984    }
985}
986
987mod scirs2_graphs_stub {
988    pub struct Graph;
989    pub struct GraphLayout;
990
991    pub fn spring_layout(
992        _edges: &[(usize, usize)],
993        n_nodes: usize,
994    ) -> Result<Vec<(f64, f64)>, Box<dyn std::error::Error>> {
995        // Simple circular layout
996        let mut positions = Vec::new();
997        for i in 0..n_nodes {
998            let angle = 2.0 * std::f64::consts::PI * i as f64 / n_nodes as f64;
999            positions.push((angle.cos(), angle.sin()));
1000        }
1001        Ok(positions)
1002    }
1003}
quantrs2_tytan/scirs_stub.rs

quantrs2_tytan/
scirs_stub.rs
