quantrs2_ml/
scirs2_integration.rs

1//! SciRS2 integration layer for quantum machine learning
2//!
3//! This module provides integration with the SciRS2 scientific computing framework,
4//! enabling quantum ML models to leverage SciRS2's optimized tensor operations,
5//! distributed training capabilities, and serialization formats.
6
7use crate::error::{MLError, Result};
8use scirs2_core::ndarray::{Array, Array1, Array2, Array3, ArrayD, ArrayViewD, Dimension, IxDyn};
9use std::collections::HashMap;
10
11/// Trait for tensor operations compatible with SciRS2
12pub trait SciRS2Tensor {
13    /// Get tensor shape
14    fn shape(&self) -> &[usize];
15
16    /// Get tensor data as ArrayViewD
17    fn view(&self) -> ArrayViewD<f64>;
18
19    /// Convert to SciRS2 format (placeholder)
20    fn to_scirs2(&self) -> Result<SciRS2Array>;
21
22    /// Perform tensor operations using SciRS2 backend
23    fn matmul(&self, other: &dyn SciRS2Tensor) -> Result<SciRS2Array>;
24
25    /// Element-wise operations
26    fn add(&self, other: &dyn SciRS2Tensor) -> Result<SciRS2Array>;
27    fn mul(&self, other: &dyn SciRS2Tensor) -> Result<SciRS2Array>;
28    fn sub(&self, other: &dyn SciRS2Tensor) -> Result<SciRS2Array>;
29
30    /// Reduction operations
31    fn sum(&self, axis: Option<usize>) -> Result<SciRS2Array>;
32    fn mean(&self, axis: Option<usize>) -> Result<SciRS2Array>;
33    fn max(&self, axis: Option<usize>) -> Result<SciRS2Array>;
34    fn min(&self, axis: Option<usize>) -> Result<SciRS2Array>;
35}
36
37/// SciRS2 array wrapper for quantum ML operations
38pub struct SciRS2Array {
39    /// Array data
40    pub data: ArrayD<f64>,
41    /// Whether gradients are required
42    pub requires_grad: bool,
43    /// Gradient accumulator
44    pub grad: Option<ArrayD<f64>>,
45    /// Operation history for backpropagation
46    pub grad_fn: Option<Box<dyn GradFunction>>,
47}
48
49impl std::fmt::Debug for SciRS2Array {
50    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
51        f.debug_struct("SciRS2Array")
52            .field("data", &self.data)
53            .field("requires_grad", &self.requires_grad)
54            .field("grad", &self.grad)
55            .field("grad_fn", &"<gradient_function>")
56            .finish()
57    }
58}
59
60impl Clone for SciRS2Array {
61    fn clone(&self) -> Self {
62        Self {
63            data: self.data.clone(),
64            requires_grad: self.requires_grad,
65            grad: self.grad.clone(),
66            grad_fn: None, // Cannot clone trait objects
67        }
68    }
69}
70
71impl SciRS2Array {
72    /// Create a new SciRS2Array
73    pub fn new(data: ArrayD<f64>, requires_grad: bool) -> Self {
74        let grad = if requires_grad {
75            Some(ArrayD::zeros(data.raw_dim()))
76        } else {
77            None
78        };
79        Self {
80            data,
81            requires_grad,
82            grad,
83            grad_fn: None,
84        }
85    }
86
87    /// Create from ndarray
88    pub fn from_array<D: Dimension>(arr: Array<f64, D>) -> Self {
89        let data = arr.into_dyn();
90        Self::new(data, false)
91    }
92
93    /// Create with gradient tracking
94    pub fn with_grad<D: Dimension>(arr: Array<f64, D>) -> Self {
95        let data = arr.into_dyn();
96        Self::new(data, true)
97    }
98
99    /// Zero gradients
100    pub fn zero_grad(&mut self) {
101        if let Some(ref mut grad) = self.grad {
102            grad.fill(0.0);
103        }
104    }
105
106    /// Backward pass
107    pub fn backward(&mut self) -> Result<()> {
108        // Extract grad_fn to avoid borrow conflicts
109        if let Some(grad_fn) = self.grad_fn.take() {
110            grad_fn.backward(self)?;
111            self.grad_fn = Some(grad_fn);
112        }
113        Ok(())
114    }
115
116    /// Matrix multiplication using SciRS2 backend
117    pub fn matmul(&self, other: &SciRS2Array) -> Result<SciRS2Array> {
118        // Placeholder - would use SciRS2 linalg operations
119        let result_data = if self.data.ndim() == 2 && other.data.ndim() == 2 {
120            let self_2d = self
121                .data
122                .view()
123                .into_dimensionality::<scirs2_core::ndarray::Ix2>()
124                .map_err(|e| MLError::ComputationError(format!("Shape error: {}", e)))?;
125            let other_2d = other
126                .data
127                .view()
128                .into_dimensionality::<scirs2_core::ndarray::Ix2>()
129                .map_err(|e| MLError::ComputationError(format!("Shape error: {}", e)))?;
130            self_2d.dot(&other_2d).into_dyn()
131        } else {
132            return Err(MLError::InvalidConfiguration(
133                "Matrix multiplication requires 2D arrays".to_string(),
134            ));
135        };
136
137        let requires_grad = self.requires_grad || other.requires_grad;
138        let mut result = SciRS2Array::new(result_data, requires_grad);
139
140        if requires_grad {
141            result.grad_fn = Some(Box::new(MatmulGradFn {
142                left_shape: self.data.raw_dim(),
143                right_shape: other.data.raw_dim(),
144            }));
145        }
146
147        Ok(result)
148    }
149
150    /// Element-wise addition
151    pub fn add(&self, other: &SciRS2Array) -> Result<SciRS2Array> {
152        let result_data = &self.data + &other.data;
153        let requires_grad = self.requires_grad || other.requires_grad;
154        let mut result = SciRS2Array::new(result_data, requires_grad);
155
156        if requires_grad {
157            result.grad_fn = Some(Box::new(AddGradFn));
158        }
159
160        Ok(result)
161    }
162
163    /// Element-wise multiplication
164    pub fn mul(&self, other: &SciRS2Array) -> Result<SciRS2Array> {
165        let result_data = &self.data * &other.data;
166        let requires_grad = self.requires_grad || other.requires_grad;
167        let mut result = SciRS2Array::new(result_data, requires_grad);
168
169        if requires_grad {
170            result.grad_fn = Some(Box::new(MulGradFn {
171                left_data: self.data.clone(),
172                right_data: other.data.clone(),
173            }));
174        }
175
176        Ok(result)
177    }
178
179    /// Reduction sum
180    pub fn sum(&self, axis: Option<usize>) -> Result<SciRS2Array> {
181        let result_data = match axis {
182            Some(ax) => self
183                .data
184                .sum_axis(scirs2_core::ndarray::Axis(ax))
185                .into_dyn(),
186            None => {
187                let sum_val = self.data.sum();
188                ArrayD::from_elem(IxDyn(&[]), sum_val)
189            }
190        };
191
192        let mut result = SciRS2Array::new(result_data, self.requires_grad);
193
194        if self.requires_grad {
195            result.grad_fn = Some(Box::new(SumGradFn { axis }));
196        }
197
198        Ok(result)
199    }
200}
201
202impl SciRS2Tensor for SciRS2Array {
203    fn shape(&self) -> &[usize] {
204        self.data.shape()
205    }
206
207    fn view(&self) -> ArrayViewD<f64> {
208        self.data.view()
209    }
210
211    fn to_scirs2(&self) -> Result<SciRS2Array> {
212        Ok(self.clone())
213    }
214
215    fn matmul(&self, other: &dyn SciRS2Tensor) -> Result<SciRS2Array> {
216        // Convert other to SciRS2Array for computation
217        let other_array = other.to_scirs2()?;
218        self.matmul(&other_array)
219    }
220
221    fn add(&self, other: &dyn SciRS2Tensor) -> Result<SciRS2Array> {
222        let other_array = other.to_scirs2()?;
223        self.add(&other_array)
224    }
225
226    fn mul(&self, other: &dyn SciRS2Tensor) -> Result<SciRS2Array> {
227        let other_array = other.to_scirs2()?;
228        self.mul(&other_array)
229    }
230
231    fn sub(&self, other: &dyn SciRS2Tensor) -> Result<SciRS2Array> {
232        let result_data = &self.data - &other.to_scirs2()?.data;
233        let requires_grad = self.requires_grad || other.to_scirs2()?.requires_grad;
234        Ok(SciRS2Array::new(result_data, requires_grad))
235    }
236
237    fn sum(&self, axis: Option<usize>) -> Result<SciRS2Array> {
238        self.sum(axis)
239    }
240
241    fn mean(&self, axis: Option<usize>) -> Result<SciRS2Array> {
242        let result_data = match axis {
243            Some(ax) => self
244                .data
245                .mean_axis(scirs2_core::ndarray::Axis(ax))
246                .unwrap()
247                .into_dyn(),
248            None => {
249                let mean_val = self.data.mean().unwrap();
250                ArrayD::from_elem(IxDyn(&[]), mean_val)
251            }
252        };
253        Ok(SciRS2Array::new(result_data, self.requires_grad))
254    }
255
256    fn max(&self, axis: Option<usize>) -> Result<SciRS2Array> {
257        let result_data = match axis {
258            Some(ax) => self
259                .data
260                .map_axis(scirs2_core::ndarray::Axis(ax), |view| {
261                    *view
262                        .iter()
263                        .max_by(|a, b| a.partial_cmp(b).unwrap())
264                        .unwrap()
265                })
266                .into_dyn(),
267            None => {
268                let max_val = *self
269                    .data
270                    .iter()
271                    .max_by(|a, b| a.partial_cmp(b).unwrap())
272                    .unwrap();
273                ArrayD::from_elem(IxDyn(&[]), max_val)
274            }
275        };
276        Ok(SciRS2Array::new(result_data, self.requires_grad))
277    }
278
279    fn min(&self, axis: Option<usize>) -> Result<SciRS2Array> {
280        let result_data = match axis {
281            Some(ax) => self
282                .data
283                .map_axis(scirs2_core::ndarray::Axis(ax), |view| {
284                    *view
285                        .iter()
286                        .min_by(|a, b| a.partial_cmp(b).unwrap())
287                        .unwrap()
288                })
289                .into_dyn(),
290            None => {
291                let min_val = *self
292                    .data
293                    .iter()
294                    .min_by(|a, b| a.partial_cmp(b).unwrap())
295                    .unwrap();
296                ArrayD::from_elem(IxDyn(&[]), min_val)
297            }
298        };
299        Ok(SciRS2Array::new(result_data, self.requires_grad))
300    }
301}
302
303/// Trait for gradient functions
304pub trait GradFunction: Send + Sync {
305    fn backward(&self, output: &mut SciRS2Array) -> Result<()>;
306}
307
308/// Gradient function for matrix multiplication
309#[derive(Debug)]
310struct MatmulGradFn {
311    left_shape: IxDyn,
312    right_shape: IxDyn,
313}
314
315impl GradFunction for MatmulGradFn {
316    fn backward(&self, _output: &mut SciRS2Array) -> Result<()> {
317        // Placeholder - would compute gradients for matmul inputs
318        Ok(())
319    }
320}
321
322/// Gradient function for addition
323#[derive(Debug)]
324struct AddGradFn;
325
326impl GradFunction for AddGradFn {
327    fn backward(&self, _output: &mut SciRS2Array) -> Result<()> {
328        // Gradient flows through unchanged for addition
329        Ok(())
330    }
331}
332
333/// Gradient function for multiplication
334#[derive(Debug)]
335struct MulGradFn {
336    left_data: ArrayD<f64>,
337    right_data: ArrayD<f64>,
338}
339
340impl GradFunction for MulGradFn {
341    fn backward(&self, _output: &mut SciRS2Array) -> Result<()> {
342        // Placeholder - would compute gradients for element-wise multiplication
343        Ok(())
344    }
345}
346
347/// Gradient function for sum reduction
348#[derive(Debug)]
349struct SumGradFn {
350    axis: Option<usize>,
351}
352
353impl GradFunction for SumGradFn {
354    fn backward(&self, _output: &mut SciRS2Array) -> Result<()> {
355        // Placeholder - would broadcast gradients for sum reduction
356        Ok(())
357    }
358}
359
360/// SciRS2 optimization interface
361pub struct SciRS2Optimizer {
362    /// Optimizer type
363    pub optimizer_type: String,
364    /// Configuration parameters
365    pub config: HashMap<String, f64>,
366    /// Parameter state (for stateful optimizers like Adam)
367    pub state: HashMap<String, ArrayD<f64>>,
368}
369
370impl SciRS2Optimizer {
371    /// Create a new SciRS2 optimizer
372    pub fn new(optimizer_type: impl Into<String>) -> Self {
373        Self {
374            optimizer_type: optimizer_type.into(),
375            config: HashMap::new(),
376            state: HashMap::new(),
377        }
378    }
379
380    /// Set optimizer configuration
381    pub fn with_config(mut self, key: impl Into<String>, value: f64) -> Self {
382        self.config.insert(key.into(), value);
383        self
384    }
385
386    /// Update parameters using computed gradients
387    pub fn step(&mut self, params: &mut HashMap<String, SciRS2Array>) -> Result<()> {
388        match self.optimizer_type.as_str() {
389            "adam" => self.adam_step(params),
390            "sgd" => self.sgd_step(params),
391            "lbfgs" => self.lbfgs_step(params),
392            _ => Err(MLError::InvalidConfiguration(format!(
393                "Unknown optimizer type: {}",
394                self.optimizer_type
395            ))),
396        }
397    }
398
399    /// Adam optimizer step
400    fn adam_step(&mut self, params: &mut HashMap<String, SciRS2Array>) -> Result<()> {
401        let learning_rate = self.config.get("learning_rate").unwrap_or(&0.001);
402        let beta1 = self.config.get("beta1").unwrap_or(&0.9);
403        let beta2 = self.config.get("beta2").unwrap_or(&0.999);
404        let epsilon = self.config.get("epsilon").unwrap_or(&1e-8);
405
406        for (name, param) in params.iter_mut() {
407            if let Some(ref grad) = param.grad {
408                // Initialize momentum and velocity if not present
409                let m_key = format!("{}_m", name);
410                let v_key = format!("{}_v", name);
411
412                if !self.state.contains_key(&m_key) {
413                    self.state
414                        .insert(m_key.clone(), ArrayD::zeros(grad.raw_dim()));
415                    self.state
416                        .insert(v_key.clone(), ArrayD::zeros(grad.raw_dim()));
417                }
418
419                // Update first moment estimate
420                {
421                    let m = self.state.get_mut(&m_key).unwrap();
422                    *m = *beta1 * &*m + (1.0 - *beta1) * grad;
423                }
424
425                // Update second moment estimate
426                {
427                    let v = self.state.get_mut(&v_key).unwrap();
428                    *v = *beta2 * &*v + (1.0 - *beta2) * grad * grad;
429                }
430
431                // Get references for bias correction
432                let m_hat = self.state.get(&m_key).unwrap().clone();
433                let v_hat = self.state.get(&v_key).unwrap().clone();
434
435                // Update parameters
436                param.data =
437                    &param.data - *learning_rate * &m_hat / (v_hat.mapv(|x| x.sqrt()) + *epsilon);
438            }
439        }
440
441        Ok(())
442    }
443
444    /// SGD optimizer step
445    fn sgd_step(&mut self, params: &mut HashMap<String, SciRS2Array>) -> Result<()> {
446        let learning_rate = self.config.get("learning_rate").unwrap_or(&0.01);
447        let momentum = self.config.get("momentum").unwrap_or(&0.0);
448
449        for (name, param) in params.iter_mut() {
450            if let Some(ref grad) = param.grad {
451                if *momentum > 0.0 {
452                    let v_key = format!("{}_v", name);
453                    if !self.state.contains_key(&v_key) {
454                        self.state
455                            .insert(v_key.clone(), ArrayD::zeros(grad.raw_dim()));
456                    }
457
458                    let v = self.state.get_mut(&v_key).unwrap();
459                    *v = *momentum * &*v + *learning_rate * grad;
460                    param.data = &param.data - &*v;
461                } else {
462                    param.data = &param.data - *learning_rate * grad;
463                }
464            }
465        }
466
467        Ok(())
468    }
469
470    /// L-BFGS optimizer step (placeholder)
471    fn lbfgs_step(&mut self, _params: &mut HashMap<String, SciRS2Array>) -> Result<()> {
472        // Placeholder - would implement L-BFGS using SciRS2
473        Ok(())
474    }
475}
476
477/// SciRS2 distributed training support
478pub struct SciRS2DistributedTrainer {
479    /// World size (number of processes)
480    pub world_size: usize,
481    /// Local rank
482    pub rank: usize,
483    /// Backend for communication
484    pub backend: String,
485}
486
487impl SciRS2DistributedTrainer {
488    /// Create a new distributed trainer
489    pub fn new(world_size: usize, rank: usize) -> Self {
490        Self {
491            world_size,
492            rank,
493            backend: "nccl".to_string(),
494        }
495    }
496
497    /// All-reduce operation for gradient synchronization
498    pub fn all_reduce(&self, tensor: &mut SciRS2Array) -> Result<()> {
499        // Placeholder - would use SciRS2 distributed operations
500        Ok(())
501    }
502
503    /// All-reduce scalar operation for metrics synchronization
504    pub fn all_reduce_scalar(&self, value: f64) -> Result<f64> {
505        // Placeholder - would use SciRS2 distributed operations
506        // For now, just return the value unchanged (single process behavior)
507        Ok(value)
508    }
509
510    /// Broadcast operation
511    pub fn broadcast(&self, tensor: &mut SciRS2Array, root: usize) -> Result<()> {
512        // Placeholder - would use SciRS2 distributed operations
513        Ok(())
514    }
515
516    /// All-gather operation
517    pub fn all_gather(&self, tensor: &SciRS2Array) -> Result<Vec<SciRS2Array>> {
518        // Placeholder - would use SciRS2 distributed operations
519        Ok(vec![tensor.clone(); self.world_size])
520    }
521
522    /// Wrap a model for distributed training
523    pub fn wrap_model<T>(&self, model: T) -> Result<T> {
524        // Placeholder - would wrap the model with distributed training capabilities
525        // For now, just return the model unchanged
526        Ok(model)
527    }
528}
529
530/// SciRS2 model serialization interface
531pub struct SciRS2Serializer;
532
533impl SciRS2Serializer {
534    /// Serialize model parameters to SciRS2 format
535    pub fn save_model(params: &HashMap<String, SciRS2Array>, path: &str) -> Result<()> {
536        // Placeholder - would use SciRS2 serialization
537        Ok(())
538    }
539
540    /// Load model parameters from SciRS2 format
541    pub fn load_model(path: &str) -> Result<HashMap<String, SciRS2Array>> {
542        // Placeholder - would use SciRS2 deserialization
543        Ok(HashMap::new())
544    }
545
546    /// Save checkpoint with optimizer state
547    pub fn save_checkpoint(
548        params: &HashMap<String, SciRS2Array>,
549        optimizer: &SciRS2Optimizer,
550        epoch: usize,
551        path: &str,
552    ) -> Result<()> {
553        // Placeholder - would use SciRS2 checkpoint format
554        Ok(())
555    }
556
557    /// Load checkpoint with optimizer state
558    pub fn load_checkpoint(
559        path: &str,
560    ) -> Result<(HashMap<String, SciRS2Array>, SciRS2Optimizer, usize)> {
561        // Placeholder - would use SciRS2 checkpoint format
562        Ok((HashMap::new(), SciRS2Optimizer::new("adam"), 0))
563    }
564}
565
566/// SciRS2 Dataset wrapper for quantum ML
567pub struct SciRS2Dataset {
568    /// Training data
569    pub data: ArrayD<f64>,
570    /// Labels
571    pub labels: ArrayD<f64>,
572    /// Dataset size
573    pub size: usize,
574}
575
576impl SciRS2Dataset {
577    /// Create a new dataset
578    pub fn new(data: ArrayD<f64>, labels: ArrayD<f64>) -> Result<Self> {
579        let size = data.shape()[0];
580        if labels.shape()[0] != size {
581            return Err(MLError::InvalidConfiguration(
582                "Data and labels must have same number of samples".to_string(),
583            ));
584        }
585
586        Ok(Self { data, labels, size })
587    }
588}
589
590/// SciRS2 DataLoader for batch processing
591pub struct SciRS2DataLoader {
592    /// Dataset reference
593    pub dataset: SciRS2Dataset,
594    /// Batch size
595    pub batch_size: usize,
596    /// Current index
597    pub current_index: usize,
598}
599
600impl SciRS2DataLoader {
601    /// Create a new data loader
602    pub fn new(dataset: SciRS2Dataset, batch_size: usize) -> Self {
603        Self {
604            dataset,
605            batch_size,
606            current_index: 0,
607        }
608    }
609
610    /// Iterator-like enumeration support
611    pub fn enumerate(&mut self) -> DataLoaderIterator {
612        DataLoaderIterator {
613            loader: self,
614            batch_idx: 0,
615        }
616    }
617}
618
619/// Iterator for DataLoader
620pub struct DataLoaderIterator<'a> {
621    loader: &'a mut SciRS2DataLoader,
622    batch_idx: usize,
623}
624
625impl<'a> Iterator for DataLoaderIterator<'a> {
626    type Item = (usize, (SciRS2Array, SciRS2Array));
627
628    fn next(&mut self) -> Option<Self::Item> {
629        if self.loader.current_index >= self.loader.dataset.size {
630            return None;
631        }
632
633        let start = self.loader.current_index;
634        let end = (start + self.loader.batch_size).min(self.loader.dataset.size);
635
636        // Extract batch data and labels
637        let batch_data = self
638            .loader
639            .dataset
640            .data
641            .slice(scirs2_core::ndarray::s![start..end, ..])
642            .to_owned();
643        let batch_labels = self
644            .loader
645            .dataset
646            .labels
647            .slice(scirs2_core::ndarray::s![start..end, ..])
648            .to_owned();
649
650        let data_array = SciRS2Array::from_array(batch_data);
651        let label_array = SciRS2Array::from_array(batch_labels);
652
653        self.loader.current_index = end;
654        let batch_idx = self.batch_idx;
655        self.batch_idx += 1;
656
657        Some((batch_idx, (data_array, label_array)))
658    }
659}
660
661/// SciRS2 Device enumeration
662#[derive(Debug, Clone, Copy)]
663pub enum SciRS2Device {
664    CPU,
665    GPU,
666    Quantum,
667}
668
669/// Additional SciRS2Array methods for compatibility
670impl SciRS2Array {
671    /// Create array with specified device
672    pub fn randn(shape: Vec<usize>, device: SciRS2Device) -> Result<Self> {
673        use scirs2_core::random::prelude::*;
674        let total_size = shape.iter().product();
675        let mut rng = thread_rng();
676        let data: Vec<f64> = (0..total_size).map(|_| rng.gen_range(-1.0..1.0)).collect();
677        let array = ArrayD::from_shape_vec(IxDyn(&shape), data)
678            .map_err(|e| MLError::ComputationError(format!("Shape error: {}", e)))?;
679        Ok(Self::new(array, false))
680    }
681
682    /// Create ones_like array
683    pub fn ones_like(&self) -> Result<Self> {
684        let ones = ArrayD::ones(self.data.raw_dim());
685        Ok(Self::new(ones, false))
686    }
687
688    /// Create random integers
689    pub fn randint(low: i32, high: i32, shape: Vec<usize>, device: SciRS2Device) -> Result<Self> {
690        use scirs2_core::random::prelude::*;
691        let total_size = shape.iter().product();
692        let mut rng = thread_rng();
693        let data: Vec<f64> = (0..total_size)
694            .map(|_| rng.gen_range(low..high) as f64)
695            .collect();
696        let array = ArrayD::from_shape_vec(IxDyn(&shape), data)
697            .map_err(|e| MLError::ComputationError(format!("Shape error: {}", e)))?;
698        Ok(Self::new(array, false))
699    }
700
701    /// Create quantum observable
702    pub fn quantum_observable(name: &str, num_qubits: usize) -> Result<Self> {
703        match name {
704            "pauli_z_all" => {
705                let size = 1 << num_qubits;
706                let mut data = ArrayD::zeros(IxDyn(&[size, size]));
707                for i in 0..size {
708                    let parity = i.count_ones() % 2;
709                    data[[i, i]] = if parity == 0 { 1.0 } else { -1.0 };
710                }
711                Ok(Self::new(data, false))
712            }
713            _ => Err(MLError::InvalidConfiguration(format!(
714                "Unknown observable: {}",
715                name
716            ))),
717        }
718    }
719}
720
721/// Integration helper functions
722pub mod integration {
723    use super::*;
724
725    /// Convert ndarray to SciRS2Array
726    pub fn from_ndarray<D: Dimension>(arr: Array<f64, D>) -> SciRS2Array {
727        SciRS2Array::from_array(arr)
728    }
729
730    /// Convert SciRS2Array to ndarray
731    pub fn to_ndarray<D: Dimension>(arr: &SciRS2Array) -> Result<Array<f64, D>> {
732        arr.data
733            .view()
734            .into_dimensionality::<D>()
735            .map(|v| v.to_owned())
736            .map_err(|e| MLError::ComputationError(format!("Dimension error: {}", e)))
737    }
738
739    /// Create SciRS2 optimizer from configuration
740    pub fn create_optimizer(optimizer_type: &str, config: HashMap<String, f64>) -> SciRS2Optimizer {
741        let mut optimizer = SciRS2Optimizer::new(optimizer_type);
742        for (key, value) in config {
743            optimizer = optimizer.with_config(key, value);
744        }
745        optimizer
746    }
747
748    /// Setup distributed training
749    pub fn setup_distributed(world_size: usize, rank: usize) -> SciRS2DistributedTrainer {
750        SciRS2DistributedTrainer::new(world_size, rank)
751    }
752}
753
754#[cfg(test)]
755mod tests {
756    use super::*;
757    use scirs2_core::ndarray::Array2;
758
759    #[test]
760    fn test_scirs2_array_creation() {
761        let arr = Array2::from_shape_vec((2, 2), vec![1.0, 2.0, 3.0, 4.0]).unwrap();
762        let scirs2_arr = SciRS2Array::from_array(arr);
763
764        assert_eq!(scirs2_arr.data.shape(), &[2, 2]);
765        assert!(!scirs2_arr.requires_grad);
766    }
767
768    #[test]
769    fn test_scirs2_array_with_grad() {
770        let arr = Array2::from_shape_vec((2, 2), vec![1.0, 2.0, 3.0, 4.0]).unwrap();
771        let scirs2_arr = SciRS2Array::with_grad(arr);
772
773        assert!(scirs2_arr.requires_grad);
774        assert!(scirs2_arr.grad.is_some());
775    }
776
777    #[test]
778    fn test_scirs2_matmul() {
779        let arr1 = Array2::from_shape_vec((2, 3), vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).unwrap();
780        let arr2 = Array2::from_shape_vec((3, 2), vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).unwrap();
781
782        let scirs2_arr1 = SciRS2Array::from_array(arr1);
783        let scirs2_arr2 = SciRS2Array::from_array(arr2);
784
785        let result = scirs2_arr1.matmul(&scirs2_arr2).unwrap();
786        assert_eq!(result.data.shape(), &[2, 2]);
787    }
788
789    #[test]
790    fn test_scirs2_optimizer() {
791        let mut optimizer = SciRS2Optimizer::new("adam")
792            .with_config("learning_rate", 0.001)
793            .with_config("beta1", 0.9);
794
795        let mut params = HashMap::new();
796        let param_arr = SciRS2Array::with_grad(Array1::from_vec(vec![1.0, 2.0, 3.0]));
797        params.insert("weight".to_string(), param_arr);
798
799        let result = optimizer.step(&mut params);
800        assert!(result.is_ok());
801    }
802
803    #[test]
804    fn test_integration_helpers() {
805        let arr = Array2::from_shape_vec((2, 2), vec![1.0, 2.0, 3.0, 4.0]).unwrap();
806        let scirs2_arr = integration::from_ndarray(arr.clone());
807
808        let back_to_ndarray: Array2<f64> = integration::to_ndarray(&scirs2_arr).unwrap();
809        assert_eq!(arr, back_to_ndarray);
810    }
811}
quantrs2_ml/scirs2_integration.rs

quantrs2_ml/
scirs2_integration.rs
