scirs2-transform 0.4.1

//! GPU-accelerated transformations
//!
//! This module provides GPU-accelerated implementations of dimensionality reduction
//! and matrix operations. Currently provides basic stubs with CPU fallback.

use crate::error::{Result, TransformError};
use scirs2_core::gpu::{GpuBackend, GpuContext};
use scirs2_core::ndarray::{Array1, Array2, ArrayView2};
use scirs2_core::validation::{check_not_empty, check_positive, checkarray_finite};

/// GPU-accelerated Principal Component Analysis
#[cfg(feature = "gpu")]
pub struct GpuPCA {
    /// Number of components to compute
    pub n_components: usize,
    /// Whether to center the data
    pub center: bool,
    /// Principal components (loading vectors)
    pub components: Option<Array2<f64>>,
    /// Explained variance for each component
    pub explained_variance: Option<Array1<f64>>,
    /// Mean values for centering
    pub mean: Option<Array1<f64>>,
    /// GPU context for GPU operations
    gpu_context: Option<GpuContext>,
}

#[cfg(feature = "gpu")]
impl GpuPCA {
    /// Create a new GPU PCA instance
    ///
    /// # Arguments
    ///
    /// * `n_components` - Number of principal components to compute
    ///
    /// # Returns
    ///
    /// Returns a new GpuPCA instance with GPU context initialized
    ///
    /// # Errors
    ///
    /// Returns an error if GPU initialization fails or if n_components is 0
    ///
    /// # Examples
    ///
    /// ```
    /// # use scirs2_transform::gpu::GpuPCA;
    /// # fn main() -> Result<(), Box<dyn std::error::Error>> {
    /// let pca = GpuPCA::new(5)?;
    /// # Ok(())
    /// # }
    /// ```
    pub fn new(n_components: usize) -> Result<Self> {
        check_positive(n_components, "n_components")?;

        let gpu_context = GpuContext::new(GpuBackend::preferred()).map_err(|e| {
            TransformError::ComputationError(format!("Failed to initialize GPU: {}", e))
        })?;

        Ok(GpuPCA {
            n_components,
            center: true,
            components: None,
            explained_variance: None,
            mean: None,
            gpu_context: Some(gpu_context),
        })
    }

    /// Fit the PCA model on GPU
    ///
    /// Currently this is a placeholder implementation that will return an error
    /// indicating that full GPU PCA support is not yet implemented.
    ///
    /// # Arguments
    ///
    /// * `x` - Input data matrix with shape (n_samples, n_features)
    ///
    /// # Errors
    ///
    /// Returns an error indicating that GPU PCA is not fully implemented yet
    ///
    /// # Examples
    ///
    /// ```should_panic
    /// # use scirs2_transform::gpu::GpuPCA;
    /// # use scirs2_core::ndarray::Array2;
    /// # fn main() -> Result<(), Box<dyn std::error::Error>> {
    /// let mut pca = GpuPCA::new(2)?;
    /// let data = Array2::zeros((100, 5));
    /// // This will return an error indicating GPU PCA is not implemented
    /// pca.fit(&data.view())?;
    /// # Ok(())
    /// # }
    /// ```
    pub fn fit(&mut self, x: &ArrayView2<f64>) -> Result<()> {
        check_not_empty(x, "x")?;
        checkarray_finite(x, "x")?;

        // Validate input
        let (n_samples, n_features) = x.dim();
        if self.n_components > n_features.min(n_samples) {
            return Err(TransformError::InvalidInput(
                "n_components cannot be larger than min(n_samples, n_features)".to_string(),
            ));
        }

        // For now, return an error indicating GPU PCA is not fully implemented
        Err(TransformError::NotImplemented(
            "GPU-accelerated PCA is not yet fully implemented. Use CPU PCA instead.".to_string(),
        ))
    }

    /// Transform data using the fitted PCA model on GPU
    ///
    /// Currently this is a placeholder implementation that will return an error
    /// indicating that full GPU PCA support is not yet implemented.
    ///
    /// # Arguments
    ///
    /// * `x` - Input data matrix with shape (n_samples, n_features)
    ///
    /// # Returns
    ///
    /// Transformed data matrix with shape (n_samples, n_components)
    ///
    /// # Errors
    ///
    /// Returns an error indicating that GPU PCA is not fully implemented yet
    pub fn transform(&self, x: &ArrayView2<f64>) -> Result<Array2<f64>> {
        check_not_empty(x, "x")?;
        checkarray_finite(x, "x")?;

        Err(TransformError::NotImplemented(
            "GPU-accelerated PCA transform is not yet fully implemented. Use CPU PCA instead."
                .to_string(),
        ))
    }

    /// Fit the PCA model and transform data in one step
    ///
    /// Currently this is a placeholder implementation that will return an error
    /// indicating that full GPU PCA support is not yet implemented.
    ///
    /// # Arguments
    ///
    /// * `x` - Input data matrix with shape (n_samples, n_features)
    ///
    /// # Returns
    ///
    /// Transformed data matrix with shape (n_samples, n_components)
    ///
    /// # Errors
    ///
    /// Returns an error indicating that GPU PCA is not fully implemented yet
    pub fn fit_transform(&mut self, x: &ArrayView2<f64>) -> Result<Array2<f64>> {
        self.fit(x)?;
        self.transform(x)
    }

    /// Get the explained variance ratio for each principal component
    ///
    /// # Returns
    ///
    /// Array of explained variance ratios with length n_components
    ///
    /// # Errors
    ///
    /// Returns an error if the model has not been fitted
    pub fn explained_variance_ratio(&self) -> Result<Array1<f64>> {
        let explained_var = self
            .explained_variance
            .as_ref()
            .ok_or_else(|| TransformError::NotFitted("PCA model not fitted".to_string()))?;

        let total_var = explained_var.sum();
        Ok(explained_var / total_var)
    }
}

/// GPU-accelerated matrix operations for transformations
#[cfg(feature = "gpu")]
pub struct GpuMatrixOps {
    #[allow(dead_code)]
    gpu_context: GpuContext,
}

#[cfg(feature = "gpu")]
impl GpuMatrixOps {
    /// Create new GPU matrix operations instance
    pub fn new() -> Result<Self> {
        let gpu_context = GpuContext::new(GpuBackend::preferred()).map_err(|e| {
            TransformError::ComputationError(format!("Failed to initialize GPU: {}", e))
        })?;

        Ok(GpuMatrixOps { gpu_context })
    }

    /// GPU-accelerated matrix multiplication (placeholder)
    pub fn matmul(self_a: &ArrayView2<f64>, b: &ArrayView2<f64>) -> Result<Array2<f64>> {
        Err(TransformError::NotImplemented(
            "GPU matrix multiplication is not yet implemented. Use CPU operations instead."
                .to_string(),
        ))
    }

    /// GPU-accelerated SVD decomposition (placeholder)
    pub fn svd(selfa: &ArrayView2<f64>) -> Result<(Array2<f64>, Array1<f64>, Array2<f64>)> {
        Err(TransformError::NotImplemented(
            "GPU SVD is not yet implemented. Use CPU operations instead.".to_string(),
        ))
    }

    /// GPU-accelerated eigendecomposition (placeholder)
    pub fn eigh(selfa: &ArrayView2<f64>) -> Result<(Array1<f64>, Array2<f64>)> {
        Err(TransformError::NotImplemented(
            "GPU eigendecomposition is not yet implemented. Use CPU operations instead."
                .to_string(),
        ))
    }
}

/// GPU-accelerated t-SNE implementation
#[cfg(feature = "gpu")]
pub struct GpuTSNE {
    /// Number of dimensions for the embedding
    pub n_components: usize,
    /// Perplexity parameter
    pub perplexity: f64,
    /// Learning rate
    pub learning_rate: f64,
    /// Maximum number of iterations
    pub max_iter: usize,
    /// GPU context
    #[allow(dead_code)]
    gpu_context: GpuContext,
}

#[cfg(feature = "gpu")]
impl GpuTSNE {
    /// Create new GPU t-SNE instance
    pub fn new(n_components: usize) -> Result<Self> {
        check_positive(n_components, "n_components")?;

        let gpu_context = GpuContext::new(GpuBackend::preferred()).map_err(|e| {
            TransformError::ComputationError(format!("Failed to initialize GPU: {}", e))
        })?;

        Ok(GpuTSNE {
            n_components,
            perplexity: 30.0,
            learning_rate: 200.0,
            max_iter: 1000,
            gpu_context,
        })
    }

    /// Set perplexity parameter
    pub fn with_perplexity(mut self, perplexity: f64) -> Self {
        self.perplexity = perplexity;
        self
    }

    /// Set learning rate
    pub fn with_learning_rate(mut self, learning_rate: f64) -> Self {
        self.learning_rate = learning_rate;
        self
    }

    /// Set maximum iterations
    pub fn with_max_iter(mut self, max_iter: usize) -> Self {
        self.max_iter = max_iter;
        self
    }

    /// Fit and transform data using GPU-accelerated t-SNE (placeholder)
    pub fn fit_transform(selfx: &ArrayView2<f64>) -> Result<Array2<f64>> {
        Err(TransformError::NotImplemented(
            "GPU t-SNE is not yet implemented. Use CPU t-SNE instead.".to_string(),
        ))
    }
}

// Stub implementations when GPU feature is not enabled
#[cfg(not(feature = "gpu"))]
pub struct GpuPCA;

#[cfg(not(feature = "gpu"))]
pub struct GpuMatrixOps;

#[cfg(not(feature = "gpu"))]
pub struct GpuTSNE;

#[cfg(not(feature = "gpu"))]
impl GpuPCA {
    pub fn new(_ncomponents: usize) -> Result<Self> {
        Err(TransformError::FeatureNotEnabled(
            "GPU acceleration requires the 'gpu' feature to be enabled".to_string(),
        ))
    }
}

#[cfg(not(feature = "gpu"))]
impl GpuMatrixOps {
    pub fn new() -> Result<Self> {
        Err(TransformError::FeatureNotEnabled(
            "GPU acceleration requires the 'gpu' feature to be enabled".to_string(),
        ))
    }
}

#[cfg(not(feature = "gpu"))]
impl GpuTSNE {
    pub fn new(_ncomponents: usize) -> Result<Self> {
        Err(TransformError::FeatureNotEnabled(
            "GPU acceleration requires the 'gpu' feature to be enabled".to_string(),
        ))
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    #[cfg(feature = "gpu")]
    fn test_gpu_pca_creation() {
        let pca = GpuPCA::new(3);
        assert!(pca.is_ok());
        let pca = pca.expect("Operation failed");
        assert_eq!(pca.n_components, 3);
        assert!(pca.center);
        assert!(pca.components.is_none());
        assert!(pca.explained_variance.is_none());
        assert!(pca.mean.is_none());
    }

    #[test]
    #[cfg(feature = "gpu")]
    fn test_gpu_pca_invalid_components() {
        let result = GpuPCA::new(0);
        assert!(result.is_err());
    }

    #[test]
    #[cfg(feature = "gpu")]
    fn test_gpu_matrix_ops_creation() {
        let ops = GpuMatrixOps::new();
        assert!(ops.is_ok());
    }

    #[test]
    #[cfg(feature = "gpu")]
    fn test_gpu_tsne_creation() {
        let tsne = GpuTSNE::new(2);
        assert!(tsne.is_ok());
        let tsne = tsne.expect("Operation failed");
        assert_eq!(tsne.n_components, 2);
        assert_eq!(tsne.perplexity, 30.0);
        assert_eq!(tsne.learning_rate, 200.0);
        assert_eq!(tsne.max_iter, 1000);
    }

    #[test]
    #[cfg(feature = "gpu")]
    fn test_gpu_tsne_with_params() {
        let tsne = GpuTSNE::new(3)
            .expect("Operation failed")
            .with_perplexity(50.0)
            .with_learning_rate(100.0)
            .with_max_iter(500);

        assert_eq!(tsne.n_components, 3);
        assert_eq!(tsne.perplexity, 50.0);
        assert_eq!(tsne.learning_rate, 100.0);
        assert_eq!(tsne.max_iter, 500);
    }

    #[test]
    #[cfg(not(feature = "gpu"))]
    fn test_gpu_features_disabled() {
        assert!(GpuPCA::new(2).is_err());
        assert!(GpuMatrixOps::new().is_err());
        assert!(GpuTSNE::new(2).is_err());
    }
}