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//! Scalar operations for CSC matrices
use super::super::CscData;
use crate::error::Result;
use crate::ops::ScalarOps;
use crate::runtime::Runtime;
impl<R: Runtime> CscData<R> {
/// Scalar multiplication: C = A * scalar
///
/// Multiplies all non-zero values by a scalar constant.
/// Preserves the sparsity pattern.
///
/// # Arguments
///
/// * `scalar` - The scalar value to multiply with
///
/// # Performance
///
/// O(nnz) - simply scales the values tensor
///
/// # Example
///
/// ```
/// # use numr::prelude::*;
/// # #[cfg(feature = "sparse")]
/// # {
/// # use numr::sparse::SparseTensor;
/// # let device = CpuDevice::new();
/// # let sp = SparseTensor::<CpuRuntime>::from_coo_slices(&[0], &[0], &[1.0f32], [1, 1], &device)?.to_csc()?;
/// # if let numr::sparse::SparseTensor::Csc(csc) = sp {
/// let result = csc.scalar_mul(2.0)?; // Multiply all values by 2
/// # }
/// # }
/// # Ok::<(), numr::error::Error>(())
/// ```
pub fn scalar_mul(&self, scalar: f64) -> Result<Self>
where
R::Client: ScalarOps<R>,
{
let device = self.values.device();
let client = R::default_client(device);
let scaled_values = client.mul_scalar(&self.values, scalar)?;
Ok(Self {
col_ptrs: self.col_ptrs.clone(),
row_indices: self.row_indices.clone(),
values: scaled_values,
shape: self.shape,
})
}
/// Add scalar to non-zero elements only (sparsity-preserving)
///
/// # ⚠️ Important: This is NOT Standard Scalar Addition!
///
/// Standard mathematical scalar addition (`A + s`) adds `s` to **ALL** elements
/// including implicit zeros, creating a dense matrix. This operation only adds
/// to existing non-zero values, preserving the sparse structure.
///
/// Use this when you want to shift all non-zero values by a constant without
/// densifying the matrix. For true scalar addition, convert to dense first.
///
/// # Mathematical Behavior
///
/// - **Non-zero elements**: `A[i,j] + s`
/// - **Zero elements**: remain `0` (NOT `s`!)
///
/// # Performance
///
/// O(nnz) - simply adds to the values tensor
///
/// # Example
///
/// ```
/// # use numr::prelude::*;
/// # #[cfg(feature = "sparse")]
/// # {
/// # use numr::sparse::SparseTensor;
/// # let device = CpuDevice::new();
/// // Sparse matrix: After scalar_add(10):
/// // [1, 0, 2] [11, 0, 12]
/// // [0, 3, 0] [ 0, 13, 0]
/// //
/// // Note: Zeros stay 0, not 10!
/// # let sp = SparseTensor::<CpuRuntime>::from_coo_slices(&[0, 0, 1], &[0, 2, 1], &[1.0f32, 2.0, 3.0], [2, 3], &device)?.to_csc()?;
/// # if let numr::sparse::SparseTensor::Csc(csc) = sp {
/// let result = csc.scalar_add(10.0)?;
/// # }
/// # }
/// # Ok::<(), numr::error::Error>(())
/// ```
///
/// # See Also
///
/// - [`add_to_nonzeros()`](Self::add_to_nonzeros) - Clearer alias for this method
pub fn scalar_add(&self, scalar: f64) -> Result<Self>
where
R::Client: ScalarOps<R>,
{
// Handle empty tensor case (no values to add to)
if self.values.numel() == 0 {
return Ok(Self {
col_ptrs: self.col_ptrs.clone(),
row_indices: self.row_indices.clone(),
values: self.values.clone(),
shape: self.shape,
});
}
let device = self.values.device();
let client = R::default_client(device);
let shifted_values = client.add_scalar(&self.values, scalar)?;
Ok(Self {
col_ptrs: self.col_ptrs.clone(),
row_indices: self.row_indices.clone(),
values: shifted_values,
shape: self.shape,
})
}
/// Alias for [`scalar_add()`](Self::scalar_add) with clearer naming
///
/// Adds a scalar value to all non-zero elements, preserving sparsity.
/// This name makes it explicit that only non-zero elements are modified.
///
/// # Example
///
/// ```
/// # use numr::prelude::*;
/// # #[cfg(feature = "sparse")]
/// # {
/// # use numr::sparse::SparseTensor;
/// # let device = CpuDevice::new();
/// // Clearer intent than scalar_add
/// # let sp = SparseTensor::<CpuRuntime>::from_coo_slices(&[0], &[0], &[1.0f32], [1, 1], &device)?.to_csc()?;
/// # if let numr::sparse::SparseTensor::Csc(csc) = sp {
/// let result = csc.add_to_nonzeros(5.0)?;
/// # }
/// # }
/// # Ok::<(), numr::error::Error>(())
/// ```
#[inline]
pub fn add_to_nonzeros(&self, scalar: f64) -> Result<Self>
where
R::Client: ScalarOps<R>,
{
self.scalar_add(scalar)
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::runtime::cpu::CpuRuntime;
use crate::sparse::SparseStorage;
#[test]
fn test_csc_scalar_mul() {
let device = <CpuRuntime as Runtime>::Device::default();
let csc = CscData::<CpuRuntime>::from_slices(
&[0i64, 2, 3, 5],
&[0i64, 2, 2, 0, 1],
&[1.0f32, 4.0, 5.0, 2.0, 3.0],
[3, 3],
&device,
)
.unwrap();
let result = csc.scalar_mul(2.0).unwrap();
assert_eq!(result.nnz(), 5);
let vals: Vec<f32> = result.values().to_vec();
assert_eq!(vals, vec![2.0, 8.0, 10.0, 4.0, 6.0]);
}
#[test]
fn test_csc_scalar_add() {
let device = <CpuRuntime as Runtime>::Device::default();
let csc = CscData::<CpuRuntime>::from_slices(
&[0i64, 2, 3, 5],
&[0i64, 2, 2, 0, 1],
&[1.0f32, 4.0, 5.0, 2.0, 3.0],
[3, 3],
&device,
)
.unwrap();
let result = csc.scalar_add(1.0).unwrap();
assert_eq!(result.nnz(), 5);
let vals: Vec<f32> = result.values().to_vec();
assert_eq!(vals, vec![2.0, 5.0, 6.0, 3.0, 4.0]);
}
#[test]
fn test_csc_scalar_ops_preserves_structure() {
let device = <CpuRuntime as Runtime>::Device::default();
let csc = CscData::<CpuRuntime>::from_slices(
&[0i64, 2, 3, 5],
&[0i64, 2, 2, 0, 1],
&[1.0f32, 4.0, 5.0, 2.0, 3.0],
[3, 3],
&device,
)
.unwrap();
let result = csc.scalar_mul(3.0).unwrap();
// Verify structure is preserved
let col_ptrs: Vec<i64> = result.col_ptrs().to_vec();
let row_indices: Vec<i64> = result.row_indices().to_vec();
assert_eq!(col_ptrs, vec![0, 2, 3, 5]);
assert_eq!(row_indices, vec![0, 2, 2, 0, 1]);
}
}