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//! Element-wise division for COO matrices
use super::super::CooData;
use crate::dtype::{DType, Element};
use crate::error::{Error, Result};
use crate::runtime::Runtime;
use crate::sparse::SparseStorage;
use crate::tensor::Tensor;
impl<R: Runtime<DType = DType>> CooData<R> {
/// Element-wise division: C = A ./ B
///
/// Computes the element-wise quotient of two sparse matrices with the same shape.
/// Only positions where BOTH matrices have non-zero values will be non-zero
/// in the result.
///
/// # Arguments
///
/// * `other` - Another COO matrix with the same shape and dtype (divisor)
///
/// # Returns
///
/// A new COO matrix containing the element-wise quotient (sorted by row, then column)
///
/// # Errors
///
/// Returns error if:
/// - Shapes don't match
/// - Dtypes don't match
///
/// # Algorithm
///
/// 1. Sort both matrices by (row, col)
/// 2. Linear merge to find matching positions
/// 3. Divide values at matching positions (A / B)
///
/// # Performance
///
/// O(nnz_a log nnz_a + nnz_b log nnz_b) for sorting, O(nnz_a + nnz_b) for merge.
/// Result has at most min(nnz_a, nnz_b) non-zeros.
///
/// # Note
///
/// Division by zero can occur if B stores an explicit zero value. The result
/// will be infinity or NaN depending on the numerator.
///
/// # Example
///
/// ```
/// # use numr::prelude::*;
/// # #[cfg(feature = "sparse")]
/// # {
/// # use numr::sparse::SparseTensor;
/// # let device = CpuDevice::new();
/// // A: B: C = A ./ B:
/// // [8, 3] [4, 0] [2, 0]
/// // [0, 10] ./ [6, 2] = [0, 5]
/// # let a_sp = SparseTensor::<CpuRuntime>::from_coo_slices(&[0, 0, 1], &[0, 1, 1], &[8.0f32, 3.0, 10.0], [2, 2], &device)?;
/// # let b_sp = SparseTensor::<CpuRuntime>::from_coo_slices(&[0, 1, 1], &[0, 0, 1], &[4.0f32, 6.0, 2.0], [2, 2], &device)?;
/// # if let numr::sparse::SparseTensor::Coo(a) = a_sp { if let numr::sparse::SparseTensor::Coo(b) = b_sp {
/// let c = a.div(&b)?;
/// # } }
/// # }
/// # Ok::<(), numr::error::Error>(())
/// ```
pub fn div(&self, other: &Self) -> Result<Self> {
// Validate shapes match
if self.shape != other.shape {
return Err(Error::ShapeMismatch {
expected: vec![self.shape[0], self.shape[1]],
got: vec![other.shape[0], other.shape[1]],
});
}
// Validate dtypes match
if self.dtype() != other.dtype() {
return Err(Error::DTypeMismatch {
lhs: self.dtype(),
rhs: other.dtype(),
});
}
let dtype = self.dtype();
let device = self.values.device();
// Handle empty cases - if either is empty, result is empty
if self.nnz() == 0 || other.nnz() == 0 {
return Ok(Self::empty(self.shape, dtype, device));
}
// Read indices to host
let rows_a: Vec<i64> = self.row_indices.to_vec();
let cols_a: Vec<i64> = self.col_indices.to_vec();
let rows_b: Vec<i64> = other.row_indices.to_vec();
let cols_b: Vec<i64> = other.col_indices.to_vec();
// Sort both matrices by (row, col)
let mut perm_a: Vec<usize> = (0..self.nnz()).collect();
perm_a.sort_by(|&i, &j| {
let row_cmp = rows_a[i].cmp(&rows_a[j]);
if row_cmp != std::cmp::Ordering::Equal {
row_cmp
} else {
cols_a[i].cmp(&cols_a[j])
}
});
let mut perm_b: Vec<usize> = (0..other.nnz()).collect();
perm_b.sort_by(|&i, &j| {
let row_cmp = rows_b[i].cmp(&rows_b[j]);
if row_cmp != std::cmp::Ordering::Equal {
row_cmp
} else {
cols_b[i].cmp(&cols_b[j])
}
});
// Dispatch on dtype to merge and divide values
crate::dispatch_dtype!(dtype, T => {
let vals_a: Vec<T> = self.values.to_vec();
let vals_b: Vec<T> = other.values.to_vec();
// Linear merge to find matching positions
let mut result_rows: Vec<i64> = Vec::new();
let mut result_cols: Vec<i64> = Vec::new();
let mut result_vals: Vec<T> = Vec::new();
let mut i = 0;
let mut j = 0;
while i < perm_a.len() && j < perm_b.len() {
let idx_a = perm_a[i];
let idx_b = perm_b[j];
let row_a = rows_a[idx_a];
let col_a = cols_a[idx_a];
let row_b = rows_b[idx_b];
let col_b = cols_b[idx_b];
if (row_a, col_a) < (row_b, col_b) {
// A only - skip (result is 0)
i += 1;
} else if (row_a, col_a) > (row_b, col_b) {
// B only - skip (result is 0)
j += 1;
} else {
// Same position - divide values
let quotient = vals_a[idx_a].to_f64() / vals_b[idx_b].to_f64();
result_rows.push(row_a);
result_cols.push(col_a);
result_vals.push(T::from_f64(quotient));
i += 1;
j += 1;
}
}
// Handle empty result
if result_rows.is_empty() {
return Ok(Self::empty(self.shape, dtype, device));
}
let row_tensor = Tensor::from_slice(&result_rows, &[result_rows.len()], device);
let col_tensor = Tensor::from_slice(&result_cols, &[result_cols.len()], device);
let val_tensor = Tensor::from_slice(&result_vals, &[result_vals.len()], device);
return Ok(Self {
row_indices: row_tensor,
col_indices: col_tensor,
values: val_tensor,
shape: self.shape,
sorted: true,
});
}, "COO element-wise div");
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::dtype::DType;
use crate::runtime::cpu::CpuRuntime;
#[test]
fn test_coo_div_overlapping() {
let device = <CpuRuntime as Runtime>::Device::default();
// A: B:
// [8, 0] [2, 5]
// [0, 35] [7, 7]
// Overlapping at (0,0) and (1,1)
let a = CooData::<CpuRuntime>::from_slices(
&[0i64, 1],
&[0i64, 1],
&[8.0f32, 35.0],
[2, 2],
&device,
)
.unwrap();
let b = CooData::<CpuRuntime>::from_slices(
&[0i64, 0, 1, 1],
&[0i64, 1, 0, 1],
&[2.0f32, 5.0, 7.0, 7.0],
[2, 2],
&device,
)
.unwrap();
let c = a.div(&b).unwrap();
// Only positions where both have values: (0,0) and (1,1)
// 8/2=4, 35/7=5
assert_eq!(c.nnz(), 2);
let rows: Vec<i64> = c.row_indices().to_vec();
let cols: Vec<i64> = c.col_indices().to_vec();
let vals: Vec<f32> = c.values().to_vec();
assert_eq!(rows, vec![0, 1]);
assert_eq!(cols, vec![0, 1]);
assert_eq!(vals, vec![4.0, 5.0]);
}
#[test]
fn test_coo_div_disjoint() {
let device = <CpuRuntime as Runtime>::Device::default();
// A: B:
// [1, 0] [0, 2]
// [0, 3] [4, 0]
// Completely disjoint - no overlapping positions
let a = CooData::<CpuRuntime>::from_slices(
&[0i64, 1],
&[0i64, 1],
&[1.0f32, 3.0],
[2, 2],
&device,
)
.unwrap();
let b = CooData::<CpuRuntime>::from_slices(
&[0i64, 1],
&[1i64, 0],
&[2.0f32, 4.0],
[2, 2],
&device,
)
.unwrap();
let c = a.div(&b).unwrap();
// Result is empty since no positions overlap
assert_eq!(c.nnz(), 0);
}
#[test]
fn test_coo_div_empty() {
let device = <CpuRuntime as Runtime>::Device::default();
let a = CooData::<CpuRuntime>::from_slices(
&[0i64, 1],
&[0i64, 1],
&[1.0f32, 2.0],
[2, 2],
&device,
)
.unwrap();
let b = CooData::<CpuRuntime>::empty([2, 2], DType::F32, &device);
// Divide with empty matrix gives empty result
let c = a.div(&b).unwrap();
assert_eq!(c.nnz(), 0);
let c2 = b.div(&a).unwrap();
assert_eq!(c2.nnz(), 0);
}
#[test]
fn test_coo_div_shape_mismatch() {
let device = <CpuRuntime as Runtime>::Device::default();
let a = CooData::<CpuRuntime>::empty([2, 3], DType::F32, &device);
let b = CooData::<CpuRuntime>::empty([3, 2], DType::F32, &device);
let result = a.div(&b);
assert!(result.is_err());
}
#[test]
fn test_coo_div_dtype_mismatch() {
let device = <CpuRuntime as Runtime>::Device::default();
let a = CooData::<CpuRuntime>::empty([2, 2], DType::F32, &device);
let b = CooData::<CpuRuntime>::empty([2, 2], DType::F64, &device);
let result = a.div(&b);
assert!(result.is_err());
}
#[test]
fn test_coo_div_same_positions() {
let device = <CpuRuntime as Runtime>::Device::default();
// Both have values at exactly the same positions
let a = CooData::<CpuRuntime>::from_slices(
&[0i64, 1, 1],
&[0i64, 0, 1],
&[10.0f32, 18.0, 28.0],
[2, 2],
&device,
)
.unwrap();
let b = CooData::<CpuRuntime>::from_slices(
&[0i64, 1, 1],
&[0i64, 0, 1],
&[2.0f32, 6.0, 7.0],
[2, 2],
&device,
)
.unwrap();
let c = a.div(&b).unwrap();
assert_eq!(c.nnz(), 3);
let vals: Vec<f32> = c.values().to_vec();
assert_eq!(vals, vec![5.0, 3.0, 4.0]); // 10/2, 18/6, 28/7
}
}