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use crate::array::Array;
use crate::error::{NumRs2Error, Result};
use num_traits::Float;
/// BLAS Level 1: Vector-Vector operations
/// Compute the dot product of two vectors
pub fn dot<T>(x: &Array<T>, y: &Array<T>) -> Result<T>
where
T: Float + Default,
{
// For simplicity, we'll implement a generic version first
// In a real implementation, we would dispatch to the appropriate BLAS routine
// based on the type (e.g., sdot, ddot)
let x_shape = x.shape();
let y_shape = y.shape();
if x_shape.len() != 1 || y_shape.len() != 1 {
return Err(NumRs2Error::DimensionMismatch(
"dot product requires 1D arrays".to_string(),
));
}
if x_shape[0] != y_shape[0] {
return Err(NumRs2Error::ShapeMismatch {
expected: x_shape,
actual: y_shape,
});
}
let x_data = x.to_vec();
let y_data = y.to_vec();
let mut result = T::zero();
for i in 0..x_shape[0] {
result = result + x_data[i] * y_data[i];
}
Ok(result)
}
/// BLAS Level 2: Matrix-Vector operations
/// Compute the matrix-vector product y = alpha*A*x + beta*y
pub fn gemv<T>(
a: &Array<T>,
x: &Array<T>,
y: &mut Array<T>,
alpha: T,
beta: T,
trans: bool,
) -> Result<()>
where
T: Float + Default,
{
// Generic implementation for now
// A real implementation would call the appropriate BLAS routine
let a_shape = a.shape();
let x_shape = x.shape();
let y_shape = y.shape();
if a_shape.len() != 2 {
return Err(NumRs2Error::DimensionMismatch(
"First argument must be a 2D matrix".to_string(),
));
}
if x_shape.len() != 1 {
return Err(NumRs2Error::DimensionMismatch(
"Second argument must be a 1D vector".to_string(),
));
}
if y_shape.len() != 1 {
return Err(NumRs2Error::DimensionMismatch(
"Third argument must be a 1D vector".to_string(),
));
}
let (m, n) = (a_shape[0], a_shape[1]);
if trans {
// y = alpha * A^T * x + beta * y
if n != y_shape[0] || m != x_shape[0] {
return Err(NumRs2Error::ShapeMismatch {
expected: vec![n],
actual: y_shape,
});
}
} else {
// y = alpha * A * x + beta * y
if m != y_shape[0] || n != x_shape[0] {
return Err(NumRs2Error::ShapeMismatch {
expected: vec![m],
actual: y_shape,
});
}
}
// Simple implementation for demonstration
// A full implementation would call BLAS
let a_data = a.to_vec();
let x_data = x.to_vec();
let mut y_data = y.to_vec();
// Scale y by beta
for value in &mut y_data {
*value = beta * *value;
}
if trans {
// y = alpha * A^T * x + beta * y
for i in 0..n {
for j in 0..m {
y_data[i] = y_data[i] + alpha * a_data[j * n + i] * x_data[j];
}
}
} else {
// y = alpha * A * x + beta * y
for i in 0..m {
for j in 0..n {
y_data[i] = y_data[i] + alpha * a_data[i * n + j] * x_data[j];
}
}
}
// Update y with the computed result
*y = Array::from_vec(y_data);
Ok(())
}
/// BLAS Level 3: Matrix-Matrix operations
/// Compute the matrix-matrix product C = alpha*A*B + beta*C
pub fn gemm<T>(
a: &Array<T>,
b: &Array<T>,
c: &mut Array<T>,
alpha: T,
beta: T,
trans_a: bool,
trans_b: bool,
) -> Result<()>
where
T: Float + Default + std::ops::AddAssign,
{
// This would be implemented with BLAS in a complete library
// For now, we'll just validate the dimensions
let a_shape = a.shape();
let b_shape = b.shape();
let c_shape = c.shape();
if a_shape.len() != 2 || b_shape.len() != 2 || c_shape.len() != 2 {
return Err(NumRs2Error::DimensionMismatch(
"All arguments must be 2D matrices".to_string(),
));
}
let (m, k_a) = if trans_a {
(a_shape[1], a_shape[0])
} else {
(a_shape[0], a_shape[1])
};
let (k_b, n) = if trans_b {
(b_shape[1], b_shape[0])
} else {
(b_shape[0], b_shape[1])
};
if k_a != k_b {
return Err(NumRs2Error::DimensionMismatch(format!(
"Inner dimensions must match: {} vs {}",
k_a, k_b
)));
}
if c_shape[0] != m || c_shape[1] != n {
return Err(NumRs2Error::ShapeMismatch {
expected: vec![m, n],
actual: c_shape,
});
}
// A simple (inefficient) implementation for demonstration purposes
// A real implementation would call the appropriate BLAS routine
let a_data = a.to_vec();
let b_data = b.to_vec();
let mut c_data = c.to_vec();
// Scale C by beta
for value in &mut c_data {
*value = beta * *value;
}
// These variable names align with typical matrix notation
let a_cols = a_shape[1];
let b_cols = b_shape[1];
if !trans_a && !trans_b {
// C = alpha * A * B + beta * C
for i in 0..m {
for j in 0..n {
for k in 0..k_a {
c_data[i * n + j] += alpha * a_data[i * a_cols + k] * b_data[k * b_cols + j];
}
}
}
} else if trans_a && !trans_b {
// C = alpha * A^T * B + beta * C
for i in 0..m {
for j in 0..n {
for k in 0..k_a {
c_data[i * n + j] += alpha * a_data[k * a_cols + i] * b_data[k * b_cols + j];
}
}
}
} else if !trans_a && trans_b {
// C = alpha * A * B^T + beta * C
for i in 0..m {
for j in 0..n {
for k in 0..k_a {
c_data[i * n + j] += alpha * a_data[i * a_cols + k] * b_data[j * b_cols + k];
}
}
}
} else {
// C = alpha * A^T * B^T + beta * C
for i in 0..m {
for j in 0..n {
for k in 0..k_a {
c_data[i * n + j] += alpha * a_data[k * a_cols + i] * b_data[j * b_cols + k];
}
}
}
}
// Update C with the computed result
*c = Array::from_vec(c_data).reshape(&[m, n]);
Ok(())
}
// Add more BLAS functions as needed for your library