Squashed 'vendor/ruvector/' content from commit b64c2172

git-subtree-dir: vendor/ruvector
git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900

crates/ruvector-sparse-inference/examples/basic_usage.rs

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+//! Basic usage example for the sparse inference engine
+
+use ndarray::Array2;
+use ruvector_sparse_inference::backend::get_backend;
+use ruvector_sparse_inference::sparse::ActivationType;
+
+fn main() {
+    // Get the best available backend for this platform
+    let backend = get_backend();
+    println!("Using backend: {}", backend.name());
+    println!("SIMD width: {} f32s per register", backend.simd_width());
+
+    // Example 1: Dot product
+    println!("\n=== Dot Product ===");
+    let a = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0];
+    let b = vec![2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0];
+    let dot = backend.dot_product(&a, &b);
+    println!("a · b = {}", dot);
+    assert_eq!(dot, 72.0);
+
+    // Example 2: ReLU activation
+    println!("\n=== ReLU Activation ===");
+    let mut data = vec![-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, -4.0, 5.0];
+    println!("Before: {:?}", data);
+    backend.activation(&mut data, ActivationType::Relu);
+    println!("After:  {:?}", data);
+    assert_eq!(data, vec![0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 0.0, 5.0]);
+
+    // Example 3: AXPY (a = a + b * scalar)
+    println!("\n=== AXPY (a = a + b * 2.5) ===");
+    let mut a = vec![1.0, 2.0, 3.0, 4.0];
+    let b = vec![1.0, 1.0, 1.0, 1.0];
+    println!("Before: a = {:?}", a);
+    backend.axpy(&mut a, &b, 2.5);
+    println!("After:  a = {:?}", a);
+    assert_eq!(a, vec![3.5, 4.5, 5.5, 6.5]);
+
+    // Example 4: Sparse matrix-vector multiplication
+    println!("\n=== Sparse MatMul ===");
+    let matrix = Array2::from_shape_vec(
+        (4, 4),
+        vec![
+            1.0, 0.0, 2.0, 0.0, 0.0, 3.0, 0.0, 4.0, 5.0, 0.0, 6.0, 0.0, 0.0, 7.0, 0.0, 8.0,
+        ],
+    )
+    .unwrap();
+    let input = vec![1.0, 2.0, 3.0, 4.0];
+
+    // Only compute rows 0 and 2 (sparse computation)
+    let active_rows = vec![0, 2];
+    let output = backend.sparse_matmul(&matrix, &input, &active_rows);
+    println!("Matrix (4x4):");
+    println!("{:?}", matrix);
+    println!("Input: {:?}", input);
+    println!("Active rows: {:?}", active_rows);
+    println!("Output: {:?}", output);
+    assert_eq!(output, vec![7.0, 23.0]); // row 0: 1*1 + 2*3 = 7, row 2: 5*1 + 6*3 = 23
+
+    // Example 5: Different activation functions
+    println!("\n=== Activation Functions ===");
+    for activation in [
+        ActivationType::Relu,
+        ActivationType::Gelu,
+        ActivationType::Silu,
+    ] {
+        let mut data = vec![-1.0, 0.0, 1.0, 2.0];
+        backend.activation(&mut data, activation);
+        println!("{:?}: {:?}", activation, data);
+    }
+
+    println!("\n✓ All examples completed successfully!");
+}