entropy working

hod_1/.gitignore

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+target/

hod_1/Cargo.toml

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+[package]
+name = "hod_1"
+version = "0.1.0"
+edition = "2024"
+
+[dependencies]
+burn = { version = "0.20.1", default-features = false, features = ["ndarray"] }
+burn-ndarray = "0.20.1"
+clap = { version = "4.5.60", features = ["derive"] }
+ndarray = "0.17.2"

hod_1/src/main.rs

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+use burn::tensor::{backend::Backend, Tensor, TensorData};
+use burn_ndarray::NdArray;
+use ndarray::{array, Array1};
+
+type B = NdArray<f64>;
+
+fn main() {
+    let device = <B as Backend>::Device::default();
+
+    // ndarray::Array1<f64>
+    let a: Array1<f64> = array![0.4, 0.6];
+
+    // Convert ndarray -> Burn tensor
+    let p = Tensor::<B, 1>::from_data(
+        TensorData::new(a.to_vec(), [a.len()]),
+        &device,
+    );
+
+    let h = entropy(p);
+    println!("{h}");
+}
+
+fn entropy(p: Tensor<B, 1>) -> f64 {
+    // Handle p = 0 safely, because 0 * log(0) should contribute 0
+    let zero_mask = p.clone().equal_elem(0.0);
+    let p_safe = p.clone().mask_fill(zero_mask.clone(), 1.0);
+
+    let terms = (p.clone() * p_safe.log()).mask_fill(zero_mask, 0.0);
+
+    (-terms).sum().into_scalar()
+}
+
+// pub fn entropy2(p: Tensor<B, 1>) -> f64 {
+//     if p == 0.0 {
+//         return 0.0;
+//     }
+//     - p * p.ln()
+// }
+
+// pub fn cross_entropy(p: Tensor<B, 1>, q: f64) -> f64 {
+//     if p == 0.0 {
+//         return 0.0;
+//     }
+//     - p * q.ln()
+// }
+
+// pub fn kl_div(p: f64, q: f64) -> f64 {
+//     if p == 0.0 {
+//         return 0.0;
+//     }
+//     p*(p.ln()-q.ln())
+// }
+
+// pub fn kl_div2(p: f64, q: f64) -> f64 {
+//     cross_entropy(p,q) - entropy(p)
+// }