59: Added `MulAdd` and `MulAddAssign` traits r=cuviper a=regexident
Both `f32` and `f64` implement fused multiply-add, which computes `(self * a) + b` with only one rounding error. This produces a more accurate result with better performance than a separate multiplication operation followed by an add:
```rust
fn mul_add(self, a: f32, b: f32) -> f32[src]
```
It is however not possible to make use of this in a generic context by abstracting over a trait.
My concrete use-case is machine learning, [gradient descent](https://en.wikipedia.org/wiki/Gradient_descent) to be specific,
where the core operation of updating the gradient could make use of `mul_add` for both its `weights: Vector` as well as its `bias: f32`:
```rust
struct Perceptron {
weights: Vector,
bias: f32,
}
impl MulAdd<f32, Self> for Vector {
// ...
}
impl Perceptron {
fn learn(&mut self, example: Vector, expected: f32, learning_rate: f32) {
let alpha = self.error(example, expected, learning_rate);
self.weights = example.mul_add(alpha, self.weights);
self.bias = self.bias.mul_add(alpha, self.bias)
}
}
```
(The actual impl of `Vector` would be generic over its value type: `Vector<T>`, thus requiring the trait.)
Co-authored-by: Vincent Esche <regexident@gmail.com>
Co-authored-by: Josh Stone <cuviper@gmail.com>
We don't actually need to compute the `trunc()` value, as long as we can
figure out the right values for the exclusive range `(MIN-1, MAX+1)` to
measure the same truncation effect.
This change adds some new macro rules used when converting from floats
to integers. There are two macro rule variants, one for signed ints, one
for unsigned ints.
Among other things, this change specifically addresses the overflow case
documented in https://github.com/rust-num/num-traits/issues/12
Josh Stone
bors[bot]
Vincent Esche
LEXUGE
Vinzent Steinberg
Dan Barella
Alexander Popiak