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feat: symcbolize lib, add, mul #21

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ab6fd51
feat: symcbolize lib, add, mul
tibetsou Mar 15, 2023
8287e1c
feat: symcbolize periodic, linear, constant, instant
tibetsou Mar 16, 2023
ff989e3
feat: symcbolize instant, exponential, and so on
tibetsou Mar 20, 2023
d098e77
feat: resolute some errors
tibetsou Mar 21, 2023
60668c4
feat: change to todo!() in ard, convolutional, spectral_mixture
tibetsou Mar 23, 2023
0c428b0
feat: change version of symbolic
tibetsou May 17, 2023
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5 changes: 4 additions & 1 deletion .vscode/settings.json
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Original file line number Diff line number Diff line change
Expand Up @@ -2,4 +2,7 @@
"editor.tabSize": 2,
"editor.detectIndentation": false,
"editor.formatOnSave": true,
}
"rust-analyzer.diagnostics.disabled": [
"unresolved-proc-macro"
],
}
3 changes: 2 additions & 1 deletion Cargo.toml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
[package]
name = "opensrdk-kernel-method"
version = "0.2.3"
version = "0.2.5"
authors = ["Kimura Yu <33382781+KimuraYu45z@users.noreply.github.com>"]
edition = "2018"
description = "Standard Kernel Method library for OpenSRDK toolchain."
Expand All @@ -12,6 +12,7 @@ categories = ["mathematics", "science"]
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html

[dependencies]
opensrdk-symbolic-computation = "0.2.0"
rayon = "1.5.1"
thiserror = "1.0.28"
opensrdk-linear-algebra = "0.8.2"
180 changes: 86 additions & 94 deletions src/add.rs
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Original file line number Diff line number Diff line change
@@ -1,141 +1,133 @@
use crate::KernelError;
use crate::ParamsDifferentiableKernel;
use crate::Value;
use crate::ValueDifferentiableKernel;
use crate::{KernelMul, PositiveDefiniteKernel};
use opensrdk_linear_algebra::Vector;
use crate::{KernelError, KernelMul, PositiveDefiniteKernel};
use opensrdk_symbolic_computation::Expression;
use std::fmt::Debug;
use std::marker::PhantomData;
use std::{ops::Add, ops::Mul};

#[derive(Clone, Debug)]
pub struct KernelAdd<L, R, T>
pub struct KernelAdd<L, R>
where
L: PositiveDefiniteKernel<T>,
R: PositiveDefiniteKernel<T>,
T: Value,
L: PositiveDefiniteKernel,
R: PositiveDefiniteKernel,
{
lhs: L,
rhs: R,
phantom: PhantomData<T>,
}

impl<L, R, T> KernelAdd<L, R, T>
impl<L, R> KernelAdd<L, R>
where
L: PositiveDefiniteKernel<T>,
R: PositiveDefiniteKernel<T>,
T: Value,
L: PositiveDefiniteKernel,
R: PositiveDefiniteKernel,
{
pub fn new(lhs: L, rhs: R) -> Self {
Self {
lhs,
rhs,
phantom: PhantomData,
}
Self { lhs, rhs }
}
}

impl<L, R, T> PositiveDefiniteKernel<T> for KernelAdd<L, R, T>
impl<L, R> PositiveDefiniteKernel for KernelAdd<L, R>
where
L: PositiveDefiniteKernel<T>,
R: PositiveDefiniteKernel<T>,
T: Value,
L: PositiveDefiniteKernel,
R: PositiveDefiniteKernel,
{
fn params_len(&self) -> usize {
self.lhs.params_len() + self.rhs.params_len()
}

fn value(&self, params: &[f64], x: &T, xprime: &T) -> Result<f64, KernelError> {
fn expression(
&self,
x: Expression,
x_prime: Expression,
params: &[Expression],
) -> Result<Expression, KernelError> {
let lhs_params_len = self.lhs.params_len();
let fx = self.lhs.value(&params[..lhs_params_len], x, xprime)?;
let gx = self.rhs.value(&params[lhs_params_len..], x, xprime)?;
let fx = self
.lhs
.expression(x.clone(), x_prime.clone(), &params[..lhs_params_len])?;
let gx = self.rhs.expression(x, x_prime, &params[lhs_params_len..])?;

let hx = fx + gx;

Ok(hx)
}
}

impl<Rhs, L, R, T> Add<Rhs> for KernelAdd<L, R, T>
impl<Rhs, L, R> Add<Rhs> for KernelAdd<L, R>
where
Rhs: PositiveDefiniteKernel<T>,
L: PositiveDefiniteKernel<T>,
R: PositiveDefiniteKernel<T>,
T: Value,
Rhs: PositiveDefiniteKernel,
L: PositiveDefiniteKernel,
R: PositiveDefiniteKernel,
{
type Output = KernelAdd<Self, Rhs, T>;
type Output = KernelAdd<Self, Rhs>;

fn add(self, rhs: Rhs) -> Self::Output {
Self::Output::new(self, rhs)
}
}

impl<Rhs, L, R, T> Mul<Rhs> for KernelAdd<L, R, T>
impl<Rhs, L, R> Mul<Rhs> for KernelAdd<L, R>
where
Rhs: PositiveDefiniteKernel<T>,
L: PositiveDefiniteKernel<T>,
R: PositiveDefiniteKernel<T>,
T: Value,
Rhs: PositiveDefiniteKernel,
L: PositiveDefiniteKernel,
R: PositiveDefiniteKernel,
{
type Output = KernelMul<Self, Rhs, T>;
type Output = KernelMul<Self, Rhs>;

fn mul(self, rhs: Rhs) -> Self::Output {
Self::Output::new(self, rhs)
}
}

impl<L, R, T> ValueDifferentiableKernel<T> for KernelAdd<L, R, T>
where
L: ValueDifferentiableKernel<T>,
R: ValueDifferentiableKernel<T>,
T: Value,
{
fn ln_diff_value(&self, params: &[f64], x: &T, xprime: &T) -> Result<Vec<f64>, KernelError> {
let diff_rhs = &self
.rhs
.ln_diff_value(params, x, xprime)
.unwrap()
.clone()
.col_mat();
let diff_lhs = &self
.lhs
.ln_diff_value(params, x, xprime)
.unwrap()
.clone()
.col_mat();
let value_rhs = vec![self.rhs.value(params, x, xprime).unwrap()].col_mat();
let value_lhs = vec![self.lhs.value(params, x, xprime).unwrap()].col_mat();
let diff = ((&value_rhs * diff_rhs + &value_lhs * diff_lhs)
* (&value_rhs + value_lhs)[(0, 0)].powi(-1))
.vec();
Ok(diff)
}
}
// impl<L, R> ValueDifferentiableKernel for KernelAdd<L, R>
// where
// L: ValueDifferentiableKernel<T>,
// R: ValueDifferentiableKernel<T>,
// T: Value,
// {
// fn ln_diff_value(&self, params: &[f64], x: &T, xprime: &T) -> Result<Vec<f64>, KernelError> {
// let diff_rhs = &self
// .rhs
// .ln_diff_value(params, x, xprime)
// .unwrap()
// .clone()
// .col_mat();
// let diff_lhs = &self
// .lhs
// .ln_diff_value(params, x, xprime)
// .unwrap()
// .clone()
// .col_mat();
// let value_rhs = vec![self.rhs.value(params, x, xprime).unwrap()].col_mat();
// let value_lhs = vec![self.lhs.value(params, x, xprime).unwrap()].col_mat();
// let diff = ((&value_rhs * diff_rhs + &value_lhs * diff_lhs)
// * (&value_rhs + value_lhs)[(0, 0)].powi(-1))
// .vec();
// Ok(diff)
// }
// }

impl<L, R, T> ParamsDifferentiableKernel<T> for KernelAdd<L, R, T>
where
L: ParamsDifferentiableKernel<T>,
R: ParamsDifferentiableKernel<T>,
T: Value,
{
fn ln_diff_params(&self, params: &[f64], x: &T, xprime: &T) -> Result<Vec<f64>, KernelError> {
let diff_rhs = &self
.rhs
.ln_diff_params(params, x, xprime)
.unwrap()
.clone()
.col_mat();
let diff_lhs = &self
.lhs
.ln_diff_params(params, x, xprime)
.unwrap()
.clone()
.col_mat();
let value_rhs = vec![self.rhs.value(params, x, xprime).unwrap()].col_mat();
let value_lhs = vec![self.lhs.value(params, x, xprime).unwrap()].col_mat();
let diff = ((&value_rhs * diff_rhs + &value_lhs * diff_lhs)
* (&value_rhs + value_lhs)[(0, 0)].powi(-1))
.vec();
Ok(diff)
}
}
// impl<L, R, T> ParamsDifferentiableKernel<T> for KernelAdd<L, R, T>
// where
// L: ParamsDifferentiableKernel<T>,
// R: ParamsDifferentiableKernel<T>,
// T: Value,
// {
// fn ln_diff_params(&self, params: &[f64], x: &T, xprime: &T) -> Result<Vec<f64>, KernelError> {
// let diff_rhs = &self
// .rhs
// .ln_diff_params(params, x, xprime)
// .unwrap()
// .clone()
// .col_mat();
// let diff_lhs = &self
// .lhs
// .ln_diff_params(params, x, xprime)
// .unwrap()
// .clone()
// .col_mat();
// let value_rhs = vec![self.rhs.value(params, x, xprime).unwrap()].col_mat();
// let value_lhs = vec![self.lhs.value(params, x, xprime).unwrap()].col_mat();
// let diff = ((&value_rhs * diff_rhs + &value_lhs * diff_lhs)
// * (&value_rhs + value_lhs)[(0, 0)].powi(-1))
// .vec();
// Ok(diff)
// }
// }
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