mixt 0.2.0

Estimate mixture model weights for a fixed log-likelihood matrix.
Documentation 
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// mixt: Riemannian conjugate gradient descent for estimating mixture model weights.
//
// Copyright 2025 mixt contributors [https://github.com/tmaklin/mixt]
//
// This library is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 2.1 of the License, or (at your option) any later version.
//
// This library is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
// Lesser General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License along with this library; if not, write to the Free Software
// Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301
// USA
//

//! C++ API for compatibility with [rcgpar](https://github.com/tmaklin/rcgpar) v1.

use crate::OptimizerOpts;
use crate::BurnBackend::NdArray32;
use crate::BurnBackend::Wgpu32;
use crate::optimize_flat;

use crate::optimizer::Algorithm;

use burn::backend::ndarray::NdArray;
use burn_tensor::{Shape, Tensor};
use cxx::CxxVector;

#[cxx::bridge(namespace = "mixt")]
mod ffi {

    extern "Rust" {
        fn rcg_optl_cpu(
            logl: &CxxVector<f32>,
            log_times_observed: &CxxVector<f32>,
            alpha0: &CxxVector<f32>,
            tol: f64,
            max_iters: usize,
        ) -> Vec<f32>;

        fn rcg_optl_gpu(
            logl: &CxxVector<f32>,
            log_times_observed: &CxxVector<f32>,
            alpha0: &CxxVector<f32>,
            tol: f64,
            max_iters: usize,
        ) -> Vec<f32>;

        fn em_cpu(
            logl: &CxxVector<f32>,
            log_times_observed: &CxxVector<f32>,
            alpha0: &CxxVector<f32>,
            tol: f64,
            max_iters: usize,
        ) -> Vec<f32>;

        fn em_gpu(
            logl: &CxxVector<f32>,
            log_times_observed: &CxxVector<f32>,
            alpha0: &CxxVector<f32>,
            tol: f64,
            max_iters: usize,
        ) -> Vec<f32>;

        fn mixture_components(
            probs: &CxxVector<f32>,
            log_times_observed: &CxxVector<f32>,
        ) -> Vec<f32>;

    }
}

fn run_optimizer(
    logl: &CxxVector<f32>,
    log_times_observed: &CxxVector<f32>,
    alpha0: &CxxVector<f32>,
    options: OptimizerOpts,
) -> Vec<f32> {
    let (_, probs) = optimize_flat(logl.as_slice(), log_times_observed.as_slice(), alpha0.as_slice(), Some(options)).unwrap();
    probs
}

pub fn rcg_optl_cpu(
    logl: &CxxVector<f32>,
    log_times_observed: &CxxVector<f32>,
    alpha0: &CxxVector<f32>,
    tolerance: f64,
    max_iters: usize,
) -> Vec<f32> {
    let options = OptimizerOpts { tolerance, max_iters, device: NdArray32, algorithm: Algorithm::RCG };
    run_optimizer(logl, log_times_observed, alpha0, options)
}

#[allow(unused_variables)]
pub fn rcg_optl_gpu(
    logl: &CxxVector<f32>,
    log_times_observed: &CxxVector<f32>,
    alpha0: &CxxVector<f32>,
    tolerance: f64,
    max_iters: usize,
) -> Vec<f32> {
    let options = OptimizerOpts { tolerance, max_iters, device: Wgpu32, algorithm: Algorithm::RCG };

    #[cfg(any(feature = "wgpu", feature = "webgpu", feature = "vulkan"))]
    return run_optimizer(logl, log_times_observed, alpha0, options);

    #[cfg(not(any(feature = "wgpu", feature = "webgpu", feature = "vulkan")))]
    panic!("mixt: mixt was not compiled with GPU support.")
}

pub fn em_cpu(
    logl: &CxxVector<f32>,
    log_times_observed: &CxxVector<f32>,
    alpha0: &CxxVector<f32>,
    tolerance: f64,
    max_iters: usize,
) -> Vec<f32> {
    let options = OptimizerOpts { tolerance, max_iters, device: NdArray32, algorithm: Algorithm::EM };
    run_optimizer(logl, log_times_observed, alpha0, options)
}

#[allow(unused_variables)]
pub fn em_gpu(
    logl: &CxxVector<f32>,
    log_times_observed: &CxxVector<f32>,
    alpha0: &CxxVector<f32>,
    tolerance: f64,
    max_iters: usize,
) -> Vec<f32> {
    let options = OptimizerOpts { tolerance, max_iters, device: Wgpu32, algorithm: Algorithm::EM };

    #[cfg(any(feature = "wgpu", feature = "webgpu", feature = "vulkan"))]
    return run_optimizer(logl, log_times_observed, alpha0, options);

    #[cfg(not(any(feature = "wgpu", feature = "webgpu", feature = "vulkan")))]
    panic!("mixt: mixt was not compiled with GPU support.")
}

pub fn mixture_components(
    probs: &cxx::CxxVector<f32>,
    log_times_observed: &cxx::CxxVector<f32>,
) -> Vec<f32> {

    let n_obs = log_times_observed.len();
    let n_targets = probs.len()/n_obs;

    let device = Default::default();
    type Backend = NdArray<f32>;

    let probs_t: Tensor::<Backend, 2> = Tensor::<Backend, 1>::from_data(probs.as_slice(), &device).reshape(Shape::new([n_targets, n_obs]));
    let log_counts_t = Tensor::<Backend, 1>::from_data(log_times_observed.as_slice(), &device);

    let thetas_t = crate::optimizer::mixture_components(probs_t, log_counts_t);
    thetas_t.into_data().to_vec().unwrap()
}