feat: add ai modul

Cargo.toml

@@ -1,7 +1,6 @@
 [workspace]
-members = [
+members = [ "stochastic-rs-ai",
     "stochastic-rs-core",
-    "stochastic-rs-ml",
     "stochastic-rs-quant",
     "stochastic-rs-stats",
 ]

stochastic-rs-ai/Cargo.toml

@@ -0,0 +1,18 @@
+[package]
+name = "stochastic-rs-ai"
+version = "0.1.0"
+edition = "2021"
+
+[dependencies]
+anyhow = "1.0.88"
+candle-core = {version = "0.6.0", features = ["accelerate", "metal"]}
+candle-datasets = {version = "0.6.0"}
+candle-metal-kernels = "0.6.0"
+candle-nn = {version = "0.6.0", features = ["accelerate", "metal"]}
+indicatif = "0.17.7"
+ndarray = "0.16.1"
+ndarray-rand = "0.15.0"
+polars = "0.43.1"
+polars-io = {version = "0.43.1", features = ["csv"]}
+rand_distr = "0.4.3"
+stochastic-rs = {version = "0.8.0", path = "../stochastic-rs-core"}

stochastic-rs-ai/src/fou_lstm.rs

@@ -0,0 +1,3 @@
+pub mod datasets;
+pub mod lstm_model_1_d;
+pub mod lstm_model_2_d;

stochastic-rs-ai/src/fou_lstm/datasets.rs

@@ -0,0 +1,128 @@
+use anyhow::Result;
+use candle_core::{Device, Tensor};
+use candle_datasets::{batcher::IterResult2, Batcher};
+use indicatif::{ProgressBar, ProgressStyle};
+use ndarray::{s, Array1};
+use ndarray_rand::RandomExt;
+use rand_distr::Uniform;
+use std::vec::IntoIter;
+use stochastic_rs::{diffusion::fou::Fou, Sampling};
+
+pub fn test_vasicek_1_d(
+  epoch_size: usize,
+  batch_size: usize,
+  n: usize,
+  device: &Device,
+) -> Result<(
+  Batcher<IterResult2<IntoIter<Result<(Tensor, Tensor), candle_core::Error>>>>,
+  Vec<f64>,
+)> {
+  let mut paths = Vec::with_capacity(epoch_size);
+  let mu = 2.8;
+  let sigma = 1.0;
+  let thetas = Array1::random(epoch_size, Uniform::new(0.0, 10.0)).to_vec();
+  let hursts = Array1::random(epoch_size, Uniform::new(0.01, 0.99)).to_vec();
+  let progress_bar = ProgressBar::new(epoch_size as u64);
+  progress_bar.set_style(
+    ProgressStyle::with_template(
+      "{spinner:.green} [{elapsed_precise}] [{wide_bar:.cyan/blue}] ({eta})",
+    )?
+    .progress_chars("#>-"),
+  );
+  for idx in 0..epoch_size {
+    let hurst = hursts[idx];
+    let theta = thetas[idx];
+    let fou = Fou::new(&Fou {
+      hurst,
+      mu,
+      sigma,
+      theta,
+      n,
+      x0: Some(0.0),
+      t: Some(16.0),
+      ..Default::default()
+    });
+    let mut path = fou.sample();
+    let mean = path.mean().unwrap();
+    let std = path.std(0.0);
+    path = (path - mean) / std;
+
+    paths.push(Ok((
+      Tensor::from_iter(path, device)?,
+      Tensor::new(&[thetas[idx]], device)?,
+    )));
+    progress_bar.inc(1);
+  }
+  progress_bar.finish();
+
+  let batcher = Batcher::new_r2(paths.into_iter())
+    .batch_size(batch_size)
+    .return_last_incomplete_batch(false);
+
+  Ok((batcher, hursts))
+}
+
+pub fn test_vasicek_2_d(
+  epoch_size: usize,
+  batch_size: usize,
+  n: usize,
+  device: &Device,
+) -> Result<(
+  Batcher<IterResult2<IntoIter<Result<(Tensor, Tensor), candle_core::Error>>>>,
+  Vec<f64>,
+)> {
+  let mut paths = Vec::with_capacity(epoch_size);
+  let mu = 2.8;
+  let sigma = 1.0;
+  let thetas = Array1::random(epoch_size, Uniform::new(0.0, 10.0)).to_vec();
+  let hursts = Array1::random(epoch_size, Uniform::new(0.01, 0.99)).to_vec();
+  let progress_bar = ProgressBar::new(epoch_size as u64);
+  progress_bar.set_style(
+    ProgressStyle::with_template(
+      "{spinner:.green} [{elapsed_precise}] [{wide_bar:.cyan/blue}] ({eta})",
+    )?
+    .progress_chars("#>-"),
+  );
+  for idx in 0..epoch_size {
+    let hurst = hursts[idx];
+    let theta = thetas[idx];
+    let fou = Fou::new(&Fou {
+      hurst,
+      mu,
+      sigma,
+      theta,
+      n,
+      x0: Some(0.0),
+      t: Some(16.0),
+      ..Default::default()
+    });
+    let mut path = fou.sample();
+    let mean = path.mean().unwrap();
+    let std = path.std(0.0);
+    path = (path - mean) / std;
+
+    let diff = &path.slice(s![1..]) - &path.slice(s![..-1]);
+    let path = path.slice(s![..-1]);
+    let paired = path.iter().zip(diff.iter()).collect::<Vec<_>>();
+    let paired_tensors = paired
+      .iter()
+      .map(|pair| {
+        let (x, y) = *pair;
+        Tensor::new(&[*x, *y], device).unwrap()
+      })
+      .collect::<Vec<_>>();
+
+    paths.push(Ok((
+      Tensor::stack(&paired_tensors, 0)?,
+      Tensor::new(&[thetas[idx]], device)?,
+    )));
+    progress_bar.inc(1);
+  }
+  progress_bar.finish();
+
+  let batcher = Batcher::new_r2(paths.into_iter())
+    .batch_size(batch_size)
+    .return_last_incomplete_batch(false);
+
+  Ok((batcher, hursts))
+}