fix: candle-version

Author: dancixx

Cargo.toml

@@ -15,10 +15,10 @@ keywords = ["stochastic", "quant", "finance", "simulation", "statistics"]
 anyhow = "1.0.89"
 approx = "0.5.1"
 argmin = "0.10.0"
-candle-core = "0.8.1"
-candle-datasets = "0.8.1"
-candle-nn = "0.8.1"
-candle-transformers = "0.8.1"
+candle-core = "=0.8.1"
+candle-datasets = "=0.8.1"
+candle-nn = "=0.8.1"
+candle-transformers = "=0.8.1"
 chrono = "0.4.38"
 cudarc = { version = "0.13.9", optional = true, features = [
     "cuda-12080",
@@ -30,7 +30,6 @@ gauss-quad = "0.2.1"
 impl-new-derive = "0.1.3"
 implied-vol = "1.0.0"
 indicatif = "0.17.8"
-# itransformer = "1.0.1"
 kendalls = "0.2.2"
 levenberg-marquardt = "0.14.0"
 libloading = { version = "0.8.6", optional = true }

src/ai.rs

@@ -1,5 +1,4 @@
 use candle_core::Tensor;
-// pub use itransformer::ITransformer;
 
 pub mod fou;
 pub mod utils;

src/stochastic/noise/fgn.rs

@@ -1,15 +1,15 @@
 use std::sync::{Arc, RwLock};
 
-#[cfg(feature = "cuda")]
-use either::Either;
 #[cfg(feature = "cuda")]
 use anyhow::Result;
+#[cfg(feature = "cuda")]
+use either::Either;
 
 use ndarray::parallel::prelude::*;
 use ndarray::{concatenate, prelude::*};
 use ndarray_rand::rand_distr::StandardNormal;
 use ndarray_rand::RandomExt;
-use ndrustfft::{ndfft, FftHandler};
+use ndrustfft::{ndfft_par, FftHandler};
 use num_complex::{Complex, ComplexDistribution};
 
 use crate::stochastic::Sampling;
@@ -51,7 +51,7 @@ impl FGN {
     let data = r.mapv(|v| Complex::new(v, 0.0));
     let r_fft = FftHandler::new(r.len());
     let mut sqrt_eigenvalues = Array1::<Complex<f64>>::zeros(r.len());
-    ndfft(&data, &mut sqrt_eigenvalues, &r_fft, 0);
+    ndfft_par(&data, &mut sqrt_eigenvalues, &r_fft, 0);
     sqrt_eigenvalues.mapv_inplace(|x| Complex::new((x.re / (2.0 * n as f64)).sqrt(), x.im));
 
     Self {
@@ -90,7 +90,7 @@ impl Sampling<f64> for FGN {
 
     let fgn = &*self.sqrt_eigenvalues * &*rnd.read().unwrap();
     let mut fgn_fft = Array1::<Complex<f64>>::zeros(2 * self.n);
-    ndfft(&fgn, &mut fgn_fft, &*self.fft_handler, 0);
+    ndfft_par(&fgn, &mut fgn_fft, &*self.fft_handler, 0);
     let scale = (self.n as f64).powf(-self.hurst) * self.t.unwrap_or(1.0).powf(self.hurst);
     let fgn = fgn_fft
       .slice(s![1..self.n - self.offset + 1])