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use tea_core::prelude::*;
/// Trait for performing rolling binary operations on valid elements in vectors.
///
/// This trait provides methods for calculating rolling covariance and correlation
/// between two vectors of potentially nullable elements.
pub trait RollingValidBinary<T: IsNone>: Vec1View<T> {
/// Calculates the rolling covariance between two vectors.
///
/// # Arguments
///
/// * `other` - The other vector to calculate covariance with.
/// * `window` - The size of the rolling window.
/// * `min_periods` - The minimum number of observations in window required to have a value.
/// * `out` - Optional output buffer to store the results.
///
/// # Returns
///
/// A vector containing the rolling covariance values.
#[no_out]
fn ts_vcov<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>(
&self,
other: &V2,
window: usize,
min_periods: Option<usize>,
out: Option<O::UninitRefMut<'_>>,
) -> O
where
T::Inner: Number,
T2::Inner: Number,
f64: Cast<U>,
{
let min_periods = min_periods.unwrap_or(window / 2).min(window);
let mut sum_a = 0.;
let mut sum_b = 0.;
let mut sum_ab = 0.;
let mut n = 0;
self.rolling2_apply(
other,
window,
|remove_values, (va, vb)| {
if va.not_none() && vb.not_none() {
n += 1;
let (va, vb) = (va.unwrap().f64(), vb.unwrap().f64());
sum_a += va;
sum_b += vb;
sum_ab += va * vb;
};
let res = if n >= min_periods {
(sum_ab - (sum_a * sum_b) / n.f64()) / (n - 1).f64()
} else {
f64::NAN
};
if let Some((va, vb)) = remove_values {
if va.not_none() && vb.not_none() {
n -= 1;
let (va, vb) = (va.unwrap().f64(), vb.unwrap().f64());
sum_a -= va;
sum_b -= vb;
sum_ab -= va * vb;
};
}
res.cast()
},
out,
)
}
/// Calculates the rolling correlation between two vectors.
///
/// # Arguments
///
/// * `other` - The other vector to calculate correlation with.
/// * `window` - The size of the rolling window.
/// * `min_periods` - The minimum number of observations in window required to have a value.
/// * `out` - Optional output buffer to store the results.
///
/// # Returns
///
/// A vector containing the rolling correlation values.
#[no_out]
fn ts_vcorr<O: Vec1<U>, U, V2: Vec1View<T2>, T2: IsNone>(
&self,
other: &V2,
window: usize,
min_periods: Option<usize>,
out: Option<O::UninitRefMut<'_>>,
) -> O
where
T::Inner: Number,
T2::Inner: Number,
f64: Cast<U>,
{
let mut sum_a = 0.;
let mut sum2_a = 0.;
let mut sum_b = 0.;
let mut sum2_b = 0.;
let mut sum_ab = 0.;
let mut n = 0;
let min_periods = min_periods.unwrap_or(window / 2).min(window);
self.rolling2_apply(
other,
window,
|remove_values, (va, vb)| {
if va.not_none() && vb.not_none() {
n += 1;
let (va, vb) = (va.unwrap().f64(), vb.unwrap().f64());
sum_a += va;
sum2_a += va * va;
sum_b += vb;
sum2_b += vb * vb;
sum_ab += va * vb;
};
let res = if n >= min_periods {
let n_f64 = n.f64();
let mean_a = sum_a / n_f64;
let mut var_a = sum2_a / n_f64;
let mean_b = sum_b / n_f64;
let mut var_b = sum2_b / n_f64;
var_a -= mean_a.powi(2);
var_b -= mean_b.powi(2);
if (var_a > EPS) & (var_b > EPS) {
let exy = sum_ab / n_f64;
let exey = sum_a * sum_b / n_f64.powi(2);
(exy - exey) / (var_a * var_b).sqrt()
} else {
f64::NAN
}
} else {
f64::NAN
};
if let Some((va, vb)) = remove_values {
if va.not_none() && vb.not_none() {
n -= 1;
let (va, vb) = (va.unwrap().f64(), vb.unwrap().f64());
sum_a -= va;
sum2_a -= va * va;
sum_b -= vb;
sum2_b -= vb * vb;
sum_ab -= va * vb;
};
}
res.cast()
},
out,
)
}
}
impl<T: IsNone, I: Vec1View<T>> RollingValidBinary<T> for I {}
#[cfg(test)]
mod tests {
use tea_core::testing::assert_vec1d_equal_numeric;
use super::*;
#[test]
fn test_cov() {
let data = vec![1, 5, 3, 2, 5];
let data2 = vec![2, 5, 4, 3, 6];
let out1: Vec<f64> = data.ts_vcov(&data2, 3, Some(2));
let out2: Vec<f64> = data
.rolling2_custom(&data2, 3, |v1, v2| v1.titer().vcov(v2.titer(), 2), None)
.unwrap();
assert_vec1d_equal_numeric(&out1, &out2, None);
assert_vec1d_equal_numeric(&out1, &vec![f64::NAN, 6., 3., 1.5, 2.333333333333332], None);
}
#[test]
fn test_corr() {
let data = vec![1, 5, 3, 2, 5];
let data2 = vec![2, 5, 4, 3, 6];
let out1: Vec<f64> = data.ts_vcorr(&data2, 3, Some(2));
let out2: Vec<f64> = data
.rolling2_custom(
&data2,
3,
|v1, v2| v1.titer().vcorr_pearson(v2.titer(), 2),
None,
)
.unwrap();
assert_vec1d_equal_numeric(&out1, &out2, None);
assert_vec1d_equal_numeric(
&out1,
&vec![f64::NAN, 1., 0.9819805060619652, 0.9819805060619652, 1.],
None,
);
}
}