initial, unoptimized algoritm
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@ -335,12 +335,27 @@ impl<T: Scalar, R: Dim, C: Dim, S: RawStorage<T, R, C>> Matrix<T, R, C, S> {
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if self.is_empty() {
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if self.is_empty() {
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T::zero()
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T::zero()
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} else {
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} else {
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let val = self.iter().cloned().fold((T::zero(), T::zero()), |a, b| {
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// let val = self.iter().cloned().fold((T::zero(), T::zero()), |a, b| {
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(a.0 + b.clone() * b.clone(), a.1 + b)
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// (a.0 + b.clone() * b.clone(), a.1 + b)
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});
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// });
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let denom = T::one() / crate::convert::<_, T>(self.len() as f64);
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// let denom = T::one() / crate::convert::<_, T>(self.len() as f64);
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let vd = val.1 * denom.clone();
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// let vd = val.1 * denom.clone();
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val.0 * denom - vd.clone() * vd
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// val.0 * denom - vd.clone() * vd
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// let mean: T = self.iter().map(|&entry| entry).sum::<T>();
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//
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// let x: Vec<T> = (0..1000).map(|_| T::zero()).collect();
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// let s: T = x.iter().cloned().fold(T::zero(), |a, b| a + b);
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// cannot use sum since `T` is not `Sum` by trait bounds
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let total_sum = self.iter().cloned().fold(T::zero(), |a, b| a + b);
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let n_elements = crate::convert::<_, T>(self.len() as f64);
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let mean = total_sum / n_elements.clone();
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let variance = self.iter().cloned().fold(T::zero(), |acc, x| {
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acc + (x.clone() - mean.clone()) * (x.clone() - mean.clone())
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}) / n_elements.clone();
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variance
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}
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}
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}
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}
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@ -2,6 +2,6 @@
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use crate::DVector;
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use crate::DVector;
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#[test]
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#[test]
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fn test_variance_new() {
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fn test_variance_new() {
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let v = DVector::repeat(10_000, 100000000.1234);
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let v = DVector::repeat(10_000, 100000000.0);
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assert_eq!(v.variance(), 0.0)
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assert_eq!(v.variance(), 0.0)
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}
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}
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