s2gpp 1.0.2

Algorithm for Highly Efficient Detection of Correlation Anomalies in Multivariate Time Series
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33

use actix::Message;
use ndarray::{ArcArray2, Array1, Array2};
use std::ops::Range;

#[derive(Message)]
#[rtype(Result = "()")]
pub(in crate::training::node_estimation) struct MultiKDEMessage {
    pub data: Array2<f32>,
}

#[derive(Message)]
#[rtype(Result = "()")]
pub(in crate::training::node_estimation) struct GaussianKDEMessage {
    pub column: ArcArray2<f32>,
}

#[derive(Message)]
#[rtype(Result = "()")]
pub(in crate::training::node_estimation) struct GaussianKDEResponse {
    pub kernel_estimate: Array1<f32>,
}

#[derive(Message)]
#[rtype(Result = "()")]
pub(in crate::training::node_estimation) struct EstimatorTask {
    pub data_range: Range<usize>,
}

#[derive(Message)]
#[rtype(Result = "()")]
pub(in crate::training::node_estimation) struct EstimatorResponse {
    pub estimate: Array1<f32>,
}