s2gpp 1.0.2

Algorithm for Highly Efficient Detection of Correlation Anomalies in Multivariate Time Series
Documentation 
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use crate::data_manager::data_reader::read_data_;
use crate::data_manager::reference_dataset_builder::ReferenceDatasetBuilder;
use crate::data_manager::stats_collector::DatasetStats;
use crate::parameters::{Parameters, Role};
use ndarray::{arr2, s, Axis};
use ndarray_linalg::close_l1;

use crate::utils::Stats;

#[test]
fn test_correct_spacing() {
    let data = read_data_("data/test.csv");
    let parameters = Parameters {
        role: Role::Main {
            data_path: Some("data/test.csv".to_string()),
        },
        local_host: "127.0.0.1:8000".parse().unwrap(),
        pattern_length: 50,
        latent: 16,
        rate: 100,
        n_threads: 20,
        n_cluster_nodes: 1,
        ..Default::default()
    };

    let dataset_stats = DatasetStats::new(
        data.std_axis(Axis(0), 0.0),
        data.to_shared().min_axis(Axis(0)),
        data.to_shared().max_axis(Axis(0)),
        0,
    );

    let rdb = ReferenceDatasetBuilder::new(dataset_stats, parameters);
    let df_ref = rdb.build();
    let expected = arr2(&[
        [0.09360752, 0.63319328, 0.0458456],
        [0.09360752, 0.63319328, 0.0458456],
        [0.09360752, 0.63319328, 0.0458456],
    ]);

    close_l1(&df_ref.slice(s![0_usize, 0..3, 0..3]), &expected, 0.0005)
}