Source code for etna.clustering.distances.euclidean_distance

from typing import TYPE_CHECKING

import numba
import numpy as np
import pandas as pd

from etna.clustering.distances.base import Distance

    from etna.datasets import TSDataset

@numba.cfunc(numba.float64(numba.float64[:], numba.float64[:]))
def euclidean_distance(x1: np.ndarray, x2: np.ndarray) -> np.floating:
    """Get euclidean distance between two arrays.

        first array
        second array

        distance between x1 and x2
    return np.linalg.norm(x1 - x2)

[docs]class EuclideanDistance(Distance): """Euclidean distance handler.""" def __init__(self, trim_series: bool = True): """Init EuclideanDistance. Parameters ---------- trim_series: if True, compare parts of series with common timestamp """ super().__init__(trim_series=trim_series) def _compute_distance(self, x1: np.ndarray, x2: np.ndarray) -> float: """Compute distance between x1 and x2.""" return euclidean_distance(x1=x1, x2=x2) def _get_average(self, ts: "TSDataset") -> pd.DataFrame: """Get series that minimizes squared distance to given ones according to the euclidean distance. Parameters ---------- ts: TSDataset with series to be averaged Returns ------- pd.DataFrame: dataframe with columns "timestamp" and "target" that contains the series """ centroid = pd.DataFrame({"timestamp": ts.index.values, "target": ts.df.mean(axis=1).values}) return centroid
__all__ = ["EuclideanDistance", "euclidean_distance"]