How to use the tslearn.utils.to_time_series function in tslearn

15.358...
    >>> unnormalized_gak([1, 2, 3],
    ...                  [1., 2., 2., 3., 4.])  # doctest: +ELLIPSIS
    3.166...

    See Also
    --------
    gak : normalized version of GAK that ensures that k(x,x) = 1
    cdist_gak : Compute cross-similarity matrix using Global Alignment kernel

    References
    ----------
    .. [1] M. Cuturi, "Fast global alignment kernels," ICML 2011.
    """
    s1 = to_time_series(s1, remove_nans=True)
    s2 = to_time_series(s2, remove_nans=True)

    gram = _gak_gram(s1, s2, sigma=sigma)

    gak_val = njit_gak(s1, s2, gram)
    return gak_val

float
        Similarity score

    Examples
    --------
    >>> path, dist = dtw_subsequence_path([2., 3.], [1., 2., 2., 3., 4.])
    >>> path
    [(0, 2), (1, 3)]
    >>> dist
    0.0

    See Also
    --------
    dtw : Get the similarity score for DTW
    """
    subseq = to_time_series(subseq)
    longseq = to_time_series(longseq)
    acc_cost_mat = njit_accumulated_matrix_subsequence(subseq=subseq,
                                                       longseq=longseq)
    path = _return_path_subsequence(acc_cost_mat)
    return path, numpy.sqrt(numpy.min(acc_cost_mat[-1, :]))

1.0

    See Also
    --------
    dtw_path : Get both the matching path and the similarity score for DTW
    cdist_dtw : Cross similarity matrix between time series datasets

    References
    ----------
    .. [1] H. Sakoe, S. Chiba, "Dynamic programming algorithm optimization for
           spoken word recognition," IEEE Transactions on Acoustics, Speech and
           Signal Processing, vol. 26(1), pp. 43--49, 1978.

    """
    s1 = to_time_series(s1, remove_nans=True)
    s2 = to_time_series(s2, remove_nans=True)

    if global_constraint is not None:
        global_constraint_str = global_constraint
    else:
        global_constraint_str = ""

    mask = compute_mask(
        s1, s2,
        GLOBAL_CONSTRAINT_CODE[global_constraint_str],
        sakoe_chiba_radius=sakoe_chiba_radius,
        itakura_max_slope=itakura_max_slope)
    return njit_dtw(s1, s2, mask=mask)

array([[2.],
           [3.],
           [3.],
           [3.],
           [2.]])

    See also
    --------
    lb_keogh : Compute LB_Keogh similarity

    References
    ----------
    .. [1] Keogh, E. Exact indexing of dynamic time warping. In International
       Conference on Very Large Data Bases, 2002. pp 406-417.
    """
    return njit_lb_envelope(to_time_series(ts), radius=radius)

Parameters
        ----------
        X: array, shape = [m, d]
            First time series.
        Y: array, shape = [n, d]
            Second time series.

        Examples
        --------
        >>> SquaredEuclidean([1, 2, 2, 3], [1, 2, 3, 4]).compute()
        array([[0., 1., 4., 9.],
               [1., 0., 1., 4.],
               [1., 0., 1., 4.],
               [4., 1., 0., 1.]])
        """
        self.X = to_time_series(X).astype(numpy.float64)
        self.Y = to_time_series(Y).astype(numpy.float64)

1.0

    See Also
    --------
    dtw : Get only the similarity score for DTW
    cdist_dtw : Cross similarity matrix between time series datasets

    References
    ----------
    .. [1] H. Sakoe, S. Chiba, "Dynamic programming algorithm optimization for
           spoken word recognition," IEEE Transactions on Acoustics, Speech and
           Signal Processing, vol. 26(1), pp. 43--49, 1978.

    """
    s1 = to_time_series(s1, remove_nans=True)
    s2 = to_time_series(s2, remove_nans=True)

    if global_constraint is not None:
        global_constraint_str = global_constraint
    else:
        global_constraint_str = ""

    mask = compute_mask(
        s1, s2, GLOBAL_CONSTRAINT_CODE[global_constraint_str],
        sakoe_chiba_radius, itakura_max_slope
    )
    acc_cost_mat = njit_accumulated_matrix(s1, s2, mask=mask)
    path = _return_path(acc_cost_mat)
    return path, numpy.sqrt(acc_cost_mat[-1, -1])

>>> dtw([1, 2, 3], [1., 2., 2., 3., 4.])
    1.0

    See Also
    --------
    dtw_path : Get both the matching path and the similarity score for DTW
    cdist_dtw : Cross similarity matrix between time series datasets

    References
    ----------
    .. [1] H. Sakoe, S. Chiba, "Dynamic programming algorithm optimization for
           spoken word recognition," IEEE Transactions on Acoustics, Speech and
           Signal Processing, vol. 26(1), pp. 43--49, 1978.

    """
    s1 = to_time_series(s1, remove_nans=True)
    s2 = to_time_series(s2, remove_nans=True)

    if global_constraint is not None:
        global_constraint_str = global_constraint
    else:
        global_constraint_str = ""

    mask = compute_mask(
        s1, s2,
        GLOBAL_CONSTRAINT_CODE[global_constraint_str],
        sakoe_chiba_radius=sakoe_chiba_radius,
        itakura_max_slope=itakura_max_slope)
    return njit_dtw(s1, s2, mask=mask)

...                  sigma=2.)  # doctest: +ELLIPSIS
    15.358...
    >>> unnormalized_gak([1, 2, 3],
    ...                  [1., 2., 2., 3., 4.])  # doctest: +ELLIPSIS
    3.166...

    See Also
    --------
    gak : normalized version of GAK that ensures that k(x,x) = 1
    cdist_gak : Compute cross-similarity matrix using Global Alignment kernel

    References
    ----------
    .. [1] M. Cuturi, "Fast global alignment kernels," ICML 2011.
    """
    s1 = to_time_series(s1, remove_nans=True)
    s2 = to_time_series(s2, remove_nans=True)

    gram = _gak_gram(s1, s2, sigma=sigma)

    gak_val = njit_gak(s1, s2, gram)
    return gak_val