class_docstr: Compute metric pairwise.
  
  Parameters
  ----------
  func : function
      Function that computes the pairwise metric.
  
  name : str
      Name of the metric
docstr: Perform jackknife resampling on numpy arrays.
  
  Jackknife resampling is a technique to generate 'n' deterministic
  samples
  of size 'n-1' from a measured sample of size 'n'. Basically, the i-th
  sample, (1<=i<=n), is generated by means of removing the i-th
  measurement
  of the original sample. Like the bootstrap resampling, this statistical
  technique finds applications in estimating variance, bias,
  and confidence
  intervals.
  
  Parameters
  ----------
  x : numpy.ndarray
      Original sample (1-D array) from which the jackknife resamples
      will be
      generated.
  
  Returns
  -------
  resamples : numpy.ndarray
      The i-th row is the i-th jackknife sample, i.e., the original
      sample
      with the i-th measurement deleted.
  
  References
  ----------
  .. [1] modified version of
  http://docs.astropy.org/en/stable/_modules/astropy/stats/jackknife.html
code: def _jackknife_resampling(x):
      """Perform jackknife resampling on numpy arrays.
  
      Jackknife resampling is a technique to generate 'n' deterministic
      samples
      of size 'n-1' from a measured sample of size 'n'. Basically, the i-th
      sample, (1<=i<=n), is generated by means of removing the i-th
      measurement
      of the original sample. Like the bootstrap resampling, this statistical
      technique finds applications in estimating variance, bias,
      and confidence
      intervals.
  
      Parameters
      ----------
      x : numpy.ndarray
          Original sample (1-D array) from which the jackknife resamples
          will be
          generated.
  
      Returns
      -------
      resamples : numpy.ndarray
          The i-th row is the i-th jackknife sample, i.e., the original
          sample
          with the i-th measurement deleted.
  
      References
      ----------
      .. [1] modified version of
      http://docs.astropy.org/en/stable/_modules/astropy/stats/jackknife.html
      """
      n_instances = x.shape[0]
  
      # preallocate array
      dtype = x.dtype
      resamples = np.empty([n_instances, n_instances - 1], dtype=dtype)
  
      # jackknife resampling
      for i in range(n_instances):
          resamples[i] = np.delete(x, i)
  
      return resamples
  
file_path: sktime/sktime/benchmarking/metrics.py
imports: import numpy
  from sktime.benchmarking.base import BaseMetric
label_desc: Use image node_id 4 for calling the AggregateMetric obj's underlying member method code with example usage: obj._jackknife_resampling(x) and returns: resamples