docstr: Compare the similarity between two given bundles, model bundle,
  and extracted bundle.
  
  Parameters
  ----------
  model_bundle : Streamlines
  pruned_streamlines : Streamlines
  slr_select : tuple
      Select the number of streamlines from model to neirborhood of
      model to perform the local SLR.
  
  Returns
  -------
  ba_value : float
      bundle adjacency value between model bundle and pruned bundle
  bmd_value : float
      bundle minimum distance value between model bundle and
      pruned bundle
docstr_tok: ["Compare","the","similarity","between","two","given","bundles",",","model","bundle",",","and","extracted","bundle",".","Parameters","--","--","--","--","--","model_bundle",":","Streamlines","pruned_streamlines",":","Streamlines","slr_select",":","tuple","Select","the","number","of","streamlines","from","model","to","neirborhood","of","model","to","perform","the","local","SLR",".","Returns","--","--","--","-","ba_value",":","float","bundle","adjacency","value","between","model","bundle","and","pruned","bundle","bmd_value",":","float","bundle","minimum","distance","value","between","model","bundle","and","pruned","bundle"]
code: def evaluate_results(
      self, model_bundle, pruned_streamlines, slr_select
  ):
      """Compare the similarity between two given bundles, model bundle,
      and extracted bundle.
  
      Parameters
      ----------
      model_bundle : Streamlines
      pruned_streamlines : Streamlines
      slr_select : tuple
          Select the number of streamlines from model to neirborhood of
          model to perform the local SLR.
  
      Returns
      -------
      ba_value : float
          bundle adjacency value between model bundle and pruned bundle
      bmd_value : float
          bundle minimum distance value between model bundle and
          pruned bundle
      """
  
      spruned_streamlines = Streamlines(pruned_streamlines)
      recog_centroids = self._cluster_model_bundle(
          spruned_streamlines, model_clust_thr=1.25
      )
      mod_centroids = self._cluster_model_bundle(
          model_bundle, model_clust_thr=1.25
      )
      recog_centroids = Streamlines(recog_centroids)
      model_centroids = Streamlines(mod_centroids)
      ba_value = bundle_adjacency(
          set_number_of_points(recog_centroids, 20),
          set_number_of_points(model_centroids, 20),
          threshold=10,
      )
  
      BMD = BundleMinDistanceMetric()
      static = select_random_set_of_streamlines(
          model_bundle, slr_select[0]
      )
      moving = select_random_set_of_streamlines(
          pruned_streamlines, slr_select[1]
      )
      nb_pts = 20
      static = set_number_of_points(static, nb_pts)
      moving = set_number_of_points(moving, nb_pts)
  
      BMD.setup(static, moving)
      x0 = np.array([0, 0, 0, 0, 0, 0, 1.0, 1.0, 1, 0, 0, 0])  # affine
      bmd_value = BMD.distance(x0.tolist())
  
      return ba_value, bmd_value
  
imports: from time import time
  from itertools import chain
  import logging
  import numpy
  from dipy.tracking.streamline import set_number_of_points, nbytes, select_random_set_of_streamlines
  from dipy.segment.clustering import qbx_and_merge
  from dipy.tracking.distances import bundles_distances_mdf, bundles_distances_mam
  from dipy.align.streamlinear import StreamlineLinearRegistration, BundleMinDistanceMetric, BundleSumDistanceMatrixMetric, BundleMinDistanceAsymmetricMetric
  from dipy.tracking.streamline import Streamlines, length
  from dipy.utils.deprecator import deprecated_params
  from nibabel.affines import apply_affine
label_desc: Use image node_id 5 for calling the RecoBundles obj's underlying member method code with example usage: obj.evaluate_results(model_bundle, pruned_streamlines, slr_select) and returns: ba_value,  bmd_value