class_docstr: Compute the affinities between samples.
  
  t-SNE takes as input an affinity matrix :math:`P`, and does not really care
  about anything else from the data. This means we can use t-SNE for any data
  where we are able to express interactions between samples with an affinity
  matrix.
  
  Attributes
  ----------
  P: array_like
      The :math:`N \times N` affinity matrix expressing interactions between
      :math:`N` initial data samples.
  
  verbose: bool
class_docstr_tok: ["Compute","the","affinities","between","samples",".","t-SNE","takes","as","input","an","affinity","matrix",":","math",":","`","P","`",",","and","does","not","really","care","about","anything","else","from","the","data",".","This","means","we","can","use","t-SNE","for","any","data","where","we","are","able","to","express","interactions","between","samples","with","an","affinity","matrix",".","Attributes","--","--","--","--","--","P",":","array_like","The",":","math",":","`","N","\\times","N","`","affinity","matrix","expressing","interactions","between",":","math",":","`","N","`","initial","data","samples",".","verbose",":","bool"]
code: def __init__(self, verbose=False):
      self.P = None
      self.verbose = verbose
      self.knn_index: nearest_neighbors.KNNIndex = None
  
imports: import logging
  import numbers
  import operator
  from functools import reduce
  import numpy
  import scipy.sparse
  from openTSNE import _tsne
  from openTSNE import nearest_neighbors
  from openTSNE import utils
  from openTSNE.utils import is_package_installed
  import warnings
label_desc: Use image node_id 1 to create a new Affinities object  with example: obj = Affinities(verbose)