code: def self_similarity_matrix(feature_vectors):
      """
      This function computes the self-similarity matrix for a sequence
      of feature vectors.
      ARGUMENTS:
       - feature_vectors:    a np matrix (nDims x nVectors) whose i-th column
                             corresponds to the i-th feature vector
  
      RETURNS:
       - sim_matrix:         the self-similarity matrix (nVectors x nVectors)
      """
      scaler = StandardScaler()
      norm_feature_vectors = scaler.fit_transform(feature_vectors.T).T
      sim_matrix = 1.0 - distance.squareform(
          distance.pdist(norm_feature_vectors.T, "cosine")
      )
      return sim_matrix
  
imports: from __future__ import print_function
  import os
  import csv
  import glob
  import scipy
  import sklearn
  import numpy
  import hmmlearn.hmm
  import sklearn.cluster
  import pickle
  import matplotlib.pyplot
  from scipy.spatial import distance
  import sklearn.discriminant_analysis
  from sklearn.preprocessing import StandardScaler
  import sys
  import pyAudioAnalysis.audioBasicIO
  import pyAudioAnalysis.audioTrainTest
  import pyAudioAnalysis.MidTermFeatures
  import pyAudioAnalysis.ShortTermFeatures
label_desc: Use image node_id 2 for calling a global function with example usage: self_similarity_matrix(feature_vectors) and returns: sim_matrix