title: How to measure performance of Gaussian mixture?
body: <p>I have a data set with 27211 samples and 90 attributes. This data set has no class label. I want to fit gaussian mixture to data set but I dont know how to measure performance. Can you help me? </p>
  
  <pre><code>import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  import random
  from sklearn.naive_bayes import GaussianNB
  from sklearn import mixture
  
  trainFile = TRAIN_PATH_NAME + "train" + str(j+1) + ".txt"
  trainData = pd.read_csv(trainFile, sep=",", header=None)
  
  np.random.seed(42)
  g = mixture.GMM(n_components=60)
  g.fit(trainData.values)
  print("IS_COVERGED: ", g.converged_)
  sampled = g.sample(trainData.values.shape[0])
  return sampled
  </code></pre>