entropy_estimators.py 文件源码

def mi(x, y, k=3, base=2):
  """Mutual information of x and y.
  x,y should be a list of vectors, e.g. x = [[1.3], [3.7], [5.1], [2.4]]
  if x is a one-dimensional scalar and we have four samples.
  """
  assert len(x)==len(y), 'Lists should have same length.'
  assert k <= len(x) - 1, 'Set k smaller than num samples - 1.'
  intens = 1e-10 # Small noise to break degeneracy, see doc.
  x = [list(p + intens*nr.rand(len(x[0]))) for p in x]
  y = [list(p + intens*nr.rand(len(y[0]))) for p in y]
  points = zip2(x,y)
  # Find nearest neighbors in joint space, p=inf means max-norm.
  tree = ss.cKDTree(points)
  dvec = [tree.query(point, k+1, p=float('inf'))[0][k] for point in points]
  a = avgdigamma(x,dvec)
  b = avgdigamma(y,dvec)
  c = digamma(k)
  d = digamma(len(x))
  return (-a-b+c+d) / log(base)