class_docstr: Use a bivariate Gaussian as a distribution model
class_docstr_tok: ["Use","a","bivariate","Gaussian","as","a","distribution","model"]
code: def loss(self, H):
      rho2 = self(H)
      I = (self.I - self.mI) / np.sqrt(nonzero(self.vI))
      J = (self.J - self.mJ) / np.sqrt(nonzero(self.vJ))
      L = rho2 * I**2 + rho2 * J**2 - 2 * self.rho * I * J
      tmp = nonzero(1.0 - rho2)
      L *= 0.5 / tmp
      L += 0.5 * np.log(tmp)
      return L
  
imports: import numpy
  from scipy.ndimage import gaussian_filter
  from ._registration import _L1_moments
label_desc: Use image node_id 1 for calling the CorrelationCoefficient obj's underlying member method code with example usage: obj.loss(H) and returns: L