code: def spectral_embedding_(self, n):
          
          aff = self._affinity.copy()
          aff.flat[::aff.shape[0]+1] = 0
          laplacian = laplace(aff)
          decomp = eigen(laplacian)
          return CoordinateMatrix(normalise_rows(decomp.vecs[:,:n]))
docs: Old method for generating coords, used on original analysis
          of yeast data. Included to reproduce yeast result from paper.
          Reason for difference - switched to using spectral embedding
          method provided by scikit-learn (mainly because it spreads 
          points over a sphere, rather than a half sphere, so looks
          better plotted). Uses a different Laplacian matrix.