class_docstr: Diagonal multivariate normal mixture model
class_docstr_tok: ["Diagonal","multivariate","normal","mixture","model"]
docstr: Multivariate normal quantile for when there is only 1 mixture component.
  
  Args:
      mus (nparray): (n,) array of means of normals
      sigmas (nparray): (n,) array of std of normals
      ps (nparray): (m,) array of probabilities
  Returns:
      quantiles (nparray): (n,m) array of quantiles for each mu and sigma
docstr_tok: ["Multivariate","normal","quantile","for","when","there","is","only","1","mixture","component",".","Args",":","mus","(","nparray",")",":","(","n",",",")","array","of","means","of","normals","sigmas","(","nparray",")",":","(","n",",",")","array","of","std","of","normals","ps","(","nparray",")",":","(","m",",",")","array","of","probabilities","Returns",":","quantiles","(","nparray",")",":","(","n",",","m",")","array","of","quantiles","for","each","mu","and","sigma"]
code: def multivariate_normal_quantile(mus, sigmas, ps):
      """
      Multivariate normal quantile for when there is only 1 mixture component.
  
      Args:
          mus (nparray): (n,) array of means of normals
          sigmas (nparray): (n,) array of std of normals
          ps (nparray): (m,) array of probabilities
      Returns:
          quantiles (nparray): (n,m) array of quantiles for each mu and sigma
      """
      ps = np.asarray(ps)
      standard_qs = np.sqrt(2) * special.erfinv(2 * ps - 1)
      standard_qs = np.reshape(standard_qs, (-1, 1))
      return mus + sigmas * standard_qs
  
file_path: elu-gelu-kernels/code/mvn_mixture/diag_mvn_mixture.py
imports: import numpy
  from scipy import special
  from scipy.stats.mstats import mquantiles
label_desc: Use image node_id 3 for calling the DiagMVNMixture obj's underlying member method code with example usage: obj.multivariate_normal_quantile(mus, sigmas, ps) and returns: unknown