class_docstr: AbstractSolver base class for mystic optimizers.
      
class_docstr_tok: ["AbstractSolver","base","class","for","mystic","optimizers","."]
docstr: Generate Initial Points from Multivariate Normal.
  
  Args:
      mean (list[float]): mean values, list of length ``self.nDim``
      var (ndarray[float,float], default=None): covariance matrix
  
  Returns:
      None
  
  Notes:
      - ``var`` must be symmetric, positive-semidefinite, and length ``self.nDim``
      - if ``var`` is None, then ``var`` becomes the Identity matrix, ``I``
      - if ``var`` is a scalar, then ``var`` is set to ``var * I``
          
code: def SetMultinormalInitialPoints(self, mean, var=None):
      """Generate Initial Points from Multivariate Normal.
  
      Args:
          mean (list[float]): mean values, list of length ``self.nDim``
          var (ndarray[float,float], default=None): covariance matrix
  
      Returns:
          None
  
      Notes:
          - ``var`` must be symmetric, positive-semidefinite, and length ``self.nDim``
          - if ``var`` is None, then ``var`` becomes the Identity matrix, ``I``
          - if ``var`` is a scalar, then ``var`` is set to ``var * I``
      """
      from mystic.tools import random_state
  
      rng = random_state(module="numpy.random")
      assert len(mean) == self.nDim
      if var is None:
          var = numpy.eye(self.nDim)
      else:
          try:  # scalar ?
              float(var)
          except:  # nope. var better be matrix of the right size (no check)
              pass
          else:
              var = var * numpy.eye(self.nDim)
      for i in range(len(self.population)):
          self.population[i] = rng.multivariate_normal(
              mean, var
          ).tolist()
      return
  
imports: import random
  import numpy
  from numpy import inf, shape, asarray, absolute, asfarray, seterr
  from mystic.tools import wrap_function, wrap_nested, wrap_reducer
  from mystic.tools import wrap_bounds, wrap_penalty, reduced
  from klepto import isvalid, validate
  from collections.abc import Callable
label_desc: Use image node_id 23 for calling the AbstractSolver obj's underlying member method code with example usage: obj.SetMultinormalInitialPoints(mean, var) without return types