class_docstr: Class representing a simple nonlinear least squares model.
docstr: Get predicted values for `design` or ``self.design``
  
  Parameters
  ----------
  design : None or array, optional
      design at which to predict data.  If None (the default) then use the
      initial ``self.design``
  
  Returns
  -------
  y_predicted : array
      predicted data at given (or initial) design
docstr_tok: ["Get","predicted","values","for","`","design","`","or","``","self.design","``","Parameters","--","--","--","--","--","design",":","None","or","array",",","optional","design","at","which","to","predict","data",".","If","None","(","the","default",")","then","use","the","initial","``","self.design","``","Returns","--","--","--","-","y_predicted",":","array","predicted","data","at","given","(","or","initial",")","design"]
code: def predict(self, design=None):
      """Get predicted values for `design` or ``self.design``
  
      Parameters
      ----------
      design : None or array, optional
          design at which to predict data.  If None (the default) then use the
          initial ``self.design``
  
      Returns
      -------
      y_predicted : array
          predicted data at given (or initial) design
      """
      if design is None:
          design = self.design
      return self.f(design, self.theta)
  
file_path: nipy/nipy/algorithms/statistics/models/nlsmodel.py
imports: import numpy
  import numpy.linalg
  from .model import Model
label_desc: Use image node_id 6 for calling the NLSModel obj's underlying member method code with example usage: obj.predict(design) and returns: self