docstr: Constructs the objective for the :func: `stoc_search_cost`.
  
  First calculates the Moore-Penrose inverse of the input `phi` and takes
  a dot product with the measured signal. The result obtained is again
  multiplied with `phi` to complete the projection of the variable into
  a transformed space. (see [1]_ and [2]_ for thorough discussion on
  Variable Projections and relevant cost functions).
  
  Parameters
  ----------
  phi : array
      Returns an array calculated from :func: `Phi`.
  
  signal : array
      The signal values measured for this model.
  
  Returns
  -------
  (signal -  S)^T(signal -  S)
  
  Notes
  -----
  to make cost function for Differential Evolution algorithm:
  .. math::
  
      (signal -  S)^T(signal -  S)
  
  References
  ----------
  .. [1] Fadnavis, Shreyas et.al. "MicroLearn: Framework for machine
         learning, reconstruction, optimization and microstructure
         modeling, Proceedings of: International Society of Magnetic
         Resonance in Medicine (ISMRM), Montreal, Canada, 2019.
  .. [2] Farooq, Hamza, et al. "Microstructure Imaging of Crossing (MIX)
         White Matter Fibers from diffusion MRI." Scientific reports 6
         (2016).
code: def ivim_mix_cost_one(self, phi, signal):
      """
      Constructs the objective for the :func: `stoc_search_cost`.
  
      First calculates the Moore-Penrose inverse of the input `phi` and takes
      a dot product with the measured signal. The result obtained is again
      multiplied with `phi` to complete the projection of the variable into
      a transformed space. (see [1]_ and [2]_ for thorough discussion on
      Variable Projections and relevant cost functions).
  
      Parameters
      ----------
      phi : array
          Returns an array calculated from :func: `Phi`.
  
      signal : array
          The signal values measured for this model.
  
      Returns
      -------
      (signal -  S)^T(signal -  S)
  
      Notes
      -----
      to make cost function for Differential Evolution algorithm:
      .. math::
  
          (signal -  S)^T(signal -  S)
  
      References
      ----------
      .. [1] Fadnavis, Shreyas et.al. "MicroLearn: Framework for machine
             learning, reconstruction, optimization and microstructure
             modeling, Proceedings of: International Society of Magnetic
             Resonance in Medicine (ISMRM), Montreal, Canada, 2019.
      .. [2] Farooq, Hamza, et al. "Microstructure Imaging of Crossing (MIX)
             White Matter Fibers from diffusion MRI." Scientific reports 6
             (2016).
  
      """
      # Moore-Penrose
      phi_mp = np.dot(np.linalg.inv(np.dot(phi.T, phi)), phi.T)
      f = np.dot(phi_mp, signal)
      yhat = np.dot(phi, f)  # - sigma
      return np.dot((signal - yhat).T, signal - yhat)
  
imports: import warnings
  import numpy
  from scipy.optimize import least_squares, differential_evolution
  from dipy.reconst.base import ReconstModel
  from dipy.reconst.multi_voxel import multi_voxel_fit
  from dipy.utils.optpkg import optional_package
label_desc: Use image node_id 4 for calling the IvimModelVP obj's underlying member method code with example usage: obj.ivim_mix_cost_one(phi, signal) and returns: np