class_docstr: Plotter for numeric independent variables with regression model.
  
  This does the computations and drawing for the `regplot` function, and
  is thus also used indirectly by `lmplot`.
class_docstr_tok: ["Plotter","for","numeric","independent","variables","with","regression","model",".","This","does","the","computations","and","drawing","for","the","`","regplot","`","function",",","and","is","thus","also","used","indirectly","by","`","lmplot","`","."]
docstr: Low-level regression and prediction using linear algebra.
docstr_tok: ["Low-level","regression","and","prediction","using","linear","algebra","."]
code: def fit_fast(self, grid):
      """Low-level regression and prediction using linear algebra."""
  
      def reg_func(_x, _y):
          return np.linalg.pinv(_x).dot(_y)
  
      X, y = np.c_[np.ones(len(self.x)), self.x], self.y
      grid = np.c_[np.ones(len(grid)), grid]
      yhat = grid.dot(reg_func(X, y))
      if self.ci is None:
          return yhat, None
  
      beta_boots = algo.bootstrap(
          X,
          y,
          func=reg_func,
          n_boot=self.n_boot,
          units=self.units,
          seed=self.seed,
      ).T
      yhat_boots = grid.dot(beta_boots).T
      return yhat, yhat_boots
  
imports: import copy
  from textwrap import dedent
  import warnings
  import numpy
  import pandas
  import matplotlib
  import matplotlib.pyplot
  from .None import utils
  from .None import algorithms
  from .axisgrid import FacetGrid, _facet_docs
label_desc: Use image node_id 6 for calling the _RegressionPlotter obj's underlying member method code with example usage: obj.fit_fast(grid) and returns: yhat,  yhat_boots, np, yhat,  None