romspline/cross_validation.py
def stats(self):
    """Compute means and medians of each set of validation errors from 
    a Monte Carlo K-fold cross-validation study. The validation errors
    considered are those greater than or equal to the reduced-order
    spline tolerance.
    
    Attributes Made
    ---------------
      means_all_errors_ge_tol   -- means of each set of validation errors
                                   that are greater than or equal to the
                                   reduced-order spline tolerance
      medians_all_errors_ge_tol -- medians of each set of validation errors
                                   that are greater than or equal to the
                                   reduced-order spline tolerance
    
    Comments
    --------
    A single trial of a Monte Carlo K-fold cross-validation study produces
    K validation errors, which are the largest absolute errors in each 
    of the K subsets. Of these K validation errors, only those greater than
    or equal to the tolerance used to build the reduced-order spline are
    used to compute the means and medians here. Each mean and median is
    computed for each of the Monte Carlo trials so that 100 trials yields
    100 such means and medians.
    """
    
    # Mean of error means and sample standard deviation
    _means = []
    _medians = []
    for ee in self.all_errors_ge_tol:
      if ee != []:
        _means.append( np.mean(ee) )
        _medians.append( np.median(ee) )
    
    self.means_all_errors_ge_tol = _means
    self.medians_all_errors_ge_tol = _medians
    
    # Specific quantiles of error means
    #self.mc_lower_quantile, self.mc_median, self.mc_upper_quantile = mquantiles(self.mc_mean_errors, prob=[lq, 0.5, uq])
Compute means and medians of each set of validation errors from 
    a Monte Carlo K-fold cross-validation study. The validation errors
    considered are those greater than or equal to the reduced-order
    spline tolerance.
    
    Attributes Made
    ---------------
      means_all_errors_ge_tol   -- means of each set of validation errors
                                   that are greater than or equal to the
                                   reduced-order spline tolerance
      medians_all_errors_ge_tol -- medians of each set of validation errors
                                   that are greater than or equal to the
                                   reduced-order spline tolerance
    
    Comments
    --------
    A single trial of a Monte Carlo K-fold cross-validation study produces
    K validation errors, which are the largest absolute errors in each 
    of the K subsets. Of these K validation errors, only those greater than
    or equal to the tolerance used to build the reduced-order spline are
    used to compute the means and medians here. Each mean and median is
    computed for each of the Monte Carlo trials so that 100 trials yields
    100 such means and medians.
    
Compute means and medians of each set of validation errors from
a Monte Carlo K-fold cross-validation study. The validation errors
considered are those greater than or equal to the reduced-order
spline tolerance.

Attributes Made



Comments

A single trial of a Monte Carlo K-fold cross-validation study produces
K validation errors, which are the largest absolute errors in each
of the K subsets. Of these K validation errors, only those greater than
or equal to the tolerance used to build the reduced-order spline are
used to compute the means and medians here. Each mean and median is
computed for each of the Monte Carlo trials so that 100 trials yields
100 such means and medians.
Compute means and medians of each set of validation errors from a Monte Carlo K - fold cross - validation study . The validation errors considered are those greater than or equal to the reduced - order spline tolerance . Attributes Made Comments A single trial of a Monte Carlo K - fold cross - validation study produces K validation errors which are the largest absolute errors in each of the K subsets . Of these K validation errors only those greater than or equal to the tolerance used to build the reduced - order spline are used to compute the means and medians here . Each mean and median is computed for each of the Monte Carlo trials so that 100 trials yields 100 such means and medians .
def stats(self):
    _means = []
    _medians = []
    for ee in self.all_errors_ge_tol:
      if ee != []:
        _means.append( np.mean(ee) )
        _medians.append( np.median(ee) )
    self.means_all_errors_ge_tol = _means
    self.medians_all_errors_ge_tol = _medians
def stats ( self ) : _means = [ ] _medians = [ ] for ee in self . all_errors_ge_tol : if ee != [ ] : _means . append ( np . mean ( ee ) ) _medians . append ( np . median ( ee ) ) self . means_all_errors_ge_tol = _means self . medians_all_errors_ge_tol = _medians
Compute means and medians of each set of validation errors from
a Monte Carlo K-fold cross-validation study.
Compute means and medians of each set of validation errors from a Monte Carlo K - fold cross - validation study .
"""Compute means and medians of each set of validation errors from 
    a Monte Carlo K-fold cross-validation study. The validation errors
    considered are those greater than or equal to the reduced-order
    spline tolerance.
    
    Attributes Made
    ---------------
      means_all_errors_ge_tol   -- means of each set of validation errors
                                   that are greater than or equal to the
                                   reduced-order spline tolerance
      medians_all_errors_ge_tol -- medians of each set of validation errors
                                   that are greater than or equal to the
                                   reduced-order spline tolerance
    
    Comments
    --------
    A single trial of a Monte Carlo K-fold cross-validation study produces
    K validation errors, which are the largest absolute errors in each 
    of the K subsets. Of these K validation errors, only those greater than
    or equal to the tolerance used to build the reduced-order spline are
    used to compute the means and medians here. Each mean and median is
    computed for each of the Monte Carlo trials so that 100 trials yields
    100 such means and medians.
    """ # Mean of error means and sample standard deviation # Specific quantiles of error means #self.mc_lower_quantile, self.mc_median, self.mc_upper_quantile = mquantiles(self.mc_mean_errors, prob=[lq, 0.5, uq])
self