eval_model.py 文件源码

def estimate_std_dev(indep_variable, dep_variable):
    """

    Parameters
    ----------
    indep_variable : ndarray, list(float)
        Independent variable (e.g., temperature, pressure)
    dep_variable : ndarray, list(float)
        Dependent variable (e.g., ignition delay)

    Returns
    -------
    standard_dev : float
        Standard deviation of difference between data and best-fit line

    """

    assert len(indep_variable) == len(dep_variable), \
        'independent and dependent variables not the same length'

    # ensure data sorted based on independent variable to avoid some problems
    sorted_vars = sorted(zip(indep_variable, dep_variable))
    indep_variable = [pt[0] for pt in sorted_vars]
    dep_variable = [pt[1] for pt in sorted_vars]

    # spline fit of the data
    if len(indep_variable) == 1 or len(indep_variable) == 2:
        # Fit of data will be perfect
        return min_deviation
    elif len(indep_variable) == 3:
        spline = UnivariateSpline(indep_variable, dep_variable, k=2)
    else:
        spline = UnivariateSpline(indep_variable, dep_variable)

    standard_dev = numpy.std(dep_variable - spline(indep_variable))

    if standard_dev < min_deviation:
        print('Standard deviation of {:.2f} too low, '
              'using {:.2f}'.format(standard_dev, min_deviation))
        standard_dev = min_deviation

    return standard_dev