PEATDB/Ekin/LM_Fitter.py
def AIC(self):
        """Computes the Akaike Information Criterion.
           see http://adorio-research.org/wordpress/?p=1932
           
           RSS-residual sum of squares of the fitting errors.
           k  - number of fitted parameters.
           n  - number of observations.
        """
        RSS = self.getRMSE()
        k = len(self.variables)
        n = len(self.exp_data)
        AIC = 2 * k + n * (log(2 * pi * RSS/n) + 1)
        return AIC
Computes the Akaike Information Criterion.
           see http://adorio-research.org/wordpress/?p=1932
           
           RSS-residual sum of squares of the fitting errors.
           k  - number of fitted parameters.
           n  - number of observations.
        
Computes the Akaike Information Criterion.

RSS-residual sum of squares of the fitting errors.
k  - number of fitted parameters.
n  - number of observations.
Computes the Akaike Information Criterion . RSS - residual sum of squares of the fitting errors . k - number of fitted parameters . n - number of observations .
def AIC(self):
        RSS = self.getRMSE()
        k = len(self.variables)
        n = len(self.exp_data)
        AIC = 2 * k + n * (log(2 * pi * RSS/n) + 1)
        return AIC
def AIC ( self ) : RSS = self . getRMSE ( ) k = len ( self . variables ) n = len ( self . exp_data ) AIC = 2 * k + n * ( log ( 2 * pi * RSS / n ) + 1 ) return AIC
Computes the Akaike Information Criterion.
Computes the Akaike Information Criterion .
"""Computes the Akaike Information Criterion.
           see http://adorio-research.org/wordpress/?p=1932
           
           RSS-residual sum of squares of the fitting errors.
           k  - number of fitted parameters.
           n  - number of observations.
        """
self