main.py 文件源码

def test(self, xtest, x, type_='smaller', alpha=0.05):
        """
        Parameters
        ----------
        type_: in ['smaller', 'equality']
            type of comparison to perform
        alpha: float
            significance level
        """
        # call function to make sure it has been evaluated a sufficient number of times
        if type_ not in ['smaller', 'equality']:
            raise NotImplementedError(type_)
        ftest, ftestse = self(xtest)
        f, fse = self(x)
        # get function values
        fxtest = np.array(self.cache[tuple(xtest)])
        fx = np.array(self.cache[tuple(x)])
        if np.mean(fxtest-fx) == 0.0:
            if type_ == 'equality':
                return True
            if type_ == 'smaller':
                return False
        if self.paired:
            # if values are paired then test on distribution of differences
            statistic, pvalue = stats.ttest_rel(fxtest, fx, axis=None)
        else:
            statistic, pvalue = stats.ttest_ind(fxtest, fx, equal_var=False, axis=None)
        if type_ == 'smaller':
            # if paired then df=N-1, else df=N1+N2-2=2*N-2 
            df = self.N-1 if self.paired else 2*self.N-2
            pvalue = stats.t.cdf(statistic, df) 
            # return true if null hypothesis rejected
            return pvalue < alpha
        if type_ == 'equality':
            # return true if null hypothesis not rejected
            return pvalue > alpha