mklmm.py 文件源码

def fit(self, X, C, y, regions, kernelType, reml=True, maxiter=100):

        #construct a list of kernel names (one for each region) 
        if (kernelType == 'adapt'): kernelNames = self.buildKernelAdapt(X, C, y, regions, reml, maxiter)
        else: kernelNames = [kernelType] * len(regions)         

        #perform optimization
        kernelObj, hyp_kernels, sig2e, fixedEffects = self.optimize(X, C, y, kernelNames, regions, reml, maxiter)

        #compute posterior distribution
        Ktraintrain = kernelObj.getTrainKernel(hyp_kernels)
        post = self.infExact_scipy_post(Ktraintrain, C, y, sig2e, fixedEffects)

        #fix intercept if phenotype is binary
        if (len(np.unique(y)) == 2):            
            controls = (y<y.mean())
            cases = ~controls
            meanVec = C.dot(fixedEffects)
            mu, var = self.getPosteriorMeanAndVar(np.diag(Ktraintrain), Ktraintrain, post, meanVec)                                     
            fixedEffects[0] -= optimize.minimize_scalar(self.getNegLL, args=(mu, np.sqrt(sig2e+var), controls, cases), method='brent').x                

        #construct trainObj
        trainObj = dict([])
        trainObj['sig2e'] = sig2e
        trainObj['hyp_kernels'] = hyp_kernels
        trainObj['fixedEffects'] = fixedEffects     
        trainObj['kernelNames'] = kernelNames

        return trainObj