glmnetPredict.py 文件源码

def lambda_interp(lambdau, s):
# lambda is the index sequence that is produced by the model
# s is the new vector at which evaluations are required.
# the value is a vector of left and right indices, and a vector of fractions.
# the new values are interpolated bewteen the two using the fraction
# Note: lambda decreases. you take:
# sfrac*left+(1-sfrac*right)
    if len(lambdau) == 1:
        nums = len(s)
        left = scipy.zeros([nums, 1], dtype = scipy.integer)
        right = left
        sfrac = scipy.zeros([nums, 1], dtype = scipy.float64)
    else:
        s[s > scipy.amax(lambdau)] = scipy.amax(lambdau)
        s[s < scipy.amin(lambdau)] = scipy.amin(lambdau)
        k = len(lambdau)
        sfrac = (lambdau[0] - s)/(lambdau[0] - lambdau[k - 1])
        lambdau = (lambdau[0] - lambdau)/(lambdau[0] - lambdau[k - 1]) 
        coord = scipy.interpolate.interp1d(lambdau, range(k))(sfrac)
        left = scipy.floor(coord).astype(scipy.integer, copy = False)
        right = scipy.ceil(coord).astype(scipy.integer, copy = False)
        #
        tf = left != right
        sfrac[tf] = (sfrac[tf] - lambdau[right[tf]])/(lambdau[left[tf]] - lambdau[right[tf]])
        sfrac[~tf] = 1.0
        #if left != right:
        #    sfrac = (sfrac - lambdau[right])/(lambdau[left] - lambdau[right])
        #else:
        #    sfrac[left == right] = 1.0

    result = dict()    
    result['left'] = left
    result['right'] = right
    result['frac'] = sfrac

    return(result)
# end of lambda_interp    
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