ee5e9a5a7320bac1b2ed27957e3a7087fdddbcad,dipy/core/stat/resampling.py,,jackknife,#Any#Any#Any#,207

Before Change

    N = len(pdf)
    pdf_mask = np.ones((N,),dtype="int16")
    if M == None:
        M = np.round(0.10 * len(pdf))
    jk_pdf = np.empty((M,))
    
    for ii in range(0, M):
        rand_index = np.round(np.random.random() * (N - 1))
        //choose a unique random sample to remove
        while pdf_mask[rand_index] == 0 :
            rand_index = np.round(np.random.random() * (N - 1))
        //set mask to zero for chosen random index
        pdf_mask[rand_index] = 0
        jk_pdf[ii] = statistic(pdf[pdf_mask > 0]) 
    
    return jk_pdf, np.mean(jk_pdf) - statistic(pdf), M/(M-1)*np.std(jk_pdf)

def residual_bootstrap(data):
    pass

After Change

    mask_index = np.copy(pdf_mask)
    if M == None:
        M = N
    M = np.minimum(M, N - 1)
    jk_pdf = np.empty((M,))
    
    for ii in range(0, M):
        rand_index = np.round(np.random.random() * (N - 1))

Instances