コード例 #1
0
def test_predict_bvals():
    [actual1, actual2, predicted11,
    predicted12, predicted22, predicted21] = pn.predict_bvals(data_pv,
                                                              bvals_pv,
                                                              bvecs_pv,
                                                              mask_pv,
                                                              ad, rd, 0, 2,
                                                              n = 10,
                                                              solver = "nnls",
                                                          mode = "kfold_xval")

    rmse00 = np.sqrt(np.mean((actual1 - predicted11)**2))
    rmse02 = np.sqrt(np.mean((actual2 - predicted12)**2))
    rmse22 = np.sqrt(np.mean((actual2 - predicted22)**2))
    rmse20 = np.sqrt(np.mean((actual1 - predicted21)**2))

    npt.assert_(rmse00<300)
    npt.assert_(rmse02<300)
    npt.assert_(rmse22<300)
    npt.assert_(rmse20<300)
コード例 #2
0
    
    bvals = np.loadtxt(os.path.join(data_path, "bvals"))
    bvecs = np.loadtxt(os.path.join(data_path, "bvecs"))
    
    low = i*2000
    # Make sure not to go over the edge of the mask:
    high = np.min([(i+1) * 2000, int(np.sum(wm_data))])
    
    # Preserve memory by getting rid of this data:
    del wm_data

    # Now set the mask:
    mask = np.zeros(wm_data_file.shape)
    mask[wm_idx[0][low:high], wm_idx[1][low:high], wm_idx[2][low:high]] = 1
       
    # Predict 10% (n = 10)
    ad = {1000:1.6386920952169737, 2000:1.2919249903637751, 3000:0.99962593218241236}
    rd = {1000:0.33450124887561905, 2000:0.28377379537043729, 3000:0.24611723207420028}
    
    [actual1, actual2, predicted11,
     predicted13, predicted33, predicted31] = pn.predict_bvals(data, bvals, bvecs,
                                                                mask, ad, rd, b_idx1,
                                                                b_idx2, n = 10, mode = "kfold_xval")
                                                   
    np.save("/hsgs/nobackup/klchan13/predict_bvals11_%d.npy"%i, predicted11)
    np.save("/hsgs/nobackup/klchan13/predict_bvals13_%d.npy"%i, predicted13)
    np.save("/hsgs/nobackup/klchan13/predict_bvals33_%d.npy"%i, predicted33)
    np.save("/hsgs/nobackup/klchan13/predict_bvals31_%d.npy"%i, predicted31)
    t2 = time.time()
    print "This program took %4.2f minutes to run."%((t2 - t1)/60.)
コード例 #3
0
    # Predict 10% (n = 10)
    ad = {
        1000: 1.6386920952169737,
        2000: 1.2919249903637751,
        3000: 0.99962593218241236
    }
    rd = {
        1000: 0.33450124887561905,
        2000: 0.28377379537043729,
        3000: 0.24611723207420028
    }

    [actual1, actual2, predicted11, predicted13, predicted33,
     predicted31] = pn.predict_bvals(data,
                                     bvals,
                                     bvecs,
                                     mask,
                                     ad,
                                     rd,
                                     b_idx1,
                                     b_idx2,
                                     n=10,
                                     mode="kfold_xval")

    np.save("/hsgs/nobackup/klchan13/predict_bvals11_%d.npy" % i, predicted11)
    np.save("/hsgs/nobackup/klchan13/predict_bvals13_%d.npy" % i, predicted13)
    np.save("/hsgs/nobackup/klchan13/predict_bvals33_%d.npy" % i, predicted33)
    np.save("/hsgs/nobackup/klchan13/predict_bvals31_%d.npy" % i, predicted31)
    t2 = time.time()
    print "This program took %4.2f minutes to run." % ((t2 - t1) / 60.)