| from nilearn.datasets import fetch_atlas_difumo | |
| from nilearn.input_data import NiftiSpheresMasker | |
| import numpy as np | |
| d256 = fetch_atlas_difumo(dimension=256) | |
| seeds = [[-52, 14, 0]] # this is your position, you may pick any you wish | |
| masker = NiftiSpheresMasker(seeds=seeds, radius=2).fit() | |
| weights = masker.transform(d256.maps) | |
| label = d256.labels[np.argmax(weights)] | |
| print('label:', label) |
| from nibabel import load | |
| tex1 = np.array(load('sample_map_lh.gii').darrays[0].data) | |
| tex2 = np.array(load('grp_stat_lh.gii').darrays[0].data) | |
| brain = Brain("fsaverage", 'lh', "inflated", title='', views=['lat']) | |
| brain.add_overlay(tex1, hemi='lh', min=3., max=8.) | |
| brain.save_montage('sample_map_lh.png') | |
| brain = Brain("fsaverage", 'lh', "inflated", title='', views=['lat']) |
| """ | |
| Get the neighbors of target voxels | |
| """ | |
| import numpy as np | |
| from nilearn import datasets | |
| from nilearn import input_data | |
| from sklearn.feature_extraction import image | |
| ### Load nyu_rest dataset ##################################################### |
| """ | |
| Experiment on gcv to understand the issue: | |
| It is indeed equivalent to leave-one-out selection with mean_squared error | |
| but in a setting without fit_intercept, and with y and X initially centered. | |
| In theory, this is correct. | |
| Author: Bertrand Thirion, 2016 | |
| """ | |
| import numpy as np | |
| from sklearn import linear_model |
| from os import path | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from nibabel import load | |
| from nibabel.loadsave import read_img_data | |
| # Local import to $NIPY/examples/labs/need_data | |
| from get_data_light import DATA_DIR, get_first_level_dataset |
| import numpy as np | |
| from sklearn import linear_model | |
| def _gcv(X, y, alphas): | |
| """Local gcv""" | |
| # singular values of the design matrix | |
| _, s, _ = np.linalg.svd(X, 0) | |
| clf = linear_model.Ridge() | |
| best_gcv = np.inf | |
| for alpha in alphas: |
This includes a test of the new GMM routines in https://github.com/bthirion/scikit-learn/tree/gmm-fixes
By changing the line
GMM = mixture.GMM
at the top of the file, we can plot the BIC and AIC for each variant of GMM. Standard GMM works beautifully: it settles in on 3 components, which are a good description of the data. DPGMM and VBGMM produce some unexpected results.