agramfort

#%% import ####################################################################
import mne
import numpy as np
import os

from mne.datasets import sample
data_path = sample.data_path()

#%% load group avg data #######################################################
os.environ["SUBJECTS_DIR"] = data_path + '/subjects'

agramfort

import numpy as np
from mayavi import mlab
from surfer import Brain
import mne
from mne.datasets import sample

data_path = sample.data_path()
subjects_dir = data_path + '/subjects'

agramfort

import numpy as np
from scipy import signal

import mne
from mne import (read_forward_solution, read_cov, read_label,
                 pick_types_evoked, pick_types_forward, read_evokeds)
from mne.io import Raw
from mne.datasets import sample
from mne.time_frequency import iir_filter_raw, morlet
from mne.simulation import generate_sparse_stc

agramfort

from scipy import stats

class GaussianKernelDensityEstimation(object):
    """docstring for GaussianKernelDensityEstimation"""
    def __init__(self):
        self.gkde = None

    def fit(self, X):
        """docstring for fit"""
        self.gkde = stats.gaussian_kde(X.T)

agramfort

"""
==========================
Feature Selection Shootout
==========================

Illustration of feature selection with :
    - RFE-SVC
    - Anova-SVC
    - L1-Logistic Regression
"""

agramfort

"""
Author: Oliver Mitevski

References:
A Generalized Linear Model for Principal Component Analysis of Binary Data,
Andrew I. Schein; Lawrence K. Saul; Lyle H. Ungar

The code was translated and adapted from Jakob Verbeek's
"Hidden Markov models and mixtures for Binary PCA" implementation in MATLAB

agramfort

from pprint import pprint
import numpy as np
from scipy import sparse
from scikits.learn.grid_search import GridSearchCV
from scikits.learn.cross_val import StratifiedKFold
from scikits.learn.metrics import f1_score, classification_report
from scikits.learn import svm
from scikits.learn.linear_model import LogisticRegression
from scikits.learn.linear_model.sparse import LogisticRegression as SparseLogisticRegression

agramfort

"""Testing the difference between two coefficients of correlation

Following:

Thöni, H. (1977), Testing the Difference Between two Coefficients of Correlation.
Biometrical Journal, 19: 355–359. doi: 10.1002/bimj.4710190506
http://onlinelibrary.wiley.com/doi/10.1002/bimj.4710190506/abstract
"""

from math import log, sqrt

agramfort

#!/usr/bin/env python

"""
Simple viewer for tri mesh files and OpenMEEG geometry files

Usage
-----

om_viz.py model.geom mesh1.tri mesh2.tri dipoles.txt

agramfort

import numpy as np
from scipy import linalg

from sklearn import datasets, svm, linear_model
from sklearn.svm import l1_min_c

iris = datasets.load_iris()
X = iris.data
y = iris.target