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

"""
This module implements the Lowess function for nonparametric regression.

Functions:
lowess Fit a smooth nonparametric regression curve to a scatterplot.

For more information, see

William S. Cleveland: "Robust locally weighted regression and smoothing
scatterplots", Journal of the American Statistical Association, December 1979,

agramfort

"""A module which implements the continuous wavelet transform
with complex Morlet wavelets.

Author : Alexandre Gramfort, gramfort@nmr.mgh.harvard.edu (2011)
License : BSD 3-clause

inspired by Matlab code from Sheraz Khan & Brainstorm & SPM
"""

from math import sqrt

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

agramfort

Dendogram = namedtuple('Dendogram', ['children', 'n_leaves', 'n_components'])


def hierarchical_tree(X, linkage_criterion='ward', connectivity=None, n_components=None, copy=True):
    ...

    for ...:


        if linkage_criterion == 'ward':