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"""
Partial Correlation in Python (clone of Matlab's partialcorr)

This uses the linear regression approach to compute the partial 
correlation (might be slow for a huge number of variables). The 
algorithm is detailed here:

    http://en.wikipedia.org/wiki/Partial_correlation#Using_linear_regression

Taking X and Y two variables of interest and Z the matrix with all the variable minus {X, Y},

fabianp

=====================
Lasso and Elastic Net
=====================

Lasso and elastic net (L1 and L2 penalisation) implemented using a
coordinate descent.

The coefficients can be forced to be positive.

fabianp

"""
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,

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"""
Check that the gradient of the logistic regression is correct
"""

import numpy as np

BIG = 1e12

def phi(t):
    """

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#/bin/bash
source $HOME/envs/cron/bin/activate
cd $HOME/cron/nisl

# clean all previous code
rm -rf *

# clean import
git fetch origin
git reset --hard origin/master

fabianp

from __future__ import print_function
import numpy as np
from sklearn import linear_model
from datetime import datetime
import pylab as pl
import pylab


def errorfill(x, y, yerr, color=None, alpha_fill=0.3, ax=None, label=None):
    # helper function, stolen from http://tonysyu.github.com/plotting-error-bars.html

fabianp

import numpy as np
from scipy import linalg
from scipy.sparse import linalg as splinalg


def prox_l1(a, b):
    return np.sign(a) * np.fmax(np.abs(a) - b, 0)

def prox(X, t, v0, n_nonzero=1000, n=0, algo='dense', n_svals=10):
    """prox operator for trace norm