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import numpy as np
from scipy import sparse
from datetime import datetime
from numba import njit

@njit
def deriv_logistic(p, b):
    """Derivative of the logistic loss"""
    p *= b
    if p > 0:

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from numba import njit

@njit
def prox_tv1d(w, stepsize):
    """
    
    Parameters
    ----------
    w: array
        vector of coefficieents

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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},

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=====================
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,

fabianp

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

import numpy as np

BIG = 1e12

def phi(t):
    """