tansey

import numpy as np
from functools import partial
from scipy.optimize import fmin_l_bfgs_b
from sklearn.linear_model import LogisticRegression


def binary_mf(Y, nembeds, lam=None, lams=30, cv=5, max_steps=30, tol=1e-4, verbose=False):
    # Convert to a log-space grid
    if lam is None and isinstance(lams, int):
        lams = np.exp(np.linspace(np.log(1e-2), np.log(1), lams))

tansey

import matplotlib.pyplot as plt
import numpy as np
import statsmodels.api as sm


def generalized_liang_sim_xy(N=500, P=500, S=100):
    '''Generates data from a simple linear model'''
    X = (np.random.normal(size=(N,1)) + np.random.normal(size=(N,P))) / 2.
    w0 = np.random.normal(1, size=S//4)
    w1 = np.random.normal(2, size=S//4)

tansey

import numpy as np

def pav(y):
    """
    PAV uses the pair adjacent violators method to produce a monotonic
    smoothing of y

    translated from matlab by Sean Collins (2006) as part of the EMAP toolbox
    Author : Alexandre Gramfort
    license : BSD

tansey

import numpy as np
import numpy.ma as ma


def cap_outliers(points, thresh=3.5, data=None, median=None, med_abs_deviation=None):
    '''
    Cap outliers to be within a certain number of median deviations.
    '''
    if type(points) is np.float64:
        points = np.array([points])