derekpowell

def horseshoe_prior(name, X, y, m, v, s):
    '''
    Regularizing horseshoe prior as introduced by Piironen & Vehtari
    https://arxiv.org/pdf/1707.01694.pdf
    
    name: variable name
    X: X (2-d array)
    y: y (for setting pseudo-variance)
    m: expected number of relevant features (must be < total N)
    v: regularizing student-t df

bshishov

import numpy as np

EPSILON = 1e-10


def _error(actual: np.ndarray, predicted: np.ndarray):
    """ Simple error """
    return actual - predicted

mishurov

CC=g++
MOC=moc-qt4
CFLAGS=-Wall
SOURCES=hello.cc hello_cls.cc
MOC_HEADERS=hello_cls.h
EXECUTABLE=hello
INCDIRS=-I/usr/include/qt4 -I/usr/include/qt4/QtGui -I/usr/include/qt4/QtCore
LIBS=-lQtCore -lQtGui
# Change postfixes
MOC_SOURCES=$(MOC_HEADERS:.h=.moc.cc)