def test_4():
"""Ensure that the basic conditions are satisfied by the multiattribute utility copula."""
constr = dict()
constr['version'] = 'scaled_archimedean'
period = np.random.randint(1, 100)
for _ in range(10):
_, _, _, copula_spec = generate_random_request(constr)
copula = UtilityCopulaCls(copula_spec)
# Normalized range and domain
np.testing.assert_equal(
copula.evaluate(x=0, y=0, t=period, is_normalized=True), 0.0)
np.testing.assert_equal(
copula.evaluate(x=1, y=1, t=period, is_normalized=True), 1.0)
# Nondecreasing in both arguments.
v = np.random.uniform(0, 1, 2)
base = copula.evaluate(*v, t=period, is_normalized=True)
v_upper = []
for item in v:
v_upper += [np.random.uniform(item, 1)]
np.testing.assert_equal(
copula.evaluate(*v_upper, t=period, is_normalized=True) >= base,
True)
def test_3():
"""Ensure that linear transformations of the uniattribute utility functions...
do not matter for the evaluation of the multiattribute utility copula.
"""
constr = dict()
constr['version'] = 'scaled_archimedean'
period = np.random.randint(1, 100)
for _ in range(10):
x, y, is_normalized, copula_spec = generate_random_request(constr)
copula = UtilityCopulaCls(copula_spec)
base = copula.evaluate(x=x, y=y, t=period, is_normalized=is_normalized)
for _ in range(10):
copula_spec['a'] = np.random.uniform(0.01, 10)
copula_spec['b'] = np.random.normal()
copula = UtilityCopulaCls(copula_spec)
np.testing.assert_almost_equal(
base,
copula.evaluate(x=x,
y=y,
t=period,
is_normalized=is_normalized))
def test_1():
"""Ensure that a multiattribute utility function is always created."""
for _ in range(10):
x, y, is_normalized, copula_spec = generate_random_request()
copula = UtilityCopulaCls(copula_spec)
# Get all possible periods t that we can evaluate the copula at.
periods = [0]
if copula_spec['version'] == 'nonstationary':
periods = copula_spec['nonstationary']['discount_factors'].keys()
for period in periods:
copula.evaluate(x=x, y=y, t=period, is_normalized=is_normalized)
#!/usr/bin/env python
"""This script is a basic property testing setup."""
from copulpy.tests.test_auxiliary import generate_random_request
from copulpy.clsUtilityCopula import UtilityCopulaCls
for _ in range(10000):
x, y, is_normalized, copula_spec = generate_random_request()
version = copula_spec['version']
if version in ['nonstationary', 'warmglow']:
periods = copula_spec[version]['discount_factors'].keys()
else:
periods = [0]
for period in periods:
UtilityCopulaCls(copula_spec).evaluate(x, y, period, is_normalized)
# from subprocess import CalledProcessError
# import pickle as pkl
# import subprocess
# import os
import numpy as np
# import pytest
from copulpy.tests.test_auxiliary import generate_random_request
from copulpy.clsUtilityCopula import UtilityCopulaCls
from copulpy.shared.auxiliary import distribute_copula_spec
# from copulpy.config_copulpy import PACKAGE_DIR
np.random.seed(123)
for _ in range(10):
x, y, is_normalized, copula_spec = generate_random_request(
{'version': 'nonstationary'})
# copula_spec['marginals'] = ['exponential', 'exponential']
# copula_spec['r'] = [-5, -5]
# copula_spec['bounds'] = [500, 500]
# print(is_normalized, 'out')
copula = UtilityCopulaCls(copula_spec)
util = copula.evaluate(x, y, is_normalized)
alpha, beta, gamma, y_scale, version = \
distribute_copula_spec(copula_spec, 'alpha', 'beta', 'gamma', 'y_scale', 'version')
print('version: {}'.format(version))
print('alpha: {0:.2f}, beta: {1:.2f}, gamma: {2:.2f}, y_scale: {3:.2f}'.
format(alpha, beta, gamma, y_scale))