Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
106 lines
2.9 KiB
106 lines
2.9 KiB
"""Tests for polyutils module.
|
|
|
|
"""
|
|
import numpy as np
|
|
import numpy.polynomial.polyutils as pu
|
|
from numpy.testing import (
|
|
assert_almost_equal, assert_raises, assert_equal, assert_,
|
|
)
|
|
|
|
|
|
class TestMisc:
|
|
|
|
def test_trimseq(self):
|
|
for i in range(5):
|
|
tgt = [1]
|
|
res = pu.trimseq([1] + [0]*5)
|
|
assert_equal(res, tgt)
|
|
|
|
def test_as_series(self):
|
|
# check exceptions
|
|
assert_raises(ValueError, pu.as_series, [[]])
|
|
assert_raises(ValueError, pu.as_series, [[[1, 2]]])
|
|
assert_raises(ValueError, pu.as_series, [[1], ['a']])
|
|
# check common types
|
|
types = ['i', 'd', 'O']
|
|
for i in range(len(types)):
|
|
for j in range(i):
|
|
ci = np.ones(1, types[i])
|
|
cj = np.ones(1, types[j])
|
|
[resi, resj] = pu.as_series([ci, cj])
|
|
assert_(resi.dtype.char == resj.dtype.char)
|
|
assert_(resj.dtype.char == types[i])
|
|
|
|
def test_trimcoef(self):
|
|
coef = [2, -1, 1, 0]
|
|
# Test exceptions
|
|
assert_raises(ValueError, pu.trimcoef, coef, -1)
|
|
# Test results
|
|
assert_equal(pu.trimcoef(coef), coef[:-1])
|
|
assert_equal(pu.trimcoef(coef, 1), coef[:-3])
|
|
assert_equal(pu.trimcoef(coef, 2), [0])
|
|
|
|
|
|
class TestDomain:
|
|
|
|
def test_getdomain(self):
|
|
# test for real values
|
|
x = [1, 10, 3, -1]
|
|
tgt = [-1, 10]
|
|
res = pu.getdomain(x)
|
|
assert_almost_equal(res, tgt)
|
|
|
|
# test for complex values
|
|
x = [1 + 1j, 1 - 1j, 0, 2]
|
|
tgt = [-1j, 2 + 1j]
|
|
res = pu.getdomain(x)
|
|
assert_almost_equal(res, tgt)
|
|
|
|
def test_mapdomain(self):
|
|
# test for real values
|
|
dom1 = [0, 4]
|
|
dom2 = [1, 3]
|
|
tgt = dom2
|
|
res = pu.mapdomain(dom1, dom1, dom2)
|
|
assert_almost_equal(res, tgt)
|
|
|
|
# test for complex values
|
|
dom1 = [0 - 1j, 2 + 1j]
|
|
dom2 = [-2, 2]
|
|
tgt = dom2
|
|
x = dom1
|
|
res = pu.mapdomain(x, dom1, dom2)
|
|
assert_almost_equal(res, tgt)
|
|
|
|
# test for multidimensional arrays
|
|
dom1 = [0, 4]
|
|
dom2 = [1, 3]
|
|
tgt = np.array([dom2, dom2])
|
|
x = np.array([dom1, dom1])
|
|
res = pu.mapdomain(x, dom1, dom2)
|
|
assert_almost_equal(res, tgt)
|
|
|
|
# test that subtypes are preserved.
|
|
class MyNDArray(np.ndarray):
|
|
pass
|
|
|
|
dom1 = [0, 4]
|
|
dom2 = [1, 3]
|
|
x = np.array([dom1, dom1]).view(MyNDArray)
|
|
res = pu.mapdomain(x, dom1, dom2)
|
|
assert_(isinstance(res, MyNDArray))
|
|
|
|
def test_mapparms(self):
|
|
# test for real values
|
|
dom1 = [0, 4]
|
|
dom2 = [1, 3]
|
|
tgt = [1, .5]
|
|
res = pu. mapparms(dom1, dom2)
|
|
assert_almost_equal(res, tgt)
|
|
|
|
# test for complex values
|
|
dom1 = [0 - 1j, 2 + 1j]
|
|
dom2 = [-2, 2]
|
|
tgt = [-1 + 1j, 1 - 1j]
|
|
res = pu.mapparms(dom1, dom2)
|
|
assert_almost_equal(res, tgt)
|
|
|