def test_bcd_against_reference(hazard):
data = [0] * 100 + [2] * 100
istreams = []
ostreams = []
detector = BayesianChangepointDetection(hazard, Gaussian(0.1, 0.1, 1, 1),
istreams, ostreams)
reference = offline_changepoint_detection(data, hazard,
Gaussian(0.1, 0.1, 1, 1))
for i, observation in enumerate(data):
pr = detector.step(observation)
# we want 0th to ith elements inclusive, so + 2
assert (pr == reference[:i + 2, i]).all()
def test_gaussian_update():
g = Gaussian(0, 0, 0, 0)
g.update(2)
g.update(-1)
g.update(1)
g.update(0)
# hand-calculated parameters:
# kappa = [0, 1, 2, 3, 4]
# alpha = [0, 1/2, 1, 3/2, 2]
# mu = [0, 0, 1/2, 0, 1/2]
# beta = [0, 0, 1/4, 1, 5/2]
assert np.isclose(g.kappa, [0, 1, 2, 3, 4]).all()
assert np.isclose(g.alpha, [0, 0.5, 1, 1.5, 2]).all()
assert np.isclose(g.mu, [0, 0, 0.5, 0, 0.5]).all()
assert np.isclose(g.beta, [0, 0, 0.25, 1, 2.5]).all()
def test_gaussian_update():
g = Gaussian(0, 0, 0, 0)
g.update(2)
g.update(-1)
g.update(1)
g.update(0)
# hand-calculated parameters:
# kappa = [0, 1, 2, 3, 4]
# alpha = [0, 1/2, 1, 3/2, 2]
# mu = [0, 0, 1/2, 0, 1/2]
# beta = [0, 0, 1/4, 1, 5/2]
assert np.isclose(g.kappa, [0, 1, 2, 3, 4]).all()
assert np.isclose(g.alpha, [0, 0.5, 1, 1.5, 2]).all()
assert np.isclose(g.mu, [0, 0, 0.5, 0, 0.5]).all()
assert np.isclose(g.beta, [0, 0, 0.25, 1, 2.5]).all()
def test_gaussian_pdf():
g = Gaussian(1, 0, 1, 1)
# g.pdf ~ t-distribution w/ np.sqrt(2) degrees of freedom
assert np.isclose(g.pdf(0), 0.25)
assert np.isclose(g.pdf(1), 0.17888543819998315)
def test_gaussian_pdf():
g = Gaussian(1, 0, 1, 1)
# g.pdf ~ t-distribution w/ np.sqrt(2) degrees of freedom
assert np.isclose(g.pdf(0), 0.25)
assert np.isclose(g.pdf(1), 0.17888543819998315)
