def testDensity(self):
"Test vonMisesSufficient.density degenerate case"
for i in xrange(100):
randno = [uniform(-4 * pi, 4 * pi), uniform(-pi, pi), uniform(0.1, 100)]
fn = vonMises(*randno)
fn2 = vonMisesSufficient(randno[0], 1, 1, randno[1], randno[2])
self.assertAlmostEqual(fn.density(), fn2.density(), delta=0.001)
def testEvaluate(self):
"tests vonMisesSufficient.evaluate"
for i in xrange(100):
randno = [uniform(-4 * pi, 4 * pi), uniform(-pi, pi), uniform(0.1, 100)]
fn = vonMises(*randno)
fn2 = vonMisesSufficient(randno[0], 1, 1, randno[1], randno[2])
self.assertAlmostEqual(fn.evaluate(), fn2.evaluate(), delta=0.001)
def testEvaluateDKappa(self):
"Test vonMisesSufficient.evaluate_derivative_kappa degenerate case"
for i in xrange(100):
randno = [uniform(-4 * pi, 4 * pi), uniform(-pi, pi), uniform(0.1, 100)]
fn = vonMises(*randno)
fn2 = vonMisesSufficient(randno[0], 1, 1, randno[1], randno[2])
self.assertAlmostEqual(fn.evaluate_derivative_kappa(), fn2.evaluate_derivative_kappa(), delta=0.001)
def testAlternative(self):
"Test alternative vonMisesSufficient constructor"
for i in xrange(100):
x = uniform(-4 * pi, 4 * pi)
N = randint(1, 20)
meanv = uniform(-pi, pi)
stdev = uniform(0, 2 * pi)
obs = array([gauss(meanv, stdev) for j in xrange(N)])
cosbar = cos(obs).sum()
sinbar = sin(obs).sum()
R = sqrt(cosbar ** 2 + sinbar ** 2)
chiexp = acos(cosbar / R)
if sinbar < 0:
chiexp = -chiexp
kappa = uniform(0.1, 100)
fn = vonMisesSufficient(x, N, R, chiexp, kappa)
fn2 = vonMisesSufficient(x, obs, kappa)
self.assertAlmostEqual(fn.evaluate(), fn2.evaluate(), delta=1e-6)
def testAlternative(self):
"Test alternative vonMisesSufficient constructor"
for i in xrange(100):
x = uniform(-4 * pi, 4 * pi)
N = randint(1, 20)
meanv = uniform(-pi, pi)
stdev = uniform(0, 2 * pi)
obs = array([gauss(meanv, stdev) for j in xrange(N)])
cosbar = cos(obs).sum()
sinbar = sin(obs).sum()
R = sqrt(cosbar**2 + sinbar**2)
chiexp = acos(cosbar / R)
if sinbar < 0:
chiexp = -chiexp
kappa = uniform(0.1, 100)
fn = vonMisesSufficient(x, N, R, chiexp, kappa)
fn2 = vonMisesSufficient(x, obs, kappa)
self.assertAlmostEqual(fn.evaluate(), fn2.evaluate(), delta=1e-6)
def testEvaluateDX(self):
"Test vonMisesSufficient.evaluate_derivative_x"
for i in xrange(100):
x = uniform(-4 * pi, 4 * pi)
N = randint(1, 20)
R = randint(1, N)
chiexp = uniform(-pi, pi)
kappa = uniform(0.1, 100)
fn = vonMisesSufficient(x, N, R, chiexp, kappa)
self.assertAlmostEqual(fn.evaluate_derivative_x(), R * kappa * sin(x - chiexp), delta=0.001)
def testDensity(self):
"Test vonMisesSufficient.density degenerate case"
for i in xrange(100):
randno = [
uniform(-4 * pi, 4 * pi),
uniform(-pi, pi),
uniform(0.1, 100)
]
fn = vonMises(*randno)
fn2 = vonMisesSufficient(randno[0], 1, 1, randno[1], randno[2])
self.assertAlmostEqual(fn.density(), fn2.density(), delta=0.001)
def testEvaluateDX(self):
"Test vonMisesSufficient.evaluate_derivative_x"
for i in xrange(100):
x = uniform(-4 * pi, 4 * pi)
N = randint(1, 20)
R = randint(1, N)
chiexp = uniform(-pi, pi)
kappa = uniform(0.1, 100)
fn = vonMisesSufficient(x, N, R, chiexp, kappa)
self.assertAlmostEqual(fn.evaluate_derivative_x(),
R * kappa * sin(x - chiexp),
delta=0.001)
def testEvaluateDKappa(self):
"Test vonMisesSufficient.evaluate_derivative_kappa degenerate case"
for i in xrange(100):
randno = [
uniform(-4 * pi, 4 * pi),
uniform(-pi, pi),
uniform(0.1, 100)
]
fn = vonMises(*randno)
fn2 = vonMisesSufficient(randno[0], 1, 1, randno[1], randno[2])
self.assertAlmostEqual(fn.evaluate_derivative_kappa(),
fn2.evaluate_derivative_kappa(),
delta=0.001)
def testEvaluateDKappa(self):
"Test vonMisesSufficient.evaluate_derivative_kappa"
try:
from scipy.special import i0, i1
except ImportError:
self.skipTest("this test requires the scipy Python module")
for i in xrange(100):
x = uniform(-4 * pi, 4 * pi)
N = randint(1, 20)
R = randint(1, N)
chiexp = uniform(-pi, pi)
kappa = uniform(0.1, 100)
fn = vonMisesSufficient(x, N, R, chiexp, kappa)
self.assertAlmostEqual(
fn.evaluate_derivative_kappa(), N * i1(kappa) / i0(kappa) - R * cos(x - chiexp), delta=0.001
)
def testEvaluateDKappa(self):
"Test vonMisesSufficient.evaluate_derivative_kappa"
try:
from scipy.special import i0, i1
except ImportError:
self.skipTest("this test requires the scipy Python module")
for i in xrange(100):
x = uniform(-4 * pi, 4 * pi)
N = randint(1, 20)
R = randint(1, N)
chiexp = uniform(-pi, pi)
kappa = uniform(0.1, 100)
fn = vonMisesSufficient(x, N, R, chiexp, kappa)
self.assertAlmostEqual(fn.evaluate_derivative_kappa(),
N * i1(kappa) / i0(kappa) -
R * cos(x - chiexp),
delta=0.001)
def testDensity(self):
"Test vonMisesSufficient.density"
try:
from scipy.special import i0, i1
except ImportError:
self.skipTest("this test requires the scipy Python module")
for i in xrange(100):
# x, N, cmu1, smu2, kappa
x = uniform(-4 * pi, 4 * pi)
N = randint(1, 20)
R = randint(1, N)
chiexp = uniform(-pi, pi)
kappa = uniform(0.1, 10)
fn = vonMisesSufficient(x, N, R, chiexp, kappa)
cpp = fn.density()
py = exp(R * kappa * cos(x - chiexp)) / (2 * pi * i0(kappa) ** N)
if py == 0.0:
self.assertAlmostEqual(cpp, 0.0, delta=0.001)
else:
self.assertAlmostEqual(cpp / py, 1.0, delta=0.001)
def testDensity(self):
"Test vonMisesSufficient.density"
try:
from scipy.special import i0, i1
except ImportError:
self.skipTest("this test requires the scipy Python module")
for i in xrange(100):
#x, N, cmu1, smu2, kappa
x = uniform(-4 * pi, 4 * pi)
N = randint(1, 20)
R = randint(1, N)
chiexp = uniform(-pi, pi)
kappa = uniform(0.1, 10)
fn = vonMisesSufficient(x, N, R, chiexp, kappa)
cpp = fn.density()
py = exp(R * kappa * cos(x - chiexp)) / (2 * pi * i0(kappa)**N)
if py == 0.0:
self.assertAlmostEqual(cpp, 0.0, delta=0.001)
else:
self.assertAlmostEqual(cpp / py, 1.0, delta=0.001)
