def testEvaluateDJA(self):
"tests FNormal.evaluate_derivative_JA"
for i in range(100):
randno = [uniform(-100, 100), uniform(0.1, 100),
uniform(-100, 100), uniform(0.1, 100)]
fn = FNormal(*randno)
self.assertAlmostEqual(fn.evaluate_derivative_JA(),
-1 / randno[1], delta=0.001)
def testEvaluateDJA(self):
"tests FNormal.evaluate_derivative_JA"
for i in range(100):
randno = [uniform(-100, 100), uniform(0.1, 100),
uniform(-100, 100), uniform(0.1, 100)]
fn = FNormal(*randno)
self.assertAlmostEqual(fn.evaluate_derivative_JA(),
-1 / randno[1], delta=0.001)
def testEvaluateDFM(self):
"tests FNormal.evaluate_derivative_FM"
for i in range(100):
randno = [uniform(-100, 100), uniform(0.1, 100),
uniform(-100, 100), uniform(0.1, 100)]
fn = FNormal(*randno)
self.assertAlmostEqual(fn.evaluate_derivative_FM(),
(randno[2] - randno[0]) / randno[3] ** 2,
delta=0.001)
def testEvaluateDFM(self):
"tests FNormal.evaluate_derivative_FM"
for i in range(100):
randno = [uniform(-100, 100), uniform(0.1, 100),
uniform(-100, 100), uniform(0.1, 100)]
fn = FNormal(*randno)
self.assertAlmostEqual(fn.evaluate_derivative_FM(),
(randno[2] - randno[0]) / randno[3] ** 2,
delta=0.001)
def testEvaluateDSigma(self):
"tests FNormal.evaluate_derivative_sigma"
for i in xrange(100):
randno = [uniform(-100,100), uniform(0.1,100),
uniform(-100,100),uniform(0.1,100)]
fn=FNormal(*randno)
self.assertAlmostEqual(fn.evaluate_derivative_sigma(),
1/randno[3]-(randno[0]-randno[2])**2/randno[3]**3,
delta=0.001)
def testDensity(self):
"tests FNormal.density"
for i in range(100):
randno = [uniform(-100, 100), uniform(0.1, 100),
uniform(-100, 100), uniform(0.1, 100)]
fn = FNormal(*randno)
self.assertAlmostEqual(fn.density(),
randno[1] / (sqrt(2 * pi) * randno[3])
* exp(-(randno[0] - randno[2]) ** 2 / (2 * randno[3] ** 2)),
delta=0.001)
def testGetDensity(self):
"tests FNormal.get_density"
for i in range(100):
randno = [uniform(-100, 100), uniform(0.1, 100),
uniform(-100, 100), uniform(0.1, 100)]
fn = FNormal(*randno)
self.assertAlmostEqual(fn.get_density(),
randno[1] / (sqrt(2 * pi) * randno[3])
* exp(-(randno[0] - randno[2]) ** 2 / (2 * randno[3] ** 2)),
delta=0.001)
def testEvaluate(self):
"tests FNormal.evaluate"
for i in xrange(100):
randno = [uniform(-100,100), uniform(0.1,100),
uniform(-100,100),uniform(0.1,100)]
fn=FNormal(*randno)
self.assertAlmostEqual(fn.evaluate(),
0.5*log(2*pi) + log(randno[3]/randno[1])
+ 0.5/randno[3]**2*(randno[0]-randno[2])**2,
delta=0.001)
def testEvaluateDSigma(self):
"tests FNormal.evaluate_derivative_sigma"
for i in range(100):
randno = [uniform(-100, 100), uniform(0.1, 100),
uniform(-100, 100), uniform(0.1, 100)]
fn = FNormal(*randno)
self.assertAlmostEqual(fn.evaluate_derivative_sigma(),
1 /
randno[3] - (
randno[
0] - randno[
2]) ** 2 / randno[3] ** 3,
delta=0.001)
def testEvaluate(self):
"tests FNormal.evaluate"
for i in range(100):
randno = [uniform(-100, 100), uniform(0.1, 100),
uniform(-100, 100), uniform(0.1, 100)]
fn = FNormal(*randno)
self.assertAlmostEqual(fn.evaluate(),
0.5 *
log(2 * pi) + log(randno[3] / randno[1])
+ 0.5 /
randno[
3] ** 2 * (randno[0] - randno[2]) ** 2,
delta=0.001)
