def test_truth_is_minimum_of_objfunc(self, nTrial=2):
vopt = HBO.beta2v(self.truebeta)
objfunc = lambda v: HBO.objectiveFunc(v, self.alpha, self.gamma, self.G, self.sumLogPi)
success = 0
fopt = objfunc(vopt)
print vopt, fopt, '**'
for trial in range(nTrial):
v = vopt + 0.01 * self.PRNG.rand(self.K)
v = np.minimum(v, 1.0 - 1e-8)
fv = objfunc(v)
if fopt < fv:
success += 1
else:
print v, fv
assert success > 0.98 * nTrial
def test_truth_is_minimum_of_objfunc(self, nTrial=2):
vopt = HBO.beta2v(self.truebeta)
objfunc = lambda v: HBO.objectiveFunc(v, self.alpha, self.gamma, self.
G, self.sumLogPi)
success = 0
fopt = objfunc(vopt)
print vopt, fopt, '**'
for trial in range(nTrial):
v = vopt + 0.01 * self.PRNG.rand(self.K)
v = np.minimum(v, 1.0 - 1e-8)
fv = objfunc(v)
if fopt < fv:
success += 1
else:
print v, fv
assert success > 0.98 * nTrial
def test_estimate_is_near_truth(self, nTrial=2):
vopt = HBO.beta2v(self.truebeta)
objfunc = lambda v: HBO.objectiveFunc(v, self.alpha, self.gamma, self.G, self.sumLogPi)
success = 0
print vopt, '*optimal*'
for trial in range(nTrial):
initV = np.random.rand(self.K)
v = HBO.estimate_v(self.gamma, self.alpha, self.G, self.K, self.sumLogPi, initV )
if np.all( np.abs(v - vopt) < .01 ):
success += 1
elif objfunc(v) < objfunc(vopt):
success += 1
else:
print v
print "%d/%d suceeded." % (success, nTrial)
assert success == nTrial
def test_estimate_is_near_truth(self, nTrial=2):
vopt = HBO.beta2v(self.truebeta)
objfunc = lambda v: HBO.objectiveFunc(v, self.alpha, self.gamma, self.
G, self.sumLogPi)
success = 0
print vopt, '*optimal*'
for trial in range(nTrial):
initV = np.random.rand(self.K)
v = HBO.estimate_v(self.gamma, self.alpha, self.G, self.K,
self.sumLogPi, initV)
if np.all(np.abs(v - vopt) < .01):
success += 1
elif objfunc(v) < objfunc(vopt):
success += 1
else:
print v
print "%d/%d suceeded." % (success, nTrial)
assert success == nTrial