def test_init(self):
self.sampler = CosmoHammerSampler(
params=self.params,
likelihoodComputationChain=LikelihoodComputationChain(),
filePrefix=self._getTempFilePrefix(),
walkersRatio=10,
burninIterations=1,
sampleIterations=1)
assert isinstance(self.sampler.storageUtil, SampleFileUtil)
assert isinstance(self.sampler.stopCriteriaStrategy,
IterationStopCriteriaStrategy)
assert isinstance(self.sampler.initPositionGenerator,
SampleBallPositionGenerator)
assert self.sampler.likelihoodComputationChain.params is not None
def setup(self):
"""
Initialise some data vectors used for the comparisons by the other functions.
"""
self.params = np.array([[70, 40, 100, 3], [0.0226, 0.005, 0.1, 0.001],
[0.122, 0.01, 0.99, 0.01],
[2.1e-9, 1.48e-9, 5.45e-9, 1e-10],
[0.96, 0.5, 1.5, 0.02],
[0.09, 0.01, 0.8, 0.03], [1, 0, 2, 0.4]])
#the real means..
means = [70.704, 0.02256, 0.1115, 2.18474E-09, 0.9688, 0.08920]
# ...and non-trivial covariance matrix.
cov = np.array(
[[6.11E+00, 0, 0, 0, 0, 0], [7.19E-04, 3.26E-07, 0, 0, 0, 0],
[-1.19E-02, -3.37E-07, 3.14E-05, 0, 0, 0],
[-3.56E-11, 1.43E-14, 1.76E-13, 5.96E-21, 0, 0],
[2.01E-02, 6.37E-06, -2.13E-05, 3.66E-13, 1.90E-04, 0],
[1.10E-02, 2.36E-06, -1.92E-05, 8.70E-13, 7.32E-05, 2.23E-04]])
cov += cov.T - np.diag(cov.diagonal())
# Invert the covariance matrix
icov = np.linalg.inv(cov)
chain = LikelihoodComputationChain()
pseudoLikelihood = PseudoCmbModule(icov, means)
chain.addLikelihoodModule(pseudoLikelihood)
chain.setup()
posGen = FlatPositionGenerator()
self.sampler = CosmoHammerSampler(params=self.params,
likelihoodComputationChain=chain,
filePrefix=self._getTempFilePrefix(),
walkersRatio=10,
burninIterations=1,
sampleIterations=11,
initPositionGenerator=posGen,
storageUtil=InMemoryStorageUtil())
def test_init(self):
self.sampler = CosmoHammerSampler(
params= self.params,
likelihoodComputationChain=LikelihoodComputationChain(),
filePrefix=self._getTempFilePrefix(),
walkersRatio=10,
burninIterations=1,
sampleIterations=1)
assert isinstance(self.sampler.storageUtil, SampleFileUtil)
assert isinstance(self.sampler.stopCriteriaStrategy, IterationStopCriteriaStrategy)
assert isinstance(self.sampler.initPositionGenerator, SampleBallPositionGenerator)
assert self.sampler.likelihoodComputationChain.params is not None
def setup(self):
"""
Initialise some data vectors used for the comparisons by the other functions.
"""
self.params = np.array([[70, 40, 100, 3],
[0.0226, 0.005, 0.1, 0.001],
[0.122, 0.01, 0.99, 0.01],
[2.1e-9, 1.48e-9, 5.45e-9, 1e-10],
[0.96, 0.5, 1.5, 0.02],
[0.09, 0.01, 0.8, 0.03],
[1,0,2,0.4] ])
#the real means..
means = [70.704, 0.02256, 0.1115, 2.18474E-09, 0.9688, 0.08920]
# ...and non-trivial covariance matrix.
cov = np.array([[6.11E+00, 0, 0, 0, 0, 0],
[7.19E-04, 3.26E-07, 0, 0, 0, 0],
[-1.19E-02, -3.37E-07, 3.14E-05, 0, 0, 0],
[-3.56E-11, 1.43E-14, 1.76E-13, 5.96E-21, 0, 0],
[2.01E-02, 6.37E-06, -2.13E-05, 3.66E-13, 1.90E-04, 0],
[1.10E-02, 2.36E-06, -1.92E-05, 8.70E-13, 7.32E-05, 2.23E-04]])
cov += cov.T - np.diag(cov.diagonal())
# Invert the covariance matrix
icov = np.linalg.inv(cov)
chain = LikelihoodComputationChain()
pseudoLikelihood = PseudoCmbModule(icov, means)
chain.addLikelihoodModule(pseudoLikelihood)
chain.setup()
posGen = FlatPositionGenerator()
self.sampler = CosmoHammerSampler(
params= self.params,
likelihoodComputationChain=chain,
filePrefix=self._getTempFilePrefix(),
walkersRatio=10,
burninIterations=1,
sampleIterations=11,
initPositionGenerator=posGen,
storageUtil=InMemoryStorageUtil())
class TestCosmoHammerSampler(object):
params = None
sampler = None
def setup(self):
"""
Initialise some data vectors used for the comparisons by the other functions.
"""
self.params = np.array([[70, 40, 100, 3],
[0.0226, 0.005, 0.1, 0.001],
[0.122, 0.01, 0.99, 0.01],
[2.1e-9, 1.48e-9, 5.45e-9, 1e-10],
[0.96, 0.5, 1.5, 0.02],
[0.09, 0.01, 0.8, 0.03],
[1,0,2,0.4] ])
#the real means..
means = [70.704, 0.02256, 0.1115, 2.18474E-09, 0.9688, 0.08920]
# ...and non-trivial covariance matrix.
cov = np.array([[6.11E+00, 0, 0, 0, 0, 0],
[7.19E-04, 3.26E-07, 0, 0, 0, 0],
[-1.19E-02, -3.37E-07, 3.14E-05, 0, 0, 0],
[-3.56E-11, 1.43E-14, 1.76E-13, 5.96E-21, 0, 0],
[2.01E-02, 6.37E-06, -2.13E-05, 3.66E-13, 1.90E-04, 0],
[1.10E-02, 2.36E-06, -1.92E-05, 8.70E-13, 7.32E-05, 2.23E-04]])
cov += cov.T - np.diag(cov.diagonal())
# Invert the covariance matrix
icov = np.linalg.inv(cov)
chain = LikelihoodComputationChain()
pseudoLikelihood = PseudoCmbModule(icov, means)
chain.addLikelihoodModule(pseudoLikelihood)
chain.setup()
posGen = FlatPositionGenerator()
self.sampler = CosmoHammerSampler(
params= self.params,
likelihoodComputationChain=chain,
filePrefix=self._getTempFilePrefix(),
walkersRatio=10,
burninIterations=1,
sampleIterations=11,
initPositionGenerator=posGen,
storageUtil=InMemoryStorageUtil())
def test_init(self):
self.sampler = CosmoHammerSampler(
params= self.params,
likelihoodComputationChain=LikelihoodComputationChain(),
filePrefix=self._getTempFilePrefix(),
walkersRatio=10,
burninIterations=1,
sampleIterations=1)
assert isinstance(self.sampler.storageUtil, SampleFileUtil)
assert isinstance(self.sampler.stopCriteriaStrategy, IterationStopCriteriaStrategy)
assert isinstance(self.sampler.initPositionGenerator, SampleBallPositionGenerator)
assert self.sampler.likelihoodComputationChain.params is not None
def test_no_burn_in(self):
self.sampler.resetSampler()
self.sampler.burninIterations = 0;
self.sampler.startSampling()
assert self.sampler.storageUtil.samplesBurnin == None
assert self.sampler.storageUtil.probBurnin == None
def test_one_iter_burn_in(self):
self.sampler.resetSampler()
self.sampler.burninIterations = 1;
self.sampler.walkersRatio = 10;
self.sampler.startSampling()
assert self.sampler.storageUtil.samplesBurnin is not None
assert self.sampler.storageUtil.probBurnin is not None
params = 7
samples = params * self.sampler.burninIterations * self.sampler.walkersRatio
assert self.sampler.storageUtil.samplesBurnin.shape == (samples, params)
assert self.sampler.storageUtil.probBurnin.shape == (samples, )
def test_one_iter_sampling(self):
self.sampler.resetSampler()
self.sampler.burninIterations = 0;
self.sampler.sampleIterations = 1;
self.sampler.walkersRatio = 10;
self.sampler.startSampling()
params = 7
samples = params * self.sampler.sampleIterations * self.sampler.walkersRatio
assert self.sampler.storageUtil.samplesBurnin is None
assert self.sampler.storageUtil.probBurnin is None
assert self.sampler.storageUtil.samples.shape == (samples, params)
assert self.sampler.storageUtil.prob.shape == (samples, )
def _getTempFilePrefix(self):
return os.path.join(tempfile.mkdtemp(), "pseudoCmb")
class TestCosmoHammerSampler(object):
params = None
sampler = None
def setup(self):
"""
Initialise some data vectors used for the comparisons by the other functions.
"""
self.params = np.array([[70, 40, 100, 3], [0.0226, 0.005, 0.1, 0.001],
[0.122, 0.01, 0.99, 0.01],
[2.1e-9, 1.48e-9, 5.45e-9, 1e-10],
[0.96, 0.5, 1.5, 0.02],
[0.09, 0.01, 0.8, 0.03], [1, 0, 2, 0.4]])
#the real means..
means = [70.704, 0.02256, 0.1115, 2.18474E-09, 0.9688, 0.08920]
# ...and non-trivial covariance matrix.
cov = np.array(
[[6.11E+00, 0, 0, 0, 0, 0], [7.19E-04, 3.26E-07, 0, 0, 0, 0],
[-1.19E-02, -3.37E-07, 3.14E-05, 0, 0, 0],
[-3.56E-11, 1.43E-14, 1.76E-13, 5.96E-21, 0, 0],
[2.01E-02, 6.37E-06, -2.13E-05, 3.66E-13, 1.90E-04, 0],
[1.10E-02, 2.36E-06, -1.92E-05, 8.70E-13, 7.32E-05, 2.23E-04]])
cov += cov.T - np.diag(cov.diagonal())
# Invert the covariance matrix
icov = np.linalg.inv(cov)
chain = LikelihoodComputationChain()
pseudoLikelihood = PseudoCmbModule(icov, means)
chain.addLikelihoodModule(pseudoLikelihood)
chain.setup()
posGen = FlatPositionGenerator()
self.sampler = CosmoHammerSampler(params=self.params,
likelihoodComputationChain=chain,
filePrefix=self._getTempFilePrefix(),
walkersRatio=10,
burninIterations=1,
sampleIterations=11,
initPositionGenerator=posGen,
storageUtil=InMemoryStorageUtil())
def test_init(self):
self.sampler = CosmoHammerSampler(
params=self.params,
likelihoodComputationChain=LikelihoodComputationChain(),
filePrefix=self._getTempFilePrefix(),
walkersRatio=10,
burninIterations=1,
sampleIterations=1)
assert isinstance(self.sampler.storageUtil, SampleFileUtil)
assert isinstance(self.sampler.stopCriteriaStrategy,
IterationStopCriteriaStrategy)
assert isinstance(self.sampler.initPositionGenerator,
SampleBallPositionGenerator)
assert self.sampler.likelihoodComputationChain.params is not None
def test_no_burn_in(self):
self.sampler.resetSampler()
self.sampler.burninIterations = 0
self.sampler.startSampling()
assert self.sampler.storageUtil.samplesBurnin == None
assert self.sampler.storageUtil.probBurnin == None
def test_one_iter_burn_in(self):
self.sampler.resetSampler()
self.sampler.burninIterations = 1
self.sampler.walkersRatio = 10
self.sampler.startSampling()
assert self.sampler.storageUtil.samplesBurnin is not None
assert self.sampler.storageUtil.probBurnin is not None
params = 7
samples = params * self.sampler.burninIterations * self.sampler.walkersRatio
assert self.sampler.storageUtil.samplesBurnin.shape == (samples,
params)
assert self.sampler.storageUtil.probBurnin.shape == (samples, )
def test_one_iter_sampling(self):
self.sampler.resetSampler()
self.sampler.burninIterations = 0
self.sampler.sampleIterations = 1
self.sampler.walkersRatio = 10
self.sampler.startSampling()
params = 7
samples = params * self.sampler.sampleIterations * self.sampler.walkersRatio
assert self.sampler.storageUtil.samplesBurnin is None
assert self.sampler.storageUtil.probBurnin is None
assert self.sampler.storageUtil.samples.shape == (samples, params)
assert self.sampler.storageUtil.prob.shape == (samples, )
def _getTempFilePrefix(self):
return os.path.join(tempfile.mkdtemp(), "pseudoCmb")