class TestMixModelEMUnifAlpha(object):
def shortDescription(self):
return None
def setUp(self):
'''
Create a stupid simple case for making sure we're calculating things correctly
'''
self.alpha0 = 1.0
self.allocM = MixModel('EM', dict(alpha0=self.alpha0))
self.N = np.asarray([1., 2., 3, 4, 5.])
self.SS = SuffStatBag(K=5, D=1)
self.SS.setField('N', self.N, dims='K')
self.resp = np.random.rand(100, 3)
self.precompEntropy = np.sum(self.resp * np.log(self.resp), axis=0)
def test_update_global_params_EM(self):
self.allocM.update_global_params_EM(self.SS)
wTrue = (self.N + self.alpha0 - 1.0)
wTrue = wTrue / np.sum(wTrue)
wEst = self.allocM.w
print wTrue
print wEst
assert np.allclose(wTrue, wEst)
def test_get_global_suff_stats(self):
Data = bnpy.data.XData(np.random.randn(10, 1))
SS = self.allocM.get_global_suff_stats(Data,
dict(resp=self.resp),
doPrecompEntropy=True)
assert np.allclose(self.precompEntropy, SS.getELBOTerm('ElogqZ'))
assert np.allclose(np.sum(self.resp, axis=0), SS.N)
class TestMixModelEMUnifAlpha(object):
def shortDescription(self):
return None
def setUp(self):
'''
Create a stupid simple case for making sure we're calculating things correctly
'''
self.alpha0 = 1.0
self.allocM = MixModel('EM', dict(alpha0=self.alpha0))
self.N = np.asarray([1.,2.,3,4,5.])
self.SS = SuffStatBag(K=5, D=1)
self.SS.setField('N', self.N, dims='K')
self.resp = np.random.rand(100,3)
self.precompEntropy = np.sum(self.resp * np.log(self.resp), axis=0)
def test_update_global_params_EM(self):
self.allocM.update_global_params_EM(self.SS)
wTrue = (self.N + self.alpha0 - 1.0)
wTrue = wTrue / np.sum(wTrue)
wEst = self.allocM.w
print wTrue
print wEst
assert np.allclose(wTrue, wEst)
def test_get_global_suff_stats(self):
Data = bnpy.data.XData(np.random.randn(10,1))
SS = self.allocM.get_global_suff_stats(Data, dict(resp=self.resp), doPrecompEntropy=True)
assert np.allclose(self.precompEntropy, SS.getELBOTerm('ElogqZ'))
assert np.allclose( np.sum(self.resp, axis=0), SS.N)
def setUp(self):
'''
Create a stupid simple case for making sure we're calculating things correctly
'''
self.alpha0 = 1.0
self.allocM = MixModel('EM', dict(alpha0=self.alpha0))
self.N = np.asarray([1., 2., 3, 4, 5.])
self.SS = SuffStatBag(K=5, D=1)
self.SS.setField('N', self.N, dims='K')
self.resp = np.random.rand(100, 3)
self.precompEntropy = np.sum(self.resp * np.log(self.resp), axis=0)
def setUp(self):
self.alpha0 = 2.0
self.allocM = MixModel('EM', dict(alpha0=self.alpha0))
self.N = np.asarray([1., 2., 3, 4, 5.])
self.SS = SuffStatBag(K=5, D=1)
self.SS.setField('N', self.N, dims='K')
self.resp = np.random.rand(100, 3)
self.precompEntropy = np.sum(self.resp * np.log(self.resp), axis=0)
def setUp(self):
'''
Create a stupid simple case for making sure we're calculating things correctly
'''
self.alpha0 = 1.0
self.allocM = MixModel('EM', dict(alpha0=self.alpha0))
self.N = np.asarray([1.,2.,3,4,5.])
self.SS = SuffStatBag(K=5, D=1)
self.SS.setField('N', self.N, dims='K')
self.resp = np.random.rand(100,3)
self.precompEntropy = np.sum(self.resp * np.log(self.resp), axis=0)