def test(self):
obs = np.concatenate((np.random.randn(10000, 1), 10 + np.random.randn(30000, 1))).T
mu, Sigma, weights = mixgauss_init(M=2, data=obs, cov_type='diag', method='kmeans')
assert np.all(np.abs(mu-np.array([[10, 0]])) < 1e-1) or np.all(np.abs(mu-np.array([[0, 10]])) < 1e-1)
assert np.abs(Sigma[:,:,0]-1)<1e-1
assert np.abs(Sigma[:,:,1]-1)<1e-1
assert np.all(np.abs(weights-np.array([[0.75], [0.25]])) < 0.2) or np.all(np.abs(weights-np.array([[0.25], [0.75]])) < 0.2)
def fit(self, obs):
obs = self._convertObs(obs)
O = obs[0].shape[0]
M = self.n_mix
Q = self.n_components
if 's' in self.init_params:
self.startprob_, _ = normalise(self.startprob_)
if 't' in self.init_params:
self.transmat_, _ = mk_stochastic(self.transmat_)
if 'm' in self.init_params or 'c' in self.init_params:
mu0, Sigma0, weights0 = mixgauss_init(
Q * M, obs, cov_type=self._covariance_type)
if 'm' in self.init_params:
self.means_ = np.transpose(np.reshape(mu0, (O, M, Q)),
(0, 2, 1))
if 'c' in self.init_params:
self.covars_ = np.transpose(np.reshape(Sigma0, (O, O, M, Q)),
(0, 1, 3, 2))
mixmat0, _ = mk_stochastic(np.random.rand(Q, M))
self.LL, prior1, transmat1, mu1, Sigma1, mixmat1 = mhmm_em(
data=obs,
prior=self.startprob_,
transmat=self.transmat_,
mu=self.means_,
Sigma=self.covars_,
mixmat=mixmat0,
max_iter=self.n_iter,
thresh=self.thresh,
cov_type=self._covariance_type,
adj_trans='t' in self.params,
adj_mix='w' in self.params,
adj_mu='m' in self.params,
adj_Sigma='c' in self.params)
self.startprob_ = prior1
self.transmat_ = transmat1
self.means_ = mu1
self.covars_ = Sigma1
self.weights_ = mixmat1
def test(self):
mean0 = [0, 10]
cov = [[.1, 0],
[0, .1]]
obs = np.random.multivariate_normal(mean0, cov, 10000)
mean1 = [5, 15]
cov = [[.1, 0],
[0, .1]]
obs = np.vstack((obs, np.random.multivariate_normal(mean1, cov, 30000)))
mu, Sigma, weights = mixgauss_init(M=2, data=obs.T, cov_type='diag', method='kmeans')
assert np.all(np.abs(mu[:,0]-mean0) < 1e-1) or np.all(np.abs(mu[:,1]-mean0) < 1e-1)
def test(self):
obs = np.concatenate(
(np.random.randn(10000, 1), 10 + np.random.randn(30000, 1))).T
mu, Sigma, weights = mixgauss_init(M=2,
data=obs,
cov_type='diag',
method='kmeans')
assert np.all(np.abs(mu - np.array([[10, 0]])) < 1e-1) or np.all(
np.abs(mu - np.array([[0, 10]])) < 1e-1)
assert np.abs(Sigma[:, :, 0] - 1) < 1e-1
assert np.abs(Sigma[:, :, 1] - 1) < 1e-1
assert np.all(
np.abs(weights - np.array([[0.75], [0.25]])) < 0.2) or np.all(
np.abs(weights - np.array([[0.25], [0.75]])) < 0.2)
def fit(self, obs):
obs = self._convertObs(obs)
O = obs[0].shape[0]
M = self.n_mix
Q = self.n_components
if "s" in self.init_params:
self.startprob_, _ = normalise(self.startprob_)
if "t" in self.init_params:
self.transmat_, _ = mk_stochastic(self.transmat_)
if "m" in self.init_params or "c" in self.init_params:
mu0, Sigma0, weights0 = mixgauss_init(Q * M, obs, cov_type=self._covariance_type)
if "m" in self.init_params:
self.means_ = np.transpose(np.reshape(mu0, (O, M, Q)), (0, 2, 1))
if "c" in self.init_params:
self.covars_ = np.transpose(np.reshape(Sigma0, (O, O, M, Q)), (0, 1, 3, 2))
mixmat0, _ = mk_stochastic(np.random.rand(Q, M))
self.LL, prior1, transmat1, mu1, Sigma1, mixmat1 = mhmm_em(
data=obs,
prior=self.startprob_,
transmat=self.transmat_,
mu=self.means_,
Sigma=self.covars_,
mixmat=mixmat0,
max_iter=self.n_iter,
thresh=self.thresh,
cov_type=self._covariance_type,
adj_trans="t" in self.params,
adj_mix="w" in self.params,
adj_mu="m" in self.params,
adj_Sigma="c" in self.params,
)
self.startprob_ = prior1
self.transmat_ = transmat1
self.means_ = mu1
self.covars_ = Sigma1
self.weights_ = mixmat1
def test(self):
mean0 = [0, 10]
cov = [[.1, 0], [0, .1]]
obs = np.random.multivariate_normal(mean0, cov, 10000)
mean1 = [5, 15]
cov = [[.1, 0], [0, .1]]
obs = np.vstack((obs, np.random.multivariate_normal(mean1, cov,
30000)))
mu, Sigma, weights = mixgauss_init(M=2,
data=obs.T,
cov_type='diag',
method='kmeans')
assert np.all(np.abs(mu[:, 0] - mean0) < 1e-1) or np.all(
np.abs(mu[:, 1] - mean0) < 1e-1)
def setUp(self):
self.prior = np.array([1, 0, 0])
self.transmat = np.matrix([[0, 1, 0], [0, 0, 1], [0, 0, 1]])
self.mu = np.zeros((2, 3, 2))
self.mu[:, :, 0] = np.array([[1, 2, 3], [1, 2, 3]])
self.mu[:, :, 1] = np.array([[4, 5, 6], [4, 5, 6]])
self.Sigma = np.zeros((2, 2, 3, 2))
for i in range(3):
self.Sigma[:, :, i, 0] = np.diag(np.ones((2, )) * 0.01)
self.Sigma[:, :, i, 1] = np.diag(np.ones((2, )) * 0.01)
self.mixmat = np.array([[.5, .5], [.5, .5], [.5, .5]])
try:
with open('MhmmEM2DTestGaussInit.cache', 'rb') as f:
cache = load(f)
self.obs = cache['obs']
self.prior0 = cache['prior0']
self.transmat0 = cache['transmat0']
self.mu0 = cache['mu0']
self.Sigma0 = cache['Sigma0']
self.mixmat0 = cache['mixmat0']
except:
self.obs, hidden = mhmm_sample(T=4,
numex=100,
initial_prob=self.prior,
transmat=self.transmat,
mu=self.mu,
Sigma=self.Sigma,
mixmat=self.mixmat)
self.prior0, _ = mk_stochastic(np.random.rand(3))
self.transmat0, _ = mk_stochastic(np.random.rand(3, 3))
O = self.obs.shape[0]
M = 2
Q = 3
mu0, Sigma0, weights0 = mixgauss_init(Q * M,
self.obs,
cov_type='diag')
self.mu0 = np.transpose(np.reshape(mu0, (O, M, Q)), (0, 2, 1))
self.Sigma0 = np.transpose(np.reshape(Sigma0, (O, O, M, Q)),
(0, 1, 3, 2))
self.mixmat0, _ = mk_stochastic(np.random.rand(Q, M))
cache = {
'obs': self.obs,
'prior0': self.prior0,
'transmat0': self.transmat0,
'mu0': self.mu0,
'Sigma0': self.Sigma0,
'mixmat0': self.mixmat0
}
with open('MhmmEM2DTestGaussInit.cache', 'wb') as f:
dump(cache, f)
savemat('MhmmEM2DTestGaussInit.mat', cache)
def setUp(self):
self.prior = np.array([1, 0, 0])
self.transmat = np.matrix([[0, 1, 0],
[0, 0, 1],
[0, 0, 1]])
self.mu = np.zeros((2,3,2))
self.mu[:,:,0] = np.array([[1, 2, 3],
[1, 2, 3]])
self.mu[:,:,1] = np.array([[4, 5, 6],
[4, 5, 6]])
self.Sigma = np.zeros((2, 2, 3, 2))
for i in range(3):
self.Sigma[:,:,i,0] = np.diag(np.ones((2,))*0.01)
self.Sigma[:,:,i,1] = np.diag(np.ones((2,))*0.01)
self.mixmat = np.array([[.5,.5],
[.5,.5],
[.5,.5]])
try:
with open('MhmmEM2DTestGaussInit.cache', 'rb') as f:
cache = load(f)
self.obs = cache['obs']
self.prior0 = cache['prior0']
self.transmat0 = cache['transmat0']
self.mu0 = cache['mu0']
self.Sigma0 = cache['Sigma0']
self.mixmat0 = cache['mixmat0']
except:
self.obs, hidden = mhmm_sample(T=4,
numex=100,
initial_prob=self.prior,
transmat=self.transmat,
mu=self.mu,
Sigma=self.Sigma,
mixmat = self.mixmat)
self.prior0, _ = mk_stochastic(np.random.rand(3))
self.transmat0, _ = mk_stochastic(np.random.rand(3,3))
O = self.obs.shape[0]
M = 2
Q = 3
mu0, Sigma0, weights0 = mixgauss_init(Q*M, self.obs, cov_type='diag')
self.mu0 = np.transpose(np.reshape(mu0, (O, M, Q)), (0,2,1))
self.Sigma0 = np.transpose(np.reshape(Sigma0, (O, O, M, Q)), (0, 1, 3, 2))
self.mixmat0, _ = mk_stochastic(np.random.rand(Q,M))
cache = {'obs':self.obs,
'prior0':self.prior0,
'transmat0':self.transmat0,
'mu0':self.mu0,
'Sigma0':self.Sigma0,
'mixmat0':self.mixmat0}
with open('MhmmEM2DTestGaussInit.cache', 'wb') as f:
dump(cache, f)
savemat('MhmmEM2DTestGaussInit.mat', cache)
