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train | HMM.generate_synthetic_observation_trajectory | Generate a synthetic realization of observables.
Parameters
----------
length : int
Length of synthetic state trajectory to be generated.
initial_Pi : np.array of shape (nstates,), optional, default=None
The initial probability distribution, if samples are not to... | bhmm/hmm/generic_hmm.py | def generate_synthetic_observation_trajectory(self, length, initial_Pi=None):
"""Generate a synthetic realization of observables.
Parameters
----------
length : int
Length of synthetic state trajectory to be generated.
initial_Pi : np.array of shape (nstates,), optio... | def generate_synthetic_observation_trajectory(self, length, initial_Pi=None):
"""Generate a synthetic realization of observables.
Parameters
----------
length : int
Length of synthetic state trajectory to be generated.
initial_Pi : np.array of shape (nstates,), optio... | [
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train | HMM.generate_synthetic_observation_trajectories | Generate a number of synthetic realization of observables from this model.
Parameters
----------
ntrajectories : int
The number of trajectories to be generated.
length : int
Length of synthetic state trajectory to be generated.
initial_Pi : np.array of sh... | bhmm/hmm/generic_hmm.py | def generate_synthetic_observation_trajectories(self, ntrajectories, length, initial_Pi=None):
"""Generate a number of synthetic realization of observables from this model.
Parameters
----------
ntrajectories : int
The number of trajectories to be generated.
length :... | def generate_synthetic_observation_trajectories(self, ntrajectories, length, initial_Pi=None):
"""Generate a number of synthetic realization of observables from this model.
Parameters
----------
ntrajectories : int
The number of trajectories to be generated.
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train | nb_to_python | convert notebook to python script | docs/sphinxext/notebook_sphinxext.py | def nb_to_python(nb_path):
"""convert notebook to python script"""
exporter = python.PythonExporter()
output, resources = exporter.from_filename(nb_path)
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train | nb_to_html | convert notebook to html | docs/sphinxext/notebook_sphinxext.py | def nb_to_html(nb_path):
"""convert notebook to html"""
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output, resources = exporter.from_filename(nb_path)
header = output.split('<head>', 1)[1].split('</head>',1)[0]
body = output.split('<body>', 1)[1].split('</body>',1)[0]
# http://imgur.com... | def nb_to_html(nb_path):
"""convert notebook to html"""
exporter = html.HTMLExporter(template_file='full')
output, resources = exporter.from_filename(nb_path)
header = output.split('<head>', 1)[1].split('</head>',1)[0]
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train | DiscreteOutputModel.p_obs | Returns the output probabilities for an entire trajectory and all hidden states
Parameters
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obs : ndarray((T), dtype=int)
a discrete trajectory of length T
Return
------
p_o : ndarray (T,N)
the probability of generating the symbol at ti... | bhmm/output_models/discrete.py | def p_obs(self, obs, out=None):
"""
Returns the output probabilities for an entire trajectory and all hidden states
Parameters
----------
obs : ndarray((T), dtype=int)
a discrete trajectory of length T
Return
------
p_o : ndarray (T,N)
... | def p_obs(self, obs, out=None):
"""
Returns the output probabilities for an entire trajectory and all hidden states
Parameters
----------
obs : ndarray((T), dtype=int)
a discrete trajectory of length T
Return
------
p_o : ndarray (T,N)
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train | DiscreteOutputModel.estimate | Maximum likelihood estimation of output model given the observations and weights
Parameters
----------
observations : [ ndarray(T_k) ] with K elements
A list of K observation trajectories, each having length T_k
weights : [ ndarray(T_k, N) ] with K elements
A li... | bhmm/output_models/discrete.py | def estimate(self, observations, weights):
"""
Maximum likelihood estimation of output model given the observations and weights
Parameters
----------
observations : [ ndarray(T_k) ] with K elements
A list of K observation trajectories, each having length T_k
... | def estimate(self, observations, weights):
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Maximum likelihood estimation of output model given the observations and weights
Parameters
----------
observations : [ ndarray(T_k) ] with K elements
A list of K observation trajectories, each having length T_k
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train | DiscreteOutputModel.sample | Sample a new set of distribution parameters given a sample of observations from the given state.
The internal parameters are updated.
Parameters
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observations : [ numpy.array with shape (N_k,) ] with nstates elements
observations[k] are all observations associate... | bhmm/output_models/discrete.py | def sample(self, observations_by_state):
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Sample a new set of distribution parameters given a sample of observations from the given state.
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train | DiscreteOutputModel.generate_observation_from_state | Generate a single synthetic observation data from a given state.
Parameters
----------
state_index : int
Index of the state from which observations are to be generated.
Returns
-------
observation : float
A single observation from the given state... | bhmm/output_models/discrete.py | def generate_observation_from_state(self, state_index):
"""
Generate a single synthetic observation data from a given state.
Parameters
----------
state_index : int
Index of the state from which observations are to be generated.
Returns
-------
... | def generate_observation_from_state(self, state_index):
"""
Generate a single synthetic observation data from a given state.
Parameters
----------
state_index : int
Index of the state from which observations are to be generated.
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train | DiscreteOutputModel.generate_observations_from_state | Generate synthetic observation data from a given state.
Parameters
----------
state_index : int
Index of the state from which observations are to be generated.
nobs : int
The number of observations to generate.
Returns
-------
observation... | bhmm/output_models/discrete.py | def generate_observations_from_state(self, state_index, nobs):
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Generate synthetic observation data from a given state.
Parameters
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state_index : int
Index of the state from which observations are to be generated.
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Generate synthetic observation data from a given state.
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state_index : int
Index of the state from which observations are to be generated.
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train | DiscreteOutputModel.generate_observation_trajectory | Generate synthetic observation data from a given state sequence.
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s_t : numpy.array with shape (T,) of int type
s_t[t] is the hidden state sampled at time t
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Generate synthetic observation data from a given state sequence.
Parameters
----------
s_t : numpy.array with shape (T,) of int type
s_t[t] is the hidden state sampled at time t
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Generate synthetic observation data from a given state sequence.
Parameters
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s_t : numpy.array with shape (T,) of int type
s_t[t] is the hidden state sampled at time t
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train | set_implementation | Sets the implementation of this module
Parameters
----------
impl : str
One of ["python", "c"] | bhmm/hidden/api.py | def set_implementation(impl):
"""
Sets the implementation of this module
Parameters
----------
impl : str
One of ["python", "c"]
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train | forward | Compute P( obs | A, B, pi ) and all forward coefficients.
Parameters
----------
A : ndarray((N,N), dtype = float)
transition matrix of the hidden states
pobs : ndarray((T,N), dtype = float)
pobs[t,i] is the observation probability for observation at time t given hidden state i
pi : ... | bhmm/hidden/api.py | def forward(A, pobs, pi, T=None, alpha_out=None):
"""Compute P( obs | A, B, pi ) and all forward coefficients.
Parameters
----------
A : ndarray((N,N), dtype = float)
transition matrix of the hidden states
pobs : ndarray((T,N), dtype = float)
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----------
A : ndarray((N,N), dtype = float)
transition matrix of the hidden states
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train | backward | Compute all backward coefficients. With scaling!
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transition matrix of the hidden states
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pobs[t,i] is the observation probability for observation at time t given hidden state i
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A : ndarray((N,N), dtype = float)
transition matrix of the hidden states
pobs : ndarray((T,N), dtype = float)
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transition matrix of the hidden states
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train | state_probabilities | Calculate the (T,N)-probabilty matrix for being in state i at time t.
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alpha : ndarray((T,N), dtype = float), optional, default = None
alpha[t,i] is the ith forward coefficient of time t.
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Parameters
----------
alpha : ndarray((T,N), dtype = float), optional, default = None
alpha[t,i] is the ith forward coefficient of time t.
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train | state_counts | Sum the probabilities of being in state i to time t
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gamma[t,i] is the probabilty at time t to be in state i !
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number of time steps
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count : numpy.array shape (N)
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gamma : ndarray((T,N), dtype = float), optional, default = None
gamma[t,i] is the probabilty at time t to be in state i !
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number of time steps
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number of time steps
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train | transition_counts | Sum for all t the probability to transition from state i to state j.
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alpha : ndarray((T,N), dtype = float), optional, default = None
alpha[t,i] is the ith forward coefficient of time t.
beta : ndarray((T,N), dtype = float), optional, default = None
beta[t,i] is the... | bhmm/hidden/api.py | def transition_counts(alpha, beta, A, pobs, T=None, out=None):
""" Sum for all t the probability to transition from state i to state j.
Parameters
----------
alpha : ndarray((T,N), dtype = float), optional, default = None
alpha[t,i] is the ith forward coefficient of time t.
beta : ndarray((... | def transition_counts(alpha, beta, A, pobs, T=None, out=None):
""" Sum for all t the probability to transition from state i to state j.
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alpha : ndarray((T,N), dtype = float), optional, default = None
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train | viterbi | Estimate the hidden pathway of maximum likelihood using the Viterbi algorithm.
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A : ndarray((N,N), dtype = float)
transition matrix of the hidden states
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""" Estimate the hidden pathway of maximum likelihood using the Viterbi algorithm.
Parameters
----------
A : ndarray((N,N), dtype = float)
transition matrix of the hidden states
pobs : ndarray((T,N), dtype = float)
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""" Estimate the hidden pathway of maximum likelihood using the Viterbi algorithm.
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A : ndarray((N,N), dtype = float)
transition matrix of the hidden states
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train | sample_path | Sample the hidden pathway S from the conditional distribution P ( S | Parameters, Observations )
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alpha : ndarray((T,N), dtype = float), optional, default = None
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""" Sample the hidden pathway S from the conditional distribution P ( S | Parameters, Observations )
Parameters
----------
alpha : ndarray((T,N), dtype = float), optional, default = None
alpha[t,i] is the ith forward coefficient of time t.
A : ndarra... | def sample_path(alpha, A, pobs, T=None):
""" Sample the hidden pathway S from the conditional distribution P ( S | Parameters, Observations )
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alpha : ndarray((T,N), dtype = float), optional, default = None
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train | logger | Retrieves the logger instance associated to the given name.
:param name: The name of the logger instance.
:type name: str
:param pattern: The associated pattern.
:type pattern: str
:param date_format: The date format to be used in the pattern.
:type date_format: str
:param handler: The logg... | bhmm/util/logger.py | def logger(name='BHMM', pattern='%(asctime)s %(levelname)s %(name)s: %(message)s',
date_format='%H:%M:%S', handler=logging.StreamHandler(sys.stdout)):
"""
Retrieves the logger instance associated to the given name.
:param name: The name of the logger instance.
:type name: str
:param patt... | def logger(name='BHMM', pattern='%(asctime)s %(levelname)s %(name)s: %(message)s',
date_format='%H:%M:%S', handler=logging.StreamHandler(sys.stdout)):
"""
Retrieves the logger instance associated to the given name.
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train | BayesianHMMSampler.sample | Sample from the BHMM posterior.
Parameters
----------
nsamples : int
The number of samples to generate.
nburn : int, optional, default=0
The number of samples to discard to burn-in, following which `nsamples` will be generated.
nthin : int, optional, defa... | bhmm/estimators/bayesian_sampling.py | def sample(self, nsamples, nburn=0, nthin=1, save_hidden_state_trajectory=False,
call_back=None):
"""Sample from the BHMM posterior.
Parameters
----------
nsamples : int
The number of samples to generate.
nburn : int, optional, default=0
Th... | def sample(self, nsamples, nburn=0, nthin=1, save_hidden_state_trajectory=False,
call_back=None):
"""Sample from the BHMM posterior.
Parameters
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nsamples : int
The number of samples to generate.
nburn : int, optional, default=0
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train | BayesianHMMSampler._update | Update the current model using one round of Gibbs sampling. | bhmm/estimators/bayesian_sampling.py | def _update(self):
"""Update the current model using one round of Gibbs sampling.
"""
initial_time = time.time()
self._updateHiddenStateTrajectories()
self._updateEmissionProbabilities()
self._updateTransitionMatrix()
final_time = time.time()
elapsed_ti... | def _update(self):
"""Update the current model using one round of Gibbs sampling.
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initial_time = time.time()
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self._updateEmissionProbabilities()
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train | BayesianHMMSampler._updateHiddenStateTrajectories | Sample a new set of state trajectories from the conditional distribution P(S | T, E, O) | bhmm/estimators/bayesian_sampling.py | def _updateHiddenStateTrajectories(self):
"""Sample a new set of state trajectories from the conditional distribution P(S | T, E, O)
"""
self.model.hidden_state_trajectories = list()
for trajectory_index in range(self.nobs):
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train | BayesianHMMSampler._sampleHiddenStateTrajectory | Sample a hidden state trajectory from the conditional distribution P(s | T, E, o)
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----------
o_t : numpy.array with dimensions (T,)
observation[n] is the nth observation
dtype : numpy.dtype, optional, default=numpy.int32
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Parameters
----------
o_t : numpy.array with dimensions (T,)
observation[n] is the nth observation
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----------
o_t : numpy.array with dimensions (T,)
observation[n] is the nth observation
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train | BayesianHMMSampler._updateEmissionProbabilities | Sample a new set of emission probabilites from the conditional distribution P(E | S, O) | bhmm/estimators/bayesian_sampling.py | def _updateEmissionProbabilities(self):
"""Sample a new set of emission probabilites from the conditional distribution P(E | S, O)
"""
observations_by_state = [self.model.collect_observations_in_state(self.observations, state)
for state in range(self.model.nstat... | def _updateEmissionProbabilities(self):
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train | BayesianHMMSampler._updateTransitionMatrix | Updates the hidden-state transition matrix and the initial distribution | bhmm/estimators/bayesian_sampling.py | def _updateTransitionMatrix(self):
"""
Updates the hidden-state transition matrix and the initial distribution
"""
# TRANSITION MATRIX
C = self.model.count_matrix() + self.prior_C # posterior count matrix
# check if we work with these options
if self.reversible... | def _updateTransitionMatrix(self):
"""
Updates the hidden-state transition matrix and the initial distribution
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train | BayesianHMMSampler._generateInitialModel | Initialize using an MLHMM. | bhmm/estimators/bayesian_sampling.py | def _generateInitialModel(self, output_model_type):
"""Initialize using an MLHMM.
"""
logger().info("Generating initial model for BHMM using MLHMM...")
from bhmm.estimators.maximum_likelihood import MaximumLikelihoodEstimator
mlhmm = MaximumLikelihoodEstimator(self.observations,... | def _generateInitialModel(self, output_model_type):
"""Initialize using an MLHMM.
"""
logger().info("Generating initial model for BHMM using MLHMM...")
from bhmm.estimators.maximum_likelihood import MaximumLikelihoodEstimator
mlhmm = MaximumLikelihoodEstimator(self.observations,... | [
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train | ensure_dtraj | r"""Makes sure that dtraj is a discrete trajectory (array of int) | bhmm/util/types.py | def ensure_dtraj(dtraj):
r"""Makes sure that dtraj is a discrete trajectory (array of int)
"""
if is_int_vector(dtraj):
return dtraj
elif is_list_of_int(dtraj):
return np.array(dtraj, dtype=int)
else:
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r"""Makes sure that dtraj is a discrete trajectory (array of int)
"""
if is_int_vector(dtraj):
return dtraj
elif is_list_of_int(dtraj):
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train | ensure_dtraj_list | r"""Makes sure that dtrajs is a list of discrete trajectories (array of int) | bhmm/util/types.py | def ensure_dtraj_list(dtrajs):
r"""Makes sure that dtrajs is a list of discrete trajectories (array of int)
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if isinstance(dtrajs, list):
# elements are ints? then wrap into a list
if is_list_of_int(dtrajs):
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for i... | def ensure_dtraj_list(dtrajs):
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train | connected_sets | Computes the connected sets of C.
C : count matrix
mincount_connectivity : float
Minimum count which counts as a connection.
strong : boolean
True: Seek strongly connected sets. False: Seek weakly connected sets. | bhmm/estimators/_tmatrix_disconnected.py | def connected_sets(C, mincount_connectivity=0, strong=True):
""" Computes the connected sets of C.
C : count matrix
mincount_connectivity : float
Minimum count which counts as a connection.
strong : boolean
True: Seek strongly connected sets. False: Seek weakly connected sets.
"""
... | def connected_sets(C, mincount_connectivity=0, strong=True):
""" Computes the connected sets of C.
C : count matrix
mincount_connectivity : float
Minimum count which counts as a connection.
strong : boolean
True: Seek strongly connected sets. False: Seek weakly connected sets.
"""
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train | closed_sets | Computes the strongly connected closed sets of C | bhmm/estimators/_tmatrix_disconnected.py | def closed_sets(C, mincount_connectivity=0):
""" Computes the strongly connected closed sets of C """
n = np.shape(C)[0]
S = connected_sets(C, mincount_connectivity=mincount_connectivity, strong=True)
closed = []
for s in S:
mask = np.zeros(n, dtype=bool)
mask[s] = True
if C[... | def closed_sets(C, mincount_connectivity=0):
""" Computes the strongly connected closed sets of C """
n = np.shape(C)[0]
S = connected_sets(C, mincount_connectivity=mincount_connectivity, strong=True)
closed = []
for s in S:
mask = np.zeros(n, dtype=bool)
mask[s] = True
if C[... | [
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train | nonempty_set | Returns the set of states that have at least one incoming or outgoing count | bhmm/estimators/_tmatrix_disconnected.py | def nonempty_set(C, mincount_connectivity=0):
""" Returns the set of states that have at least one incoming or outgoing count """
# truncate to states with at least one observed incoming or outgoing count.
if mincount_connectivity > 0:
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train | estimate_P | Estimates full transition matrix for general connectivity structure
Parameters
----------
C : ndarray
count matrix
reversible : bool
estimate reversible?
fixed_statdist : ndarray or None
estimate with given stationary distribution
maxiter : int
Maximum number of ... | bhmm/estimators/_tmatrix_disconnected.py | def estimate_P(C, reversible=True, fixed_statdist=None, maxiter=1000000, maxerr=1e-8, mincount_connectivity=0):
""" Estimates full transition matrix for general connectivity structure
Parameters
----------
C : ndarray
count matrix
reversible : bool
estimate reversible?
fixed_sta... | def estimate_P(C, reversible=True, fixed_statdist=None, maxiter=1000000, maxerr=1e-8, mincount_connectivity=0):
""" Estimates full transition matrix for general connectivity structure
Parameters
----------
C : ndarray
count matrix
reversible : bool
estimate reversible?
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train | transition_matrix_partial_rev | Maximum likelihood estimation of transition matrix which is reversible on parts
Partially-reversible estimation of transition matrix. Maximizes the likelihood:
.. math:
P_S &=& arg max prod_{S, :} (p_ij)^c_ij \\
\Pi_S P_{S,S} &=& \Pi_S P_{S,S}
where the product runs over all elements of t... | bhmm/estimators/_tmatrix_disconnected.py | def transition_matrix_partial_rev(C, P, S, maxiter=1000000, maxerr=1e-8):
"""Maximum likelihood estimation of transition matrix which is reversible on parts
Partially-reversible estimation of transition matrix. Maximizes the likelihood:
.. math:
P_S &=& arg max prod_{S, :} (p_ij)^c_ij \\
\... | def transition_matrix_partial_rev(C, P, S, maxiter=1000000, maxerr=1e-8):
"""Maximum likelihood estimation of transition matrix which is reversible on parts
Partially-reversible estimation of transition matrix. Maximizes the likelihood:
.. math:
P_S &=& arg max prod_{S, :} (p_ij)^c_ij \\
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train | enforce_reversible_on_closed | Enforces transition matrix P to be reversible on its closed sets. | bhmm/estimators/_tmatrix_disconnected.py | def enforce_reversible_on_closed(P):
""" Enforces transition matrix P to be reversible on its closed sets. """
import msmtools.analysis as msmana
n = np.shape(P)[0]
Prev = P.copy()
# treat each weakly connected set separately
sets = closed_sets(P)
for s in sets:
I = np.ix_(s, s)
... | def enforce_reversible_on_closed(P):
""" Enforces transition matrix P to be reversible on its closed sets. """
import msmtools.analysis as msmana
n = np.shape(P)[0]
Prev = P.copy()
# treat each weakly connected set separately
sets = closed_sets(P)
for s in sets:
I = np.ix_(s, s)
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train | is_reversible | Returns if P is reversible on its weakly connected sets | bhmm/estimators/_tmatrix_disconnected.py | def is_reversible(P):
""" Returns if P is reversible on its weakly connected sets """
import msmtools.analysis as msmana
# treat each weakly connected set separately
sets = connected_sets(P, strong=False)
for s in sets:
Ps = P[s, :][:, s]
if not msmana.is_transition_matrix(Ps):
... | def is_reversible(P):
""" Returns if P is reversible on its weakly connected sets """
import msmtools.analysis as msmana
# treat each weakly connected set separately
sets = connected_sets(P, strong=False)
for s in sets:
Ps = P[s, :][:, s]
if not msmana.is_transition_matrix(Ps):
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train | stationary_distribution | Simple estimator for stationary distribution for multiple strongly connected sets | bhmm/estimators/_tmatrix_disconnected.py | def stationary_distribution(P, C=None, mincount_connectivity=0):
""" Simple estimator for stationary distribution for multiple strongly connected sets """
# can be replaced by msmtools.analysis.stationary_distribution in next msmtools release
from msmtools.analysis.dense.stationary_vector import stationary_... | def stationary_distribution(P, C=None, mincount_connectivity=0):
""" Simple estimator for stationary distribution for multiple strongly connected sets """
# can be replaced by msmtools.analysis.stationary_distribution in next msmtools release
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train | SampledGaussianHMM.means_samples | r""" Samples of the Gaussian distribution means | bhmm/hmm/gaussian_hmm.py | def means_samples(self):
r""" Samples of the Gaussian distribution means """
res = np.empty((self.nsamples, self.nstates, self.dimension), dtype=config.dtype)
for i in range(self.nsamples):
for j in range(self.nstates):
res[i, j, :] = self._sampled_hmms[i].means[j]
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r""" Samples of the Gaussian distribution means """
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for i in range(self.nsamples):
for j in range(self.nstates):
res[i, j, :] = self._sampled_hmms[i].means[j]
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train | SampledGaussianHMM.sigmas_samples | r""" Samples of the Gaussian distribution standard deviations | bhmm/hmm/gaussian_hmm.py | def sigmas_samples(self):
r""" Samples of the Gaussian distribution standard deviations """
res = np.empty((self.nsamples, self.nstates, self.dimension), dtype=config.dtype)
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train | _guess_output_type | Suggests a HMM model type based on the observation data
Uses simple rules in order to decide which HMM model type makes sense based on observation data.
If observations consist of arrays/lists of integer numbers (irrespective of whether the python type is
int or float), our guess is 'discrete'.
If obse... | bhmm/api.py | def _guess_output_type(observations):
""" Suggests a HMM model type based on the observation data
Uses simple rules in order to decide which HMM model type makes sense based on observation data.
If observations consist of arrays/lists of integer numbers (irrespective of whether the python type is
int o... | def _guess_output_type(observations):
""" Suggests a HMM model type based on the observation data
Uses simple rules in order to decide which HMM model type makes sense based on observation data.
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train | lag_observations | r""" Create new trajectories that are subsampled at lag but shifted
Given a trajectory (s0, s1, s2, s3, s4, ...) and lag 3, this function will generate 3 trajectories
(s0, s3, s6, ...), (s1, s4, s7, ...) and (s2, s5, s8, ...). Use this function in order to parametrize a MLE
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r""" Create new trajectories that are subsampled at lag but shifted
Given a trajectory (s0, s1, s2, s3, s4, ...) and lag 3, this function will generate 3 trajectories
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r""" Create new trajectories that are subsampled at lag but shifted
Given a trajectory (s0, s1, s2, s3, s4, ...) and lag 3, this function will generate 3 trajectories
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train | gaussian_hmm | Initializes a 1D-Gaussian HMM
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pi : ndarray(nstates, )
Initial distribution.
P : ndarray(nstates,nstates)
Hidden transition matrix
means : ndarray(nstates, )
Means of Gaussian output distributions
sigmas : ndarray(nstates, )
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""" Initializes a 1D-Gaussian HMM
Parameters
----------
pi : ndarray(nstates, )
Initial distribution.
P : ndarray(nstates,nstates)
Hidden transition matrix
means : ndarray(nstates, )
Means of Gaussian output distributions
sigma... | def gaussian_hmm(pi, P, means, sigmas):
""" Initializes a 1D-Gaussian HMM
Parameters
----------
pi : ndarray(nstates, )
Initial distribution.
P : ndarray(nstates,nstates)
Hidden transition matrix
means : ndarray(nstates, )
Means of Gaussian output distributions
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train | discrete_hmm | Initializes a discrete HMM
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pi : ndarray(nstates, )
Initial distribution.
P : ndarray(nstates,nstates)
Hidden transition matrix
pout : ndarray(nstates,nsymbols)
Output matrix from hidden states to observable symbols
pi : ndarray(nstates, )
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""" Initializes a discrete HMM
Parameters
----------
pi : ndarray(nstates, )
Initial distribution.
P : ndarray(nstates,nstates)
Hidden transition matrix
pout : ndarray(nstates,nsymbols)
Output matrix from hidden states to observable symbols... | def discrete_hmm(pi, P, pout):
""" Initializes a discrete HMM
Parameters
----------
pi : ndarray(nstates, )
Initial distribution.
P : ndarray(nstates,nstates)
Hidden transition matrix
pout : ndarray(nstates,nsymbols)
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observations : list of ndarray((T_i))
list of arrays of length T_i with observation data
nstates : int
The number of states.
output : str, optional, default=None
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"""Use a heuristic scheme to generate an initial model.
Parameters
----------
observations : list of ndarray((T_i))
list of arrays of length T_i with observation data
nstates : int
The number of states.
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----------
observations : list of ndarray((T_i))
list of arrays of length T_i with observation data
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The number of states.
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train | init_gaussian_hmm | Use a heuristic scheme to generate an initial model.
Parameters
----------
observations : list of ndarray((T_i))
list of arrays of length T_i with observation data
nstates : int
The number of states.
Examples
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Generate initial model for a gaussian output model.
... | bhmm/api.py | def init_gaussian_hmm(observations, nstates, lag=1, reversible=True):
""" Use a heuristic scheme to generate an initial model.
Parameters
----------
observations : list of ndarray((T_i))
list of arrays of length T_i with observation data
nstates : int
The number of states.
Exam... | def init_gaussian_hmm(observations, nstates, lag=1, reversible=True):
""" Use a heuristic scheme to generate an initial model.
Parameters
----------
observations : list of ndarray((T_i))
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train | init_discrete_hmm | Use a heuristic scheme to generate an initial model.
Parameters
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observations : list of ndarray((T_i))
list of arrays of length T_i with observation data
nstates : int
The number of states.
lag : int
Lag time at which the observations should be counted.
reversi... | bhmm/api.py | def init_discrete_hmm(observations, nstates, lag=1, reversible=True, stationary=True, regularize=True,
method='connect-spectral', separate=None):
"""Use a heuristic scheme to generate an initial model.
Parameters
----------
observations : list of ndarray((T_i))
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train | estimate_hmm | r""" Estimate maximum-likelihood HMM
Generic maximum-likelihood estimation of HMMs
Parameters
----------
observations : list of numpy arrays representing temporal data
`observations[i]` is a 1d numpy array corresponding to the observed trajectory index `i`
nstates : int
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mincount_connectivity=1e-2):
r""" Estimate maximum-likelihood HMM
Generic maximum-likelihood estimation of HMMs... | def estimate_hmm(observations, nstates, lag=1, initial_model=None, output=None,
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train | bayesian_hmm | r""" Bayesian HMM based on sampling the posterior
Generic maximum-likelihood estimation of HMMs
Parameters
----------
observations : list of numpy arrays representing temporal data
`observations[i]` is a 1d numpy array corresponding to the observed trajectory index `i`
estimated_hmm : HMM
... | bhmm/api.py | def bayesian_hmm(observations, estimated_hmm, nsample=100, reversible=True, stationary=False,
p0_prior='mixed', transition_matrix_prior='mixed', store_hidden=False, call_back=None):
r""" Bayesian HMM based on sampling the posterior
Generic maximum-likelihood estimation of HMMs
Parameters
... | def bayesian_hmm(observations, estimated_hmm, nsample=100, reversible=True, stationary=False,
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r""" Bayesian HMM based on sampling the posterior
Generic maximum-likelihood estimation of HMMs
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train | logsumexp | Computes the sum of arr assuming arr is in the log domain.
Returns log(sum(exp(arr))) while minimizing the possibility of
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Examples
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>>> import numpy as np
>>> from sklearn.utils.extmath import logsumexp
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9.... | bhmm/_external/sklearn/utils.py | def logsumexp(arr, axis=0):
"""Computes the sum of arr assuming arr is in the log domain.
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Examples
--------
>>> import numpy as np
>>> from sklearn.utils.extmath import logsumexp
>>> a = np.arange(10)
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"""Computes the sum of arr assuming arr is in the log domain.
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Examples
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>>> import numpy as np
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train | _ensure_sparse_format | Convert a sparse matrix to a given format.
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Parameters
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spmatrix : scipy sparse matrix
Input to validate and convert.
accept_sparse : string, list of string or None (default=None)
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Checks the sparse format of spmatrix and converts if necessary.
Parameters
----------
spmatrix : scipy sparse matrix
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train | check_array | Input validation on an array, list, sparse matrix or similar.
By default, the input is converted to an at least 2nd numpy array.
If the dtype of the array is object, attempt converting to float,
raising on failure.
Parameters
----------
array : object
Input object to check / convert.
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train | beta_confidence_intervals | Compute confidence intervals of beta distributions.
Parameters
----------
ci_X : numpy.array
Computed confidence interval estimate from `ntrials` experiments
ntrials : int
The number of trials that were run.
ci : float, optional, default=0.95
Confidence interval to report (e... | bhmm/util/analysis.py | def beta_confidence_intervals(ci_X, ntrials, ci=0.95):
"""
Compute confidence intervals of beta distributions.
Parameters
----------
ci_X : numpy.array
Computed confidence interval estimate from `ntrials` experiments
ntrials : int
The number of trials that were run.
ci : flo... | def beta_confidence_intervals(ci_X, ntrials, ci=0.95):
"""
Compute confidence intervals of beta distributions.
Parameters
----------
ci_X : numpy.array
Computed confidence interval estimate from `ntrials` experiments
ntrials : int
The number of trials that were run.
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train | empirical_confidence_interval | Compute specified symmetric confidence interval for empirical sample.
Parameters
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sample : numpy.array
The empirical samples.
interval : float, optional, default=0.95
Size of desired symmetric confidence interval (0 < interval < 1)
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"""
Compute specified symmetric confidence interval for empirical sample.
Parameters
----------
sample : numpy.array
The empirical samples.
interval : float, optional, default=0.95
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Compute specified symmetric confidence interval for empirical sample.
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sample : numpy.array
The empirical samples.
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train | generate_latex_table | Generate a LaTeX column-wide table showing various computed properties and uncertainties.
Parameters
----------
conf : float
confidence interval. Use 0.68 for 1 sigma, 0.95 for 2 sigma etc. | bhmm/util/analysis.py | def generate_latex_table(sampled_hmm, conf=0.95, dt=1, time_unit='ms', obs_name='force', obs_units='pN',
caption='', outfile=None):
"""
Generate a LaTeX column-wide table showing various computed properties and uncertainties.
Parameters
----------
conf : float
confi... | def generate_latex_table(sampled_hmm, conf=0.95, dt=1, time_unit='ms', obs_name='force', obs_units='pN',
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Generate a LaTeX column-wide table showing various computed properties and uncertainties.
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----------
conf : float
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train | confidence_interval | Computes the mean and alpha-confidence interval of the given sample set
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data : ndarray
a 1D-array of samples
alpha : float in [0,1]
the confidence level, i.e. percentage of data included in the interval
Returns
-------
[m,l,r] where m is the me... | bhmm/util/statistics.py | def confidence_interval(data, alpha):
"""
Computes the mean and alpha-confidence interval of the given sample set
Parameters
----------
data : ndarray
a 1D-array of samples
alpha : float in [0,1]
the confidence level, i.e. percentage of data included in the interval
... | def confidence_interval(data, alpha):
"""
Computes the mean and alpha-confidence interval of the given sample set
Parameters
----------
data : ndarray
a 1D-array of samples
alpha : float in [0,1]
the confidence level, i.e. percentage of data included in the interval
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train | confidence_interval_arr | r""" Computes element-wise confidence intervals from a sample of ndarrays
Given a sample of arbitrarily shaped ndarrays, computes element-wise confidence intervals
Parameters
----------
data : ndarray (K, (shape))
ndarray of ndarrays, the first index is a sample index, the remaining indexes ar... | bhmm/util/statistics.py | def confidence_interval_arr(data, conf=0.95):
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Given a sample of arbitrarily shaped ndarrays, computes element-wise confidence intervals
Parameters
----------
data : ndarray (K, (shape))
ndarray of ndarrays, the first... | def confidence_interval_arr(data, conf=0.95):
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train | Connection.status | Return the connection status, both locally and remotely.
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* the count of multiple queries sent to the server.
* the count of single queries sent to the server.
* the count of actions sent to the server.
* the count of action... | umapi_client/connection.py | def status(self, remote=False):
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Return the connection status, both locally and remotely.
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* the count of single queries sent to the server.
* the count of actio... | def status(self, remote=False):
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train | Connection.query_single | Query for a single object.
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:param url_params: required list of strings to provide as additional URL components
:param query_params: optional dictionary of query options
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Query for a single object.
:param object_type: string query type (e.g., "users" or "groups")
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Query for a single object.
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:param page: numeric page (0-based) of results to get (up to 200 in a page)
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Query for a page of objects. Defaults to the (0-based) first page.
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Query for a page of objects. Defaults to the (0-based) first page.
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Execute a single action (containing commands on a single object).
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Execute multiple Actions (each containing commands on a single object).
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train | Connection._execute_batch | Execute a single batch of Actions.
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train | Connection.make_call | Make a single UMAPI call with error handling and retry on temporary failure.
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Add commands at the end of the sequence.
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"""
Insert commands at the beginning of the sequence.
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"""
Report a server error executing a command.
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"""
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train | Action.maybe_split_groups | Check if group lists in add/remove directives should be split and split them if needed
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Rerun the query (lazily).
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train | QueryMultiple._next_page | Fetch the next page of the query. | umapi_client/api.py | def _next_page(self):
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train | QueryMultiple.all_results | Eagerly fetch all the results.
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all the previously-iterated results as well as any results that weren't yet iterated.
:return: a list of all the results. | umapi_client/api.py | def all_results(self):
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Eagerly fetch all the results.
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Eagerly fetch all the results.
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train | QuerySingle._fetch_result | Fetch the queried object. | umapi_client/api.py | def _fetch_result(self):
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Fetch the queried object.
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train | UserAction.create | Create the user on the Adobe back end.
See [Issue 32](https://github.com/adobe-apiplatform/umapi-client.py/issues/32): because
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create call if the user already exists (possibly from an earlier call... | umapi_client/functional.py | def create(self, first_name=None, last_name=None, country=None, email=None,
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"""
Create the user on the Adobe back end.
See [Issue 32](https://github.com/adobe-apiplatform/umapi-client.py/issues/32): because
we retr... | def create(self, first_name=None, last_name=None, country=None, email=None,
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Create the user on the Adobe back end.
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train | UserAction.update | Update values on an existing user. See the API docs for what kinds of update are possible.
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Update values on an existing user. See the API docs for what kinds of update are possible.
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Update values on an existing user. See the API docs for what kinds of update are possible.
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train | UserAction.add_to_groups | Add user to some (typically PLC) groups. Note that, if you add to no groups, the effect
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:param groups: list of group names the user should be added to
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Add user to some (typically PLC) groups. Note that, if you add to no groups, the effect
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Add user to some (typically PLC) groups. Note that, if you add to no groups, the effect
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train | UserAction.remove_from_groups | Remove user from some PLC groups, or all of them.
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Remove user from some PLC groups, or all of them.
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train | UserAction.add_role | Make user have a role (typically PLC admin) with respect to some PLC groups.
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Make user have a role (typically PLC admin) with respect to some PLC groups.
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Remove user from a role (typically admin) of some groups.
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train | UserAction.remove_from_organization | Remove a user from the organization's list of visible users. Optionally also delete the account.
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Remove a user from the organization's list of visible users. Optionally also delete the account.
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Delete a user's account.
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Delete a user's account.
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train | UserGroupAction._validate | Validates the group name
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"""
Validates the group name
Input values must be strings (standard or unicode). Throws ArgumentError if any input is invalid
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"""
if group_name and not cls._group_name_regex.match(group_name):
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train | UserGroupAction.add_to_products | Add user group to some product license configuration groups (PLCs), or all of them.
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Add user group to some product license configuration groups (PLCs), or all of them.
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Add user group to some product license configuration groups (PLCs), or all of them.
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train | UserGroupAction.remove_from_products | Remove user group from some product license configuration groups (PLCs), or all of them.
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Remove user group from some product license configuration groups (PLCs), or all of them.
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train | UserGroupAction.add_users | Add users (specified by email address) to this user group.
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:param users: list of emails for users to add to the group.
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Add users (specified by email address) to this user group.
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Add users (specified by email address) to this user group.
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train | UserGroupAction.remove_users | Remove users (specified by email address) from this user group.
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train | transformer.scale2x | Scales using the AdvanceMAME Scale2X algorithm which does a
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"""
Scales using the AdvanceMAME Scale2X algorithm which does a
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"""
assert(self._scale == 2)
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train | transformer.smoothscale | Smooth scaling using MMX or SSE extensions if available | luma/emulator/render.py | def smoothscale(self, surface):
"""
Smooth scaling using MMX or SSE extensions if available
"""
return self._pygame.transform.smoothscale(surface, self._output_size) | def smoothscale(self, surface):
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train | transformer.identity | Fast scale operation that does not sample the results | luma/emulator/render.py | def identity(self, surface):
"""
Fast scale operation that does not sample the results
"""
return self._pygame.transform.scale(surface, self._output_size) | def identity(self, surface):
"""
Fast scale operation that does not sample the results
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train | transformer.led_matrix | Transforms the input surface into an LED matrix (1 pixel = 1 LED) | luma/emulator/render.py | def led_matrix(self, surface):
"""
Transforms the input surface into an LED matrix (1 pixel = 1 LED)
"""
scale = self._led_on.get_width()
w, h = self._input_size
pix = self._pygame.PixelArray(surface)
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Transforms the input surface into an LED matrix (1 pixel = 1 LED)
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train | rgb2short | Converts RGB values to the nearest equivalent xterm-256 color. | luma/emulator/clut.py | def rgb2short(r, g, b):
"""
Converts RGB values to the nearest equivalent xterm-256 color.
"""
# Using list of snap points, convert RGB value to cube indexes
r, g, b = [len(tuple(s for s in snaps if s < x)) for x in (r, g, b)]
# Simple colorcube transform
return (r * 36) + (g * 6) + b + 16 | def rgb2short(r, g, b):
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Converts RGB values to the nearest equivalent xterm-256 color.
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# Using list of snap points, convert RGB value to cube indexes
r, g, b = [len(tuple(s for s in snaps if s < x)) for x in (r, g, b)]
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train | emulator.to_surface | Converts a :py:mod:`PIL.Image` into a :class:`pygame.Surface`,
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train | capture.display | Takes a :py:mod:`PIL.Image` and dumps it to a numbered PNG file. | luma/emulator/device.py | def display(self, image):
"""
Takes a :py:mod:`PIL.Image` and dumps it to a numbered PNG file.
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self._count += 1
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train | gifanim.display | Takes an image, scales it according to the nominated transform, and
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"""
Takes an image, scales it according to the nominated transform, and
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assert(image.size == self.size)
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train | pygame.display | Takes a :py:mod:`PIL.Image` and renders it to a pygame display surface. | luma/emulator/device.py | def display(self, image):
"""
Takes a :py:mod:`PIL.Image` and renders it to a pygame display surface.
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train | asciiart._char_density | Count the number of black pixels in a rendered character. | luma/emulator/device.py | def _char_density(self, c, font=ImageFont.load_default()):
"""
Count the number of black pixels in a rendered character.
"""
image = Image.new('1', font.getsize(c), color=255)
draw = ImageDraw.Draw(image)
draw.text((0, 0), c, fill="white", font=font)
return collec... | def _char_density(self, c, font=ImageFont.load_default()):
"""
Count the number of black pixels in a rendered character.
"""
image = Image.new('1', font.getsize(c), color=255)
draw = ImageDraw.Draw(image)
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train | asciiart._generate_art | Return an iterator that produces the ascii art. | luma/emulator/device.py | def _generate_art(self, image, width, height):
"""
Return an iterator that produces the ascii art.
"""
# Characters aren't square, so scale the output by the aspect ratio of a charater
height = int(height * self._char_width / float(self._char_height))
image = image.resize... | def _generate_art(self, image, width, height):
"""
Return an iterator that produces the ascii art.
"""
# Characters aren't square, so scale the output by the aspect ratio of a charater
height = int(height * self._char_width / float(self._char_height))
image = image.resize... | [
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] | rm-hull/luma.emulator | python | https://github.com/rm-hull/luma.emulator/blob/ca3db028b33d17cda9247ea5189873ff0408d013/luma/emulator/device.py#L262-L273 | [
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train | asciiart.display | Takes a :py:mod:`PIL.Image` and renders it to the current terminal as
ASCII-art. | luma/emulator/device.py | def display(self, image):
"""
Takes a :py:mod:`PIL.Image` and renders it to the current terminal as
ASCII-art.
"""
assert(image.size == self.size)
self._last_image = image
surface = self.to_surface(self.preprocess(image), alpha=self._contrast)
rawbytes = ... | def display(self, image):
"""
Takes a :py:mod:`PIL.Image` and renders it to the current terminal as
ASCII-art.
"""
assert(image.size == self.size)
self._last_image = image
surface = self.to_surface(self.preprocess(image), alpha=self._contrast)
rawbytes = ... | [
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