hidden-markov-models

问题 I'm playing around with Hidden Markov Models for a stock market prediction problem. My data matrix contains various features for a particular security: 01-01-2001, .025, .012, .01 01-02-2001, -.005, -.023, .02 I fit a simple GaussianHMM: from hmmlearn import GaussianHMM mdl = GaussianHMM(n_components=3,covariance_type='diag',n_iter=1000) mdl.fit(train[:,1:]) With the model (λ), I can decode an observation vector to find the most likely hidden state sequence corresponding to the observation

问题 I'm playing around with Hidden Markov Models for a stock market prediction problem. My data matrix contains various features for a particular security: 01-01-2001, .025, .012, .01 01-02-2001, -.005, -.023, .02 I fit a simple GaussianHMM: from hmmlearn import GaussianHMM mdl = GaussianHMM(n_components=3,covariance_type='diag',n_iter=1000) mdl.fit(train[:,1:]) With the model (λ), I can decode an observation vector to find the most likely hidden state sequence corresponding to the observation

问题 I'm playing around with Hidden Markov Models for a stock market prediction problem. My data matrix contains various features for a particular security: 01-01-2001, .025, .012, .01 01-02-2001, -.005, -.023, .02 I fit a simple GaussianHMM: from hmmlearn import GaussianHMM mdl = GaussianHMM(n_components=3,covariance_type='diag',n_iter=1000) mdl.fit(train[:,1:]) With the model (λ), I can decode an observation vector to find the most likely hidden state sequence corresponding to the observation

问题 I'm trying to make a basic markov model using the package msm and things were working fine until I've suddenly started receiving the following error code. I don't know why it's suddenly started throwing this as it was working fine earlier, and I don't think I've changed anything. The error code seems to be pointing to the linear algebra library but I don't know what to do with it exactly ... Error in balance(baP$z, "S") : BLAS/LAPACK routine 'DGEBAL' gave error code -3 The code is as follows:

问题 I had a question about how I can use gaussianHMM in the scikit-learn package to train on several different observation sequences all at once. The example is here: visualizing the stock market structure shows EM converging on 1 long observation sequence. But in many scenarios, we want to break up the observations (like training on set of sentences) with each observation sequence having a START and END state. That is, I would like to globally train on multiple observation sequences. How can one

问题 I had a question about how I can use gaussianHMM in the scikit-learn package to train on several different observation sequences all at once. The example is here: visualizing the stock market structure shows EM converging on 1 long observation sequence. But in many scenarios, we want to break up the observations (like training on set of sentences) with each observation sequence having a START and END state. That is, I would like to globally train on multiple observation sequences. How can one

来源： https://stackoverflow.com/questions/44350447/hmmlearn-how-to-get-the-prediction-for-the-hidden-state-probability-at-time-t1

问题 To learn PyMC, I'm trying to do a simple Hidden Markov Model as shown below: with pymc3.Model() as hmm: # Transition "matrix" a_t = np.ones(num_states) T = [pymc3.Dirichlet('T{0}'.format(i), a = a_t, shape = num_states) for i in xrange(num_states)] # Emission "matrix" a_e = np.ones(num_emissions) E = [pymc3.Dirichlet('E{0}'.format(i), a = a_e, shape = num_emissions) for i in xrange(num_states)] # State models p0 = np.ones(num_states) / num_states # No shape, so each state is a scalar tensor

问题 I am working on a prototype framework. Basically I need to generate a model or profile for each individual's lifestyle based on some sensor data about him/her, such as GPS, motions, heart rate, surrounding environment readings, temperature etc. The proposed model or profile is a knowledge representation of an individual's lifestyle pattern. Maybe a graph with probabilities. I am thinking to use Hidden Markov Model to implement this. As the states in HMM can be Working, Sleeping, Leisure,

问题 Yes, I'm aware that speech recognition is fairly complicated (as an understatement). What I'm looking for is a method for distinguishing between maybe 20-30 phrases. An ability to split words (discrete speech is fine) would be nice, but isn't required. The software will be user-dependent(i.e. for use by me). I'm not looking for existing software, but for a good way of going about doing this myself. I've looked into various existing methods and it seems like splitting the sound into phonemes,