From 06f2a54ab8298021bcc639c0636971624ba0a69d Mon Sep 17 00:00:00 2001
From: Scott Linderman <scott.linderman@gmail.com>
Date: Fri, 21 Oct 2016 21:42:18 -0400
Subject: [PATCH] bernoulli music generation!
---
examples/hmm.py | 27 +++++++++++++++++++++++++--
1 file changed, 25 insertions(+), 2 deletions(-)
diff --git a/examples/hmm.py b/examples/hmm.py
index e15810e..6ed561c 100644
--- a/examples/hmm.py
+++ b/examples/hmm.py
@@ -2,6 +2,8 @@ from __future__ import division
from builtins import range
import numpy as np
np.seterr(divide='ignore') # these warnings are usually harmless for this code
+np.random.seed(0)
+
from matplotlib import pyplot as plt
import matplotlib
import os
@@ -24,6 +26,7 @@ fit the model). Maybe this demo should use multinomial emissions...
###############
data = np.loadtxt(os.path.join(os.path.dirname(__file__),'example-data.txt'))[:2500]
+T = data.shape[0]
#########################
# posterior inference #
@@ -40,9 +43,9 @@ obs_hypparams = {'mu_0':np.zeros(obs_dim),
'nu_0':obs_dim+2}
### HDP-HMM without the sticky bias
-
obs_distns = [pyhsmm.distributions.Gaussian(**obs_hypparams) for state in range(Nmax)]
-posteriormodel = pyhsmm.models.WeakLimitHDPHMM(alpha=6.,gamma=6.,init_state_concentration=1.,
+posteriormodel = pyhsmm.models.WeakLimitHDPHMM(alpha=6.,gamma=6.,
+ init_state_concentration=1.,
obs_distns=obs_distns)
posteriormodel.add_data(data)
@@ -52,6 +55,26 @@ for idx in progprint_xrange(100):
posteriormodel.plot()
plt.gcf().suptitle('HDP-HMM sampled model after 100 iterations')
+### HDP-HMM with "sticky" initialization
+obs_distns = [pyhsmm.distributions.Gaussian(**obs_hypparams) for state in range(Nmax)]
+posteriormodel = pyhsmm.models.WeakLimitHDPHMM(alpha=6.,gamma=6.,
+ init_state_concentration=1.,
+ obs_distns=obs_distns)
+
+# Start with a "sticky" state sequence
+z_init = np.random.randint(0, Nmax, size=(T//5)).repeat(5)
+posteriormodel.add_data(data, stateseq=z_init)
+
+# Initialize the parameters of the model, holding the stateseq fixed
+for _ in progprint_xrange(10):
+ posteriormodel.resample_parameters()
+
+for idx in progprint_xrange(100):
+ posteriormodel.resample_model()
+
+posteriormodel.plot()
+plt.gcf().suptitle('HDP-HMM (sticky initialization) sampled model after 100 iterations')
+
### Sticky-HDP-HMM
obs_distns = [pyhsmm.distributions.Gaussian(**obs_hypparams) for state in range(Nmax)]
--
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