with hmmlearn0.3.2

MACS3/Signal/HMMR_HMM.pyx

@@ -1,6 +1,6 @@
 # cython: language_level=3
 # cython: profile=True
-# Time-stamp: <2024-02-18 16:21:00 Tao Liu>
+# Time-stamp: <2024-03-01 23:34:51 Tao Liu>
 
 """Module description:
 
@@ -20,7 +20,8 @@ from math import sqrt
 import numpy as np
 cimport numpy as np
 from cpython cimport bool
-from hmmlearn import hmm, _utils
+import hmmlearn
+from hmmlearn.hmm import GaussianHMM
 from sklearn import cluster
 import json
 # from hmmlearn cimport hmm
@@ -51,34 +52,6 @@ cdef inline float get_weighted_density( int x, float m, float v, w ):
 # Classes
 # ------------------------------------
 
-class GaussianHMM_modified( hmm.GaussianHMM ):
-    def _init(self, X, lengths=None):
-        super()._init(X, lengths)
-        # we will overwrite initial means_ and covars_
-        kmeans = cluster.KMeans(n_clusters=self.n_components,
-                                random_state=self.random_state,
-                                n_init=10)  # https://github.com/hmmlearn/hmmlearn/pull/545
-                                # the idea is to do the random seeds
-                                # for 10 times orginally, hmmlearn 0.3
-                                # will do this only once.  However,
-                                # due to the change in scikit-learn
-                                # 1.3, the random seeding in KMeans
-                                # will generate different results with
-                                # previous scikit-learn. It will make
-                                # the results irreproducible between
-                                # sklearn <1.3 and sklearn
-                                # >=1.3. Hopefully, if we choose to do
-                                # the process 10 times, the results
-                                # will be more similar.
-        kmeans.fit(X)
-        self.means_ = kmeans.cluster_centers_
-
-        cv = np.cov(X.T) + self.min_covar * np.eye(X.shape[1])
-        if not cv.shape:
-            cv.shape = (1, 1)
-        self.covars_ = \
-            _utils.distribute_covar_matrix_to_match_covariance_type( cv, self.covariance_type, self.n_components ).copy()
-
 # ------------------------------------
 # public functions
 # ------------------------------------
@@ -90,7 +63,7 @@ cpdef hmm_training( list training_data, list training_data_lengths, int n_states
     # according to base documentation, if init_prob not stated, it is set to be equally likely for any state (1/ # of components)
     # if we have other known parameters, we should set these (ie: means_weights, covariance_type etc.)
     rs = np.random.RandomState(np.random.MT19937(np.random.SeedSequence(random_seed)))
-    hmm_model = GaussianHMM_modified( n_components= n_states, covariance_type = covar, random_state = rs, verbose = False )
+    hmm_model = GaussianHMM( n_components= n_states, covariance_type = covar, random_state = rs, verbose = False )
     hmm_model = hmm_model.fit( training_data, training_data_lengths )
     assert hmm_model.n_features == 4
     return hmm_model
@@ -121,7 +94,7 @@ cpdef void hmm_model_save( str model_file, object hmm_model, int hmm_binsize, in
 cpdef list hmm_model_init( str model_file ):
     with open( model_file ) as f:
         m = json.load( f )
-        hmm_model = GaussianHMM_modified( n_components=3, covariance_type=m["covariance_type"] )
+        hmm_model = GaussianHMM( n_components=3, covariance_type=m["covariance_type"] )
         hmm_model.startprob_ = np.array(m["startprob"])
         hmm_model.transmat_ = np.array(m["transmat"])
         hmm_model.means_ = np.array(m["means"])

pyproject.toml

@@ -1,3 +1,3 @@
 [build-system]
-requires=['setuptools>=60.0', 'numpy>=1.24.2', 'scipy>=1.11.4', 'cykhash>=2.0,<3.0', 'Cython~=3.0', 'scikit-learn>=1.2.1', 'hmmlearn==0.3.0']
+requires=['setuptools>=60.0', 'numpy>=1.24.2', 'scipy>=1.11.4', 'cykhash>=2.0,<3.0', 'Cython~=3.0', 'scikit-learn>=1.2.1', 'hmmlearn>=0.3.2']
 

requirements.txt

@@ -2,7 +2,7 @@ Cython~=3.0
 numpy>=1.24.2
 scipy>=1.11.4
 scikit-learn>=1.2.1
-hmmlearn==0.3.0
+hmmlearn>=0.3.2
 cykhash>=2.0,<3.0 
 pytest>=7.0
 setuptools>=60.0

setup.py

@@ -39,7 +39,7 @@
 
 install_requires = [ "numpy>=1.24.2",
                      "scipy>=1.11.4",
-                     "hmmlearn==0.3.0",
+                     "hmmlearn>=0.3.2",
                      "scikit-learn>=1.2.1",
                      "cykhash>=2.0,<3.0"]