hmm.py

import numpy as np
from hmmlearn import hmm
from sklearn.datasets import load_iris,load_diabetes
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

CLASSES = 4
ITERATIONS = 100000

iris = load_iris()
X = iris.data
y = iris.target


X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

m1 = hmm.GaussianHMM(n_components=CLASSES, covariance_type="full", n_iter=ITERATIONS)
m1.fit(X)

y_pred = m1.predict(X)
print(accuracy_score(y,y_pred))
print("modele stand prob ", m1.startprob_)

m2 = hmm.GMMHMM(n_components=CLASSES, covariance_type="full", n_iter=ITERATIONS)
m2.fit(X)

y_pred = m2.predict(X)
print(accuracy_score(y,y_pred))
print("modele stand prob ", m2.startprob_)