fit_hmm_to_r_indicator.py

# Create an HMM object with two hidden states
model = hmm.GaussianHMM(n_components = 2, covariance_type = "diag", n_iter = 100, random_state = 42)

# Create an array input for the HMM model
X = data[['R']].values

# Estimate the HMM model
results = model.fit(X)

# Use the Viterbi algorithm to find the fitted hidden states
data['states'] = results.predict(X)

# Count the number of days in which we find the two states
data['states'].value_counts()