Hands-On Markov Models with Python

Code

Let's now try to code up the preceding algorithm:

def weather_fit(data):    """    Learn the transition and emission probabilities from the given data    for the weather model.    Parameters    ----------    data: 2-D list (array-like)    Each data point should be a tuple of size 2 with the first element    representing the state of Weather and the second element representing    whether it rained or not.    Sunny = 0, Cloudy = 1, Windy = 2    Rain = 0, No Rain = 1     Returns    -------    transition probability: 2-D array    The conditional distribution representing the transition probability     of the model.    emission probability: 2-D array    The conditional distribution representing the emission probability     of the model.    """    data = np.array(data)  transition_counts = np.zeros((3, ...