/Python/Convert time series to transition matrix

Convert time series to transition matrix

def compute_transition_matrix(data, n, step = 1):
    P = np.zeros((n, n))
    m = len(data)
    for i in range(m):
        initial, final = i, i + step
        if final < m:
            P[data[initial]][data[final]] += 1
    sums = np.sum(P, axis = 1)
    for i in range(n):
        for j in range(n):
            P[i][j] = P[i][j] / sums[i]
    return P

print(compute_transition_matrix([3, 0, 1, 3, 2, 6, 5, 4, 7, 5, 4], 8, 1))

# data is the input time series data, n is the total number of states in the Markov chain, step is the transition step.

Convert time series to transition matrix

def compute_transition_matrix2(data, n, step = 1):
    
    t = np.array(data)
    step = step
    total_inds = t.size - (step + 1) + 1
    t_strided = np.lib.stride_tricks.as_strided(
                                    t,
                                    shape = (total_inds, 2),
                                    strides = (t.strides[0], step * t.strides[0]))
    
    inds, counts = np.unique(t_strided, axis = 0, return_counts = True)

    P = np.zeros((n, n))
    P[inds[:, 0], inds[:, 1]] = counts
    
    sums = P.sum(axis = 1)
    # Avoid divide by zero error by normalizing only non-zero rows
    P[sums != 0] = P[sums != 0] / sums[sums != 0][:, None]
    
    # P = P / P.sum(axis = 1)[:, None]
    return P

print(compute_transition_matrix2([3, 0, 1, 3, 2, 6, 5, 4, 7, 5, 4], 8, 1))

# data is the input time series data, n is the total number of states in the Markov chain, step is the transition step.

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