b46c893e2c0f9cd505b597c2ff215a0df04b6050,lifelines/fitters/npmle.py,,log_likelihood,#,171

Before Change

def log_likelihood(observation_intervals, p, turnbull_interval_lookup, weights):

    ll = 0
    for observation_interval, w in zip(observation_intervals, weights):
        min_, max_ = turnbull_interval_lookup[observation_interval]
        ll += w * np.log(p[min_ : max_ + 1].sum())
    return ll


def reconstruct_survival_function(probabilities, turnbull_intervals, timeline=None, label="NPMLE"):

After Change

def log_likelihood(p, turnbull_interval_lookup, weights):
    P = cumulative_sum(p)
    ix = np.array(list(turnbull_interval_lookup.values()))
    return (weights * np.log(P[ix[:, 1] + 1] - P[ix[:, 0]])).sum()


def reconstruct_survival_function(probabilities, turnbull_intervals, timeline=None, label="NPMLE"):

Instances