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pam.array.distance

accuracy(actual, predicted) #

Source code in pam/array/distance.py 
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def accuracy(actual: np.array, predicted: np.array) -> float:
    assert actual.shape == predicted.shape
    correct = 0
    for ia, ib in zip(actual, predicted):
        if np.argmax(ia) == np.argmax(ib):
            correct += 1
    return correct / len(predicted)

cross_entropy(actual, predicted) #

Source code in pam/array/distance.py 
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def cross_entropy(actual: np.array, predicted: np.array) -> float:
    assert actual.shape == predicted.shape
    epsilon = 1e-12
    predicted = np.clip(predicted, epsilon, 1.0 - epsilon)
    return -np.sum(actual * np.log(predicted)) / actual.shape[0]