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object Probability

A collection of utility functions related to probability theory.

Deprecated

Remains for backwards compatibility. Use Distribution instead which has higher precision.

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  4. def argMax(values: Vector[Double]): Option[Int]

    Returns an index to the vector, pointing to the highest value in the vector.

    Returns an index to the vector, pointing to the highest value in the vector. If multiple maxima exist, it returns an index to one of those at random. If the vector values is empty, it returns None.

    values

    Values represented in a vector of doubles (e.g., a probability distribution).

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  7. def entropy(distribution: Vector[Double]): Double

    Returns the Shannon information entropy of a distribution.

    Returns the Shannon information entropy of a distribution.

    For distributions that deviate from probability assumptions (i.e., the sum of the values equals 1.0), Shannon information entropy is ill-defined.

    distribution

    The probability distribution, the values in this list should add to 1.0.

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  17. def softArgMax(values: Vector[Double], beta: Double): Option[Int]

    Returns an index to the distribution according to soft argMax with parameter beta.

    Returns an index to the distribution according to soft argMax with parameter beta. If beta -> Inf, this function is equivalent to argMax. If the vector values is empty, it returns None.

    values

    Values represented in a vector of doubles (e.g., a probability distribution).

    beta

    The beta parameter, >=0. Soft argmax is ill-defined for negative beta values.

    returns

    An index pointing to the value in the distribution

    See also

    See this Wikipedia page for a mathmatical definition of soft argmax https://en.wikipedia.org/wiki/Softmax_function.

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