Trait potpourri::Parametrizable

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pub trait Parametrizable {
    type SufficientStatistics: Send + Sync;
    type Likelihood;
    type DataIn<'a>: Sync;
    type DataOut;

    // Required methods
    fn expect(
        &self,
        data: &Self::DataIn<'_>
    ) -> Result<(Self::Likelihood, AvgLLH), Error>;
    fn compute(
        &self,
        data: &Self::DataIn<'_>,
        responsibilities: &Self::Likelihood
    ) -> Result<Self::SufficientStatistics, Error>;
    fn maximize(
        &mut self,
        sufficient_statistics: &Self::SufficientStatistics
    ) -> Result<(), Error>;
    fn predict(&self, data: &Self::DataIn<'_>) -> Result<Self::DataOut, Error>;
    fn update(
        &mut self,
        sufficient_statistics: &Self::SufficientStatistics,
        weight: f64
    ) -> Result<(), Error>;
    fn merge(
        sufficient_statistics: &[&Self::SufficientStatistics],
        weights: &[f64]
    ) -> Result<Self::SufficientStatistics, Error>;

    // Provided method
    fn expect_rand(
        &self,
        _data: &Self::DataIn<'_>,
        _k: usize
    ) -> Result<Self::Likelihood, Error> { ... }
}

Required Associated Types§

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type SufficientStatistics: Send + Sync

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type Likelihood

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type DataIn<'a>: Sync

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type DataOut

Required Methods§

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fn expect( &self, data: &Self::DataIn<'_> ) -> Result<(Self::Likelihood, AvgLLH), Error>

The E-Step. Computes the likelihood for each component in the mixture Note that for Mixables, this is the log-likelihood

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fn compute( &self, data: &Self::DataIn<'_>, responsibilities: &Self::Likelihood ) -> Result<Self::SufficientStatistics, Error>

Computes the sufficient statistics from the responsibility matrix. The Optionally, stores the sufficient statistics (for incremental learning and store.restore functionality) can be disabled for performance (defaults to True)

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fn maximize( &mut self, sufficient_statistics: &Self::SufficientStatistics ) -> Result<(), Error>

Maximize the model parameters from

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fn predict(&self, data: &Self::DataIn<'_>) -> Result<Self::DataOut, Error>

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fn update( &mut self, sufficient_statistics: &Self::SufficientStatistics, weight: f64 ) -> Result<(), Error>

Update the stored sufficient statistics (for incremental learning) Weights is a tuple (a float should suffice, if summing to one)

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fn merge( sufficient_statistics: &[&Self::SufficientStatistics], weights: &[f64] ) -> Result<Self::SufficientStatistics, Error>

merge multiple sufficient statistics into one.

Provided Methods§

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fn expect_rand( &self, _data: &Self::DataIn<'_>, _k: usize ) -> Result<Self::Likelihood, Error>

Generate a random expectation. Used as an initalization. It is recommended to draw the expectations from a univorm Dirichlet distribution. Note: This works better than an initialization method, because the layers such as the Probabilistic trait don’t need to implement backend-specific random samplers.

Implementors§