## gov.sandia.cognition.learning.function.categorization Interface ConfidenceWeightedBinaryCategorizer

All Superinterfaces:
BinaryCategorizer<Vectorizable>, Categorizer<Vectorizable,Boolean>, Cloneable, CloneableSerializable, DiscriminantBinaryCategorizer<Vectorizable>, DiscriminantCategorizer<Vectorizable,Boolean,Double>, Evaluator<Vectorizable,Boolean>, Serializable, ThresholdBinaryCategorizer<Vectorizable>, VectorInputEvaluator<Vectorizable,Boolean>
All Known Implementing Classes:
AbstractConfidenceWeightedBinaryCategorizer, DefaultConfidenceWeightedBinaryCategorizer, DiagonalConfidenceWeightedBinaryCategorizer

`public interface ConfidenceWeightedBinaryCategorizerextends VectorInputEvaluator<Vectorizable,Boolean>, ThresholdBinaryCategorizer<Vectorizable>`

Interface for a confidence-weighted binary categorizer, which defines a distribution over linear binary categorizers. It extends the vector input evaluator and threshold binary categorizer like a `LinearBinaryCategorizer` so that it can behave as a binary categorizer, it but also has methods for accessing the distribution of binary categorizers that it represents. It is typically represented using a mean vector and a covariance matrix.

Since:
3.3.0
Author:
Justin Basilico

Method Summary
` MultivariateGaussian` `createWeightDistribution()`
Creates a multivariate Gaussian distribution that represents the distribution of weight vectors that the algorithm has learned.
` BernoulliDistribution` `evaluateAsBernoulli(Vectorizable input)`
Returns a Bernoulli distribution over the output of the distribution of weight vectors times the input, with the confidence that the categorizer was trained using.
` UnivariateGaussian` `evaluateAsGaussian(Vectorizable input)`
Returns the univariate Gaussian distribution over the output of the distribution of weight vectors times the input, with the confidence that the categorizer was trained using.
` Matrix` `getCovariance()`
Gets the covariance matrix of the categorizer.
` Vector` `getMean()`
Gets the mean of the categorizer, which is the weight vector.
` boolean` `isInitialized()`
Determines if this category has been initialized with a mean and covariance.

Methods inherited from interface gov.sandia.cognition.math.matrix.VectorInputEvaluator
`getInputDimensionality`

Methods inherited from interface gov.sandia.cognition.learning.function.categorization.ThresholdBinaryCategorizer
`getThreshold, setThreshold`

Methods inherited from interface gov.sandia.cognition.learning.function.categorization.DiscriminantBinaryCategorizer
`evaluateAsDouble`

Methods inherited from interface gov.sandia.cognition.learning.function.categorization.DiscriminantCategorizer
`evaluateWithDiscriminant`

Methods inherited from interface gov.sandia.cognition.learning.function.categorization.Categorizer
`getCategories`

Methods inherited from interface gov.sandia.cognition.evaluator.Evaluator
`evaluate`

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable
`clone`

Method Detail

### evaluateAsGaussian

`UnivariateGaussian evaluateAsGaussian(Vectorizable input)`
Returns the univariate Gaussian distribution over the output of the distribution of weight vectors times the input, with the confidence that the categorizer was trained using.

Parameters:
`input` - The input to evaluate.
Returns:
The distribution of outputs as a Gaussian.

### evaluateAsBernoulli

`BernoulliDistribution evaluateAsBernoulli(Vectorizable input)`
Returns a Bernoulli distribution over the output of the distribution of weight vectors times the input, with the confidence that the categorizer was trained using.

Parameters:
`input` - The input to evaluate.
Returns:
The distribution over outputs as a Bernoulli.

### createWeightDistribution

`MultivariateGaussian createWeightDistribution()`
Creates a multivariate Gaussian distribution that represents the distribution of weight vectors that the algorithm has learned.

Returns:
The distribution of weight vectors.

### isInitialized

`boolean isInitialized()`
Determines if this category has been initialized with a mean and covariance.

Returns:
True if this categorizer has been initialized. Otherwise, false.

### getMean

`Vector getMean()`
Gets the mean of the categorizer, which is the weight vector.

Returns:
The mean of the categorizer.

### getCovariance

`Matrix getCovariance()`
Gets the covariance matrix of the categorizer.

Returns:
The covariance matrix.