private static Action <LatentDirichletAllocationTransformer.LdaSummary> Wrap(LatentDirichletAllocationFitResult.OnFit onFit)
{
if (onFit == null)
{
return(null);
}
return(ldaTopicSummary => onFit(new LatentDirichletAllocationFitResult(ldaTopicSummary)));
}
/// <include file='../Microsoft.ML.Transforms/Text/doc.xml' path='doc/members/member[@name="LightLDA"]/*' />
/// <param name="input">A vector of floats representing the document.</param>
/// <param name="numberOfTopics">The number of topics.</param>
/// <param name="alphaSum">Dirichlet prior on document-topic vectors.</param>
/// <param name="beta">Dirichlet prior on vocab-topic vectors.</param>
/// <param name="samplingStepCount">Number of Metropolis Hasting step.</param>
/// <param name="maximumNumberOfIterations">Number of iterations.</param>
/// <param name="likelihoodInterval">Compute log likelihood over local dataset on this iteration interval.</param>
/// <param name="numberOfThreads">The number of training threads. Default value depends on number of logical processors.</param>
/// <param name="maximumTokenCountPerDocument">The threshold of maximum count of tokens per doc.</param>
/// <param name="numberOfSummaryTermsPerTopic">The number of words to summarize the topic.</param>
/// <param name="numberOfBurninIterations">The number of burn-in iterations.</param>
/// <param name="resetRandomGenerator">Reset the random number generator for each document.</param>
/// <param name="onFit">Called upon fitting with the learnt enumeration on the dataset.</param>
public static Vector <float> LatentDirichletAllocation(this Vector <float> input,
int numberOfTopics = LatentDirichletAllocationEstimator.Defaults.NumberOfTopics,
Single alphaSum = LatentDirichletAllocationEstimator.Defaults.AlphaSum,
Single beta = LatentDirichletAllocationEstimator.Defaults.Beta,
int samplingStepCount = LatentDirichletAllocationEstimator.Defaults.SamplingStepCount,
int maximumNumberOfIterations = LatentDirichletAllocationEstimator.Defaults.MaximumNumberOfIterations,
int likelihoodInterval = LatentDirichletAllocationEstimator.Defaults.LikelihoodInterval,
int numberOfThreads = LatentDirichletAllocationEstimator.Defaults.NumberOfThreads,
int maximumTokenCountPerDocument = LatentDirichletAllocationEstimator.Defaults.MaximumTokenCountPerDocument,
int numberOfSummaryTermsPerTopic = LatentDirichletAllocationEstimator.Defaults.NumberOfSummaryTermsPerTopic,
int numberOfBurninIterations = LatentDirichletAllocationEstimator.Defaults.NumberOfBurninIterations,
bool resetRandomGenerator = LatentDirichletAllocationEstimator.Defaults.ResetRandomGenerator,
LatentDirichletAllocationFitResult.OnFit onFit = null)
{
Contracts.CheckValue(input, nameof(input));
return(new ImplVector(input,
new Config(numberOfTopics, alphaSum, beta, samplingStepCount, maximumNumberOfIterations, likelihoodInterval, numberOfThreads, maximumTokenCountPerDocument, numberOfSummaryTermsPerTopic,
numberOfBurninIterations, resetRandomGenerator, Wrap(onFit))));
}
