pub struct LdaResult<F: BixverseFloat> {
pub cell_topic: Mat<F>,
pub topic_region: Mat<F>,
pub bound: F,
pub perplexity: F,
pub n_iter: usize,
pub converged: bool,
}Expand description
A fitted LDA model.
Fields§
§cell_topic: Mat<F>Topic proportions per document, shape (k, n_docs). Columns sum to one,
so a document’s topic vector is one contiguous column.
topic_region: Mat<F>Term probabilities per topic, shape (n_terms, k). Columns sum to one,
so a topic’s term vector is one contiguous column.
bound: FFinal variational bound (ELBO). Higher is better.
perplexity: FPer-token perplexity, exp(-bound / total_tokens). Lower is better.
n_iter: usizeOuter iterations actually run.
converged: boolWhether the relative bound change fell below tol.
Trait Implementations§
Auto Trait Implementations§
impl<F> Freeze for LdaResult<F>
impl<F> RefUnwindSafe for LdaResult<F>
impl<F> Send for LdaResult<F>
impl<F> Sync for LdaResult<F>
impl<F> Unpin for LdaResult<F>
impl<F> UnsafeUnpin for LdaResult<F>
impl<F> UnwindSafe for LdaResult<F>
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The inclusion map: converts
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