pub struct Options {Show 16 fields
pub dim: u32,
pub epochs: u32,
pub min_count: u32,
pub bucket: u32,
pub minn: u32,
pub maxn: u32,
pub threads: u32,
pub seed: u64,
pub ham_weight: f64,
pub costs: Vec<f64>,
pub threshold_spam: f64,
pub threshold_unsure: f64,
pub least_of_each: u64,
pub replace: bool,
pub model: Model,
pub lexicon: SpamWords,
}Expand description
How training goes; Default holds the values of docs/spam-filter.md.
Fields§
§dim: u32fastText: the vectors’ dimension.
epochs: u32fastText: passes over the corpus (the classifier’s 25; the centroid’s
language model, 5: Model::epochs).
min_count: u32fastText: words seen fewer times are left out of the vocabulary.
bucket: u32fastText: the character n-grams’ hash buckets.
minn: u32fastText: character n-grams from minn to maxn characters.
maxn: u32§threads: u32Threads for fastText and the vectors; the computer’s cores by default.
seed: u64The same seed and one thread give the same model.
ham_weight: f64The SVM: ham’s weight (calling ham spam costs this many times more).
costs: Vec<f64>The SVM’s costs to choose from.
threshold_spam: f64Your thresholds (spam.threshold_spam, spam.threshold_unsure).
threshold_unsure: f64§least_of_each: u64Fewer messages of a kind than this: no training.
replace: boolThe table in place may be replaced (when no worse). False: a trial, which changes nothing of the filter and says what it would do.
model: ModelWhich model learns.
lexicon: SpamWordsThe tokenizer’s words (the spam lists of the languages in use and your
changes, [words]): it reads the messages with them, and the table
keeps them (spam::Meta::lexicon).
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impl StructuralPartialEq for Options
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impl Freeze for Options
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impl UnwindSafe for Options
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