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Training: * A fixed vocabulary is set to all tokens that appear in, at least, 2 samples. * All out-of-vocabulary tokens are discarded. * For every token, we set its Inverse Class Frequency (ICF) to `log(ct / cf) + 1` where `ct` is the total number of classes and `cf` is the number of classes where the token occurs. * Each sample is converted to a vector of `tf * icf` for every token in the vocabulary. `tf` is `1 + log(freq)`, where `freq` is the number of occurrences of the token in the given sample. * Samples are L2-normalized. * For each class (language), we compute the centroid of all its training samples by averaging them and L2-normalizing the result. Classification: * For a new sample, we get the L2-normalized vector with `tf * icf` terms for every known token, then classify the sample using the nearest centroid. Cosine similarity is used as similarity measure for this.
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