analiticcl 0.4.9

use ibig::UBig;
use std::cmp::Ordering;
use std::fmt;
use std::io::Error;
use std::io::ErrorKind;
use std::str::FromStr;

use serde::{Deserialize, Serialize};

///Each type gets assigned an ID integer, carries no further meaning
pub type VocabId = u64;

pub type CharIndexType = u8;

pub type CharType = u32;

///A normalized string encoded via the alphabet
pub type NormString = Vec<CharIndexType>;

pub const PRIMES: &[CharType] = &[
    2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97,
    101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193,
    197, 199, 211, 223, 227, 229, 233, 239, 241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307,
    311, 313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397, 401, 409, 419, 421,
    431, 433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521, 523, 541, 547,
    557, 563, 569, 571, 577, 587, 593, 599, 601, 607, 613, 617, 619, 631, 641, 643, 647, 653, 659,
    661, 673, 677, 683, 691, 701, 709, 719, 727, 733, 739, 743, 751, 757, 761, 769, 773, 787, 797,
    809, 811, 821, 823, 827, 829, 839, 853, 857, 859, 863, 877, 881, 883, 887, 907, 911, 919, 929,
    937, 941, 947, 953, 967, 971, 977, 983, 991, 997,
];

///The anagram hash: uses a bag-of-characters representation where each bit flags the presence/absence of a certain character (the order of the bits are defined by Alphabet)
pub type AnaValue = UBig;

///Defines the alphabet, index corresponds how things are encoded, multiple strings may be encoded
///in the same way
pub type Alphabet = Vec<Vec<String>>;

#[derive(Clone, PartialEq, Debug, Deserialize, Serialize)]
pub struct Weights {
    ///Weight for the Levenshtein (or Damarau-Levenshtein) distance
    pub ld: f64,

    ///Weight for the Longest common substring length
    pub lcs: f64,

    ///Weight for the prefix length
    pub prefix: f64,

    ///Weight for the suffix length
    pub suffix: f64,

    ///Weight to assign to difference in case (lowercase/uppercase)
    pub case: f64,
}

impl Default for Weights {
    fn default() -> Self {
        Self {
            ld: 0.5,
            lcs: 0.125,
            prefix: 0.125,
            suffix: 0.125,
            case: 0.125,
        }
    }
}

impl Weights {
    pub fn sum(&self) -> f64 {
        self.ld + self.lcs + self.prefix + self.suffix + self.case
    }
}

#[derive(Clone, Copy, Debug, Serialize, Deserialize)]
pub enum DistanceThreshold {
    ///The distance threshold is expressed as a ratio of the total length of the text fragment under consideration, should be in range 0-1
    Ratio(f32),
    ///Like ratio, but with a set absolute maximum
    RatioWithLimit(f32, u8),
    ///Absolute distance threshold
    Absolute(u8),
}

impl FromStr for DistanceThreshold {
    type Err = std::io::Error;

    fn from_str(s: &str) -> Result<Self, std::io::Error> {
        if s.contains(";") {
            let fields: Vec<&str> = s.split(";").collect();
            if fields.len() == 2 {
                if let Ok(ratio) = fields[0].parse::<f32>() {
                    if let Ok(limit) = fields[1].parse::<u8>() {
                        return Ok(Self::RatioWithLimit(ratio, limit));
                    }
                }
            }
            return Err(Error::new(ErrorKind::InvalidInput, "Expected a combination of a ratio (float) and and absolute maximum (integer) separated by a semicolon"));
        } else if let Ok(num) = s.parse::<u8>() {
            return Ok(Self::Absolute(num));
        } else if let Ok(num) = s.parse::<f32>() {
            if num >= 0.0 && num <= 1.0 {
                return Ok(Self::Ratio(num));
            }
        }
        Err(Error::new(ErrorKind::InvalidInput, "Input must be integer (absolute threshold) or float between 0.0 and 1.0 (ratio), or a combination of a ratio and and absolute maximum separated by a semicolon"))
    }
}

#[derive(Clone, Debug, Serialize, Deserialize)]
#[serde(default)]
pub struct SearchParameters {
    /// Maximum anagram distance. The difference in characters (regardless of order)
    pub max_anagram_distance: DistanceThreshold,

    /// Maximum edit distance (levenshtein-damarau). The maximum edit distance according to Levenshtein-Damarau. Insertions, deletions, substitutions and transposition all have the same cost (1). It is recommended to set this value slightly lower than the maximum anagram distance.
    pub max_edit_distance: DistanceThreshold,

    /// Number of matches to return per input (set to 0 for unlimited if you want to exhaustively return every possibility within the specified anagram and edit distance)
    pub max_matches: usize,

    /// Require scores to meet this threshold, they are pruned otherwise
    pub score_threshold: f64,

    /// Cut-off threshold: if a score in the ranking is a specific factor greater than the best score, the ranking will be cut-off at that point and the score not included. Should be set to a value like 2.
    pub cutoff_threshold: f64,

    /// Determines when to stop searching for matches. Setting this can speed up the process at the
    /// cost of lower accuracy
    pub stop_criterion: StopCriterion,

    /// Maximum ngram order (1 for unigrams, 2 for bigrams, etc..).
    pub max_ngram: u8,

    /// Maximum ngram order for Language Models (2 for bigrams, etc..).
    pub lm_order: u8,

    /// Maximum number of candidate sequences to take along to the language modelling stage
    pub max_seq: usize,

    /// Use only a single-thread instead of leveraging multiple cores (lowers resource use and
    /// performance)
    pub single_thread: bool,

    /// Weight attributed to the language model in relation to the variant model (e.g. 2.0 = twice
    /// as much weight) when considering input context and rescoring.
    pub context_weight: f32,

    /// Weight attributed to the variant model in finding the most likely sequence
    pub variantmodel_weight: f32,

    /// Weight attributed to the language model in finding the most likely sequence
    pub lm_weight: f32,

    /// Weight attributed to the context rules model in finding the most likely sequence
    pub contextrules_weight: f32,

    /// Weight attributed to the frequency information in frequency reranking, in relation to
    /// the similarity component. 0 = disabled)
    pub freq_weight: f32,

    /// Consolidate matches and extract a single most likely sequence, if set
    /// to false, all possible matches (including overlapping ones) are returned.
    pub consolidate_matches: bool,

    /// Output text offsets in unicode points rather than UTF-8 byte offsets
    pub unicodeoffsets: bool,
}

impl Default for SearchParameters {
    fn default() -> Self {
        Self {
            max_anagram_distance: DistanceThreshold::Absolute(3),
            max_edit_distance: DistanceThreshold::Absolute(3),
            max_matches: 20,
            score_threshold: 0.25,
            cutoff_threshold: 2.0,
            stop_criterion: StopCriterion::Exhaustive,
            max_ngram: 3,
            lm_order: 3,
            single_thread: false,
            max_seq: 250,
            context_weight: 0.0,
            freq_weight: 0.0,
            variantmodel_weight: 3.0,
            lm_weight: 1.0,
            contextrules_weight: 1.0,
            consolidate_matches: true,
            unicodeoffsets: false,
        }
    }
}

impl fmt::Display for SearchParameters {
    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
        writeln!(f, " max_anagram_distance={:?}", self.max_anagram_distance)?;
        writeln!(f, " max_edit_distance={:?}", self.max_edit_distance)?;
        writeln!(f, " max_matches={}", self.max_matches)?;
        writeln!(f, " score_threshold={}", self.score_threshold)?;
        writeln!(f, " cutoff_threshold={}", self.cutoff_threshold)?;
        writeln!(f, " max_ngram={}", self.max_ngram)?;
        writeln!(f, " lm_order={}", self.lm_order)?;
        writeln!(f, " single_thread={}", self.single_thread)?;
        writeln!(f, " max_seq={}", self.max_seq)?;
        writeln!(f, " freq_weight={}", self.freq_weight)?;
        writeln!(f, " variantmodel_weight={}", self.variantmodel_weight)?;
        writeln!(f, " lm_weight={}", self.lm_weight)?;
        writeln!(f, " contextrules_weight={}", self.contextrules_weight)?;
        writeln!(f, " consolidate_matches={}", self.consolidate_matches)?;
        writeln!(f, " unicodeoffsets={}", self.unicodeoffsets)
    }
}

impl SearchParameters {
    pub fn with_edit_distance(mut self, distance: DistanceThreshold) -> Self {
        self.max_edit_distance = distance;
        self
    }
    pub fn with_anagram_distance(mut self, distance: DistanceThreshold) -> Self {
        self.max_anagram_distance = distance;
        self
    }
    pub fn with_max_matches(mut self, matches: usize) -> Self {
        self.max_matches = matches;
        self
    }
    pub fn with_score_threshold(mut self, threshold: f64) -> Self {
        self.score_threshold = threshold;
        self
    }
    pub fn with_cutoff_threshold(mut self, threshold: f64) -> Self {
        self.cutoff_threshold = threshold;
        self
    }
    pub fn with_stop_criterion(mut self, criterion: StopCriterion) -> Self {
        self.stop_criterion = criterion;
        self
    }
    pub fn with_max_ngram(mut self, max_ngram: u8) -> Self {
        self.max_ngram = max_ngram;
        self
    }
    pub fn with_max_seq(mut self, max_seq: usize) -> Self {
        self.max_seq = max_seq;
        self
    }
    pub fn with_single_thread(mut self) -> Self {
        self.single_thread = true;
        self
    }
    pub fn with_unicodeoffsets(mut self) -> Self {
        self.unicodeoffsets = true;
        self
    }
    pub fn with_utf8offsets(mut self) -> Self {
        self.unicodeoffsets = false;
        self
    }
    pub fn with_context_weight(mut self, weight: f32) -> Self {
        self.context_weight = weight;
        self
    }
    pub fn with_lm_weight(mut self, weight: f32) -> Self {
        self.lm_weight = weight;
        self
    }
    pub fn with_lm_order(mut self, order: u8) -> Self {
        self.lm_order = order;
        self
    }
    pub fn with_freq_weight(mut self, weight: f32) -> Self {
        self.freq_weight = weight;
        self
    }
    pub fn with_variantmodel_weight(mut self, weight: f32) -> Self {
        self.variantmodel_weight = weight;
        self
    }
    pub fn with_contextrules_weight(mut self, weight: f32) -> Self {
        self.contextrules_weight = weight;
        self
    }
    pub fn with_consolidate_matches(mut self, value: bool) -> Self {
        self.consolidate_matches = value;
        self
    }
}

#[derive(Debug, Clone)]
pub struct Distance {
    ///Levenshtein (or Damarau-Levenshtein) distance
    pub ld: CharIndexType,

    ///Longest common substring length
    pub lcs: u16,

    ///Common prefix length
    pub prefixlen: u16,

    ///Common suffix length
    pub suffixlen: u16,

    ///Is the casing different or not?
    pub samecase: bool,
}

#[derive(Debug, Clone, Copy, PartialEq, Serialize, Deserialize)]
pub enum StopCriterion {
    Exhaustive,

    /// Stop when we find an exact match with a lexical weight higher or equal than the specified value here
    StopAtExactMatch,
}

#[derive(Debug, Clone, PartialEq, PartialOrd, Serialize, Deserialize)]
pub enum VariantReference {
    ///The current item is a reference for a variant. The score expressed similarity from the
    ///variant to the reference.
    ReferenceFor((VocabId, f64)),

    ///The current item is a variant of a reference item. The score expressed similarity from the
    ///variant to the reference.
    VariantOf((VocabId, f64)),
}

#[derive(Debug, Clone, PartialEq)]
pub struct VariantResult {
    pub vocab_id: VocabId,
    pub dist_score: f64,
    pub freq_score: f64,
    pub via: Option<VocabId>,
}

impl VariantResult {
    pub fn score(&self, freq_weight: f32) -> f64 {
        if freq_weight == 0.0 {
            self.dist_score
        } else {
            (self.dist_score + (freq_weight as f64 * self.freq_score)) / (1.0 + freq_weight as f64)
        }
    }

    /// Custom comparison function for ranking, takes an extra freq_weight parameter
    pub fn rank_cmp(&self, other: &Self, freq_weight: f32) -> Option<Ordering> {
        if freq_weight > 0.0 {
            other
                .score(freq_weight)
                .partial_cmp(&self.score(freq_weight)) //reverse parameters because we want decreasing order
        } else {
            if self.dist_score > other.dist_score {
                Some(Ordering::Less) //opposite because we want decreasing order
            } else if self.dist_score < other.dist_score {
                Some(Ordering::Greater)
            } else {
                //when tied, fall back to frequency score
                if self.freq_score > other.freq_score {
                    Some(Ordering::Less)
                } else if self.freq_score < other.freq_score {
                    Some(Ordering::Greater)
                } else {
                    Some(Ordering::Equal)
                }
            }
        }
    }
}

///A simple lower-order n-gram type that does not require heap allocation
#[derive(Clone, Hash, PartialEq, Eq, PartialOrd)]
pub enum NGram {
    Empty,
    UniGram(VocabId),
    BiGram(VocabId, VocabId),
    TriGram(VocabId, VocabId, VocabId),
    QuadGram(VocabId, VocabId, VocabId, VocabId),
    QuintGram(VocabId, VocabId, VocabId, VocabId, VocabId),
}

impl NGram {
    pub fn from_list(v: &[VocabId]) -> Result<Self, &'static str> {
        match v.len() {
            0 => Ok(NGram::Empty),
            1 => Ok(NGram::UniGram(v[0])),
            2 => Ok(NGram::BiGram(v[0], v[1])),
            3 => Ok(NGram::TriGram(v[0], v[1], v[2])),
            4 => Ok(NGram::QuadGram(v[0], v[1], v[2], v[3])),
            5 => Ok(NGram::QuintGram(v[0], v[1], v[2], v[3], v[4])),
            _ => Err("Only supporting at most 5-grams"),
        }
    }

    pub fn from_option_list(v: &[Option<VocabId>]) -> Result<Self, &'static str> {
        match v {
            [] => Ok(NGram::Empty),
            [Some(a)] => Ok(NGram::UniGram(*a)),
            [Some(a), Some(b)] => Ok(NGram::BiGram(*a, *b)),
            [Some(a), Some(b), Some(c)] => Ok(NGram::TriGram(*a, *b, *c)),
            [Some(a), Some(b), Some(c), Some(d)] => Ok(NGram::QuadGram(*a, *b, *c, *d)),
            [Some(a), Some(b), Some(c), Some(d), Some(e)] => {
                Ok(NGram::QuintGram(*a, *b, *c, *d, *e))
            }
            _ => Err("Only supporting at most 5-grams"),
        }
    }

    pub fn to_vec(&self) -> Vec<VocabId> {
        match *self {
            NGram::Empty => {
                vec![]
            }
            NGram::UniGram(x) => {
                vec![x]
            }
            NGram::BiGram(x, y) => {
                vec![x, y]
            }
            NGram::TriGram(x, y, z) => {
                vec![x, y, z]
            }
            NGram::QuadGram(a, b, c, d) => {
                vec![a, b, c, d]
            }
            NGram::QuintGram(a, b, c, d, e) => {
                vec![a, b, c, d, e]
            }
        }
    }

    pub fn new() -> Self {
        NGram::Empty
    }

    pub fn len(&self) -> usize {
        match self {
            NGram::Empty => 0,
            NGram::UniGram(_) => 1,
            NGram::BiGram(..) => 2,
            NGram::TriGram(..) => 3,
            NGram::QuadGram(..) => 4,
            NGram::QuintGram(..) => 5,
        }
    }

    pub fn push(&mut self, item: VocabId) -> bool {
        match *self {
            NGram::Empty => {
                *self = NGram::UniGram(item);
                true
            }
            NGram::UniGram(x) => {
                *self = NGram::BiGram(x, item);
                true
            }
            NGram::BiGram(x, y) => {
                *self = NGram::TriGram(x, y, item);
                true
            }
            NGram::TriGram(x, y, z) => {
                *self = NGram::QuadGram(x, y, z, item);
                true
            }
            NGram::QuadGram(a, b, c, d) => {
                *self = NGram::QuintGram(a, b, c, d, item);
                true
            }
            _ => false,
        }
    }

    pub fn first(&self) -> Option<VocabId> {
        match *self {
            NGram::Empty => None,
            NGram::UniGram(x)
            | NGram::BiGram(x, _)
            | NGram::TriGram(x, _, _)
            | NGram::QuadGram(x, _, _, _)
            | NGram::QuintGram(x, _, _, _, _) => Some(x),
        }
    }

    pub fn pop_first(&mut self) -> NGram {
        match *self {
            NGram::Empty => NGram::Empty,
            NGram::UniGram(x) => {
                *self = NGram::Empty;
                NGram::UniGram(x)
            }
            NGram::BiGram(x, y) => {
                *self = NGram::UniGram(y);
                NGram::UniGram(x)
            }
            NGram::TriGram(x, y, z) => {
                *self = NGram::BiGram(y, z);
                NGram::UniGram(x)
            }
            NGram::QuadGram(a, b, c, d) => {
                *self = NGram::TriGram(b, c, d);
                NGram::UniGram(a)
            }
            NGram::QuintGram(a, b, c, d, e) => {
                *self = NGram::QuadGram(b, c, d, e);
                NGram::UniGram(a)
            }
        }
    }

    pub fn pop_last(&mut self) -> NGram {
        match *self {
            NGram::Empty => NGram::Empty,
            NGram::UniGram(x) => {
                *self = NGram::Empty;
                NGram::UniGram(x)
            }
            NGram::BiGram(x, y) => {
                *self = NGram::UniGram(x);
                NGram::UniGram(y)
            }
            NGram::TriGram(x, y, z) => {
                *self = NGram::BiGram(x, y);
                NGram::UniGram(z)
            }
            NGram::QuadGram(a, b, c, d) => {
                *self = NGram::TriGram(a, b, c);
                NGram::UniGram(d)
            }
            NGram::QuintGram(a, b, c, d, e) => {
                *self = NGram::QuadGram(a, b, c, d);
                NGram::UniGram(e)
            }
        }
    }
}