NLP.Tfidf

This document is auto-generated for Owl’s APIs. #39 entries have been extracted.

Github: {Signature} {Implementation}

Type definition

type tf_typ = Binary | Count | Frequency | Log_norm

Type of term frequency.

type df_typ = Unary | Idf | Idf_Smooth

Type of inverse document frequency.

type t

Type of a TFIDF model

Query model

val length : t -> int

Size of Tfidf model, i.e. number of documents contained.

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val term_freq : tf_typ -> float -> float -> float

term_freq term_count num_words calculates the term frequency weight.

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val doc_freq : df_typ -> float -> float -> float

doc_freq doc_count num_docs calculates the document frequency weight.

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val get_uri : t -> string

Return the path of the TFIDF model.

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val get_corpus : t -> Owl_nlp_corpus.t

Return the corpus contained in TFIDF model

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val vocab_len : t -> int

Return the size of the vocabulary contained in the TFIDF model.

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val get_handle : t -> in_channel

Geht the file handle associated with TFIDF model.

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val doc_count_of : t -> string -> float

doc_count_of tfidf w calculate document frequency for a given word w.

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val doc_count : Owl_nlp_vocabulary.t -> string -> float array * int

doc_count vocab fname``count occurrency in all documents contained in the raw text corpus of file ``fname, for all words

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val term_count : ('a, float) Hashtbl.t -> 'a array -> unit

term_count count doc counts the term occurrency in a document, and saves the result in count hashtbl.

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val density : t -> float

Return the percentage of non-zero elements in doc-term matrix.

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val doc_to_vec : (float, 'a) Bigarray.kind -> t -> (int * float) array -> (float, 'a) Owl_dense.Ndarray.Generic.t

doc_to_vec kind tfidf vec converts a TFIDF vector from its sparse represents to dense ndarray vector whose length equals the vocabulary size.

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Iteration functions

val get : t -> int -> (int * float) array

Return the ith TFIDF vector in the model. The format of return is (vocabulary index, weight) tuple array of a document.

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val next : t -> (int * float) array

Return the next document vector in the model. The format of return is (vocabulary index, weight) tuple array of a document.

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val next_batch : ?size:int -> t -> (int * float) array array

Return the next batch of document vectors in the model, the default size is 100.

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val iteri : (int -> (int * float) array -> unit) -> t -> unit

Iterate all the document vectors in a TFIDF model. The format of document vector is (vocabulary index, weight) tuple array of a document.

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val mapi : (int -> (int * float) array -> 'a) -> t -> 'a array

Map all the document vectors in a TFIDF model. The format of document vector is (vocabulary index, weight) tuple array of a document.

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val reset_iterators : t -> unit

Reset the iterator to the begining of the TFIDF model.

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Core functions

val build : ?norm:bool -> ?sort:bool -> ?tf:tf_typ -> ?df:df_typ -> Owl_nlp_corpus.t -> t

This function builds up a TFIDF model according to the passed in paramaters.

Parameters: * norm: whether to normalise the vectors in the TFIDF model, default is false. * sort: whether to sort the terms in a TFIDF vector in increasing order w.r.t their vocabulary indices. The default is false. * tf: type of term frequency used in building TFIDF. The default is Count. * df: type of document frequency used in building TFIDF. The default is Idf. * corpus: the corpus built by Owl_nlp_corpus model atop of which TFIDF will be built.

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I/O functions

val save : t -> string -> unit

save tfidf fname saves the TFIDF to a file of given file name fname.

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val load : string -> t

load fname loads a TFIDF from a file of name fname.

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val to_string : t -> string

Convert a TFIDF to its string representation, contains summary information.

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val print : t -> unit

Pretty print out the summary information of a TFIDF model.

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Helper functions

val tf_typ_string : tf_typ -> string

Convert term frequency type into string.

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val df_typ_string : df_typ -> string

Convert document frequency type into string.

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val apply : t -> string -> (int * float) array

Convert a single document according to a given model

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val normalise : ('a * float) array -> ('a * float) array

normalise x makes x a unit vector by dividing its l2norm.

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val create : tf_typ -> df_typ -> Owl_nlp_corpus.t -> t

Wrap up a TFIDF model type. Low-level function and you are not supposed to use it.

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val all_pairwise_distance : Owl_nlp_similarity.t -> t -> ('a * float) array -> (int * float) array

Calculate pairwise distance for the whole model, return format is (id,dist) array.

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val nearest : ?typ:Owl_nlp_similarity.t -> t -> ('a * float) array -> int -> (int * float) array

Return K-nearest neighbours, it is very slow due to linear search.

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