Xapian::MSet Class Reference

Class representing a list of search results. More...

## Public Types

enum | { SNIPPET_BACKGROUND_MODEL = 1 , SNIPPET_EXHAUSTIVE = 2 , SNIPPET_EMPTY_WITHOUT_MATCH = 4 , SNIPPET_CJK_NGRAM = 2048 }

## Public Member Functions

| MSet (const MSet &o)
| Copying is allowed. More...
MSet & | operator= (const MSet &o)
| Copying is allowed. More...
| MSet ()
| Default constructor. More...
| ~MSet ()
| Destructor.
int | convert_to_percent (double weight) const
| Convert a weight to a percentage. More...
int | convert_to_percent (const MSetIterator &it) const
| Convert the weight of the current iterator position to a percentage. More...
Xapian::doccount | get_termfreq (const std::string &term) const
| Get the termfreq of a term. More...
double | get_termweight (const std::string &term) const
| Get the term weight of a term. More...
Xapian::doccount | get_firstitem () const
| Rank of first item in this MSet. More...
Xapian::doccount | get_matches_lower_bound () const
| Lower bound on the total number of matching documents.
Xapian::doccount | get_matches_estimated () const
| Estimate of the total number of matching documents.
Xapian::doccount | get_matches_upper_bound () const
| Upper bound on the total number of matching documents.
Xapian::doccount | get_uncollapsed_matches_lower_bound () const
| Lower bound on the total number of matching documents before collapsing. More...
Xapian::doccount | get_uncollapsed_matches_estimated () const
| Estimate of the total number of matching documents before collapsing. More...
Xapian::doccount | get_uncollapsed_matches_upper_bound () const
| Upper bound on the total number of matching documents before collapsing. More...
double | get_max_attained () const
| The maximum weight attained by any document.
double | get_max_possible () const
| The maximum possible weight any document could achieve.
std::string | snippet (const std::string &text, size_t length=500, const Xapian::Stem &stemmer=Xapian::Stem(), unsigned flags=SNIPPET_BACKGROUND_MODEL|SNIPPET_EXHAUSTIVE, const std::string &hi_start="<b>", const std::string &hi_end="</b>", const std::string &omit="...") const
| Generate a snippet. More...
void | fetch (const MSetIterator &begin, const MSetIterator &end) const
| Prefetch hint a range of items. More...
void | fetch (const MSetIterator &item) const
| Prefetch hint a single MSet item. More...
void | fetch () const
| Prefetch hint the whole MSet. More...
Xapian::doccount | size () const
| Return number of items in this MSet object.
bool | empty () const
| Return true if this MSet object is empty.
void | swap (MSet &o)
| Efficiently swap this MSet object with another.
MSetIterator | begin () const
| Return iterator pointing to the first item in this MSet.
MSetIterator | end () const
| Return iterator pointing to just after the last item in this MSet.
MSetIterator | operator[] (Xapian::doccount i) const
| Return iterator pointing to the i-th object in this MSet.
MSetIterator | back () const
| Return iterator pointing to the last object in this MSet.
std::string | get_description () const
| Return a string describing this object.

## Detailed Description

Class representing a list of search results.

## Member Enumeration Documentation

## anonymous enum

anonymous enum

Enumerator

SNIPPET_BACKGROUND_MODEL

Model the relevancy of non-query terms in MSet::snippet().

        Non-query terms will be assigned a small weight, and the snippet
        will tend to prefer snippets which contain a more interesting
        background (where the query term content is equivalent).

SNIPPET_EXHAUSTIVE

Exhaustively evaluate candidate snippets in MSet::snippet().

        Without this flag, snippet generation will stop once it thinks
        it has found a "good enough" snippet, which will generally reduce
        the time taken to generate a snippet.

SNIPPET_EMPTY_WITHOUT_MATCH

Return the empty string if no term got matched.

        If enabled, snippet() returns an empty string if not a single match
        was found in text. If not enabled, snippet() returns a (sub)string
        of text without any highlighted terms.

SNIPPET_CJK_NGRAM

Enable generation of n-grams from CJK text.

        This option highlights CJK searches made using the QueryParser
        FLAG_CJK_NGRAM flag.  Non-CJK characters are split into words as
        normal.

        The TermGenerator FLAG_CJK_NGRAM flag needs to have been used at
        index time.

        This mode can also be enabled by setting environment variable
        XAPIAN_CJK_NGRAM to a non-empty value (but doing so was deprecated
        in 1.4.11).

        @since Added in Xapian 1.4.11.

## Constructor & Destructor Documentation

## MSet() [1/2]

Xapian::MSet::MSet | ( | const MSet & | o | ) |

Copying is allowed.

The internals are reference counted, so copying is cheap.

## MSet() [2/2]

Xapian::MSet::MSet | ( |  | ) |

Default constructor.

Creates an empty MSet, mostly useful as a placeholder.

## Member Function Documentation

## convert_to_percent() [1/2]

int Xapian::MSet::convert_to_percent | ( | const MSetIterator & | it | ) | const

inline

Convert the weight of the current iterator position to a percentage.

The matching document with the highest weight will get 100% if it matches all the weighted query terms, and proportionally less if it only matches some, and other weights are scaled by the same factor.

Documents with a non-zero score will always score at least 1%.

Note that these generally aren't percentages of anything meaningful (unless you use a custom weighting formula where they are!)

References convert_to_percent(), and Xapian::MSetIterator::get_weight().

## convert_to_percent() [2/2]

int Xapian::MSet::convert_to_percent | ( | double | weight | ) | const

Convert a weight to a percentage.

The matching document with the highest weight will get 100% if it matches all the weighted query terms, and proportionally less if it only matches some, and other weights are scaled by the same factor.

Documents with a non-zero score will always score at least 1%.

Note that these generally aren't percentages of anything meaningful (unless you use a custom weighting formula where they are!)

Referenced by convert_to_percent(), and Xapian::MSetIterator::get_percent().

## fetch() [1/3]

void Xapian::MSet::fetch | ( |  | ) | const

inline

Prefetch hint the whole MSet.

For a remote database, this may start a pipelined fetch of the requested documents from the remote server.

For a disk-based database, this may send prefetch hints to the operating system such that the disk blocks the requested documents are stored in are more likely to be in the cache when we come to actually read them.

## fetch() [2/3]

void Xapian::MSet::fetch | ( | const MSetIterator & | begin,
|  | const MSetIterator & | end
| ) |  | const

inline

Prefetch hint a range of items.

For a remote database, this may start a pipelined fetch of the requested documents from the remote server.

For a disk-based database, this may send prefetch hints to the operating system such that the disk blocks the requested documents are stored in are more likely to be in the cache when we come to actually read them.

## fetch() [3/3]

void Xapian::MSet::fetch | ( | const MSetIterator & | item | ) | const

inline

Prefetch hint a single MSet item.

For a remote database, this may start a pipelined fetch of the requested documents from the remote server.

For a disk-based database, this may send prefetch hints to the operating system such that the disk blocks the requested documents are stored in are more likely to be in the cache when we come to actually read them.

## get_firstitem()

Xapian::doccount Xapian::MSet::get_firstitem | ( |  | ) | const

Rank of first item in this MSet.

This is the parameter `first` passed to Xapian::Enquire::get_mset().

Referenced by Xapian::MSetIterator::get_rank().

## get_termfreq()

Xapian::doccount Xapian::MSet::get_termfreq | ( | const std::string & | term | ) | const

Get the termfreq of a term.

Returns
  The number of documents which term occurs in. This considers all documents in the database being searched, so gives the same answer as `db.get_termfreq(term)` (but is more efficient for query terms as it returns a value cached during the search.)

## get_termweight()

double Xapian::MSet::get_termweight | ( | const std::string & | term | ) | const

Get the term weight of a term.

Returns
  The maximum weight that term could have contributed to a document.

## get_uncollapsed_matches_estimated()

Xapian::doccount Xapian::MSet::get_uncollapsed_matches_estimated | ( |  | ) | const

Estimate of the total number of matching documents before collapsing.

Conceptually the same as get_matches_estimated() for the same query without any collapse part (though the actual value may differ).

## get_uncollapsed_matches_lower_bound()

Xapian::doccount Xapian::MSet::get_uncollapsed_matches_lower_bound | ( |  | ) | const

Lower bound on the total number of matching documents before collapsing.

Conceptually the same as get_matches_lower_bound() for the same query without any collapse part (though the actual value may differ).

## get_uncollapsed_matches_upper_bound()

Xapian::doccount Xapian::MSet::get_uncollapsed_matches_upper_bound | ( |  | ) | const

Upper bound on the total number of matching documents before collapsing.

Conceptually the same as get_matches_upper_bound() for the same query without any collapse part (though the actual value may differ).

## operator=()

MSet& Xapian::MSet::operator= | ( | const MSet & | o | ) |

Copying is allowed.

The internals are reference counted, so assignment is cheap.

## snippet()

std::string Xapian::MSet::snippet | ( | const std::string & | text,
|  | size_t | length = `500`,
|  | const Xapian::Stem & | stemmer = `Xapian::Stem()`,
|  | unsigned | flags = `SNIPPET_BACKGROUND_MODEL|SNIPPET_EXHAUSTIVE`,
|  | const std::string & | hi_start = `"<b>"`,
|  | const std::string & | hi_end = `"</b>"`,
|  | const std::string & | omit = `"..."`
| ) |  | const

Generate a snippet.

This method selects a continuous run of words from text, based mainly on where the query matches (currently terms, exact phrases and wildcards are taken into account). If flag SNIPPET_BACKGROUND_MODEL is used (which it is by default) then the selection algorithm also considers the non-query terms in the text with the aim of showing a context which provides more useful information.

The size of the text selected can be controlled by the length parameter, which specifies a number of bytes of text to aim to select. However slightly more text may be selected. Also the size of any escaping, highlighting or omission markers is not considered.

The returned text is escaped to make it suitable for use in HTML (though beware that in upstream releases 1.4.5 and earlier this escaping was sometimes incomplete), and matches with the query will be highlighted using hi_start and hi_end.

If the snippet seems to start or end mid-sentence, then omit is prepended or append (respectively) to indicate this.

The same stemming algorithm which was used to build the query should be specified in stemmer.

And flags contains flags controlling behaviour.

Added in 1.3.5.

The documentation for this class was generated from the following file:

- xapian/mset.h