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add docstrings to similarity methods

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oliver 2016-04-09 15:57:11 +02:00
parent 53fda5caaf
commit 4df159ba7e
2 changed files with 121 additions and 34 deletions
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91
app/lib/Similarity.inc
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@ -22,8 +22,13 @@
/**
* TODO readDocument()
* used
* Read a file and return its text.
*
* Currently only PDF-files are supported and “pdftotext” needs to be
* installed. If reading fails, false is returned.
*
* @param string $filename Name of file to read
* @return mixed Text of document (string) or false (boolean)
*/
public static function readDocument($filename)
{
@ -46,12 +51,16 @@
/**
* TODO splitNgrams()
* used
* Split a text into N-grams.
*
* The default N is 3.
*
* @param string $document Text to be splitted
* @param int $n Size of grams to split into (N)
* @return array List of n-grams
*/
public static function splitNgrams($document)
public static function splitNgrams($document, $n=3)
{
$n = 3;
$affix = implode(' ', array_fill(0, $n-1, ' '));
$document = $affix.$document.$affix;
$ngrams = array();
@ -65,8 +74,17 @@
/**
* TODO compare()
* used
* Compare to documents, represented by there Term Frequencies (TFs)
* values.
*
* $tfsA, $tfsB and $idf_n are expected to be associative arrays with
* the term as key and the corresponding frequency as value.
*
* @param array $tfsA Term Frequencies of document A
* @param array $tfsB Term Frequencies of document B
* @param int $idf_N Total count of documents in corpus
* @param array $idf_n Inverse Document Frequencies of all terms
* @return float Similarity value (between 0.0 and 1.0)
*/
public static function compare($tfsA, $tfsB, $idf_N, $idf_n)
{
@ -88,8 +106,17 @@
/**
* TODO getVector()
* used
* Calculate the vector for a document based on TF and IDF.
*
* $tfs and $idf_n are expected to be associative arrays with the term
* as key and the corresponding frequency as value. The resulting
* vector is an associative array with the terms as keys and their
* corresponding values as value.
*
* @param array $tfs Term Frequencies of document
* @param int $idf_N Total count of documents in corpus
* @param array $idf_n Inverse Document Frequencies of all terms
* @return array Document vector
*/
protected static function getVector($tfs, $idf_N, $idf_n)
{
@ -101,9 +128,18 @@
}
/**
* TODO getTFIDFs()
* used
/*
* Calculate TF*IDF values for a document.
*
* $tfs and $idf_n are expected to be associative arrays with the term
* as key and the corresponding frequency as value. The resulting
* value is an associative array with the terms as keys and their
* corresponding TF*IDF as values.
*
* @param array $tfs Term Frequencies of document
* @param int $idf_N Total count of documents in corpus
* @param array $idf_n Inverse Document Frequencies of all terms
* @return array TF*IDF values
*/
protected static function getTFIDFs($tfs, $idf_N, $idf_n)
{
@ -128,9 +164,13 @@
/**
* TODO cosinus()
* Calculate cosinus similarity between two vectors.
*
* sim(a, b) = (a・b) / (||a|| * ||b||)
* used
*
* @param array $a Vector A
* @param array $b Vector B
* @return float Similarity value (between 0.0 and 1.0)
*/
protected static function cosinus(array $a, array $b)
{
@ -146,9 +186,13 @@
/**
* TODO Dot product
* Calculate the dot-product for two vectors.
*
* a・b = summation{i=1,n}(a[i] * b[i])
* used
*
* @param array $a Vector A
* @param array $b Vector B
* @return float Dot-product
*/
protected static function dotProduct(array $a, array $b)
{
@ -156,10 +200,8 @@
$keysA = array_keys(array_filter($a));
$keysB = array_keys(array_filter($b));
$uniqueKeys = array_unique(array_merge($keysA, $keysB));
foreach($uniqueKeys as $key)
{
if(!empty($a[$key]) && !empty($b[$key]))
{
foreach($uniqueKeys as $key) {
if(!empty($a[$key]) && !empty($b[$key])) {
$dotProduct += ($a[$key] * $b[$key]);
}
}
@ -169,9 +211,12 @@
/**
* TODO Euclidean norm
* Caculate the Euclidean norm for a vector.
*
* ||x|| = sqrt(x・x) // ・ is a dot product
* used
*
* @param array $vector Vector
* @return float Euclidean norm
*/
protected static function norm(array $vector)
{