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[Clucene-cvs] SF.net SVN: clucene:[3022] branches/lucene2_3_2/src/core/CLucene/search
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From: <ust...@us...> - 2009-09-27 08:58:46
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Revision: 3022
http://clucene.svn.sourceforge.net/clucene/?rev=3022&view=rev
Author: ustramooner
Date: 2009-09-27 08:58:40 +0000 (Sun, 27 Sep 2009)
Log Message:
-----------
cleanup and fixed a bug in FuzzyTermEnum
Modified Paths:
--------------
branches/lucene2_3_2/src/core/CLucene/search/FuzzyQuery.cpp
branches/lucene2_3_2/src/core/CLucene/search/FuzzyQuery.h
Modified: branches/lucene2_3_2/src/core/CLucene/search/FuzzyQuery.cpp
===================================================================
--- branches/lucene2_3_2/src/core/CLucene/search/FuzzyQuery.cpp 2009-09-27 08:58:03 UTC (rev 3021)
+++ branches/lucene2_3_2/src/core/CLucene/search/FuzzyQuery.cpp 2009-09-27 08:58:40 UTC (rev 3022)
@@ -28,7 +28,7 @@
FuzzyTermEnum::FuzzyTermEnum(IndexReader* reader, Term* term, float_t minSimilarity, size_t _prefixLength):
- FilteredTermEnum(),d(NULL),dWidth(0),dHeight(0),_similarity(0),_endEnum(false),searchTerm(_CL_POINTER(term)),
+ FilteredTermEnum(),d(NULL),_similarity(0),_endEnum(false),searchTerm(_CL_POINTER(term)),
text(NULL),textLen(0),prefix(NULL)/* ISH: was STRDUP_TtoT(LUCENE_BLANK_STRING)*/,prefixLength(_prefixLength),
minimumSimilarity(minSimilarity)
{
@@ -58,8 +58,6 @@
prefix[realPrefixLength]='\0';
initializeMaxDistances();
- dWidth = LUCENE_TYPICAL_LONGEST_WORD_IN_INDEX; // default length of the d array
- dHeight = textLen + 1;
Term* trm = _CLNEW Term(searchTerm->field(), prefix); // _CLNEW Term(term, prefix); -- not intern'd?
setEnum(reader->terms(trm));
@@ -134,13 +132,6 @@
const size_t targetLen = termTextLen-prefixLength;
_similarity = similarity(target, targetLen);
return (_similarity > minimumSimilarity);
-
- /* LEGACY:
- //Calculate the Levenshtein distance
- int32_t dist = editDistance(text, target, textLen, targetLen);
- distance = 1 - ((float_t)dist / (float_t)cl_min(textLen, targetLen));
- return (distance > minimumSimilarity);
- */
}
_endEnum = true;
return false;
@@ -177,23 +168,20 @@
//let's make sure we have enough room in our array to do the distance calculations.
//Check if the array must be reallocated because it is too small or does not exist
-
- // TODO: realloc should be able to allocate memory for NULL pointers; if thats the case the NULL
- // check here is redundant
- if (d == NULL){
- dWidth = cl_max(dWidth, n+1);
- dHeight = cl_max(dHeight, m+1);
- d = reinterpret_cast<int32_t*>(malloc(sizeof(int32_t)*dWidth*dHeight));
- } else if (dWidth <= n || dHeight <= m) {
- //growDistanceArray
- dWidth = cl_max(dWidth, n+1);
- dHeight = cl_max(dHeight, m+1);
- d = reinterpret_cast<int32_t*>(realloc(d, sizeof(int32_t)*dWidth*dHeight));
+ size_t dWidth = n+1;
+ size_t dHeight = m+1;
+ if (d == NULL){
+ dLen = dWidth*dHeight;
+ d = (int32_t*)(malloc(sizeof(int32_t)*dLen));
+ } else if (dLen < dWidth*dHeight) {
+ dLen = dWidth*dHeight;
+ d = (int32_t*)(realloc(d, sizeof(int32_t)*dLen));
}
+ memset(d,0,dLen);
+
+ size_t i; // iterates through the source string
+ size_t j; // iterates through the target string
- size_t i; // iterates through the source string
- size_t j; // iterates through the target string
-
// init matrix d
for (i = 0; i <= n; i++){
d[i + (0*dWidth)] = i;
@@ -254,72 +242,6 @@
return (int32_t) ((1-minimumSimilarity) * (cl_min(textLen, m) + prefixLength));
}
- /* LEGACY:
- int32_t FuzzyTermEnum::editDistance(const TCHAR* s, const TCHAR* t, const int32_t n, const int32_t m) {
- //Func - Calculates the Levenshtein distance also known as edit distance is a measure of similiarity
- // between two strings where the distance is measured as the number of character
- // deletions, insertions or substitutions required to transform one string to
- // the other string.
- //Pre - s != NULL and contains the source string
- // t != NULL and contains the target string
- // n >= 0 and contains the length of the source string
- // m >= 0 and containts the length of the target string
- //Post - The distance has been returned
-
- CND_PRECONDITION(s != NULL, "s is NULL");
- CND_PRECONDITION(t != NULL, "t is NULL");
- CND_PRECONDITION(n >= 0," n is a negative number");
- CND_PRECONDITION(n >= 0," n is a negative number");
-
- int32_t i; // iterates through s
- int32_t j; // iterates through t
- TCHAR s_i; // ith character of s
-
- if (n == 0)
- return m;
- if (m == 0)
- return n;
-
- //Check if the array must be reallocated because it is too small or does not exist
- if (e == NULL || eWidth <= n || eHeight <= m) {
- //Delete e if possible
- _CLDELETE_ARRAY(e);
- //resize e
- eWidth = cl_max(eWidth, n+1);
- eHeight = cl_max(eHeight, m+1);
- e = _CL_NEWARRAY(int32_t,eWidth*eHeight);
- }
-
- CND_CONDITION(e != NULL,"e is NULL");
-
- // init matrix e
- for (i = 0; i <= n; i++){
- e[i + (0*eWidth)] = i;
- }
- for (j = 0; j <= m; j++){
- e[0 + (j*eWidth)] = j;
- }
-
- int32_t __t; //temporary variable for min3
-
- // start computing edit distance
- for (i = 1; i <= n; i++) {
- s_i = s[i - 1];
- for (j = 1; j <= m; j++) {
- if (s_i != t[j-1]){
- min3(e[i + (j*eWidth) - 1], e[i + ((j-1)*eWidth)], e[i + ((j-1)*eWidth)-1]);
- e[i + (j*eWidth)] = __t+1;
- }else{
- min3(e[i + (j*eWidth) -1]+1, e[i + ((j-1)*eWidth)]+1, e[i + ((j-1)*eWidth)-1]);
- e[i + (j*eWidth)] = __t;
- }
- }
- }
-
- // we got the result!
- return e[n + ((m)*eWidth)];
- }*/
-
class FuzzyQuery::ScoreTerm {
public:
Term* term;
Modified: branches/lucene2_3_2/src/core/CLucene/search/FuzzyQuery.h
===================================================================
--- branches/lucene2_3_2/src/core/CLucene/search/FuzzyQuery.h 2009-09-27 08:58:03 UTC (rev 3021)
+++ branches/lucene2_3_2/src/core/CLucene/search/FuzzyQuery.h 2009-09-27 08:58:40 UTC (rev 3022)
@@ -90,8 +90,7 @@
* everytime similarity is called.
*/
int32_t* d;
- size_t dWidth;
- size_t dHeight;
+ size_t dLen;
//float_t distance;
float_t _similarity;
@@ -108,24 +107,6 @@
double scale_factor;
int32_t maxDistances[LUCENE_TYPICAL_LONGEST_WORD_IN_INDEX];
-
-
- /* LEGACY:
- int32_t* e;
- int32_t eWidth;
- int32_t eHeight;
- **
- Levenshtein distance also known as edit distance is a measure of similiarity
- between two strings where the distance is measured as the number of character
- deletions, insertions or substitutions required to transform one string to
- the other string.
- <p>This method takes in four parameters; two strings and their respective
- lengths to compute the Levenshtein distance between the two strings.
- The result is returned as an integer.
- *
- int32_t editDistance(const TCHAR* s, const TCHAR* t, const int32_t n, const int32_t m);
- */
-
/******************************
* Compute Levenshtein distance
******************************/
@@ -170,17 +151,6 @@
float_t similarity(const TCHAR* target, const size_t targetLen);
/**
- * Grow the second dimension of the array, so that we can calculate the
- * Levenshtein difference.
- */
- /*
- void growDistanceArray(int32_t m) {
- for (int i = 0; i < d.length; i++) {
- d[i] = new int[m+1];
- }
- }*/
-
- /**
* The max Distance is the maximum Levenshtein distance for the text
* compared to some other value that results in score that is
* better than the minimum similarity.
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