** variations.
**
** The fuzzer data table must contain exactly four columns (more precisely,
** the statement "SELECT * FROM <fuzzer_data_table>" must return records
** that consist of four columns). It does not matter what the columns are
** named. 
**
** Each row in the fuzzer table represents a single character transformation. 
** The left most column of the row (column 0) contains an integer value -
** the identifier of the ruleset to which the transformation rule belongs
** (see "MULTIPLE RULE SETS" below). The second column of the row (column 0)
** contains the input character or characters. The third column contains the
** output character or characters. And the fourth column contains the integer 
** cost of making the transformation. For example:
**
**    CREATE TABLE f_data(ruleset, cFrom, cTo, Cost);
**    INSERT INTO f_data(ruleset, cFrom, cTo, Cost) VALUES(0, '', 'a', 100);
**    INSERT INTO f_data(ruleset, cFrom, cTo, Cost) VALUES(0, 'b', '', 87);
**    INSERT INTO f_data(ruleset, cFrom, cTo, Cost) VALUES(0, 'o', 'oe', 38);
**    INSERT INTO f_data(ruleset, cFrom, cTo, Cost) VALUES(0, 'oe', 'o', 40);
**
** The first row inserted into the fuzzer data table by the SQL script
** above indicates that the cost of inserting a letter 'a' is 100.  (All 
** costs are integers.  We recommend that costs be scaled so that the 
** average cost is around 100.) The second INSERT statement creates a rule
** that the cost of that the cost of deleting a single letter 'b' is 87.
** The third and fourth INSERT statements mean that the cost of transforming 
** a single letter "o" into the two-letter sequence "oe" is 38 and that the
** cost of transforming "oe" back into "o" is 40.
**
** The contents of the fuzzer data table are loaded into main memory when
** a fuzzer table is first created, and may be internally reloaded by the
** system at any subsequent time. Therefore, the fuzzer data table should be 
** populated before the fuzzer table is created and not modified thereafter.
** If you do need to modify the contents of the fuzzer data table, it is
** recommended that the associated fuzzer table be dropped, the fuzzer data
** table edited, and the fuzzer table recreated within a single transaction.


**
** Once it has been created, the fuzzer table can be queried as follows:
**
**    SELECT word, distance FROM f
**     WHERE word MATCH 'abcdefg'
**       AND distance<200;
**
................................................................................
** can be derived from that string by appling the specified transformations.
** The strings are output together with their total transformation cost
** (called "distance") and appear in order of increasing cost.  No string
** is output more than once.  If there are multiple ways to transform the
** target string into the output string then the lowest cost transform is
** the one that is returned.  In the example, the search is limited to 
** strings with a total distance of less than 200.



**
** It is important to put some kind of a limit on the fuzzer output.  This
** can be either in the form of a LIMIT clause at the end of the query,
** or better, a "distance<NNN" constraint where NNN is some number.  The
** running time and memory requirement is exponential in the value of NNN 
** so you want to make sure that NNN is not too big.  A value of NNN that
** is about twice the average transformation cost seems to give good results.
................................................................................
**      AND f.distance<=200
**      AND f.word=vocabulary.w
**      AND f.ruleset=1  -- Specify the ruleset to use here
**    LIMIT 20
**
** If no "ruleset = ?" constraint is specified in the WHERE clause, ruleset 
** 0 is used.






*/
#include "sqlite3.h"
#include <stdlib.h>
#include <string.h>
#include <assert.h>
#include <stdio.h>

................................................................................
*/
static fuzzer_stem *fuzzerNewStem(
  fuzzer_cursor *pCur,
  const char *zWord,
  fuzzer_cost rBaseCost
){
  fuzzer_stem *pNew;

  unsigned int h;

  pNew = sqlite3_malloc( sizeof(*pNew) + strlen(zWord) + 1 );
  if( pNew==0 ) return 0;
  memset(pNew, 0, sizeof(*pNew));
  pNew->zBasis = (char*)&pNew[1];
  pNew->nBasis = strlen(zWord);
  memcpy(pNew->zBasis, zWord, pNew->nBasis+1);
  pNew->pRule = pCur->pVtab->pRule;
  while( pNew->pRule && pNew->pRule->iRuleset!=pCur->iRuleset ){
    pNew->pRule = pNew->pRule->pNext;
  }

  pNew->n = -1;
  pNew->rBaseCost = pNew->rCostX = rBaseCost;
  h = fuzzerHash(pNew->zBasis);
  pNew->pHash = pCur->apHash[h];
  pCur->apHash[h] = pNew;
  pCur->nStem++;
  return pNew;

** variations.
**
** The fuzzer data table must contain exactly four columns (more precisely,
** the statement "SELECT * FROM <fuzzer_data_table>" must return records
** that consist of four columns). It does not matter what the columns are
** named. 
**
** Each row in the fuzzer data table represents a single character
** transformation. The left most column of the row (column 0) contains an
** integer value - the identifier of the ruleset to which the transformation
** rule belongs (see "MULTIPLE RULE SETS" below). The second column of the
** row (column 0) contains the input character or characters. The third 
** column contains the output character or characters. And the fourth column
** contains the integer cost of making the transformation. For example:
**
**    CREATE TABLE f_data(ruleset, cFrom, cTo, Cost);
**    INSERT INTO f_data(ruleset, cFrom, cTo, Cost) VALUES(0, '', 'a', 100);
**    INSERT INTO f_data(ruleset, cFrom, cTo, Cost) VALUES(0, 'b', '', 87);
**    INSERT INTO f_data(ruleset, cFrom, cTo, Cost) VALUES(0, 'o', 'oe', 38);
**    INSERT INTO f_data(ruleset, cFrom, cTo, Cost) VALUES(0, 'oe', 'o', 40);
**
** The first row inserted into the fuzzer data table by the SQL script
** above indicates that the cost of inserting a letter 'a' is 100.  (All 
** costs are integers.  We recommend that costs be scaled so that the 
** average cost is around 100.) The second INSERT statement creates a rule
** saying that the cost of deleting a single letter 'b' is 87.  The third
** and fourth INSERT statements mean that the cost of transforming a
** single letter "o" into the two-letter sequence "oe" is 38 and that the
** cost of transforming "oe" back into "o" is 40.
**
** The contents of the fuzzer data table are loaded into main memory when
** a fuzzer table is first created, and may be internally reloaded by the
** system at any subsequent time. Therefore, the fuzzer data table should be 
** populated before the fuzzer table is created and not modified thereafter.
** If you do need to modify the contents of the fuzzer data table, it is
** recommended that the associated fuzzer table be dropped, the fuzzer data
** table edited, and the fuzzer table recreated within a single transaction.
** Alternatively, the fuzzer data table can be edited then the database
** connection can be closed and reopened.
**
** Once it has been created, the fuzzer table can be queried as follows:
**
**    SELECT word, distance FROM f
**     WHERE word MATCH 'abcdefg'
**       AND distance<200;
**
................................................................................
** can be derived from that string by appling the specified transformations.
** The strings are output together with their total transformation cost
** (called "distance") and appear in order of increasing cost.  No string
** is output more than once.  If there are multiple ways to transform the
** target string into the output string then the lowest cost transform is
** the one that is returned.  In the example, the search is limited to 
** strings with a total distance of less than 200.
**
** The fuzzer is a read-only table.  Any attempt to DELETE, INSERT, or
** UPDATE on a fuzzer table will throw an error.
**
** It is important to put some kind of a limit on the fuzzer output.  This
** can be either in the form of a LIMIT clause at the end of the query,
** or better, a "distance<NNN" constraint where NNN is some number.  The
** running time and memory requirement is exponential in the value of NNN 
** so you want to make sure that NNN is not too big.  A value of NNN that
** is about twice the average transformation cost seems to give good results.
................................................................................
**      AND f.distance<=200
**      AND f.word=vocabulary.w
**      AND f.ruleset=1  -- Specify the ruleset to use here
**    LIMIT 20
**
** If no "ruleset = ?" constraint is specified in the WHERE clause, ruleset 
** 0 is used.
**
** LIMITS
**
** The maximum ruleset number is 2147483647.  The maximum length of either
** of the strings in the second or third column of the fuzzer data table
** is 50 bytes.  The maximum cost on a rule is 1000.
*/
#include "sqlite3.h"
#include <stdlib.h>
#include <string.h>
#include <assert.h>
#include <stdio.h>

................................................................................
*/
static fuzzer_stem *fuzzerNewStem(
  fuzzer_cursor *pCur,
  const char *zWord,
  fuzzer_cost rBaseCost
){
  fuzzer_stem *pNew;
  fuzzer_rule *pRule;
  unsigned int h;

  pNew = sqlite3_malloc( sizeof(*pNew) + strlen(zWord) + 1 );
  if( pNew==0 ) return 0;
  memset(pNew, 0, sizeof(*pNew));
  pNew->zBasis = (char*)&pNew[1];
  pNew->nBasis = strlen(zWord);
  memcpy(pNew->zBasis, zWord, pNew->nBasis+1);
  pRule = pCur->pVtab->pRule;
  while( pRule && pRule->iRuleset!=pCur->iRuleset ){
    pRule = pRule->pNext;
  }
  pNew->pRule = pRule;
  pNew->n = -1;
  pNew->rBaseCost = pNew->rCostX = rBaseCost;
  h = fuzzerHash(pNew->zBasis);
  pNew->pHash = pCur->apHash[h];
  pCur->apHash[h] = pNew;
  pCur->nStem++;
  return pNew;

} {abcde 0 axcde 1 abcye 10}
do_test fuzzer1-1.13 {
  db eval {
    SELECT word, distance FROM f1
    WHERE word MATCH 'abcde' AND distance<=11 AND ruleset=1
  }
} {abcde 0 axcde 1 abcye 10 axcye 11}











do_test fuzzer1-2.0 {
  execsql {
    -- costs based on English letter frequencies
    CREATE TEMP TABLE f2_rules(ruleset, cFrom, cTo, cost);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('a','e',24);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('a','o',47);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('a','u',50);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('e','a',23);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('e','i',33);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('e','o',37);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('i','e',33);
................................................................................
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('w','',96);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('','x',120);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('x','',120);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('','y',100);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('y','',100);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('','z',120);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('z','',120);













    CREATE VIRTUAL TABLE temp.f2 USING fuzzer(f2_rules);

    -- Street names for the 28269 ZIPCODE.
    --
    CREATE TEMP TABLE streetname(n TEXT UNIQUE);
    INSERT INTO streetname VALUES('abbotsinch');
................................................................................
  execsql {
    SELECT DISTINCT streetname.n FROM f2, streetname
     WHERE f2.word MATCH 'tayle'
       AND f2.distance<=200
       AND streetname.n>=f2.word AND streetname.n<=(f2.word || x'F7BFBFBF')
  }
} {{tyler finley} trailer taymouth steelewood tallia tallu talwyn thelema}























forcedelete test.db2
do_execsql_test fuzzer1-4.1 {
  ATTACH 'test.db2' AS aux;
  CREATE TABLE aux.f3_rules(ruleset, cfrom, cto, cost);
  INSERT INTO f3_rules(ruleset, cfrom, cto, cost) VALUES(0, 'x','y', 10);
  INSERT INTO f3_rules(ruleset, cfrom, cto, cost) VALUES(1, 'a','b', 10);
  CREATE VIRTUAL TABLE aux.f3 USING fuzzer(f3_rules);
  SELECT word FROM f3 WHERE word MATCH 'ax'
} {ax ay}

finish_test

} {abcde 0 axcde 1 abcye 10}
do_test fuzzer1-1.13 {
  db eval {
    SELECT word, distance FROM f1
    WHERE word MATCH 'abcde' AND distance<=11 AND ruleset=1
  }
} {abcde 0 axcde 1 abcye 10 axcye 11}
do_test fuzzer1-1.14 {
  catchsql {INSERT INTO f1 VALUES(1)}
} {1 {table f1 may not be modified}}
do_test fuzzer1-1.15 {
  catchsql {DELETE FROM f1}
} {1 {table f1 may not be modified}}
do_test fuzzer1-1.16 {
  catchsql {UPDATE f1 SET rowid=rowid+10000}
} {1 {table f1 may not be modified}}


do_test fuzzer1-2.0 {
  execsql {
    -- costs based on English letter frequencies
    CREATE TEMP TABLE f2_rules(ruleset DEFAULT 0, cFrom, cTo, cost);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('a','e',24);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('a','o',47);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('a','u',50);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('e','a',23);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('e','i',33);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('e','o',37);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('i','e',33);
................................................................................
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('w','',96);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('','x',120);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('x','',120);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('','y',100);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('y','',100);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('','z',120);
    INSERT INTO f2_rules(cFrom,cTo,cost) VALUES('z','',120);
    INSERT INTO f2_rules(ruleset,cFrom,cTo,cost)
      SELECT 1, cFrom, cTo, 100 FROM f2_rules WHERE ruleset=0;
    INSERT INTO f2_rules(ruleset,cFrom,cTo,cost)
      SELECT 2, cFrom, cTo, 200-cost FROM f2_rules WHERE ruleset=0;
    INSERT INTO f2_rules(ruleset,cFrom,cTo,cost)
      SELECT 3, cFrom, cTo, cost FROM f2_rules WHERE ruleset=0;
    INSERT INTO f2_rules(ruleset,cFrom,cTo,cost)
      VALUES(3, 'mallard','duck',50),
            (3, 'duck', 'mallard', 50),
            (3, 'rock', 'stone', 50),
            (3, 'stone', 'rock', 50);


    CREATE VIRTUAL TABLE temp.f2 USING fuzzer(f2_rules);

    -- Street names for the 28269 ZIPCODE.
    --
    CREATE TEMP TABLE streetname(n TEXT UNIQUE);
    INSERT INTO streetname VALUES('abbotsinch');
................................................................................
  execsql {
    SELECT DISTINCT streetname.n FROM f2, streetname
     WHERE f2.word MATCH 'tayle'
       AND f2.distance<=200
       AND streetname.n>=f2.word AND streetname.n<=(f2.word || x'F7BFBFBF')
  }
} {{tyler finley} trailer taymouth steelewood tallia tallu talwyn thelema}
do_test fuzzer1-2.4 {
  execsql {
    SELECT DISTINCT streetname.n
      FROM f2 JOIN streetname
        ON (streetname.n>=f2.word AND streetname.n<=(f2.word || 'zzzzzz'))
     WHERE f2.word MATCH 'duck'
       AND f2.distance<150
       AND f2.ruleset=3
     ORDER BY 1
  }
} {mallard {mallard cove} {mallard forest} {mallard grove} {mallard hill} {mallard park} {mallard ridge} {mallard view}}
do_test fuzzer1-2.5 {
  execsql {
    SELECT DISTINCT streetname.n
      FROM f2 JOIN streetname
        ON (streetname.n>=f2.word AND streetname.n<=(f2.word || 'zzzzzz'))
     WHERE f2.word MATCH 'duck'
       AND f2.distance<150
       AND f2.ruleset=2
     ORDER BY 1
  }
} {}

forcedelete test.db2
do_execsql_test fuzzer1-4.1 {
  ATTACH 'test.db2' AS aux;
  CREATE TABLE aux.f3_rules(ruleset, cfrom, cto, cost);
  INSERT INTO f3_rules(ruleset, cfrom, cto, cost) VALUES(0, 'x','y', 10);
  INSERT INTO f3_rules(ruleset, cfrom, cto, cost) VALUES(1, 'a','b', 10);
  CREATE VIRTUAL TABLE aux.f3 USING fuzzer(f3_rules);
  SELECT word FROM f3 WHERE word MATCH 'ax'
} {ax ay}

finish_test