From c6cbb35538571c2938f5e527dd9607add3233b04 Mon Sep 17 00:00:00 2001 From: programarivm Date: Mon, 30 Dec 2024 16:48:10 +0100 Subject: [PATCH] Deployed 57440b1 with MkDocs version: 1.5.3 --- data-mining/index.html | 83 ++++++++++++++++++++++++++++++--------- index.html | 2 +- search/search_index.json | 2 +- sitemap.xml.gz | Bin 224 -> 224 bytes 4 files changed, 67 insertions(+), 20 deletions(-) diff --git a/data-mining/index.html b/data-mining/index.html index ce344f60..91e2460e 100644 --- a/data-mining/index.html +++ b/data-mining/index.html @@ -78,7 +78,7 @@ -
  • Stored Procedures +
  • Functions
    • @@ -361,18 +361,69 @@

    Example

    +------+ 36 rows in set (0.00 sec) -

    Stored Procedures

    -

    eval_array_sum()

    +

    Functions

    +

    EVAL_ARRAY_COUNT()

    +

    Average count of an evaluation feature given a result.

    +
    DELIMITER //
+DROP FUNCTION IF EXISTS EVAL_ARRAY_COUNT//
+CREATE FUNCTION EVAL_ARRAY_COUNT(res VARCHAR(7), i INT) RETURNS FLOAT
+READS SQL DATA
+DETERMINISTIC
+BEGIN
+    DECLARE count INT DEFAULT 0;
+    DECLARE total FLOAT DEFAULT 0;
+    DECLARE done INT DEFAULT 0;
+    DECLARE heuristic JSON;
+    DECLARE cur CURSOR FOR
+        SELECT
+          JSON_EXTRACT(heuristics_mine, concat('$[*][', i, ']'))
+        FROM
+          games
+        WHERE
+          heuristics_mine IS NOT NULL
+          AND Result = res;
+    DECLARE CONTINUE HANDLER FOR NOT FOUND SET done = 1;     
+    OPEN cur;
+    label:LOOP
+        FETCH cur INTO heuristic;
+        IF done = 1 THEN
+            LEAVE label;
+        END IF;
+        SELECT
+            SUM(CASE WHEN value > 0 THEN 1 ELSE 0 END) - SUM(CASE WHEN value < 0 THEN 1 ELSE 0 END) AS diff
+        INTO
+          @sum
+        FROM
+          JSON_TABLE(
+            heuristic,
+            "$[*]" COLUMNS(value FLOAT PATH "$")
+        ) material;
+        SET total = total + @sum;
+        SET count = count + 1;
+    END LOOP label;
+    CLOSE cur;
+    RETURN total / count;
+END//
+DELIMITER ;
+
    +

    The example below returns the average count of the center evaluation of all games won with the white pieces.

    +
    mysql> SELECT ROUND(EVAL_ARRAY_COUNT('1-0', 1), 2) as center_count;
++--------------+
+| center_count |
++--------------+
+|        25.28 |
++--------------+
+1 row in set (0.19 sec)
+
    +

    EVAL_ARRAY_SUM()

    Average sum of an evaluation feature given a result.

    DELIMITER //
-DROP PROCEDURE IF EXISTS eval_array_sum//
-CREATE PROCEDURE eval_array_sum(
-    IN res VARCHAR(7),
-    IN i INT,
-    OUT avg FLOAT)
+DROP FUNCTION IF EXISTS EVAL_ARRAY_SUM//
+CREATE FUNCTION EVAL_ARRAY_SUM(res VARCHAR(7), i INT) RETURNS FLOAT
+READS SQL DATA
+DETERMINISTIC
 BEGIN
     DECLARE count INT DEFAULT 0;
-    DECLARE sum FLOAT DEFAULT 0;
     DECLARE total FLOAT DEFAULT 0;
     DECLARE done INT DEFAULT 0;
     DECLARE heuristic JSON;
@@ -400,26 +451,22 @@ 
    eval_array_sum()
    
             heuristic,
             "$[*]" COLUMNS(value FLOAT PATH "$")
         ) material;
-
         SET total = total + @sum;
         SET count = count + 1;
     END LOOP label;
     CLOSE cur;
-    SET avg = total / count;
+    RETURN total / count;
 END//
 DELIMITER ;

    The example below returns the average sum of the center evaluation of all games won with the white pieces.

    -
    CALL eval_array_sum('1-0', 1, @center_avg);
-Query OK, 0 rows affected (0.10 sec)
-
-mysql> SELECT ROUND(@center_avg, 2) AS center_avg;
+
    mysql> SELECT ROUND(EVAL_ARRAY_SUM('1-0', 1), 2) as center_sum;
 +------------+
-| center_avg |
+| center_sum |
 +------------+
-|      28.47 |
+|       24.2 |
 +------------+
-1 row in set (0.00 sec)
+1 row in set (0.18 sec)
 
    
             
         
diff --git a/index.html b/index.html
index a8982f9f..4f9efb3c 100644
--- a/index.html
+++ b/index.html
@@ -240,5 +240,5 @@ 
 
 
diff --git a/search/search_index.json b/search/search_index.json
index a9eed320..e6032ee9 100644
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
-{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Chess Data The simplest yet powerful MySQL chess database to learn and play chess online. Installation Clone the chesslablab/chess-data repo into your projects folder. Then cd the chess-data directory and create an .env file: cp .env.example .env Run the Docker container in detached mode in the background: docker compose up -d Update the .env file to your specific needs and create the chess database: docker exec -itu 1000:1000 chess_data_php php cli/db-create.php Bootstrapping The chess database consists of these tables: annotations , games , openings and users . mysql> show tables; +-----------------+ | Tables_in_chess | +-----------------+ | annotations | | games | | openings | | users | +-----------------+ 4 rows in set (0.00 sec) \ud83d\uddd2 annotations This table is seeded with the CSV files in the data/annotations folder. docker exec -itu 1000:1000 chess_data_php php cli/seed/annotations.php data/annotations \ud83d\uddd2 games This table can be seeded with the PGN files in the data/example folder either all at once or file by file as it is shown in the examples below. Seed the games table with all the files in the data/example folder: docker exec -itu 1000:1000 chess_data_php php cli/seed/games.php data/example \u2717 2 games did not pass the validation. \u2713 4142 games out of a total of 4144 are OK. \u2713 597 games out of a total of 597 are OK. \u2717 1 games did not pass the validation. \u2713 1824 games out of a total of 1825 are OK. \u2713 3878 games out of a total of 3878 are OK. \u2713 4646 games out of a total of 4646 are OK. \u2717 2 games did not pass the validation. \u2713 2126 games out of a total of 2128 are OK. \u2713 2275 games out of a total of 2275 are OK. \u2713 1218 games out of a total of 1218 are OK. \u2713 827 games out of a total of 827 are OK. \u2717 5 games did not pass the validation. \u2713 1341 games out of a total of 1346 are OK. \u2713 5662 games out of a total of 5662 are OK. \u2713 3444 games out of a total of 3444 are OK. Seed the games table file by file: docker exec -itu 1000:1000 chess_data_php php cli/seed/games.php data/example/Anand.pgn \u2717 2 games did not pass the validation. \u2713 4142 games out of a total of 4144 are OK. The games table can also be seeded with your own set of PGN files in the data/games folder: docker exec -itu 1000:1000 chess_data_php php cli/seed/games.php data/games Please note that all files in the data folder are gitignored except those in data/example . The chess games won't be loaded into the database if containing PGN tags other than the ones supported by the tables created in the cli/db-create.php script. If that is the case you may want to remove the unsupported tags as shown in the example below. find . -name '*.pgn' -print0 | xargs -0 sed -i \"/\\[PlyCount .*\\]/d\" \ud83d\uddd2 openings This table is seeded with the CSV files in the data/openings folder. docker exec -itu 1000:1000 chess_data_php php cli/seed/openings.php data/openings \ud83d\uddd2 users This is how to seed the users table with fake random generated usernames: docker exec -itu 1000:1000 chess_data_php php cli/seed/users.php 5000","title":"Home"},{"location":"#chess-data","text":"The simplest yet powerful MySQL chess database to learn and play chess online.","title":"Chess Data"},{"location":"#installation","text":"Clone the chesslablab/chess-data repo into your projects folder. Then cd the chess-data directory and create an .env file: cp .env.example .env Run the Docker container in detached mode in the background: docker compose up -d Update the .env file to your specific needs and create the chess database: docker exec -itu 1000:1000 chess_data_php php cli/db-create.php","title":"Installation"},{"location":"#bootstrapping","text":"The chess database consists of these tables: annotations , games , openings and users . mysql> show tables; +-----------------+ | Tables_in_chess | +-----------------+ | annotations | | games | | openings | | users | +-----------------+ 4 rows in set (0.00 sec)","title":"Bootstrapping"},{"location":"#annotations","text":"This table is seeded with the CSV files in the data/annotations folder. docker exec -itu 1000:1000 chess_data_php php cli/seed/annotations.php data/annotations","title":"\ud83d\uddd2 annotations"},{"location":"#games","text":"This table can be seeded with the PGN files in the data/example folder either all at once or file by file as it is shown in the examples below. Seed the games table with all the files in the data/example folder: docker exec -itu 1000:1000 chess_data_php php cli/seed/games.php data/example \u2717 2 games did not pass the validation. \u2713 4142 games out of a total of 4144 are OK. \u2713 597 games out of a total of 597 are OK. \u2717 1 games did not pass the validation. \u2713 1824 games out of a total of 1825 are OK. \u2713 3878 games out of a total of 3878 are OK. \u2713 4646 games out of a total of 4646 are OK. \u2717 2 games did not pass the validation. \u2713 2126 games out of a total of 2128 are OK. \u2713 2275 games out of a total of 2275 are OK. \u2713 1218 games out of a total of 1218 are OK. \u2713 827 games out of a total of 827 are OK. \u2717 5 games did not pass the validation. \u2713 1341 games out of a total of 1346 are OK. \u2713 5662 games out of a total of 5662 are OK. \u2713 3444 games out of a total of 3444 are OK. Seed the games table file by file: docker exec -itu 1000:1000 chess_data_php php cli/seed/games.php data/example/Anand.pgn \u2717 2 games did not pass the validation. \u2713 4142 games out of a total of 4144 are OK. The games table can also be seeded with your own set of PGN files in the data/games folder: docker exec -itu 1000:1000 chess_data_php php cli/seed/games.php data/games Please note that all files in the data folder are gitignored except those in data/example . The chess games won't be loaded into the database if containing PGN tags other than the ones supported by the tables created in the cli/db-create.php script. If that is the case you may want to remove the unsupported tags as shown in the example below. find . -name '*.pgn' -print0 | xargs -0 sed -i \"/\\[PlyCount .*\\]/d\"","title":"\ud83d\uddd2 games"},{"location":"#openings","text":"This table is seeded with the CSV files in the data/openings folder. docker exec -itu 1000:1000 chess_data_php php cli/seed/openings.php data/openings","title":"\ud83d\uddd2 openings"},{"location":"#users","text":"This is how to seed the users table with fake random generated usernames: docker exec -itu 1000:1000 chess_data_php php cli/seed/users.php 5000","title":"\ud83d\uddd2 users"},{"location":"data-mining/","text":"Data Mining Data mining provides an additional boost to the SQL queries that can be performed on the games table. The precondition for it is to seed the games table with data. The CLI commands described below are to populate the columns suffixed with the word _mine with pre-calculated data for further analysis. CLI Commands Time Description \ud83d\udc8e The command is not too time-consuming. \ud83d\udc8e\ud83d\udc8e The command is time-consuming. \ud83d\udc8e fen.php The example below populates the fen_mine column with chess positions in FEN format on a player basis: docker exec -itu 1000:1000 chess_data_php php cli/mine/fen.php \"Anand,V\" This column is intended to store a text string of comma-separated values representing the chess positions in a game. It allows to search games by piece placement in FEN format. Example Fetch all games matching a particular position. SELECT * FROM games WHERE fen_mine LIKE '%r1bqkbnr/pp1ppppp/2n5/2p5/4P3/5N2/PPPP1PPP/RNBQKB1R%'; \ud83d\udc8e\ud83d\udc8e heuristics.php The example below calculates the heuristics_mine column on a player basis: docker exec -itu 1000:1000 chess_data_php php cli/mine/heuristics.php \"Anand,V\" The heuristics_mine column is intended to store a JSON object representing the PHP Chess heuristics in a game. It allows to gather insights about the decisions that have been made to make the moves. With this data, you can take advantage of MySQL JSON functions to perform operations on JSON values like in the following examples. Example Fetch the material evaluation in all games won by Anand with the white pieces. SELECT JSON_EXTRACT(heuristics_mine, '$[*][0]') as material FROM games WHERE heuristics_mine IS NOT NULL AND White = \"Anand,V\" AND Result = '1-0'; The index in the second parameter of the JSON_EXTRACT function $[*][0] corresponds to the index of the PHP Chess function being used in the cli/mine/heuristics.php script. See: Chess\\Function\\FastFunction Thus, [0] corresponds to the material evaluation in the fast function array. Example Convert the material evaluation array of a random game won by Anand with the white pieces from JSON to MySQL. SET @j = ( SELECT JSON_EXTRACT(heuristics_mine, '$[*][0]') FROM games WHERE heuristics_mine IS NOT NULL AND White = \"Anand,V\" AND Result = '1-0' ORDER BY RAND() LIMIT 1 ); SELECT * FROM JSON_TABLE( @j, \"$[*]\" COLUMNS( id FOR ORDINALITY, value FLOAT PATH \"$\" ) ) material; +------+-------+ | id | value | +------+-------+ | 1 | 0 | | 2 | 0 | | 3 | 0 | | 4 | 0 | | 5 | 0 | | 6 | 0 | | 7 | 0 | | 8 | 0 | | 9 | 0 | | 10 | 0 | | 11 | 0 | | 12 | 0 | | 13 | 0 | | 14 | 0 | | 15 | 0 | | 16 | 0 | | 17 | 0 | | 18 | 0 | | 19 | 0 | | 20 | 0 | | 21 | 0.13 | | 22 | -0.07 | | 23 | -0.07 | | 24 | -0.07 | | 25 | -0.07 | | 26 | -0.07 | | 27 | -0.07 | | 28 | -0.07 | | 29 | -0.07 | | 30 | -0.07 | | 31 | -0.07 | | 32 | -0.07 | | 33 | -0.07 | | 34 | -0.07 | | 35 | -0.07 | | 36 | -0.07 | | 37 | -0.07 | | 38 | -0.13 | | 39 | -0.13 | | 40 | -0.13 | | 41 | 0.1 | | 42 | -0.78 | | 43 | -0.66 | | 44 | -1 | | 45 | -0.66 | | 46 | -1 | | 47 | -0.44 | | 48 | -1 | | 49 | -0.26 | | 50 | -0.26 | | 51 | 0.2 | | 52 | 0.03 | | 53 | 0.02 | | 54 | 0.02 | | 55 | 0.02 | | 56 | 0.02 | | 57 | 0.06 | | 58 | -0.19 | | 59 | 0.02 | | 60 | 0.01 | | 61 | 0.02 | | 62 | 0.02 | | 63 | 0.08 | | 64 | -0.12 | | 65 | 0.08 | | 66 | 0.08 | | 67 | 0.07 | | 68 | 0.06 | | 69 | 0.14 | | 70 | 0.14 | | 71 | 0.08 | | 72 | 0.08 | | 73 | 0.06 | | 74 | -0.14 | | 75 | -0.14 | | 76 | -0.21 | | 77 | -0.21 | | 78 | -0.1 | | 79 | -1 | | 80 | -0.21 | | 81 | -0.43 | | 82 | -0.43 | | 83 | -1 | | 84 | -1 | | 85 | -0.21 | | 86 | -0.21 | | 87 | -0.21 | | 88 | -0.21 | | 89 | -0.21 | | 90 | -0.14 | | 91 | 0.01 | | 92 | -1 | | 93 | -0.44 | | 94 | -0.12 | | 95 | 0.11 | | 96 | -0.72 | | 97 | 0.24 | | 98 | 0.59 | | 99 | 0.88 | | 100 | 0.21 | | 101 | 0.25 | | 102 | 0.13 | | 103 | 0.11 | | 104 | 0.39 | | 105 | 0.24 | | 106 | 0.14 | | 107 | 0.06 | +------+-------+ 107 rows in set (0.00 sec) Example Sum all elements in the previous array. SELECT ROUND(SUM(value), 2) as sum FROM JSON_TABLE( @j, \"$[*]\" COLUMNS(value FLOAT PATH \"$\") ) material; +--------+ | sum | +--------+ | -11.32 | +--------+ 1 row in set (0.00 sec) Example Select the indexes in the previous material evaluation array where White has a material advantage. SELECT id FROM JSON_TABLE( @j, \"$[*]\" COLUMNS( id FOR ORDINALITY, value FLOAT PATH \"$\" ) ) material WHERE value > 0; +------+ | id | +------+ | 21 | | 41 | | 51 | | 52 | | 53 | | 54 | | 55 | | 56 | | 57 | | 59 | | 60 | | 61 | | 62 | | 63 | | 65 | | 66 | | 67 | | 68 | | 69 | | 70 | | 71 | | 72 | | 73 | | 91 | | 95 | | 97 | | 98 | | 99 | | 100 | | 101 | | 102 | | 103 | | 104 | | 105 | | 106 | | 107 | +------+ 36 rows in set (0.00 sec) Stored Procedures eval_array_sum() Average sum of an evaluation feature given a result. DELIMITER // DROP PROCEDURE IF EXISTS eval_array_sum// CREATE PROCEDURE eval_array_sum( IN res VARCHAR(7), IN i INT, OUT avg FLOAT) BEGIN DECLARE count INT DEFAULT 0; DECLARE sum FLOAT DEFAULT 0; DECLARE total FLOAT DEFAULT 0; DECLARE done INT DEFAULT 0; DECLARE heuristic JSON; DECLARE cur CURSOR FOR SELECT JSON_EXTRACT(heuristics_mine, concat('$[*][', i, ']')) FROM games WHERE heuristics_mine IS NOT NULL AND Result = res; DECLARE CONTINUE HANDLER FOR NOT FOUND SET done = 1; OPEN cur; label:LOOP FETCH cur INTO heuristic; IF done = 1 THEN LEAVE label; END IF; SELECT ROUND(SUM(value), 2) INTO @sum FROM JSON_TABLE( heuristic, \"$[*]\" COLUMNS(value FLOAT PATH \"$\") ) material; SET total = total + @sum; SET count = count + 1; END LOOP label; CLOSE cur; SET avg = total / count; END// DELIMITER ; The example below returns the average sum of the center evaluation of all games won with the white pieces. CALL eval_array_sum('1-0', 1, @center_avg); Query OK, 0 rows affected (0.10 sec) mysql> SELECT ROUND(@center_avg, 2) AS center_avg; +------------+ | center_avg | +------------+ | 28.47 | +------------+ 1 row in set (0.00 sec)","title":"Data Mining"},{"location":"data-mining/#data-mining","text":"Data mining provides an additional boost to the SQL queries that can be performed on the games table. The precondition for it is to seed the games table with data. The CLI commands described below are to populate the columns suffixed with the word _mine with pre-calculated data for further analysis.","title":"Data Mining"},{"location":"data-mining/#cli-commands","text":"Time Description \ud83d\udc8e The command is not too time-consuming. \ud83d\udc8e\ud83d\udc8e The command is time-consuming.","title":"CLI Commands"},{"location":"data-mining/#fenphp","text":"The example below populates the fen_mine column with chess positions in FEN format on a player basis: docker exec -itu 1000:1000 chess_data_php php cli/mine/fen.php \"Anand,V\" This column is intended to store a text string of comma-separated values representing the chess positions in a game. It allows to search games by piece placement in FEN format.","title":"\ud83d\udc8e fen.php"},{"location":"data-mining/#example","text":"Fetch all games matching a particular position. SELECT * FROM games WHERE fen_mine LIKE '%r1bqkbnr/pp1ppppp/2n5/2p5/4P3/5N2/PPPP1PPP/RNBQKB1R%';","title":"Example"},{"location":"data-mining/#heuristicsphp","text":"The example below calculates the heuristics_mine column on a player basis: docker exec -itu 1000:1000 chess_data_php php cli/mine/heuristics.php \"Anand,V\" The heuristics_mine column is intended to store a JSON object representing the PHP Chess heuristics in a game. It allows to gather insights about the decisions that have been made to make the moves. With this data, you can take advantage of MySQL JSON functions to perform operations on JSON values like in the following examples.","title":"\ud83d\udc8e\ud83d\udc8e heuristics.php"},{"location":"data-mining/#example_1","text":"Fetch the material evaluation in all games won by Anand with the white pieces. SELECT JSON_EXTRACT(heuristics_mine, '$[*][0]') as material FROM games WHERE heuristics_mine IS NOT NULL AND White = \"Anand,V\" AND Result = '1-0'; The index in the second parameter of the JSON_EXTRACT function $[*][0] corresponds to the index of the PHP Chess function being used in the cli/mine/heuristics.php script. See: Chess\\Function\\FastFunction Thus, [0] corresponds to the material evaluation in the fast function array.","title":"Example"},{"location":"data-mining/#example_2","text":"Convert the material evaluation array of a random game won by Anand with the white pieces from JSON to MySQL. SET @j = ( SELECT JSON_EXTRACT(heuristics_mine, '$[*][0]') FROM games WHERE heuristics_mine IS NOT NULL AND White = \"Anand,V\" AND Result = '1-0' ORDER BY RAND() LIMIT 1 ); SELECT * FROM JSON_TABLE( @j, \"$[*]\" COLUMNS( id FOR ORDINALITY, value FLOAT PATH \"$\" ) ) material; +------+-------+ | id | value | +------+-------+ | 1 | 0 | | 2 | 0 | | 3 | 0 | | 4 | 0 | | 5 | 0 | | 6 | 0 | | 7 | 0 | | 8 | 0 | | 9 | 0 | | 10 | 0 | | 11 | 0 | | 12 | 0 | | 13 | 0 | | 14 | 0 | | 15 | 0 | | 16 | 0 | | 17 | 0 | | 18 | 0 | | 19 | 0 | | 20 | 0 | | 21 | 0.13 | | 22 | -0.07 | | 23 | -0.07 | | 24 | -0.07 | | 25 | -0.07 | | 26 | -0.07 | | 27 | -0.07 | | 28 | -0.07 | | 29 | -0.07 | | 30 | -0.07 | | 31 | -0.07 | | 32 | -0.07 | | 33 | -0.07 | | 34 | -0.07 | | 35 | -0.07 | | 36 | -0.07 | | 37 | -0.07 | | 38 | -0.13 | | 39 | -0.13 | | 40 | -0.13 | | 41 | 0.1 | | 42 | -0.78 | | 43 | -0.66 | | 44 | -1 | | 45 | -0.66 | | 46 | -1 | | 47 | -0.44 | | 48 | -1 | | 49 | -0.26 | | 50 | -0.26 | | 51 | 0.2 | | 52 | 0.03 | | 53 | 0.02 | | 54 | 0.02 | | 55 | 0.02 | | 56 | 0.02 | | 57 | 0.06 | | 58 | -0.19 | | 59 | 0.02 | | 60 | 0.01 | | 61 | 0.02 | | 62 | 0.02 | | 63 | 0.08 | | 64 | -0.12 | | 65 | 0.08 | | 66 | 0.08 | | 67 | 0.07 | | 68 | 0.06 | | 69 | 0.14 | | 70 | 0.14 | | 71 | 0.08 | | 72 | 0.08 | | 73 | 0.06 | | 74 | -0.14 | | 75 | -0.14 | | 76 | -0.21 | | 77 | -0.21 | | 78 | -0.1 | | 79 | -1 | | 80 | -0.21 | | 81 | -0.43 | | 82 | -0.43 | | 83 | -1 | | 84 | -1 | | 85 | -0.21 | | 86 | -0.21 | | 87 | -0.21 | | 88 | -0.21 | | 89 | -0.21 | | 90 | -0.14 | | 91 | 0.01 | | 92 | -1 | | 93 | -0.44 | | 94 | -0.12 | | 95 | 0.11 | | 96 | -0.72 | | 97 | 0.24 | | 98 | 0.59 | | 99 | 0.88 | | 100 | 0.21 | | 101 | 0.25 | | 102 | 0.13 | | 103 | 0.11 | | 104 | 0.39 | | 105 | 0.24 | | 106 | 0.14 | | 107 | 0.06 | +------+-------+ 107 rows in set (0.00 sec)","title":"Example"},{"location":"data-mining/#example_3","text":"Sum all elements in the previous array. SELECT ROUND(SUM(value), 2) as sum FROM JSON_TABLE( @j, \"$[*]\" COLUMNS(value FLOAT PATH \"$\") ) material; +--------+ | sum | +--------+ | -11.32 | +--------+ 1 row in set (0.00 sec)","title":"Example"},{"location":"data-mining/#example_4","text":"Select the indexes in the previous material evaluation array where White has a material advantage. SELECT id FROM JSON_TABLE( @j, \"$[*]\" COLUMNS( id FOR ORDINALITY, value FLOAT PATH \"$\" ) ) material WHERE value > 0; +------+ | id | +------+ | 21 | | 41 | | 51 | | 52 | | 53 | | 54 | | 55 | | 56 | | 57 | | 59 | | 60 | | 61 | | 62 | | 63 | | 65 | | 66 | | 67 | | 68 | | 69 | | 70 | | 71 | | 72 | | 73 | | 91 | | 95 | | 97 | | 98 | | 99 | | 100 | | 101 | | 102 | | 103 | | 104 | | 105 | | 106 | | 107 | +------+ 36 rows in set (0.00 sec)","title":"Example"},{"location":"data-mining/#stored-procedures","text":"","title":"Stored Procedures"},{"location":"data-mining/#eval_array_sum","text":"Average sum of an evaluation feature given a result. DELIMITER // DROP PROCEDURE IF EXISTS eval_array_sum// CREATE PROCEDURE eval_array_sum( IN res VARCHAR(7), IN i INT, OUT avg FLOAT) BEGIN DECLARE count INT DEFAULT 0; DECLARE sum FLOAT DEFAULT 0; DECLARE total FLOAT DEFAULT 0; DECLARE done INT DEFAULT 0; DECLARE heuristic JSON; DECLARE cur CURSOR FOR SELECT JSON_EXTRACT(heuristics_mine, concat('$[*][', i, ']')) FROM games WHERE heuristics_mine IS NOT NULL AND Result = res; DECLARE CONTINUE HANDLER FOR NOT FOUND SET done = 1; OPEN cur; label:LOOP FETCH cur INTO heuristic; IF done = 1 THEN LEAVE label; END IF; SELECT ROUND(SUM(value), 2) INTO @sum FROM JSON_TABLE( heuristic, \"$[*]\" COLUMNS(value FLOAT PATH \"$\") ) material; SET total = total + @sum; SET count = count + 1; END LOOP label; CLOSE cur; SET avg = total / count; END// DELIMITER ; The example below returns the average sum of the center evaluation of all games won with the white pieces. CALL eval_array_sum('1-0', 1, @center_avg); Query OK, 0 rows affected (0.10 sec) mysql> SELECT ROUND(@center_avg, 2) AS center_avg; +------------+ | center_avg | +------------+ | 28.47 | +------------+ 1 row in set (0.00 sec)","title":"eval_array_sum()"}]}
\ No newline at end of file
+{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Chess Data The simplest yet powerful MySQL chess database to learn and play chess online. Installation Clone the chesslablab/chess-data repo into your projects folder. Then cd the chess-data directory and create an .env file: cp .env.example .env Run the Docker container in detached mode in the background: docker compose up -d Update the .env file to your specific needs and create the chess database: docker exec -itu 1000:1000 chess_data_php php cli/db-create.php Bootstrapping The chess database consists of these tables: annotations , games , openings and users . mysql> show tables; +-----------------+ | Tables_in_chess | +-----------------+ | annotations | | games | | openings | | users | +-----------------+ 4 rows in set (0.00 sec) \ud83d\uddd2 annotations This table is seeded with the CSV files in the data/annotations folder. docker exec -itu 1000:1000 chess_data_php php cli/seed/annotations.php data/annotations \ud83d\uddd2 games This table can be seeded with the PGN files in the data/example folder either all at once or file by file as it is shown in the examples below. Seed the games table with all the files in the data/example folder: docker exec -itu 1000:1000 chess_data_php php cli/seed/games.php data/example \u2717 2 games did not pass the validation. \u2713 4142 games out of a total of 4144 are OK. \u2713 597 games out of a total of 597 are OK. \u2717 1 games did not pass the validation. \u2713 1824 games out of a total of 1825 are OK. \u2713 3878 games out of a total of 3878 are OK. \u2713 4646 games out of a total of 4646 are OK. \u2717 2 games did not pass the validation. \u2713 2126 games out of a total of 2128 are OK. \u2713 2275 games out of a total of 2275 are OK. \u2713 1218 games out of a total of 1218 are OK. \u2713 827 games out of a total of 827 are OK. \u2717 5 games did not pass the validation. \u2713 1341 games out of a total of 1346 are OK. \u2713 5662 games out of a total of 5662 are OK. \u2713 3444 games out of a total of 3444 are OK. Seed the games table file by file: docker exec -itu 1000:1000 chess_data_php php cli/seed/games.php data/example/Anand.pgn \u2717 2 games did not pass the validation. \u2713 4142 games out of a total of 4144 are OK. The games table can also be seeded with your own set of PGN files in the data/games folder: docker exec -itu 1000:1000 chess_data_php php cli/seed/games.php data/games Please note that all files in the data folder are gitignored except those in data/example . The chess games won't be loaded into the database if containing PGN tags other than the ones supported by the tables created in the cli/db-create.php script. If that is the case you may want to remove the unsupported tags as shown in the example below. find . -name '*.pgn' -print0 | xargs -0 sed -i \"/\\[PlyCount .*\\]/d\" \ud83d\uddd2 openings This table is seeded with the CSV files in the data/openings folder. docker exec -itu 1000:1000 chess_data_php php cli/seed/openings.php data/openings \ud83d\uddd2 users This is how to seed the users table with fake random generated usernames: docker exec -itu 1000:1000 chess_data_php php cli/seed/users.php 5000","title":"Home"},{"location":"#chess-data","text":"The simplest yet powerful MySQL chess database to learn and play chess online.","title":"Chess Data"},{"location":"#installation","text":"Clone the chesslablab/chess-data repo into your projects folder. Then cd the chess-data directory and create an .env file: cp .env.example .env Run the Docker container in detached mode in the background: docker compose up -d Update the .env file to your specific needs and create the chess database: docker exec -itu 1000:1000 chess_data_php php cli/db-create.php","title":"Installation"},{"location":"#bootstrapping","text":"The chess database consists of these tables: annotations , games , openings and users . mysql> show tables; +-----------------+ | Tables_in_chess | +-----------------+ | annotations | | games | | openings | | users | +-----------------+ 4 rows in set (0.00 sec)","title":"Bootstrapping"},{"location":"#annotations","text":"This table is seeded with the CSV files in the data/annotations folder. docker exec -itu 1000:1000 chess_data_php php cli/seed/annotations.php data/annotations","title":"\ud83d\uddd2 annotations"},{"location":"#games","text":"This table can be seeded with the PGN files in the data/example folder either all at once or file by file as it is shown in the examples below. Seed the games table with all the files in the data/example folder: docker exec -itu 1000:1000 chess_data_php php cli/seed/games.php data/example \u2717 2 games did not pass the validation. \u2713 4142 games out of a total of 4144 are OK. \u2713 597 games out of a total of 597 are OK. \u2717 1 games did not pass the validation. \u2713 1824 games out of a total of 1825 are OK. \u2713 3878 games out of a total of 3878 are OK. \u2713 4646 games out of a total of 4646 are OK. \u2717 2 games did not pass the validation. \u2713 2126 games out of a total of 2128 are OK. \u2713 2275 games out of a total of 2275 are OK. \u2713 1218 games out of a total of 1218 are OK. \u2713 827 games out of a total of 827 are OK. \u2717 5 games did not pass the validation. \u2713 1341 games out of a total of 1346 are OK. \u2713 5662 games out of a total of 5662 are OK. \u2713 3444 games out of a total of 3444 are OK. Seed the games table file by file: docker exec -itu 1000:1000 chess_data_php php cli/seed/games.php data/example/Anand.pgn \u2717 2 games did not pass the validation. \u2713 4142 games out of a total of 4144 are OK. The games table can also be seeded with your own set of PGN files in the data/games folder: docker exec -itu 1000:1000 chess_data_php php cli/seed/games.php data/games Please note that all files in the data folder are gitignored except those in data/example . The chess games won't be loaded into the database if containing PGN tags other than the ones supported by the tables created in the cli/db-create.php script. If that is the case you may want to remove the unsupported tags as shown in the example below. find . -name '*.pgn' -print0 | xargs -0 sed -i \"/\\[PlyCount .*\\]/d\"","title":"\ud83d\uddd2 games"},{"location":"#openings","text":"This table is seeded with the CSV files in the data/openings folder. docker exec -itu 1000:1000 chess_data_php php cli/seed/openings.php data/openings","title":"\ud83d\uddd2 openings"},{"location":"#users","text":"This is how to seed the users table with fake random generated usernames: docker exec -itu 1000:1000 chess_data_php php cli/seed/users.php 5000","title":"\ud83d\uddd2 users"},{"location":"data-mining/","text":"Data Mining Data mining provides an additional boost to the SQL queries that can be performed on the games table. The precondition for it is to seed the games table with data. The CLI commands described below are to populate the columns suffixed with the word _mine with pre-calculated data for further analysis. CLI Commands Time Description \ud83d\udc8e The command is not too time-consuming. \ud83d\udc8e\ud83d\udc8e The command is time-consuming. \ud83d\udc8e fen.php The example below populates the fen_mine column with chess positions in FEN format on a player basis: docker exec -itu 1000:1000 chess_data_php php cli/mine/fen.php \"Anand,V\" This column is intended to store a text string of comma-separated values representing the chess positions in a game. It allows to search games by piece placement in FEN format. Example Fetch all games matching a particular position. SELECT * FROM games WHERE fen_mine LIKE '%r1bqkbnr/pp1ppppp/2n5/2p5/4P3/5N2/PPPP1PPP/RNBQKB1R%'; \ud83d\udc8e\ud83d\udc8e heuristics.php The example below calculates the heuristics_mine column on a player basis: docker exec -itu 1000:1000 chess_data_php php cli/mine/heuristics.php \"Anand,V\" The heuristics_mine column is intended to store a JSON object representing the PHP Chess heuristics in a game. It allows to gather insights about the decisions that have been made to make the moves. With this data, you can take advantage of MySQL JSON functions to perform operations on JSON values like in the following examples. Example Fetch the material evaluation in all games won by Anand with the white pieces. SELECT JSON_EXTRACT(heuristics_mine, '$[*][0]') as material FROM games WHERE heuristics_mine IS NOT NULL AND White = \"Anand,V\" AND Result = '1-0'; The index in the second parameter of the JSON_EXTRACT function $[*][0] corresponds to the index of the PHP Chess function being used in the cli/mine/heuristics.php script. See: Chess\\Function\\FastFunction Thus, [0] corresponds to the material evaluation in the fast function array. Example Convert the material evaluation array of a random game won by Anand with the white pieces from JSON to MySQL. SET @j = ( SELECT JSON_EXTRACT(heuristics_mine, '$[*][0]') FROM games WHERE heuristics_mine IS NOT NULL AND White = \"Anand,V\" AND Result = '1-0' ORDER BY RAND() LIMIT 1 ); SELECT * FROM JSON_TABLE( @j, \"$[*]\" COLUMNS( id FOR ORDINALITY, value FLOAT PATH \"$\" ) ) material; +------+-------+ | id | value | +------+-------+ | 1 | 0 | | 2 | 0 | | 3 | 0 | | 4 | 0 | | 5 | 0 | | 6 | 0 | | 7 | 0 | | 8 | 0 | | 9 | 0 | | 10 | 0 | | 11 | 0 | | 12 | 0 | | 13 | 0 | | 14 | 0 | | 15 | 0 | | 16 | 0 | | 17 | 0 | | 18 | 0 | | 19 | 0 | | 20 | 0 | | 21 | 0.13 | | 22 | -0.07 | | 23 | -0.07 | | 24 | -0.07 | | 25 | -0.07 | | 26 | -0.07 | | 27 | -0.07 | | 28 | -0.07 | | 29 | -0.07 | | 30 | -0.07 | | 31 | -0.07 | | 32 | -0.07 | | 33 | -0.07 | | 34 | -0.07 | | 35 | -0.07 | | 36 | -0.07 | | 37 | -0.07 | | 38 | -0.13 | | 39 | -0.13 | | 40 | -0.13 | | 41 | 0.1 | | 42 | -0.78 | | 43 | -0.66 | | 44 | -1 | | 45 | -0.66 | | 46 | -1 | | 47 | -0.44 | | 48 | -1 | | 49 | -0.26 | | 50 | -0.26 | | 51 | 0.2 | | 52 | 0.03 | | 53 | 0.02 | | 54 | 0.02 | | 55 | 0.02 | | 56 | 0.02 | | 57 | 0.06 | | 58 | -0.19 | | 59 | 0.02 | | 60 | 0.01 | | 61 | 0.02 | | 62 | 0.02 | | 63 | 0.08 | | 64 | -0.12 | | 65 | 0.08 | | 66 | 0.08 | | 67 | 0.07 | | 68 | 0.06 | | 69 | 0.14 | | 70 | 0.14 | | 71 | 0.08 | | 72 | 0.08 | | 73 | 0.06 | | 74 | -0.14 | | 75 | -0.14 | | 76 | -0.21 | | 77 | -0.21 | | 78 | -0.1 | | 79 | -1 | | 80 | -0.21 | | 81 | -0.43 | | 82 | -0.43 | | 83 | -1 | | 84 | -1 | | 85 | -0.21 | | 86 | -0.21 | | 87 | -0.21 | | 88 | -0.21 | | 89 | -0.21 | | 90 | -0.14 | | 91 | 0.01 | | 92 | -1 | | 93 | -0.44 | | 94 | -0.12 | | 95 | 0.11 | | 96 | -0.72 | | 97 | 0.24 | | 98 | 0.59 | | 99 | 0.88 | | 100 | 0.21 | | 101 | 0.25 | | 102 | 0.13 | | 103 | 0.11 | | 104 | 0.39 | | 105 | 0.24 | | 106 | 0.14 | | 107 | 0.06 | +------+-------+ 107 rows in set (0.00 sec) Example Sum all elements in the previous array. SELECT ROUND(SUM(value), 2) as sum FROM JSON_TABLE( @j, \"$[*]\" COLUMNS(value FLOAT PATH \"$\") ) material; +--------+ | sum | +--------+ | -11.32 | +--------+ 1 row in set (0.00 sec) Example Select the indexes in the previous material evaluation array where White has a material advantage. SELECT id FROM JSON_TABLE( @j, \"$[*]\" COLUMNS( id FOR ORDINALITY, value FLOAT PATH \"$\" ) ) material WHERE value > 0; +------+ | id | +------+ | 21 | | 41 | | 51 | | 52 | | 53 | | 54 | | 55 | | 56 | | 57 | | 59 | | 60 | | 61 | | 62 | | 63 | | 65 | | 66 | | 67 | | 68 | | 69 | | 70 | | 71 | | 72 | | 73 | | 91 | | 95 | | 97 | | 98 | | 99 | | 100 | | 101 | | 102 | | 103 | | 104 | | 105 | | 106 | | 107 | +------+ 36 rows in set (0.00 sec) Functions EVAL_ARRAY_COUNT() Average count of an evaluation feature given a result. DELIMITER // DROP FUNCTION IF EXISTS EVAL_ARRAY_COUNT// CREATE FUNCTION EVAL_ARRAY_COUNT(res VARCHAR(7), i INT) RETURNS FLOAT READS SQL DATA DETERMINISTIC BEGIN DECLARE count INT DEFAULT 0; DECLARE total FLOAT DEFAULT 0; DECLARE done INT DEFAULT 0; DECLARE heuristic JSON; DECLARE cur CURSOR FOR SELECT JSON_EXTRACT(heuristics_mine, concat('$[*][', i, ']')) FROM games WHERE heuristics_mine IS NOT NULL AND Result = res; DECLARE CONTINUE HANDLER FOR NOT FOUND SET done = 1; OPEN cur; label:LOOP FETCH cur INTO heuristic; IF done = 1 THEN LEAVE label; END IF; SELECT SUM(CASE WHEN value > 0 THEN 1 ELSE 0 END) - SUM(CASE WHEN value < 0 THEN 1 ELSE 0 END) AS diff INTO @sum FROM JSON_TABLE( heuristic, \"$[*]\" COLUMNS(value FLOAT PATH \"$\") ) material; SET total = total + @sum; SET count = count + 1; END LOOP label; CLOSE cur; RETURN total / count; END// DELIMITER ; The example below returns the average count of the center evaluation of all games won with the white pieces. mysql> SELECT ROUND(EVAL_ARRAY_COUNT('1-0', 1), 2) as center_count; +--------------+ | center_count | +--------------+ | 25.28 | +--------------+ 1 row in set (0.19 sec) EVAL_ARRAY_SUM() Average sum of an evaluation feature given a result. DELIMITER // DROP FUNCTION IF EXISTS EVAL_ARRAY_SUM// CREATE FUNCTION EVAL_ARRAY_SUM(res VARCHAR(7), i INT) RETURNS FLOAT READS SQL DATA DETERMINISTIC BEGIN DECLARE count INT DEFAULT 0; DECLARE total FLOAT DEFAULT 0; DECLARE done INT DEFAULT 0; DECLARE heuristic JSON; DECLARE cur CURSOR FOR SELECT JSON_EXTRACT(heuristics_mine, concat('$[*][', i, ']')) FROM games WHERE heuristics_mine IS NOT NULL AND Result = res; DECLARE CONTINUE HANDLER FOR NOT FOUND SET done = 1; OPEN cur; label:LOOP FETCH cur INTO heuristic; IF done = 1 THEN LEAVE label; END IF; SELECT ROUND(SUM(value), 2) INTO @sum FROM JSON_TABLE( heuristic, \"$[*]\" COLUMNS(value FLOAT PATH \"$\") ) material; SET total = total + @sum; SET count = count + 1; END LOOP label; CLOSE cur; RETURN total / count; END// DELIMITER ; The example below returns the average sum of the center evaluation of all games won with the white pieces. mysql> SELECT ROUND(EVAL_ARRAY_SUM('1-0', 1), 2) as center_sum; +------------+ | center_sum | +------------+ | 24.2 | +------------+ 1 row in set (0.18 sec)","title":"Data Mining"},{"location":"data-mining/#data-mining","text":"Data mining provides an additional boost to the SQL queries that can be performed on the games table. The precondition for it is to seed the games table with data. The CLI commands described below are to populate the columns suffixed with the word _mine with pre-calculated data for further analysis.","title":"Data Mining"},{"location":"data-mining/#cli-commands","text":"Time Description \ud83d\udc8e The command is not too time-consuming. \ud83d\udc8e\ud83d\udc8e The command is time-consuming.","title":"CLI Commands"},{"location":"data-mining/#fenphp","text":"The example below populates the fen_mine column with chess positions in FEN format on a player basis: docker exec -itu 1000:1000 chess_data_php php cli/mine/fen.php \"Anand,V\" This column is intended to store a text string of comma-separated values representing the chess positions in a game. It allows to search games by piece placement in FEN format.","title":"\ud83d\udc8e fen.php"},{"location":"data-mining/#example","text":"Fetch all games matching a particular position. SELECT * FROM games WHERE fen_mine LIKE '%r1bqkbnr/pp1ppppp/2n5/2p5/4P3/5N2/PPPP1PPP/RNBQKB1R%';","title":"Example"},{"location":"data-mining/#heuristicsphp","text":"The example below calculates the heuristics_mine column on a player basis: docker exec -itu 1000:1000 chess_data_php php cli/mine/heuristics.php \"Anand,V\" The heuristics_mine column is intended to store a JSON object representing the PHP Chess heuristics in a game. It allows to gather insights about the decisions that have been made to make the moves. With this data, you can take advantage of MySQL JSON functions to perform operations on JSON values like in the following examples.","title":"\ud83d\udc8e\ud83d\udc8e heuristics.php"},{"location":"data-mining/#example_1","text":"Fetch the material evaluation in all games won by Anand with the white pieces. SELECT JSON_EXTRACT(heuristics_mine, '$[*][0]') as material FROM games WHERE heuristics_mine IS NOT NULL AND White = \"Anand,V\" AND Result = '1-0'; The index in the second parameter of the JSON_EXTRACT function $[*][0] corresponds to the index of the PHP Chess function being used in the cli/mine/heuristics.php script. See: Chess\\Function\\FastFunction Thus, [0] corresponds to the material evaluation in the fast function array.","title":"Example"},{"location":"data-mining/#example_2","text":"Convert the material evaluation array of a random game won by Anand with the white pieces from JSON to MySQL. SET @j = ( SELECT JSON_EXTRACT(heuristics_mine, '$[*][0]') FROM games WHERE heuristics_mine IS NOT NULL AND White = \"Anand,V\" AND Result = '1-0' ORDER BY RAND() LIMIT 1 ); SELECT * FROM JSON_TABLE( @j, \"$[*]\" COLUMNS( id FOR ORDINALITY, value FLOAT PATH \"$\" ) ) material; +------+-------+ | id | value | +------+-------+ | 1 | 0 | | 2 | 0 | | 3 | 0 | | 4 | 0 | | 5 | 0 | | 6 | 0 | | 7 | 0 | | 8 | 0 | | 9 | 0 | | 10 | 0 | | 11 | 0 | | 12 | 0 | | 13 | 0 | | 14 | 0 | | 15 | 0 | | 16 | 0 | | 17 | 0 | | 18 | 0 | | 19 | 0 | | 20 | 0 | | 21 | 0.13 | | 22 | -0.07 | | 23 | -0.07 | | 24 | -0.07 | | 25 | -0.07 | | 26 | -0.07 | | 27 | -0.07 | | 28 | -0.07 | | 29 | -0.07 | | 30 | -0.07 | | 31 | -0.07 | | 32 | -0.07 | | 33 | -0.07 | | 34 | -0.07 | | 35 | -0.07 | | 36 | -0.07 | | 37 | -0.07 | | 38 | -0.13 | | 39 | -0.13 | | 40 | -0.13 | | 41 | 0.1 | | 42 | -0.78 | | 43 | -0.66 | | 44 | -1 | | 45 | -0.66 | | 46 | -1 | | 47 | -0.44 | | 48 | -1 | | 49 | -0.26 | | 50 | -0.26 | | 51 | 0.2 | | 52 | 0.03 | | 53 | 0.02 | | 54 | 0.02 | | 55 | 0.02 | | 56 | 0.02 | | 57 | 0.06 | | 58 | -0.19 | | 59 | 0.02 | | 60 | 0.01 | | 61 | 0.02 | | 62 | 0.02 | | 63 | 0.08 | | 64 | -0.12 | | 65 | 0.08 | | 66 | 0.08 | | 67 | 0.07 | | 68 | 0.06 | | 69 | 0.14 | | 70 | 0.14 | | 71 | 0.08 | | 72 | 0.08 | | 73 | 0.06 | | 74 | -0.14 | | 75 | -0.14 | | 76 | -0.21 | | 77 | -0.21 | | 78 | -0.1 | | 79 | -1 | | 80 | -0.21 | | 81 | -0.43 | | 82 | -0.43 | | 83 | -1 | | 84 | -1 | | 85 | -0.21 | | 86 | -0.21 | | 87 | -0.21 | | 88 | -0.21 | | 89 | -0.21 | | 90 | -0.14 | | 91 | 0.01 | | 92 | -1 | | 93 | -0.44 | | 94 | -0.12 | | 95 | 0.11 | | 96 | -0.72 | | 97 | 0.24 | | 98 | 0.59 | | 99 | 0.88 | | 100 | 0.21 | | 101 | 0.25 | | 102 | 0.13 | | 103 | 0.11 | | 104 | 0.39 | | 105 | 0.24 | | 106 | 0.14 | | 107 | 0.06 | +------+-------+ 107 rows in set (0.00 sec)","title":"Example"},{"location":"data-mining/#example_3","text":"Sum all elements in the previous array. SELECT ROUND(SUM(value), 2) as sum FROM JSON_TABLE( @j, \"$[*]\" COLUMNS(value FLOAT PATH \"$\") ) material; +--------+ | sum | +--------+ | -11.32 | +--------+ 1 row in set (0.00 sec)","title":"Example"},{"location":"data-mining/#example_4","text":"Select the indexes in the previous material evaluation array where White has a material advantage. SELECT id FROM JSON_TABLE( @j, \"$[*]\" COLUMNS( id FOR ORDINALITY, value FLOAT PATH \"$\" ) ) material WHERE value > 0; +------+ | id | +------+ | 21 | | 41 | | 51 | | 52 | | 53 | | 54 | | 55 | | 56 | | 57 | | 59 | | 60 | | 61 | | 62 | | 63 | | 65 | | 66 | | 67 | | 68 | | 69 | | 70 | | 71 | | 72 | | 73 | | 91 | | 95 | | 97 | | 98 | | 99 | | 100 | | 101 | | 102 | | 103 | | 104 | | 105 | | 106 | | 107 | +------+ 36 rows in set (0.00 sec)","title":"Example"},{"location":"data-mining/#functions","text":"","title":"Functions"},{"location":"data-mining/#eval_array_count","text":"Average count of an evaluation feature given a result. DELIMITER // DROP FUNCTION IF EXISTS EVAL_ARRAY_COUNT// CREATE FUNCTION EVAL_ARRAY_COUNT(res VARCHAR(7), i INT) RETURNS FLOAT READS SQL DATA DETERMINISTIC BEGIN DECLARE count INT DEFAULT 0; DECLARE total FLOAT DEFAULT 0; DECLARE done INT DEFAULT 0; DECLARE heuristic JSON; DECLARE cur CURSOR FOR SELECT JSON_EXTRACT(heuristics_mine, concat('$[*][', i, ']')) FROM games WHERE heuristics_mine IS NOT NULL AND Result = res; DECLARE CONTINUE HANDLER FOR NOT FOUND SET done = 1; OPEN cur; label:LOOP FETCH cur INTO heuristic; IF done = 1 THEN LEAVE label; END IF; SELECT SUM(CASE WHEN value > 0 THEN 1 ELSE 0 END) - SUM(CASE WHEN value < 0 THEN 1 ELSE 0 END) AS diff INTO @sum FROM JSON_TABLE( heuristic, \"$[*]\" COLUMNS(value FLOAT PATH \"$\") ) material; SET total = total + @sum; SET count = count + 1; END LOOP label; CLOSE cur; RETURN total / count; END// DELIMITER ; The example below returns the average count of the center evaluation of all games won with the white pieces. mysql> SELECT ROUND(EVAL_ARRAY_COUNT('1-0', 1), 2) as center_count; +--------------+ | center_count | +--------------+ | 25.28 | +--------------+ 1 row in set (0.19 sec)","title":"EVAL_ARRAY_COUNT()"},{"location":"data-mining/#eval_array_sum","text":"Average sum of an evaluation feature given a result. DELIMITER // DROP FUNCTION IF EXISTS EVAL_ARRAY_SUM// CREATE FUNCTION EVAL_ARRAY_SUM(res VARCHAR(7), i INT) RETURNS FLOAT READS SQL DATA DETERMINISTIC BEGIN DECLARE count INT DEFAULT 0; DECLARE total FLOAT DEFAULT 0; DECLARE done INT DEFAULT 0; DECLARE heuristic JSON; DECLARE cur CURSOR FOR SELECT JSON_EXTRACT(heuristics_mine, concat('$[*][', i, ']')) FROM games WHERE heuristics_mine IS NOT NULL AND Result = res; DECLARE CONTINUE HANDLER FOR NOT FOUND SET done = 1; OPEN cur; label:LOOP FETCH cur INTO heuristic; IF done = 1 THEN LEAVE label; END IF; SELECT ROUND(SUM(value), 2) INTO @sum FROM JSON_TABLE( heuristic, \"$[*]\" COLUMNS(value FLOAT PATH \"$\") ) material; SET total = total + @sum; SET count = count + 1; END LOOP label; CLOSE cur; RETURN total / count; END// DELIMITER ; The example below returns the average sum of the center evaluation of all games won with the white pieces. mysql> SELECT ROUND(EVAL_ARRAY_SUM('1-0', 1), 2) as center_sum; +------------+ | center_sum | +------------+ | 24.2 | +------------+ 1 row in set (0.18 sec)","title":"EVAL_ARRAY_SUM()"}]}
\ No newline at end of file
diff --git a/sitemap.xml.gz b/sitemap.xml.gz
index 67316d7507d45cbc84f75036b4a5921f055d497a..6589a0b674dd345faca6c6ed616351ffa068965a 100644
GIT binary patch
delta 14
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