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347 lines
10 KiB
347 lines
10 KiB
--
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-- Determine the Jenks classifications from a numeric array
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--
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-- @param in_array A numeric array of numbers to determine the best
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-- bins based on the Jenks method.
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--
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-- @param breaks The number of bins you want to find.
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--
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-- @param iterations The number of different starting positions to test.
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--
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-- @param invert Optional wheter to return the top of each bin (default)
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-- or the bottom. BOOLEAN, default=FALSE.
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--
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--
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CREATE OR REPLACE FUNCTION @extschema@.CDB_JenksBins(in_array NUMERIC[], breaks INT, iterations INT DEFAULT 0, invert BOOLEAN DEFAULT FALSE)
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RETURNS NUMERIC[] as
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$$
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DECLARE
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in_matrix NUMERIC[][];
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in_unique_count BIGINT;
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shuffles INT;
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arr_mean NUMERIC;
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sdam NUMERIC;
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i INT;
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bot INT;
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top INT;
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tops INT[];
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classes INT[][];
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j INT := 1;
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curr_result NUMERIC[];
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best_result NUMERIC[];
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seedtarget TEXT;
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BEGIN
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-- We clean the input array (remove NULLs) and create 2 arrays
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-- [1] contains the unique values in in_array
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-- [2] contains the number of appearances of those unique values
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SELECT ARRAY[array_agg(value), array_agg(count)] FROM
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(
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SELECT value, count(1)::numeric as count
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FROM unnest(in_array) AS value
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WHERE value is NOT NULL
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GROUP BY value
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ORDER BY value
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) __clean_array_q INTO in_matrix;
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-- Get the number of unique values
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in_unique_count := array_length(in_matrix[1:1], 2);
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IF in_unique_count IS NULL THEN
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RETURN NULL;
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END IF;
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IF in_unique_count <= breaks THEN
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-- There isn't enough distinct values for the requested breaks
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RETURN ARRAY(Select unnest(in_matrix[1:1])) _a;
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END IF;
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-- If not declated explicitly we iterate based on the length of the array
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IF iterations < 1 THEN
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-- This is based on a 'looks fine' heuristic
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iterations := log(in_unique_count)::integer + 1;
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END IF;
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-- We set the number of shuffles per iteration as the number of unique values but
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-- this is just another 'looks fine' heuristic
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shuffles := in_unique_count;
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-- Get the mean value of the whole vector (already ignores NULLs)
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SELECT avg(v) INTO arr_mean FROM ( SELECT unnest(in_array) as v ) x;
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-- Calculate the sum of squared deviations from the array mean (SDAM).
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SELECT sum(((arr_mean - v)^2) * w) INTO sdam FROM (
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SELECT unnest(in_matrix[1:1]) as v, unnest(in_matrix[2:2]) as w
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) x;
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-- To start, we create ranges with approximately the same amount of different values
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top := 0;
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i := 1;
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LOOP
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bot := top + 1;
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top := ROUND(i * in_unique_count::numeric / breaks::NUMERIC);
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IF i = 1 THEN
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classes = ARRAY[ARRAY[bot,top]];
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ELSE
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classes = ARRAY_CAT(classes, ARRAY[bot,top]);
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END IF;
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i := i + 1;
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IF i > breaks THEN EXIT; END IF;
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END LOOP;
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best_result = @extschema@.CDB_JenksBinsIteration(in_matrix, breaks, classes, invert, sdam, shuffles);
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--set the seed so we can ensure the same results
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SELECT setseed(0.4567) INTO seedtarget;
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--loop through random starting positions
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LOOP
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IF j > iterations-1 THEN EXIT; END IF;
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i = 1;
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tops = ARRAY[in_unique_count];
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LOOP
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IF i = breaks THEN EXIT; END IF;
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SELECT array_agg(distinct e) INTO tops FROM (
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SELECT unnest(array_cat(tops, ARRAY[trunc(random() * in_unique_count::float8)::int + 1])) as e ORDER BY e
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) x;
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i = array_length(tops, 1);
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END LOOP;
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top := 0;
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i = 1;
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LOOP
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bot := top + 1;
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top = tops[i];
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IF i = 1 THEN
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classes = ARRAY[ARRAY[bot,top]];
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ELSE
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classes = ARRAY_CAT(classes, ARRAY[bot,top]);
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END IF;
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i := i+1;
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IF i > breaks THEN EXIT; END IF;
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END LOOP;
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curr_result = @extschema@.CDB_JenksBinsIteration(in_matrix, breaks, classes, invert, sdam, shuffles);
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IF curr_result[1] > best_result[1] THEN
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best_result = curr_result;
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END IF;
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j = j+1;
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END LOOP;
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RETURN (best_result)[2:array_upper(best_result, 1)];
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END;
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$$ LANGUAGE PLPGSQL IMMUTABLE PARALLEL RESTRICTED;
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--
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-- Perform a single iteration of the Jenks classification
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--
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-- Returns an array with:
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-- - First element: gvf
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-- - Second to 2+n: Category limits
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DROP FUNCTION IF EXISTS @extschema@.CDB_JenksBinsIteration ( in_matrix NUMERIC[], breaks INT, classes INT[], invert BOOLEAN, element_count INT4, arr_mean NUMERIC, max_search INT); -- Old signature
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CREATE OR REPLACE FUNCTION @extschema@.CDB_JenksBinsIteration ( in_matrix NUMERIC[], breaks INT, classes INT[], invert BOOLEAN, sdam NUMERIC, max_search INT DEFAULT 50) RETURNS NUMERIC[] as $$
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DECLARE
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i INT;
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iterations INT = 0;
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side INT := 2;
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gvf numeric := 0.0;
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new_gvf numeric;
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arr_gvf numeric[];
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arr_avg numeric[];
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class_avg numeric;
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class_dev numeric;
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class_max_i INT = 0;
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class_min_i INT = 0;
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dev_max numeric;
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dev_min numeric;
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best_classes INT[] = classes;
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best_gvf numeric[];
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best_avg numeric[];
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move_elements INT = 1;
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reply numeric[];
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BEGIN
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-- We fill the arrays with the initial values
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i = 0;
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LOOP
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IF i = breaks THEN EXIT; END IF;
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i = i + 1;
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-- Get class mean
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SELECT (sum(v * w) / sum(w)) INTO class_avg FROM (
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SELECT unnest(in_matrix[1:1][classes[i][1]:classes[i][2]]) as v,
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unnest(in_matrix[2:2][classes[i][1]:classes[i][2]]) as w
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) x;
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-- Get class deviation
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SELECT sum((class_avg - v)^2 * w) INTO class_dev FROM (
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SELECT unnest(in_matrix[1:1][classes[i][1]:classes[i][2]]) as v,
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unnest(in_matrix[2:2][classes[i][1]:classes[i][2]]) as w
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) x;
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IF i = 1 THEN
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arr_avg = ARRAY[class_avg];
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arr_gvf = ARRAY[class_dev];
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ELSE
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arr_avg = array_append(arr_avg, class_avg);
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arr_gvf = array_append(arr_gvf, class_dev);
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END IF;
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END LOOP;
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-- We copy the values to avoid recalculation when a failure happens
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best_avg = arr_avg;
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best_gvf = arr_gvf;
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iterations = 0;
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LOOP
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IF iterations = max_search THEN EXIT; END IF;
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iterations = iterations + 1;
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-- calculate our new GVF
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SELECT sdam - sum(e) INTO new_gvf FROM ( SELECT unnest(arr_gvf) as e ) x;
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-- Check if any improvement was made
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IF new_gvf <= gvf THEN
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-- If we were moving too many elements, go back and move less
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IF move_elements <= 2 OR class_max_i = class_min_i THEN
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EXIT;
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END IF;
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move_elements = GREATEST(move_elements / 8, 1);
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-- Rollback from saved statuses
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classes = best_classes;
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new_gvf = gvf;
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i = class_min_i;
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LOOP
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arr_avg[i] = best_avg[i];
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arr_gvf[i] = best_gvf[i];
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IF i = class_max_i THEN EXIT; END IF;
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i = i + 1;
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END LOOP;
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END IF;
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-- We search for the classes with the min and max deviation
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i = 1;
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class_min_i = 1;
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class_max_i = 1;
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dev_max = arr_gvf[1];
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dev_min = arr_gvf[1];
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LOOP
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IF i = breaks THEN EXIT; END IF;
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i = i + 1;
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IF arr_gvf[i] < dev_min THEN
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dev_min = arr_gvf[i];
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class_min_i = i;
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ELSE
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IF arr_gvf[i] > dev_max THEN
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dev_max = arr_gvf[i];
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class_max_i = i;
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END IF;
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END IF;
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END LOOP;
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-- Save best values for comparison and output
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gvf = new_gvf;
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best_classes = classes;
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-- Limit the moved elements as to not remove everything from class_max_i
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move_elements = LEAST(move_elements, classes[class_max_i][2] - classes[class_max_i][1]);
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-- Move `move_elements` from class_max_i to class_min_i
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IF class_min_i < class_max_i THEN
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i := class_min_i;
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LOOP
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IF i = class_max_i THEN EXIT; END IF;
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classes[i][2] = classes[i][2] + move_elements;
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i := i + 1;
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END LOOP;
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i := class_max_i;
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LOOP
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IF i = class_min_i THEN EXIT; END IF;
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classes[i][1] = classes[i][1] + move_elements;
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i := i - 1;
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END LOOP;
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ELSE
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i := class_min_i;
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LOOP
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IF i = class_max_i THEN EXIT; END IF;
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classes[i][1] = classes[i][1] - move_elements;
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i := i - 1;
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END LOOP;
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i := class_max_i;
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LOOP
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IF i = class_min_i THEN EXIT; END IF;
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classes[i][2] = classes[i][2] - move_elements;
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i := i + 1;
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END LOOP;
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END IF;
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-- Recalculate avg and deviation ONLY for the affected classes
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i = LEAST(class_min_i, class_max_i);
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class_max_i = GREATEST(class_min_i, class_max_i);
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class_min_i = i;
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LOOP
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SELECT (sum(v * w) / sum(w)) INTO class_avg FROM (
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SELECT unnest(in_matrix[1:1][classes[i][1]:classes[i][2]]) as v,
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unnest(in_matrix[2:2][classes[i][1]:classes[i][2]]) as w
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) x;
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SELECT sum((class_avg - v)^2 * w) INTO class_dev FROM (
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SELECT unnest(in_matrix[1:1][classes[i][1]:classes[i][2]]) as v,
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unnest(in_matrix[2:2][classes[i][1]:classes[i][2]]) as w
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) x;
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-- Save status (in case it's needed for rollback) and store the new one
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best_avg[i] = arr_avg[i];
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arr_avg[i] = class_avg;
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best_gvf[i] = arr_gvf[i];
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arr_gvf[i] = class_dev;
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IF i = class_max_i THEN EXIT; END IF;
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i = i + 1;
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END LOOP;
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move_elements = move_elements * 2;
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END LOOP;
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i = 1;
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LOOP
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IF invert = TRUE THEN
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side = 1; --default returns bottom side of breaks, invert returns top side
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END IF;
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reply = array_append(reply, unnest(in_matrix[1:1][best_classes[i][side]:best_classes[i][side]]));
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i = i+1;
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IF i > breaks THEN EXIT; END IF;
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END LOOP;
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reply = array_prepend(gvf, reply);
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RETURN reply;
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END;
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$$ LANGUAGE PLPGSQL IMMUTABLE PARALLEL SAFE;
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