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Jenks: Remove log messages

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This commit is contained in:
Raul Marin 2018-09-10 15:53:02 +02:00
parent 272d5be776
commit e605234d38
1 changed files with 0 additions and 14 deletions
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14
scripts-available/CDB_JenksBins.sql
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@ -50,7 +50,6 @@ BEGIN
-- Get the number of unique values
in_unique_count := array_length(in_matrix[1:1], 2);
RAISE INFO 'Unique %', in_unique_count;
IF in_unique_count IS NULL THEN
RETURN NULL;
@ -66,22 +65,18 @@ BEGIN
-- This is based on a 'looks fine' heuristic
iterations := log(in_unique_count)::integer + 1;
END IF;
RAISE INFO 'Iterations: %', iterations;
-- We set the number of shuffles per iteration as the number of unique values but
-- this is just another 'looks fine' heuristic
shuffles := in_unique_count;
RAISE INFO 'Suffles %', shuffles;
-- Get the mean value of the whole vector (already ignores NULLs)
SELECT avg(v) INTO arr_mean FROM ( SELECT unnest(in_array) as v ) x;
RAISE INFO 'Mean %', arr_mean;
-- Calculate the sum of squared deviations from the array mean (SDAM).
SELECT sum(((arr_mean - v)^2) * w) INTO sdam FROM (
SELECT unnest(in_matrix[1:1]) as v, unnest(in_matrix[2:2]) as w
) x;
RAISE INFO 'Deviation %', sdam;
-- To start, we create ranges with approximately the same amount of different values
top := 0;
@ -99,8 +94,6 @@ BEGIN
i := i + 1;
IF i > breaks THEN EXIT; END IF;
END LOOP;
RAISE INFO 'Initial classes %', classes;
best_result = CDB_JenksBinsIteration(in_matrix, breaks, classes, invert, sdam, shuffles);
@ -118,7 +111,6 @@ BEGIN
) x;
i = array_length(tops, 1);
END LOOP;
RAISE INFO 'Tops %', tops;
top := 0;
i = 1;
LOOP
@ -134,7 +126,6 @@ BEGIN
IF i > breaks THEN EXIT; END IF;
END LOOP;
RAISE INFO 'Classes %', classes;
curr_result = CDB_JenksBinsIteration(in_matrix, breaks, classes, invert, sdam, shuffles);
IF curr_result[1] > best_result[1] THEN
@ -187,7 +178,6 @@ BEGIN
LOOP
IF i = breaks THEN EXIT; END IF;
i = i + 1;
RAISE INFO 'Loop %', i;
-- Get class mean
SELECT (sum(v * w) / sum(w)) INTO class_avg FROM (
@ -249,8 +239,6 @@ BEGIN
-- Save best values for comparison and output
gvf = new_gvf;
best_classes = classes;
RAISE INFO 'Deviations %', arr_gvf;
RAISE INFO 'Min %. Max %', class_min_i, class_max_i;
-- Iterate by moving an element from class_max_i to class_min_i
IF class_min_i < class_max_i THEN
@ -282,7 +270,6 @@ BEGIN
i := i + 1;
END LOOP;
END IF;
RAISE INFO 'Classes %', classes;
-- Recalculate avg and deviation for the affected classes
i = LEAST(class_min_i, class_max_i);
@ -320,7 +307,6 @@ BEGIN
END LOOP;
reply = array_prepend(gvf, reply);
RAISE INFO 'Reply: %', reply;
RETURN reply;
END;