Updates to use log as argument to multiLevel.

examples/costanza.lua

@@ -1,6 +1,8 @@
 -- @example Basic example for testing goodness-of-fit.
 -- This example reproduces Figure 4 of from Costanza R.
 -- Model goodness of fit: a multiple resolution procedure. Ecological modelling. 1989 Sep 15;47(3-4):199-215.
+-- @image costanza.png
+
 import("calibration")
 
 scene1 = CellularSpace{

images/costanza.lua

@@ -0,0 +1,6 @@
+
+dofile(packageInfo("calibration").path.."/examples/costanza.lua")
+
+chart:save("costanza.png")
+
+clean()

lua/GoodnessOfFit.lua

@@ -237,6 +237,9 @@ end
 -- @arg data.discrete A boolean value indicating whether the values are discrete. The default value
 -- is false, meaning that the atributes are continuous.
 -- @arg data.k Value that determines the weigth for each square calibration. The default value is 0.1.
+-- @arg data.log Value indicating whether the levels should be computed using log scale: 1, 2, 4, ... until
+-- the maximum length in both directions. The default value (fales) means that all levels will be computed:
+-- 1, 2, 3, ... until the maximum length in both directions.
 -- @usage import("calibration")
 --
 -- cs1 = CellularSpace{
@@ -258,8 +261,9 @@ function multiLevel(data)
 	verifyBasicArguments(data)
 
 	defaultTableValue(data, "k", 0.1)
+	defaultTableValue(data, "log", false)
 
-	verifyUnnecessaryArguments(data, {"target", "select", "discrete", "k"})
+	verifyUnnecessaryArguments(data, {"target", "select", "discrete", "k", "log"})
 
 	local k = data.k
 	local cs1 = data.target[1]
@@ -285,7 +289,8 @@ function multiLevel(data)
 	local resolutionTable = {}
 	local expTable = {}
 	if data.discrete then
-		for i = 1, maxSquare do
+		local i = 1
+		while i <= maxSquare do
 			-- increase the square size and calculate fitness for each square.
 			local fitSquare = discreteSquareBySquare(i, cs1, cs2, attribute1, attribute2, minSquare, maxSquare)
 			if fitSquare ~= -1 then
@@ -296,9 +301,22 @@ function multiLevel(data)
 				fitnessSum = fitnessSum + (fitSquare * myexp)
 				exp = exp + myexp
 			end
+
+			if data.log then
+				if i == maxSquare then
+					i = i + 1
+				else
+					i = i * 2
+
+					if i > maxSquare then i = maxSquare end
+				end
+			else
+				i = i + 1
+			end
 		end
 	else
-		for i = 1, maxSquare do
+		local i = 1
+		while i <= maxSquare do
 			-- increase the square size and calculate fitness for each square.
 			local fitSquare = continuousSquareBySquare(i, cs1, cs2, attribute1, attribute2, minSquare, maxSquare)
 			if fitSquare ~= -1 then
@@ -309,6 +327,18 @@ function multiLevel(data)
 				fitnessSum = fitnessSum + (fitSquare * myexp)
 				exp = exp + myexp
 			end
+
+			if data.log then
+				if i == maxSquare then
+					i = i + 1
+				else
+					i = i * 2
+
+					if i > maxSquare then i = maxSquare end
+				end
+			else
+				i = i + 1
+			end
 		end
 	end
 

tests/alternative/MultipleRuns.lua

@@ -195,7 +195,7 @@ return{
 		end
 
 		unitTest:assertError(error_func, "Argument 'y' is mandatory.")
-			error_func = function()
+		error_func = function()
 			m = MultipleRuns{
 				model = MyModel2,
 				strategy = "factorial",
@@ -284,7 +284,7 @@ return{
 		end
 
 		unitTest:assertError(error_func, "Parameters used in strategy 'selected' must be in a table of scenarios.")
-				error_func = function()
+		error_func = function()
 			m = MultipleRuns{
 				model = MyModel4,
 				strategy = "selected",

tests/basic/GoodnessOfFit.lua

@@ -59,6 +59,15 @@ return{
 
 		unitTest:assertEquals(result, 0.865, 0.01) -- 0.84 is the Total Fitness in Costanza Paper Example.
 
+		result = multiLevel{
+			target = {cs, cs2},
+			select = "costanza",
+			log = true,
+			discrete = true
+		}
+
+		unitTest:assertEquals(result, 0.85, 0.01)
+
 		local warningFunc = function()
 			result = multiLevel{
 				target = {sugar, sugar2},