mimgrating from another repo

lib/ann/examples/cascade/test.lua

@@ -0,0 +1,14 @@
+#! /usr/bin/env lua
+
+require("lfann")
+
+-- Load the network from a file
+local net = fann.Net.create_from_file("xor.net")
+
+-- Test using command line args
+local input = {arg[1] and tonumber(arg[1]) or 1, arg[1] and tonumber(arg[2]) or 0}
+local output = net:run(input)
+
+for i, j in ipairs(output) do
+    print(j)
+end

lib/ann/examples/cascade/train.data

@@ -0,0 +1,9 @@
+4 2 1
+1 1
+0
+1 0
+1
+0 1
+1
+0 0
+0

lib/ann/examples/cascade/train.lua

@@ -0,0 +1,13 @@
+#! /usr/bin/env lua
+
+require("lfann")
+
+-- Create a shortcut neural network to serve as base to the cascade training
+local net = fann.Net.create_shortcut{2, 1}
+
+-- Train the net from a file
+net:set_train_stop_function(fann.STOPFUNC_MSE)
+net:cascade_train_on_file("train.data", 10, 1, 0.0001)
+
+-- Save the net to a file for a latter execution 
+net:save("xor.net");

lib/ann/examples/rpg/test.lua

@@ -0,0 +1,86 @@
+#! /usr/bin/env lua
+
+require("lfann")
+
+local classes = {
+    "Fighter", "Paladin", "Druid", "Cleric", "Monk",
+    "Thief", "Sorceress", "Mage", "Bard"
+}
+
+-- Load the network from a file
+local net = fann.Net.create_from_file("rpg.net")
+math.randomseed(os.time())
+
+while true do
+    print("Enter the character attributes or: -1 to exit, -2 to generate:")
+    
+    local input = {}
+    local aux = io.read("*n")
+    
+    if not aux or aux == -1 then
+        -- Quit
+        break 
+    elseif aux == -2 then
+        -- Random
+        
+        for i = 1, 6 do
+            local d = {}
+            for i = 1, 4 do table.insert(d, math.random(1, 6)) end
+            table.sort(d)
+            table.insert(input, d[2] + d[3] + d[4])
+        end
+        
+        print("Generated attributes:")
+        print(table.concat(input, " "))
+    else
+        -- Read the attributes from stdin
+        table.insert(input, aux)
+        
+        for i = 2, 6 do
+            aux = io.read("*n")
+            table.insert(input, aux)
+        end
+    end
+    
+    -- Get the result and calculate suggest the character class
+    local output = net:run(input)
+    local maxClass, maxValue = -1, -1
+    
+    -- Get the max value
+    for class, value in ipairs(output) do
+        if value > maxValue then
+            maxClass = class
+            maxValue = value
+        end
+    end
+    
+    -- Only check if there's a class with at least 0.4 fitting
+    if maxValue < 0.4 then
+        print("-> The attributes are too low to suggest a class")
+    else
+        -- Calculate the diff using the maxValue
+        local diff = maxValue / 6
+        diff = math.max(diff, 0.1)
+        
+        -- Get the suggestions
+        local suggestions = {}
+        
+        for class, value in ipairs(output) do
+            if value > maxValue - diff then
+                table.insert(suggestions, {["class"] = class, ["value"] = value})
+            end
+        end
+        
+        -- Sort the suggestions according to the fitness
+        table.sort(suggestions, function(a, b) return a.value > b.value end)
+        
+        -- Inform the user about the suggestions
+        print("\nClass suggestions:")
+        
+        for i, tbl in ipairs(suggestions) do
+            print(string.format("-> %s (%d%%)", classes[tbl.class], tbl.value * 100))
+        end
+    end
+    
+    print()
+end

lib/ann/examples/rpg/train.data

@@ -0,0 +1,53 @@
+26 6 9
+18 13 15 10 9 10
+1.0 0.3 0.0 0.0 0.1 0.4 0.0 0.0 0.0
+15 10 14 10 14 14
+0.6 1.0 0.6 0.5 0.4 0.0 0.4 0.0 0.3
+10 12 10 14 16 12
+0.1 0.4 1.0 0.6 0.2 0.1 0.1 0.4 0.1
+14 8 12 10 16 12
+0.4 0.8 0.6 1.0 0.3 0.0 0.1 0.0 0.1
+14 14 14 10 16 10
+0.7 0.8 0.9 0.9 1.0 0.2 0.0 0.0 0.0
+10 18 11 14 10 12
+0.2 0.1 0.0 0.0 0.1 1.0 0.1 0.3 0.2
+10 12 14 12 10 17
+0.1 0.1 0.0 0.0 0.1 0.2 0.9 0.1 0.6
+12 14 12 12 10 18
+0.1 0.1 0.0 0.0 0.1 0.2 1.0 0.1 0.9
+8 11 16 18 10 10
+0.1 0.1 0.0 0.0 0.2 0.2 0.0 1.0 0.0
+18 10 16 10 10 10
+1.0 0.1 0.0 0.0 0.2 0.0 0.0 0.0 0.0
+16 14 14 10 18 10
+0.7 0.8 0.6 1.0 0.9 0.6 0.0 0.0 0.0
+10 12 14 16 10 10
+0.1 0.1 0.0 0.0 0.1 0.2 0.0 1.0 0.0
+10 12 14 20 10 10
+0.1 0.1 0.0 0.0 0.1 0.2 0.0 1.0 0.0
+12 12 14 12 16 10
+0.2 0.5 1.0 0.7 0.2 0.2 0.0 0.1 0.0
+3 3 3 3 3 3
+0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
+18 18 18 18 18 18
+1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
+14 10 9 9 11 15
+0.2 0.0 0.0 0.0 0.1 0.0 0.7 0.0 0.2
+13 15 14 14 14 16
+0.2 0.4 0.3 0.4 0.2 0.5 0.7 0.3 1.0
+12 16 12 10 16 11
+0.2 0.3 0.5 0.4 0.4 0.5 0.0 0.0 0.0
+15 17 12 12 12 13
+1.0 0.3 0.2 0.1 0.3 0.5 0.1 0.2 0.1
+16 12 14 10 16 15
+0.7 1.0 0.8 0.9 0.8 0.1 0.3 0.0 0.2
+10 16 14 18 10 14
+0.0 0.0 0.0 0.0 0.1 0.8 0.2 1.0 0.3
+12 12 12 12 12 12
+0.2 0.1 0.2 0.2 0.1 0.2 0.1 0.1 0.2
+14 14 14 14 13 14
+0.4 0.5 0.5 0.5 0.4 0.4 0.1 0.2 0.6
+8 8 8 18 18 18
+0.0 0.0 0.1 0.1 0.0 0.0 1.0 1.0 1.0
+18 18 18 8 8 8
+1.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0

lib/ann/examples/rpg/train.lua

@@ -0,0 +1,24 @@
+#! /usr/bin/env lua
+
+--[[
+    This example classifies a RPG character in classes, according to its
+    attributes (strength, dexterity, constitution, intelligence, wisdom and
+        charisma).
+--]]
+
+require("lfann")
+
+local net = fann.Net.create_standard{6, 18, 18, 9}
+
+-- Configure the activation function
+net:set_activation_function_hidden(fann.SIGMOID_SYMMETRIC)
+net:set_activation_function_output(fann.SIGMOID_SYMMETRIC)
+
+-- Configure other parameters
+net:set_training_algorithm(fann.TRAIN_RPROP)
+
+-- Train the net from a file
+net:train_on_file("train.data", 100000, 50, 0.001)
+
+-- Save the net to a file for a latter execution 
+net:save("rpg.net");

lib/ann/examples/xor/test.lua

@@ -0,0 +1,41 @@
+#! /usr/bin/env lua
+
+-- load the library
+require("lfann")
+
+-- load the network from the exported file
+local net = fann.Net.create_from_file("xor.net")
+
+local n_input, n_output = 2, 1
+local input, output = {}
+local write, sf = io.write, string.format
+
+-- test using the stdio (enter q to quit)
+while true do
+    local cont = true
+    
+    -- input
+    for i = 1, n_input do
+        local aux = io.read("*n")
+        
+        if not aux then
+            cont = false
+            break
+        end
+        
+        input[i] = aux
+    end
+    
+    if not cont then break end
+    
+    -- run the network
+    output = net:run(input)
+    
+    -- output
+    for i = 1, n_output do
+        if i >  1 then write(" ") end
+        write( sf("%.6f", output[i]) )
+    end
+    
+    write("\n")
+end

lib/ann/examples/xor/train.data

@@ -0,0 +1,9 @@
+4 2 1
+1 1
+-1
+1 -1
+1
+-1 1
+1
+-1 -1
+-1

lib/ann/examples/xor/train.lua

@@ -0,0 +1,17 @@
+#! /usr/bin/env lua
+
+require("lfann")
+
+-- Create a Neural Network with tree layers, with 2, 3 and 1 neurons, plus one
+-- bias neuron per layer
+local net = fann.Net.create_standard{2, 3, 1}
+
+-- Configure the activation function
+net:set_activation_function_hidden(fann.GAUSSIAN_SYMMETRIC)
+net:set_activation_function_output(fann.GAUSSIAN_SYMMETRIC)
+
+-- Train the net from a file
+net:train_on_file("train.data", 1000, 10, 0.0001)
+
+-- Save the net to a file for a latter execution 
+net:save("xor.net");