# \[SOLUTION\] Kalah (#58) **URL:** \ **Category:** ruby-talk\ **Created:** [16 December 2005 01:45 UTC](https://rubytalk.org/t/solution-kalah-58/23332 "2005-12-16T01:45:54Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![Jay\_Anderson](https://avatars.discourse-cdn.com/v4/letter/j/ba9def/32.png) [@Jay\_Anderson](https://rubytalk.org/u/Jay_Anderson)\ **Post date:** [16 December 2005 01:45 UTC](https://rubytalk.org/t/solution-kalah-58/23332/1 "2005-12-16T01:45:54Z")
I know this is a bit late, but I've been trying to train my neural net. I've never used them before and I've been wanting to learn. I created a neural net with the input being the kalah board plus one bias node(so 15 input nodes), 20 hidden nodes, and 6 output nodes. A move is selected by running the neural net and then picking the valid move whose corresponding output node is highest. I'm training the net right now using Particle Swarm Optimization to find good weights. I trained it against dave's player and adam's remove right. It found a pretty good solution against the remove right player, but not dave's. The best I could get after searching for a while was losing by 12. I might have better luck if I just take dave's solution and replace the function it uses to rate the board's goodness with a neural net and then train it. That might be fun for later. Anyways sorry about the lateness, but here it is. -----Horndude77 NeuralPlayer.rb (Sorry about the long section of weights. I had them on one line each.): > **···** > > ================================================= > require 'NeuralNet' > > class NeuralPlayer \< Player >     def initialize(name) >         super(name) >         @net = NeuralNet.new(15, 20, 6) >         @num\_weights = 15\*20 + 20\*6 > >         #weights for RemoveRight >         #self.weights = [-0.428456550403913, -0.909217917148082, > -0.468284431041651, 0.357903882475321, -0.104252998861521, > -0.460447451110633, 0.264105895124314, -0.220335064829405, > 0.344066370486408, 0.110887382259345, -0.112061654833158, > 0.387531331474845, 0.253470699210902, 0.58235888221721, > 0.306268984507146, 0.692431343604429, -0.0536485504199445, > 1.25989522777867, -1.10462932704233, -0.351286944668706, > -0.567170711584581, 1.29779740339143, 0.907906329763339, > -0.207925256569637, 0.177102138750371, 0.128961825966903, > -1.40103570953856, 0.60460561949975, -0.293757627893575, > 0.519854674076599, -0.121247185426499, -0.48243591070721, > 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-0.494767892310519, 0.875913986718127, > 0.249256338465321, -0.362941176741653, 0.937178999188274, > -1.3214093512053, -0.243068895971681, 0.179619525348131, > 0.0273438374107876, 0.968372096980664, -0.209418553863336, > 1.06698122835133, 0.112620291820417, 0.554996780348471, > 0.34794240961505, -0.34321078192519, 0.513339264686877, > -1.24218990057805, 0.0172507054843588, 1.73545904209529, > -0.310201933026909] >     end > >     def weights=(weights) >         @net.weights = weights >     end > >     def choose\_move >         #the board is the input to the neural net plus a bias node. >         output = @net.run(@game.board+[1]) >         max\_index = 0 >         max = -1000000 >         valid\_moves.each do |m| >             max\_index, max = m, output[m] if output[m] \> max >         end >         offset = @side == KalahGame::TOP ? 7 : 0 >         max\_index + offset >     end > >     def valid\_moves >         offset = @side == KalahGame::TOP ? 7 : 0 >         moves = [] >         6.times do |i| >             moves \<\< i if @game.legal\_move?(i+offset) >         end >         moves >     end > end > > # NeuralNet.rb: > > class NeuralNet >     attr\_reader :num\_input, :num\_hidden, :num\_output, :weights > >     def initialize(num\_input, num\_hidden, num\_output) >         @num\_input = num\_input >         @num\_hidden = num\_hidden >         @num\_output = num\_output >         @weights = Array.new(@num\_input\*@num\_hidden + > @num\_hidden\*@num\_output) { rand } >     end > >     def weights=(weights) >         @weights = weights >     end > >     def run(inputs) >         index = 0 >         hidden = Array.new(@num\_hidden, 0) > >         inputs.each do |i| >             @num\_hidden.times do |h| >                 hidden[h] += @weights[index] \* i >                 index += 1 >             end >         end > >         output = Array.new(@num\_output, 0) > >         hidden.each do |h| >             @num\_output.times do |o| >                 output[o] += @weights[index] \* h >                 index += 1 >             end >         end > >         output >     end > end ---
**Author:** ![David\_Balmain1](https://avatars.discourse-cdn.com/v4/letter/d/76d3ee/32.png) [@David\_Balmain1](https://rubytalk.org/u/David_Balmain1)\ **Post date:** [16 December 2005 03:18 UTC](https://rubytalk.org/t/solution-kalah-58/23332/2 "2005-12-16T03:18:54Z")
Dude, nice work. I really must take a closer look later on. > **···** > > On 12/16/05, horndude77@gmail.com \ wrote: > > > I know this is a bit late, but I've been trying to train my neural net. > > I've never used them before and I've been wanting to learn. I created a > > neural net with the input being the kalah board plus one bias node(so > > 15 input nodes), 20 hidden nodes, and 6 output nodes. A move is > > selected by running the neural net and then picking the valid move > > whose corresponding output node is highest. I'm training the net right > > now using Particle Swarm Optimization to find good weights. > > > > I trained it against dave's player and adam's remove right. It found a > > pretty good solution against the remove right player, but not dave's. > > The best I could get after searching for a while was losing by 12. I > > might have better luck if I just take dave's solution and replace the > > function it uses to rate the board's goodness with a neural net and > > then train it. That might be fun for later. > > > > Anyways sorry about the lateness, but here it is. > > > > -----Horndude77 > > > > NeuralPlayer.rb (Sorry about the long section of weights. I had them on > > one line each.): ---
**Author:** ![Ezra\_Zygmuntowicz3](https://avatars.discourse-cdn.com/v4/letter/e/c57346/32.png) [@Ezra\_Zygmuntowicz3](https://rubytalk.org/u/Ezra_Zygmuntowicz3)\ **Post date:** [16 December 2005 03:22 UTC](https://rubytalk.org/t/solution-kalah-58/23332/3 "2005-12-16T03:22:28Z")
Wow! -Ezra > **···** > > On Dec 15, 2005, at 5:45 PM, horndude77@gmail.com wrote: > > > I know this is a bit late, but I've been trying to train my neural net. > > I've never used them before and I've been wanting to learn. I created a > > neural net with the input being the kalah board plus one bias node(so > > 15 input nodes), 20 hidden nodes, and 6 output nodes. A move is > > selected by running the neural net and then picking the valid move > > whose corresponding output node is highest. I'm training the net right > > now using Particle Swarm Optimization to find good weights. > > > > I trained it against dave's player and adam's remove right. It found a > > pretty good solution against the remove right player, but not dave's. > > The best I could get after searching for a while was losing by 12. I > > might have better luck if I just take dave's solution and replace the > > function it uses to rate the board's goodness with a neural net and > > then train it. That might be fun for later. > > > > Anyways sorry about the lateness, but here it is. > > > > -----Horndude77 > > > > # NeuralPlayer.rb (Sorry about the long section of weights. I had them on one line each.): > > > > require 'NeuralNet' > > > > class NeuralPlayer \< Player > >     def initialize(name) > >         super(name) > >         @net = NeuralNet.new(15, 20, 6) > >         @num\_weights = 15\*20 + 20\*6 > > > >         #weights for RemoveRight > >         #self.weights = [-0.428456550403913, -0.909217917148082, > > -0.468284431041651, 0.357903882475321, -0.104252998861521, > > -0.460447451110633, 0.264105895124314, -0.220335064829405, > > 0.344066370486408, 0.110887382259345, -0.112061654833158, > > 0.387531331474845, 0.253470699210902, 0.58235888221721, > > 0.306268984507146, 0.692431343604429, -0.0536485504199445, > > 1.25989522777867, -1.10462932704233, -0.351286944668706, > > -0.567170711584581, 1.29779740339143, 0.907906329763339, > > -0.207925256569637, 0.177102138750371, 0.128961825966903, > > -1.40103570953856, 0.60460561949975, -0.293757627893575, > > 0.519854674076599, -0.121247185426499, -0.48243591070721, > > -0.5635586279583, 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-0.363234787452163, 0.0239440462433509, 0.906154588569019, > > 0.158870148658544] > > > >         #weights for Dave > >         self.weights = [0.595765520562772, 0.572313565308922, > > -1.01593258550904, -1.63154027608213, -0.242177969065067, > > -1.44498150429411, -0.611346758262896, -0.714767503318283, > > 0.133920007475619, -0.0280544755220122, 0.805544561666266, > > -0.194520526790607, 1.1332800840179, -0.899871016430959, > > 0.247063828714958, 0.224254662718752, -0.618371544496958, > > 0.684186664735964, -0.190457855344232, 0.799862285892088, > > -0.555844449171634, 0.0238775186945493, -0.180573650320856, > > 0.354543728300561, 0.513768996058762, 0.23348678845317, > > 0.178371745419366, -0.591680504803924, -0.201463863457764, > > 1.68690987229618, 0.444379703941704, 0.516277936677377, > > -0.165293331915385, 0.183888467391197, -0.207605328542711, > > 0.0336538540792326, 0.0830359695274841, 0.791089393285359, > > 0.617361158680298, 0.860187793570229, -0.85071993055499, > > 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choose\_move > >         #the board is the input to the neural net plus a bias node. > >         output = @net.run(@game.board+[1]) > >         max\_index = 0 > >         max = -1000000 > >         valid\_moves.each do |m| > >             max\_index, max = m, output[m] if output[m] \> max > >         end > >         offset = @side == KalahGame::TOP ? 7 : 0 > >         max\_index + offset > >     end > > > >     def valid\_moves > >         offset = @side == KalahGame::TOP ? 7 : 0 > >         moves = > >         6.times do |i| > >             moves \<\< i if @game.legal\_move?(i+offset) > >         end > >         moves > >     end > > end > > > > # NeuralNet.rb: > > > > class NeuralNet > >     attr\_reader :num\_input, :num\_hidden, :num\_output, :weights > > > >     def initialize(num\_input, num\_hidden, num\_output) > >         @num\_input = num\_input > >         @num\_hidden = num\_hidden > >         @num\_output = num\_output > >         @weights = Array.new(@num\_input\*@num\_hidden + > > @num\_hidden\*@num\_output) { rand } > >     end > > > >     def weights=(weights) > >         @weights = weights > >     end > > > >     def run(inputs) > >         index = 0 > >         hidden = Array.new(@num\_hidden, 0) > > > >         inputs.each do |i| > >             @num\_hidden.times do |h| > >                 hidden[h] += @weights[index] \* i > >                 index += 1 > >             end > >         end > > > >         output = Array.new(@num\_output, 0) > > > >         hidden.each do |h| > >             @num\_output.times do |o| > >                 output[o] += @weights[index] \* h > >                 index += 1 > >             end > >         end > > > >         output > >     end > > end > > -Ezra Zygmuntowicz > WebMaster > Yakima Herald-Republic Newspaper > ezra@yakima-herald.com > 509-577-7732