Created
March 10, 2014 21:53
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Artificial Neural Network
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| module Main where | |
| config_input_cells = 1 :: Int | |
| config_hidden_layer_cells = 10 :: Int | |
| config_hidden_layers = 2 :: Int | |
| config_output_cells = 1 :: Int | |
| -- [ [ (Threshhold, [Connection Weights] ) ] ] | |
| type Net = [ [ (Double, [Double] ) ] ] | |
| net :: Net | |
| net = [input_layer] ++ hidden_layers ++ [output_layer] | |
| where input_layer = take config_input_cells $ repeat (0.0, take config_input_cells $ repeat 0.5) | |
| first_hidden_layer = take config_hidden_layer_cells $ repeat (0.0, take config_input_cells $ repeat 0.5) | |
| hidden_layer_cells = take config_hidden_layer_cells $ repeat 0.5 | |
| hidden_layers = [first_hidden_layer] ++ (take (max (config_hidden_layers - 1) 0) $ repeat $ take config_hidden_layer_cells $ repeat (0.0, hidden_layer_cells)) | |
| output_layer = take config_output_cells $ repeat (0.0, hidden_layer_cells) | |
| propagate :: Net -> [Double] -> [[Double]] | |
| propagate net seed = scanl propagate_function (seed) net | |
| where propagate_function inputs layer = map (neuron_fires inputs) layer | |
| neuron_fires inputs neuron = if fst neuron < (sum $ neuron_inputs inputs neuron) then 1.0 else 0.0 | |
| neuron_inputs inputs neuron = zipWith (*) inputs (snd neuron) | |
| --fire :: [(Double, Double)] -> Double -> Bool | |
| --fire inputs threshhold = threshhold < (sum $ map (\(value, weight) -> value * weight) inputs) | |
| --input_layer :: [[Double]] -> [Double] | |
| --input_layer net = head net | |
| --output_layer :: [[Double]] -> [Double] | |
| --output_layer net = last net | |
| main :: IO () | |
| main = return () |
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