progress on getDistancesBetweenCases
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@ -15,9 +15,9 @@ assignIndiciesToData oldData = zipWith (\dat idx -> dat{_downsampleIndex = Just
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-- |Initializes cases distances for passed training data.
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initializeCaseDistances :: PushArgs -> [PushData]
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initializeCaseDistances (PushArgs {trainingData = tData, populationSize = popSize}) = [ dat{_caseDistances = Just (replicate (length tData) (fromIntegral @Int @Double popSize))} | dat <- tData ]
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initializeCaseDistances (PushArgs {trainingData = tData, populationSize = popSize}) = [ dat{_caseDistances = Just (replicate (length tData) popSize)} | dat <- tData ]
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-- |Updates the cases distances when downsampling
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-- |Updates the cases distances when downsampling.
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updateCaseDistances :: [Individual] -> [PushData] -> [PushData] -> String -> Double -> [PushData]
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updateCaseDistances evaledPop downsampleData trainingData informedDownsamplingType solutionThreshold = undefined
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@ -70,7 +70,7 @@ selectDownsampleMaxminAdaptive (PushArgs {caseDelta = cDelta}) pushData = do
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-- original pushData wrapped in a list, the second [PushData] holds the rest of the list
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-- without the aformentioned head. The Int is the caseDelta derived from the downsample rate
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-- and the length of the original [pushData].
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selectDownsampleMaxminAdaptive' :: [PushData] -> [PushData] -> Double -> IO [PushData]
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selectDownsampleMaxminAdaptive' :: [PushData] -> [PushData] -> Int -> IO [PushData]
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selectDownsampleMaxminAdaptive' newDownsample casesToPickFrom cDelta = do
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let newDistances = map extractDistance newDownsample
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let minCaseDistances = minOfColumns (map (\distList -> filterByIndex distList (map extractIndex casesToPickFrom)) newDistances)
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@ -82,3 +82,15 @@ selectDownsampleMaxminAdaptive' newDownsample casesToPickFrom cDelta = do
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((casesToPickFrom !! selectedCaseIndex) : newDownsample)
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(shuffle' (deleteAt selectedCaseIndex casesToPickFrom) (length casesToPickFrom - 1) stdGen)
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cDelta
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-- |Returns the distance between two cases given a list of individual error vectors, and the index these
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-- cases exist in the error vector. Only makes the distinction between zero and nonzero errors"
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getDistanceBetweenCases :: [[Int]] -> Int -> Int -> Int
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getDistanceBetweenCases errorLists caseIndex0 caseIndex1 =
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if lhe < caseIndex0 || lhe < caseIndex1 || caseIndex0 < 0 || caseIndex1 < 0
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then length errorLists
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else undefined
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where
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lhe :: Int
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lhe = length $ head errorLists
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errors0 :: [Int]
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errors0 = map (\lst -> lst !! caseIndex0) errorLists
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@ -96,7 +96,7 @@ data PushArgs = PushArgs
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epsilons :: Maybe [Double],
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-- | Used with the CaseMaxminAuto downsampling strategy. Tells downsampling to stop when
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-- the maximum minimum distance is too far away.
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caseDelta :: Double
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caseDelta :: Int
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}
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-- | The default values for which all runs of Hush derive
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@ -9,12 +9,12 @@ data PushData = PushData {
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_inputData :: [Gene],
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_outputData :: Gene,
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_downsampleIndex :: Maybe Int,
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_caseDistances :: Maybe [Double]
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_caseDistances :: Maybe [Int]
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} deriving (Show)
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-- |Extracts the case distances from a PushData object. Errors if the
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-- _caseDistances list is Nothing.
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extractDistance :: PushData -> [Double]
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extractDistance :: PushData -> [Int]
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extractDistance PushData{_caseDistances = Nothing} = error "Error: Case distances are empty!. This should never happen"
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extractDistance PushData{_caseDistances = Just xs} = xs
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@ -5,7 +5,7 @@ import System.Random
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import System.Random.Shuffle
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-- |Maps minimum over the transposed [[Double]].
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minOfColumns :: [[Double]] -> [Double]
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minOfColumns :: [[Int]] -> [Int]
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minOfColumns columns = map minimum (transpose columns)
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-- |Returns the index of the maximum value in a list, randomly tiebreaking.
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