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对于 25mb 的文件,内存使用量恒定为 792mb!我以为和我的使用习惯有关从列表中移动,但移动向量代码的某些部分(例如应用 fft 的数组)根本不会改变正在使用的内存量!
{-# LANGUAGE OverloadedStrings,BangPatterns #-}
import qualified Data.Attoparsec.Char8 as Ap
import Data.Attoparsec
import Control.Monad
import Control.Applicative
--import Control.DeepSeq (force)
import System.IO
import System.Environment
import Data.List (zipWith4,unzip4,zip4,foldl')
import Data.Bits
import Data.Complex
import Data.String (fromString)
import Data.ByteString.Internal
import qualified Data.ByteString.Char8 as B
import qualified Data.ByteString.Lazy.Char8 as Bl
import qualified Data.Vector.Unboxed as Vu
import qualified Statistics.Transform as St
{-
I run a test on a collection of data from a file
[(1,t),(2,t),(3,t),(4,t),(5,t)]
- - -
| - - -
| | - - -
| | |
[y++t, n, y++t]
To do that, I use splitN to create a list of list
[[(1,t),(2,t),(3,t)],[(2,t),(3,t),(4,t)],[(3,t),(4,t),(5,t)]]
Map a serie of functions to determine a value for each inner collection,
and return when an event happened.
-}
data FourD b a = FourD a a a b
instance Functor (FourD c) where
fmap f (FourD x y z d) = FourD (f x) (f y) (f z) d
mgrav_per_bit = [ 18, 36, 71, 143, 286, 571, 1142 ]
--Converting raw data to mg
aToG :: Int -> Double
aToG a = fromIntegral . sign $ uresult
where
twocomp = if a>128
then 256-a
else a
uresult = sum $ zipWith (*) mgrav_per_bit (map (fromEnum . testBit twocomp) [0..7])
sign = if a > 128
then negate
else id
--Data is (int,int,int,time)
--Converted to (St.CD^3,Bytestring) in place of maping afterwards.
parseAcc :: Parser (FourD B.ByteString St.CD)
parseAcc = do Ap.char '('
x <- fmap ((:+0) . aToG) Ap.decimal
Ap.char ','
y <- fmap ((:+0) . aToG) Ap.decimal
Ap.char ','
z <- fmap ((:+0) . aToG) Ap.decimal
Ap.char ','
time <- takeTill (== 41)
Ap.char ')'
return $! FourD x y z time
--applies parseAcc to many lines, fails at the end of file (Need to add a newline)
parseFile = many $ parseAcc <* (Ap.endOfInput <|> Ap.endOfLine)
readExpr input = case parse parseFile input of
Done b val -> val
Partial p -> undefined
Fail a b c -> undefined
unType (FourD x y d z) = (x ,y ,d ,z)
-- Breaks a list of FourD into smaller lists, apply f and g to those lists, then filter the result based if an even happened or not
amap :: (Num c, Ord c) => ([a] -> [c]) -> ([d] -> [ByteString]) -> [FourD d a] -> [Bl.ByteString]
amap f g = (uncurry4 (zipWith4 (filterAcc))). map4 f g . unzip4 . map (unType)
where map4 f g (a,b,c,d) = (f a,f b,f c,g d)
uncurry4 f (a,b,c,d) = f a b c d
-- before i had map filterAcc,outside amap. Tried to fuse everything to eliminate intermediaries
-- An event is detected if x > 50
filterAcc x y z t = if x > 50
then (Bl.pack . B.unpack) $ "yes: " `B.append` t
else ""
-- split [St.CD] in [(Vector St.CD)], apply fft to each, and compress to a single value.
-- Core of the application
fftAcross :: [St.CD] -> [Int]
fftAcross = map (floor . noiseEnergy . St.fft) . splitN 32
-- how the value is determined (sum of all magnitudes but the first one)
noiseEnergy :: (RealFloat a, Vu.Unbox a) => Vu.Vector (Complex a) -> a
noiseEnergy x = (Vu.foldl' (\b a-> b+(magnitude a)) 0 (Vu.drop 1 x))/32
-- how the values are split in (Vector St.CD), if lenght > 32, takes 32, otherwhise I'm done
splitN :: Vu.Unbox a => Int -> [a] -> [Vu.Vector a]
splitN n x = helper x
where
helper x = if atLeast n x
then (Vu.take n (Vu.fromList x)) : (helper (drop 1 x) )
else []
-- Replacing the test by atLeast in place of a counter (that compared to length x,calculated once) reduced the behaviour that memory usage was constant.
-- this is replicated so the behaviour of splitN happens on the time part of FourD, Can't use the same since there is no Vector Bytestring instance
splitN2 n x = helper x
where
helper x = if atLeast n x
then (head x) : (helper (drop 1 x))
else []
atLeast :: Int -> [a] -> Bool
atLeast 0 _ = True
atLeast _ [] = False
atLeast n (_:ys) = atLeast (n-1) ys
main = do
filename <- liftM head getArgs
filehandle <- openFile "results.txt" WriteMode
contents <- liftM readExpr $ B.readFile filename
Bl.hPutStr (filehandle) . Bl.unlines . splitAndApplyAndFilter $ contents where
splitAndApplyAndFilter = amap fftAcross (splitN2 32)
编辑:经过一些重构、融合一些映射、减少长度后,我设法使用 25mb 输入文件在 400~ 下工作。不过,如果是 100mb,则需要 1.5gb。
该程序旨在确定某个时间点是否发生了某个事件,因为它需要一组值(我使用的是 32 atm),在其中运行 fft,对这些值求和并查看是否通过阈值。如果是,则将时间打印到文件中。
http://db.tt/fT8kXPKz 25mb 测试文件
最佳答案
由于 Reddit 中关于同一问题的主题,我找到了解决方案! Parsing with Haskell and Attoparsec
我的大部分问题是由于 attoparsec 很严格并且 haskell 数据相当大(因此 100mb 的文本文件在运行时实际上可能要大得多)引起的
另一半是分析使内存使用量增加了一倍,但我没有考虑到这一点。
将解析器更改为惰性后,我的程序使用 120mb 代替 800mb(当输入大小为 116mb 时),所以成功了!
如果有人对此感兴趣,以下是相关的代码更改:
readExpr input = case parse (parseAcc<*(Ap.endOfLine<*Ap.endOfInput<|>Ap.endOfLine)) input of
Done b val -> val : readExpr b
Partial e -> []
Fail _ _ c -> error c
完整代码:
{-# LANGUAGE OverloadedStrings,BangPatterns #-}
import qualified Data.Attoparsec.Char8 as Ap
import Data.Attoparsec
import Control.Monad
import Control.Applicative
--import Control.DeepSeq (force)
import System.IO
import System.Environment
import Data.List (zipWith4,unzip4,zip4,foldl')
import Data.Bits
import Data.Complex
import Data.String (fromString)
import Data.ByteString.Internal
import qualified Data.ByteString.Char8 as B
import qualified Data.ByteString.Lazy.Char8 as Bl
import qualified Data.Vector.Unboxed as Vu
import qualified Statistics.Transform as St
{-
I run a test on a collection of data from a file
[(1,t),(2,t),(3,t),(4,t),(5,t)]
- - -
| - - -
| | - - -
| | |
[y++t, n, y++t]
To do that, I use splitN to create a list of list
[[(1,t),(2,t),(3,t)],[(2,t),(3,t),(4,t)],[(3,t),(4,t),(5,t)]]
Map a serie of functions to determine a value for each inner collection,
and return when an event happened.
-}
data FourD b a = FourD a a a b
instance Functor (FourD c) where
fmap f (FourD x y z d) = FourD (f x) (f y) (f z) d
mgrav_per_bit = [ 18, 36, 71, 143, 286, 571, 1142 ]
--Converting raw data to mg
aToG :: Int -> Double
aToG a = fromIntegral . sign $ uresult
where
twocomp
| a>128 = 256-a
| otherwise = a
uresult = sum $ zipWith (*) mgrav_per_bit (map (fromEnum . testBit twocomp) [0..7])
sign
| a > 128 = negate
| otherwise = id
--Data is (int,int,int,time)
--Converted to (St.CD^3,Bytestring) in place of maping afterwards.
parseAcc :: Parser (FourD B.ByteString St.CD)
parseAcc = do Ap.char '('
x <- fmap ((:+0) . aToG) Ap.decimal -- Parse, transform to mg, convert to complex
Ap.char ','
y <- fmap ((:+0) . aToG) Ap.decimal
Ap.char ','
z <- fmap ((:+0) . aToG) Ap.decimal
Ap.char ','
time <- takeTill (== 41)
Ap.char ')'
return $! FourD x y z time
--applies parseAcc to many lines, fails at the end of file (Need to add a newline)
parseFile = many $ parseAcc <* (Ap.endOfInput <|> Ap.endOfLine)
readExpr input = case parse (parseAcc<*(Ap.endOfLine<*Ap.endOfInput<|>Ap.endOfLine)) input of
Done b val -> val : readExpr b
Partial e -> []
Fail _ _ c -> error c
unType (FourD x y d z) = (x ,y ,d ,z)
-- Breaks a list of FourD into smaller lists, apply f and g to those lists, then filter the result based if an even happened or not
amap :: (Num c, Ord c) => ([a] -> [c]) -> ([d] -> [ByteString]) -> [FourD d a] -> [ByteString]
amap f g = (uncurry4 (zipWith4 (filterAcc))). map4 f g . unzip4 . map (unType)
where map4 f g (a,b,c,d) = (f a,f b,f c,g d)
uncurry4 f (a,b,c,d) = f a b c d
-- before i had map filterAcc,outside amap. Tried to fuse everything to eliminate intermediaries
-- An event is detected if x > 50
filterAcc x y z t
| x > 50 = t
| otherwise = ""
-- split [St.CD] in [(Vector St.CD)], apply fft to each, and compress to a single value.
-- Core of the application
fftAcross :: [St.CD] -> [Int]
fftAcross = map (floor . noiseEnergy . St.fft) . splitN 32
-- how the value is determined (sum of all magnitudes but the first one)
noiseEnergy :: (RealFloat a, Vu.Unbox a) => Vu.Vector (Complex a) -> a
noiseEnergy x = (Vu.foldl' (\b a-> b+(magnitude a)) 0 (Vu.drop 1 x))/32
-- how the values are split in (Vector St.CD), if lenght > 32, takes 32, otherwhise I'm done
splitN :: Vu.Unbox a => Int -> [a] -> [Vu.Vector a]
splitN n x = helper x
where
helper x
| atLeast n x = (Vu.take n (Vu.fromList x)) : (helper (drop 1 x) )
| otherwise = []
-- Replacing the test by atLeast in place of a counter (that compared to length x,calculated once) reduced the behaviour that memory usage was constant.
-- this is replicated so the behaviour of splitN happens on the time part of FourD, Can't use the same since there is no Vector Bytestring instance
splitN2 n x = helper x
where
helper x
| atLeast n x = (head x) : (helper (drop 1 x))
| otherwise = []
atLeast :: Int -> [a] -> Bool
atLeast 0 _ = True
atLeast _ [] = False
atLeast n (_:ys) = atLeast (n-1) ys
intervalFinder :: [ByteString]->[B.ByteString]
intervalFinder x = helper x ""
where
helper (x:xs) ""
| x /= "" = ("Start Time: " `B.append` x `B.append` "\n"):(helper xs x)
| otherwise = helper xs ""
helper (x:xs) y
| x == "" = ( "End Time: "`B.append` y `B.append` "\n\n" ):(helper xs "")
| otherwise = helper xs x
helper _ _ = []
main = do
filename <- liftM head getArgs
filehandle <- openFile "results.txt" WriteMode
contents <- liftM readExpr $ B.readFile filename
Bl.hPutStr (filehandle) . Bl.fromChunks . intervalFinder . splitAndApplyAndFilter $ contents
hClose filehandle
where
splitAndApplyAndFilter = amap fftAcross (splitN2 32)
--contents <- liftM ((map ( readExpr )) . B.lines) $ B.readFile filename
{- *Main> let g = liftM ((amap fftAcross (splitN2 32)) . readExpr) $ B.readFile "te
stpattern2.txt"
-}
-- B.hPutStrLn (filehandle) . B.unlines . map (B.pack . show ) . amap (map (floor .quare) . (filter (/=[])) . map ( (drop 1) . (map (/32)) . fft ) . splitN 32) . map ( fmap(fromIntegral . aToG)) . map readExpr $ contents
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