Safe Haskell | None |
---|---|

Language | Haskell2010 |

This module implements dynamic time warping as described here: http://en.wikipedia.org/w/index.php?title=Dynamic_time_warping

Additionally `fastDtw`

is implemented as described in the paper:
"FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and
Space" by Stan Salvador and Philip Chan.

Please note that `fastDtw`

is only an approximative solution. If you
need the optimal solution and can bear with the heavily increased demand
both in cpu and in memory you should use `dtwMemo`

or `dtwMemoWindowed`

.

## Example

`>>>`

`-- create two sample datasets`

`>>>`

`let as = [ sin x | x <- [0,0.1..pi] ]`

`>>>`

`let bs = [ sin (x+0.1) | x <- [0,0.1..pi] ]`

`>>>`

`-- define a cost function between two datapoints`

`>>>`

`let dist x y = abs (x-y)`

`>>>`

`-- define a function that will half the size of a dataset (see below)`

`>>>`

`let shrink xs = case xs of (a:b:cs) -> (a+b)/2 : shrink cs; a:[] -> [a]; [] -> []`

`>>>`

`-- calculate the cost with fastDtw and dtwMemo for comparison`

`>>>`

0.19879311`cost $ fastDtw dist shrink 2 as bs :: Float`

`>>>`

0.19879311`cost $ dtwMemo (\x y -> abs (x-y)) as bs :: Float`

## Some words on the shrink function

Care must be taken when choosing a shrink function. It's vital that the resolution is halfed, this is not exactly a problem with the algorithm but with the implementation. The lower resolution dataset should be an honest representation of the higher resolution dataset. For starters binning as in the example above should suffice.

- dtwNaive :: (Ord c, Fractional c, DataSet a, DataSet b) => (Item a -> Item b -> c) -> a -> b -> c
- dtwMemo :: (Ord c, Fractional c, DataSet a, DataSet b) => (Item a -> Item b -> c) -> a -> b -> Result c
- fastDtw :: (Ord c, Fractional c, DataSet a) => (Item a -> Item a -> c) -> (a -> a) -> Int -> a -> a -> Result c
- class DataSet dataset where
- data Result a = Result {}
- type Path = [Index]
- type Index = (Int, Int)

# Documentation

dtwNaive :: (Ord c, Fractional c, DataSet a, DataSet b) => (Item a -> Item b -> c) -> a -> b -> c Source #

this is the naive implementation of dynamic time warping no caching what so ever is taking place this should not be used and is just used as a reference for the other implementations

dtwMemo :: (Ord c, Fractional c, DataSet a, DataSet b) => (Item a -> Item b -> c) -> a -> b -> Result c Source #

this is the "standard" implementation of dynamic time warping O(N^2) is achieved by memoization of previous results

:: (Ord c, Fractional c, DataSet a) | |

=> (Item a -> Item a -> c) | |

-> (a -> a) | function that shrinks a dataset by a factor of two |

-> Int | radius that the search window is expanded at each resolution level |

-> a | first dataset |

-> a | second dataset |

-> Result c | result |

this is the "fast" implementation of dynamic time warping as per the authors this methods calculates a good approximate result in O(N), depending on the usecase the windowsize should be tweaked

class DataSet dataset where Source #

a generic dataset is basically just an indexing function | and an indicator of the dataset size