Config the size of shared memory(/dev/shm) for docker containers

Due to the issue from github, the size of shared memory is fixed at 64M.

If you want larger size /dev/shm, please upgrade your docker to version newer than 1.10, and specify --shm-size when you run a docker image. Below is the help message of --shm-size.

--shm-size=""
Size of /dev/shm. The format is . number must be greater than 0. Unit is optional and can be b (bytes), k (kilobytes), m (megabytes), or g (gigabytes). If you omit the unit, the system uses bytes. If you omit the size entirely, the system uses 64m.

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A simple implementation of DTW(Dynamic Time Warping) in C#/python

DTW(Dynamic Time Warping) is a very useful tools for time series analysis. This is a very simple (but not very efficient) c# implementation of DTW, the source code is available at https://gist.github.com/1966342 . Use the program as below: double[] x = {9,3,1,5,1,2,0,1,0,2,2,8,1,7,0,6,4,4,5}; double[] y = {1,0,5,5,0,1,0,1,0,3,3,2,8,1,0,6,4,4,5}; SimpleDTW dtw = new SimpleDTW(x,y); dtw.calculateDTW(); The python implementation is available at https://gist.github.com/3265694 . from python-dtw import Dtw import math dtw = Dtw([1, 2, 3, 4, 6], [1, 2, 3, 5], distance_func=lambda x, y: math.fabs(x - y)) print dtw.calculate() #calculate the distance print dtw.get_path() #calculate the mapping path

Change the default user when start a docker container

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Notes on Sequential Pattern Mining (2) -- Partial Order Pattern Mining and Contrast Mining

1. In , the authors induce TEIRESIAS algorithms to mining combinatorial patterns with gap constraints in biological sequences. The patterns TEIRESIAS mined is similiar with the common sequential patterns, but it could contain "." the wild card which is also in the alphbel of the sequences database standing for any other item available, for example pattern "A..B" is a length-4 pattern, with two arbitrary items between the first A and the last B. Patterns "AC.B", "AADB" are all said to be more specific than pattern "A..B". TEIRESIAS mining all the maximal patterns () with a support over a min threshold K. There some key points of TEIRESIAS algorithms: 1)The growth of the patterns The growth of the patterns is accomplished by convolute current pattern by a short length pattern. Pattern A and pattern B are convolutable if the last L(very small) characters of pattern A is the same as the first L characters of pattern B, then ...

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