Data Matters

Infer.NET is a .NET framework for machine learning. It provides state-of-the-art message-passing algorithms and statistical routines for performing Bayesian inference. It has applications in a wide variety of domains including information retrieval, bioinformatics, epidemiology, vision, and many others. Please refer to  http://research.microsoft.com/en-us/um/cambridge/projects/infernet/

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