I want to do a repeated measures MANCOVA in python but have been struggling to find resources/code online. Does anyone have examples of code/packages recommendations for this type of analysis on python?
Thank you!
I have tried the package statsmodels for ANOVA, but cannot find code for MANCOVA.
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I am working on a problem in which i have to generate a pattern similar to the one shown in this screenshot:
I have tried many different ways, but none are working.
I have seen some embroidery software which can generate this type of pattern, but I don't know the algorithm used to generate this patter.
Please help me, if someone knows anything about the algorithm.
Thank you!
I need to compare the two for a presentation I'm preparing, but I cant use the data I've gathered on my own computer since that would be unreliable. If anyone has any literature on this it would be very helpful.
Thank you!
You can refer Chapter 8 of O'Reillys' Hadoop:The Definitive Guide book for Map and Reduce join.It has good comparision.
I have a simple linear programming problem. After solving it, I get the correct result. I want to speed it up using hot-start feature of MOSEK, but I don't know how to set some parameters like "res.sol.bas.sku", "res.sol.bas.skn", .... I only know an initial solution, i.e, "res.sol.bas.xx", where the value of the variables are stored for a near to optimal solution. Is it possible for me to accelerate the engine using Hot start feature in this way!
Regards
You seem to using MATLAB. Did you read
http://docs.mosek.com/6.0/toolbox/node009.html#238393032
Does it solve the issue?
This topic has many thread. But also I am posting another one. All the post may be a way to do a sentiment analysis, but I found no way.
I want to implement the doing ways of sentiment analysis. So I would request to show me a way. During my research, I found that this is used anyway. I guess Bayesian algorithm is used to calculate positive words and negative words and calculate the probability of the sentence being positive or negative using bag of words.
This is only for the words, I guess we have to do language processing too. So is there anyone who has more knowledge? If yes, can you guide me with some algorithms with their links for reference so that I can implement. Anything in particular that may help me in my analysis.
Also can you prefer me language that I can work with? Some says Java is comparably time consuming so they don't recommend Java to work with.
Any type of help is much appreciated.
First of all, sentiment analysis is done on various levels, such as document, sentence, phrase, and feature level. Which one are you working on? There are many different approaches to each of them. You can find a very good intro to this topic here. For machine-learning approaches, the most important element is feature engineering and it's not limited to bag of words. You can find many other useful features in different applications from the tutorial I linked. What language processing you need to do depends on what features you want to use. You may need POS-tagging if POS information is needed for your features for example.
For classifiers, you can try Support Vector Machines, Maximum Entropy, and Naive Bayes (probably as a baseline) and these are frequently used in the literature, about which you can also find a pretty comprehensive list in the link. The Mallet toolkit contains ME and NB, and if you use SVMlight, you can easily convert the feature formats to the Mallet format with a function. Of course there are many other implementations of these classifiers.
For rule-based methods, Pointwise Mutual Information is frequently used, and some kinds of scoring-based methods, etc.
Hope this helps.
For the text analyzing there is no language stronger than SNOBOL. In SNOBOL-4 the Fortran interpretator, for example, takes only 60 lines.
NLTK offers really good Algorithm for sentiment analysis. It is open source so you can have a look at the source code and check out the algorithm used. You can even download NLTK book which is free and has some good material on sentiment analysis.
Coming to your second point I dont think Java is that slow. I am myself coding in c++ for years but lately also started with java as if you see a lot of very popular open source softwares like lucene, solr, hadoop, neo4j are all written in java.
I am planning to implement spam filter using Naive Bayesian classification model.
Online I see a lot of info on Naive Bayesian classification, but the problem is its a lot of mathematical stuff, than clearly stating how its done. And the problem is I am more of a programmer than a mathematician (yes I had learnt Probability and Bayesian theorem back in school, but out of touch for a long long time, and I don't have luxury of learning it now (Have nearly 3 weeks to come-up with a working prototype)).
So if someone can explain or point me to location where its explained for programmers than a mathematician, it would be a great help.
PS: By the way I have to implement it in C, if you want to know. :(
Regards,
Microkernel
The book Programming Collective Intelligence has chapter that covers this and other methods. The chapter (#6) can be understood without reference to previous chapters, is written clearly, and discusses only the minimal mathematics necessary to get the job done.
You could try this website. It's got some source code.
I would highly recommend Andrew Moore's tutorials and I think you should start with this one.
You could also take a look at POPFile, an open source spam filter engine.
Have you looked at dspam?
http://dspam.irontec.com/faq.shtml#1.0
http://www.nuclearelephant.com/