Added DTM wrapper, supports training on a corpus

What is included
-----------
* Initial implementation for the DTM wrapper. Made it look  like the Malletwrapper as much as possible (for consistency)
* Included a test_dtm script  to test out the model. I thought of putting this in the unittest, but was not sure. 
* Computes Influence Model by default
* Computes on any corpus that can be transoformed to BleiCorpus and has len (currently a part of my own research corpus is used).


To Run the test_script
-----------

* You just need to set the DTM_HOME variable or just supply the path to the binary.
* You can get the binaries either by compiling them from the  [original] or a [fork] ,which also has precompiled binaries for Linux and Windows.
* Supply another corpus if you wish .


What is missing
-----------
* Inference on a holdout corpus.
* Tests
* Some comments and parameter explanations

P,S Still slightly new to the world of contributing with git. Please point out obvious mistakes.

 [original]:https://code.google.com/p/princeton-statistical-learning/downloads/detailname=dtm_release.tgz&can=2&q=
 [fork]:  https://github.com/magsilva/dtm


It's an example of the parameters used by the binary. Can remove it.
________________________________
From: Radim Řehůřek<mailto:notifications@github.com>
Sent: ‎01.‎07.‎2014 11:28
To: piskvorky/gensim<mailto:gensim@noreply.github.com>
Cc: Arttii<mailto:artyom.topchyan@live.com>
Subject: Re: [gensim] Added DTM wrapper, supports training on a corpus (#205)

> +
> +>>> model = gensim.models.DTMmodel('dtm-win64.exe',
> +    my_corpus, my_timeslices, num_topics=20, id2word=dictionary)
> +
> +
> +   ntopics = 20 \
> +   mode = fit \
> +   rng_seed = 0 \
> +   initialize_lda = true \
> +   corpus_prefix = example/test \
> +   outname = example/model_run \
> +   top_chain_var = 0.005 \
> +   alpha = 0.01 \
> +   lda_sequence_min_iter = 6 \
> +   lda_sequence_max_iter = 20 \
> +   lda_max_em_iter = 10

What is this list of values?

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