v0.1.0, 4/28/2014 -- Initial commit

v0.1.1, 4/28/2014 -- Documentation and examples provided

v0.1.2, 5/5/2014 -- Changed instances of dtype=numpy.int to 
	dtype=numpy.int32, as other int types are not properly 
	recognized as integers on Unix machines, but as shorts or longs.

v0.1.3, 5/8/2014 -- Added optional removal of orphan states and chains
	in the bake method. Added two underflow protections: row
	normalization in the forward/backward algorithms, and a better
	logsumexp function. Fixed writing and reading so that the
	original distributions work. Still need to fix the functions
	under InverseGamma. Added more documentation.

v0.1.4, 5/11/2014 -- Infinite models are now possible, by simply not adding a transition to the end state. All algorithms accept this. You can make sure your model is an infinite model by calling "Model.is_infinite()". 

Added maximum a posteriori decoding, via "Model.maximum_a_posteriori( sequence )". This returns the mode of the posterior distribution of the states. This sequence may not necessarily be a valid sequence.

Added sampling from infinite models. If your model is infinite, you can pass in length to sample to get a sample of a certain length. You can also pass "path=True" to get the path of hidden states that generated that sample.

v0.1.5 5/18/2014 -- Fixed a bug with training that was introduced in v0.1.4. Because self.f and self.b were cast as double [:,:], they allowed for quick access. They were changed to f, and b, forgetting to cast them, making access extremely slow. This was fixed by casting f and b as double [:,:].

Fixed writing and reading. Previously, reading and writing only worked on Normal, Exponential, Gamma, InverseGamma distributions. Now works on all distributions, including mixtures of arbitrary distributions.

Sped up exponential and gamma distributions a tiny bit by changing the log function to the C log function.