Pydlm

* Add an auto noise initializer which initializes the model noise according to the scale of the time series. It is proved to improve the model performance over small scale data. To use auto initializer, simply call `dlm.noisePrior()` after constructing the model.
python
mydlm = dlm(data) + trend(1)
mydlm.noisePrior()
mydlm.fit()

* Update the default variance of the components to be 100.
* Fixed `map()` delayed evaluation bug for python3 (Thanks bdewilde!).
* Fixed bug in `continuePrediction()` for `autoReg` component. Now `predictN()` can work with `autoReg` (Thanks Usman!).
* Fixed a bug in `predictN()` which modifies the status of the dlm object. Now `dlm.predictN()` can be followed by `dlm.append()` and `dlm.fit()`. (Thanks albertotb)

* Add an example from Google data science blog and updated the homepage
* Add `dlm.plotPredictN()` which plots the prediction result from `dlm.predictN()` on top of the time series data.
* Add `dlm.predictN()` which allows prediction over multiple days.
* Change the `degree` of `trend` to match the actual meaning in polynomial, i.e, `degree=0` stands for constant and `degree=1` stands for linear trend and so on so forth.
* Add support for missing data in `modelTuner` and `.getMSE()` (Thanks sun137653577)

* Add an modelTuner class to auto-tune the discounting factors using gradient descent.
* Add model evaluation methods for geting residuals and MSE (one-day a head predicted loss).
* Bug fix: Fix the incorrect return length of the DLM results.
* Add travis Build test and coverage test (Thanks liguopku).
* Modify the tests on all Numpy matrix to pass Python3 tests.
* dynamic component now accepts Numpy Matrix as feature input (Thanks xgdgsc).
* Update the doc to be more human readable (Thanks xgdgsc).

(Special Thanks to Dr. Nick Gayeski for helping identify all these issues!)

* Add an option to let different component evolve independently (default)
* Bug fixing: change the default prior covariance for the components to match the results of BATS
* Bug fixing: deprecate the hand-written generalized inverse function and switch to numpy's built-in one.
* Add an easy specification for component prior on covariance and the model prior on observational noise (see the example and the user manual)

+ Fix bugs in latent states retrieval
+ Rewrite all the get methods (simpler and concise). Allows easy fetching individual component.
+ Add a longSeason component
+ Add more plot functionalities
+ Add the ribbon confidence interval
+ Add a simple example in documentation for using pydlm