Tomotopy

* An issue was fixed where `tomotopy.utils.Corpus.extract_ngrams` craches with empty input.
* An issue was fixed where `tomotopy.LDAModel.infer` raises exception with valid input.
* An issue was fixed where `tomotopy.HLDAModel.infer` generates wrong `tomotopy.Document.path`.
* Since a new parameter `freeze_topics` for `tomotopy.HLDAModel.train` was added, you can control whether to create a new topic or not when training.

* The interface of `tomotopy.utils.Corpus` and of `tomotopy.LDAModel.docs` were unified. Now you can access the document in corpus with the same manner.
* __getitem__ of `tomotopy.utils.Corpus` was improved. Not only indexing by int, but also by Iterable[int], slicing are supported. Also indexing by uid is supported.
* New methods `tomotopy.utils.Corpus.extract_ngrams` and `tomotopy.utils.Corpus.concat_ngrams` were added. They extracts n-gram collocations using PMI and concatenates them into a single words.
* A new method `tomotopy.LDAModel.add_corpus` was added, and `tomotopy.LDAModel.infer` can receive corpus as input.
* A new module `tomotopy.coherence` was added. It provides the way to calculate coherence of the model.
* A paramter `window_size` was added to `tomotopy.label.FoRelevance`.
* An issue was fixed where NaN often occurs when training `tomotopy.HDPModel`.
* Now Python3.9 is supported.
* A dependency to py-cpuinfo was removed and the initializing of the module was improved.

* Memory leaks of version 0.9.0 was fixed.
* `tomotopy.CTModel.summary()` was fixed.

* The `tomotopy.LDAModel.summary()` method, which prints human-readable summary of the model, has been added.
* The random number generator of package has been replaced with [EigenRand](https://github.com/bab2min/EigenRand). It speeds up the random number generation and solves the result difference between platforms.
* Due to above, even if `seed` is the same, the model training result may be different from the version before 0.9.0.
* Fixed a training error in `tomotopy.HDPModel`.
* `tomotopy.DMRModel.alpha` now shows Dirichlet prior of per-document topic distribution by metadata.
* `tomotopy.DTModel.get_count_by_topics()` has been modified to return a 2-dimensional `ndarray`.
* `tomotopy.DTModel.alpha` has been modified to return the same value as `tomotopy.DTModel.get_alpha()`.
* Fixed an issue where the `metadata` value could not be obtained for the document of `tomotopy.GDMRModel`.
* `tomotopy.HLDAModel.alpha` now shows Dirichlet prior of per-document depth distribution.
* `tomotopy.LDAModel.global_step` has been added.
* `tomotopy.MGLDAModel.get_count_by_topics()` now returns the word count for both global and local topics.
* `tomotopy.PAModel.alpha`, `tomotopy.PAModel.subalpha`, and `tomotopy.PAModel.get_count_by_super_topic()` have been added.

* New properties `tomotopy.DTModel.num_timepoints` and `tomotopy.DTModel.num_docs_by_timepoint` have been added.
* A bug which causes different results with the different platform even if `seeds` were the same was partially fixed.
As a result of this fix, now `tomotopy` in 32 bit yields different training results from earlier version.

* A bug where `tomotopy.LDAModel.used_vocabs` returned an incorrect value was fixed.
* Now `tomotopy.CTModel.prior_cov` returns a covariance matrix with shape `[k, k]`.
* Now `tomotopy.CTModel.get_correlations` with empty arguments returns a correlation matrix with shape `[k, k]`.