Conifer

Bug fixes:
- Fix faulty include on fixed-point emulation for VHDL backend

New features:
- Support for [TensorFlow Decision Forests](https://www.tensorflow.org/decision_forests)
- 'Unrolled' Xilinx HLS optimization for much faster C Synthesis time, enabled by default with `Unroll` configuration parameter (see performance plots on [the PR](https://github.com/thesps/conifer/pull/41))
- Synthesis report reading for HLS and VHDL backends: `conifer_model.read_report()` for models of those backends
- `new_config` parameter of `conifer.model.load_model` to override a saved model's configuration (e.g. to change backend or precision)
- Simulator discovery for VHDL backend (use whichever is installed)
- Model metadata saved with model JSON export for provenance tracking - conifer version, model conversion time
- Documentation webpages at https://ssummers.web.cern.ch/conifer/
- Significantly overhauled internal representation

Bug fixes:
- Fix to `sklearn` converter for newer `sklearn` versions

New features:
- Support for [TensorFlow Decision Forests](https://www.tensorflow.org/decision_forests)
- 'Unrolled' Xilinx HLS optimization for much faster C Synthesis time, enabled by default with `Unroll` configuration parameter (see performance plots on [the PR](https://github.com/thesps/conifer/pull/41))
- Synthesis report reading for HLS and VHDL backends: `conifer_model.read_report()` for models of those backends
- `new_config` parameter of `conifer.model.load_model` to override a saved model's configuration (e.g. to change backend or precision)
- Simulator discovery for VHDL backend (use whichever is installed)
- Model metadata saved with model JSON export for provenance tracking - conifer version, model conversion time
- Documentation webpages at https://ssummers.web.cern.ch/conifer/
- Significantly overhauled internal representation

Bug fixes:
- Fix to `sklearn` converter for newer `sklearn` versions

New features:
- Model save/load functionality. `model.save()` to export a JSON file, `conifer.model.load_model(‘my_prj.json’)` to load a saved model. The JSON file can also be used for C++ evaluation.
- Better agreement of output predictions between VHDL backend and others using new `FixedPointConverter` module
- `model.build` returns success status

Bug fixes:
- Fix crash when writing project to existing directory

New features:
- ONNX converter
- Different data types for inputs/thresholds and scores
- GHDL simulation support for VHDL backend
- C++ backend for emulation
- logging messages

Bug fixes:
- Support named features in xgboost

What's new:
- Make VHDL backend code compatible with Vivado xsim (remove VHDL 2008 usage)
- Add xsim option for VHDL backend. The simulator can be set with `conifer.backends.vhdl.simulator = conifer.backends.vhdl.Simulators.{xsim, modelsim}`