# How to Add New Features or Results
Features, Results and Autoencoders all implement the `ParameterizedInterface`, making it very simple to add new types. 

## Adding a New Feature

### 1. Define a New Feature Class
To add a new feature, first implement your new feature as a new class in `python/src/backend/features.py` that implements `Feature`. When writing this class, be sure to implement the necessary abstract methods declared in `Feature`. These methods include: 
* `__init__(self)`: Initialize your Feature with any instance variable settings required. (Make sure to call `super().__init__()` as well)
* `process(self, data)`: Compute the Feature given the `data` argument in the form of a 2d matrix of filtered raw data, where the first dimension is time and the second dimension is channels. The length of `data` in the first dimension is defined in `globals.raw_settings.buffer_size`.

### 2. Create a New Feature Type
In `python/src/common/enums.py`, add a new enum name for your new Feature to the `FeatureType` Enum. The name of this new type should be short and sweet as it will be used in the code, and the value should be readable since it will be displayed to the user. 

### 4. Update your new class
At the top of your new class, define the class attribute `_type` as the new `FeatureType` enum name that you created. 

Now you are done! If your new Feature class is implemented correctly, it will show up in the GUI and CLI, and the user will be use it to compute features from training and testing.

*Remember to update the class attribute `excluded_params` on your new Feature class to declare the names of any instance variables in your class that shouldn't be saved or displayed to the user. See https://github.com/DamonGeorge/Parameterized for more information.


## Adding a New Result
Following the same steps described above for adding a new Feature, except this time add a new class in `python/src/backend/results.py` that implements the `Result` class and add a new Enum type to the `ResultType` enum in `python/src/common/enums.py`. Your new Result class should define the `good_threshold` class attribute which defines the a threshold on your result that defines a 'Good' result. Your new class should also define the following methods:
* `__init__(self)`: Initialize your Result with any instance variable settings required. (Make sure to call `super().__init__()` as well)
* `calc(self, data, timestep)`: Compute the Result at the given `timestep` of the `data` object, which is of the type `Data`, containing the decoder output in `data.x_hat` and the desired decoder output in `data.x_d`. This should return the result. 
* `compare(cls, res1, res2=None)`: This `@staticmethod` function compares two results of your type to see which is better. 
* `def print(cls, printer=print, data=None)`: This `@classmethod` function is optional and defines how to print your result to the user in the console.