Machine learning is a vast and exiciting discipline, garnering attention from specialists of many fields. Unfortunately, for C++ programmers and enthusiasts, there appears to be a lack of support for this magnificient language in the field of machine learning. As a consequence, this library was created in order to fill that void and give C++ a true foothold in the ML sphere to act as a crossroad between low level developers and machine learning engineers.
Please note that ML++ uses the ```std::vector<double>``` data type for emulating vectors, and the ```std::vector<std::vector<double>>``` data type for emulating matricies.
Next, call the optimizer that you would like to use. For iterative optimizers such as gradient descent, include the learning rate, epoch number, and whether or not to utilize the UI pannel.
```cpp
model.gradientDescent(0.001, 1000, 0);
```
Great, you are now ready to test! To test a singular testing instance, utilize the following function:
```cpp
model.modelTest(testSetInstance);
```
This will return the model's singular prediction for that example.
To test an entire dataset of instances, use the following function:
```cpp
model.modelSetTest(testSet);
```
The result will be the model's predictions for the entire dataset.
