This repository contains Python code to perform 1-D regression with:
- Install the latest version of Python 3.X.
- Install the required packages:
pip install -r requirements.txt
pip install https://github.com/JamesRitchie/scikit-rvm/archive/master.zip
python main.py
The ground truth is the sinc function.
The variable noise_level
is set to 0.1
.
The variable training_data_range
is set to a large value (15
).
The results are shown with increasing number of training samples.
The variable num_samples
is set to 100
.
The variable training_data_range
is set to a large value (15
).
The results are shown with increasing noise level.
The variable num_samples
is set to 100
.
The variable noise_level
is set to 0.1
.
The results are shown with increasing range of training data
- Python module scikit-learn
- Documentation: Gaussian Process with scikit-learn
- Python module scikit-rvm
- Python module sklearn-rvm
- Slides about Relevance Vector Regression
- Slides about Gaussian Process Regression