This repo includes implementation of problems 4(a) and 4(b) defined here. Both problems are modelled as a Gaussian Process. The training input and output data is used fit a mapping from the input to the output data which is then used to predict the output mean and standard deviation values for the test input data.
To run the code in this repo the following dependencies are required:
- Scikit-learn
- numpy
- matplotlib
To run the code for problem 4(a) type in terminal:
python problem4a_sol.py
You can also use the flag --kernel=rbf for squared exponential kernel.
To run the code for problem 4(a) type in terminal:
python problem4b_sol.py
The results and implementation details for the two problems can be found in this document.