Add Gaussian Process Regression#235
Add Gaussian Process Regression#235AtharvaPatange wants to merge 9 commits intoTheAlgorithms:masterfrom
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Pull Request Overview
Adds a Gaussian Process (GP) regression example using kernlab, alongside new k-NN, CNN, and Kadane’s algorithm scripts. Key changes:
- Introduces a GP regression script with rbfdot kernel and plotting.
- Adds a from-scratch k-NN implementation with classification/regression and examples.
- Adds Kadane’s algorithm and a simple CNN architecture example.
Reviewed Changes
Copilot reviewed 4 out of 4 changed files in this pull request and generated 8 comments.
| File | Description |
|---|---|
| machine_learning/guassian_process.r | New GP regression script using kernlab with example and plotting |
| machine_learning/k-NN.r | New k-NN implementation (classification/regression) with normalization and examples |
| machine_learning/cnn.r | CNN architecture definition using keras; prints model summary |
| dynamic_programming/kadane's_algo.r | Kadane’s algorithm with examples and a circular variant |
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
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Please resolve conflicts |
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| # Kadane's Algorithm in R | |||
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This algorithm is already implemented
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| # ============================================== | |||
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Filename contains typo: 'guassian_process.r' should be 'gaussian_process.r' (missing 'i' in Gaussian).
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| # ============================================== | |||
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File extension must be lowercase '.r'. The filename 'guassian_process.r' uses lowercase extension, which is correct, but the filename itself contains a spelling error ('guassian' instead of 'gaussian'). Please rename to 'gaussian_process.r'.
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This PR is stale because it has been open 30 days with no activity. Remove stale label or comment or this will be closed in 7 days. |
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This PR was closed because it has been stalled for 7 days with no activity. |
Overview
This implementation defines a Gaussian Process (GP) regression model in R using the kernlab package. GP is a non-parametric Bayesian regression algorithm that predicts continuous outputs while providing uncertainty estimates. It models a prior over functions via a kernel and updates the posterior distribution using observed data.
Features
Performs regression with uncertainty quantification for predictions.
Supports small datasets effectively and works well with sparse data.
Uses a kernel function (e.g., RBF) to define similarity between data points.
Provides mean and variance (confidence) for each prediction.
Handles non-linear relationships automatically through the kernel.
Demonstrates predictions on synthetic or real regression datasets.
Plotting functionality included for visualizing predictions against observations.
Complexity
Time Complexity: O(n³) due to kernel matrix inversion
Space Complexity: O(n²) for storing the kernel matrix
Demonstration
The included R script trains a GP model on a synthetic sine-wave dataset with noise.
Predictions are made on test inputs, including plotting mean predictions.
Replace x and y with other datasets for custom regression tasks.
For large datasets, consider sparse GP approximations.