Snippets and data from the blog of Nirpy Research.
This folder contains the original datasets used in some of the posts.
This dataset is used in the following posts:
Used in the following posts:
- Classification of NIR Spectra by Linear Discriminant Analysis in Python
- PLS Discriminant Analysis for binary classification in Python
This dataset is used in the following posts:
- Principal Component Regression in Python
- Partial Least Square Regression in Python
- A Variable Selection Method for PLS in Python
- Two Scatter Correction Techniques for NIR Spectroscopy in Python
- Exporting NIR Regression Models Built in Python
- Principal Component Regression in Python Revisited
- Principal Component Selection with a Greedy Algorithm
- Principal Component Selection with Simulated Annealing
- Minimal prediction models for linear regression
- The Akaike Information Criterion for model selection
- Parallel computation of loops for cross-validation analysis
Used in the following posts:
Used in the following posts:
Whatever piece of code that can be of general use or will not make it in the last versions of the posts will be (in time) posted here.
- bootstrap.py - Data splitter implementing the Bootstrap cross-validation method. This is not currently available in scikit-learn. This class is consistent with scikit-learn usage in a limited case. For more info on how to use this class, read the tutorial K-fold and Montecarlo cross-validation vs Bootstrap: a primer.
- Scatter Correction - Jupyter notebook associated with the post: Two Scatter Correction Techniques for NIR Spectroscopy in Python.
- Basic PLS Regression - Jupyter notebook associated with the Partial Least Square Regression in Python post.
- PCA vs Kernel PCA - Jupyter notebook with the code described in the PCA and kernel PCA explained post.
- Simulated annealing - Companion Jupyter notebook to the simulated annealing post.