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https://github.com/waikato-datamining/matrix-algorithms
Java library of 2-dimensional matrix algorithms.
https://github.com/waikato-datamining/matrix-algorithms
java matrix-calculations pca pls
Last synced: about 2 months ago
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Java library of 2-dimensional matrix algorithms.
- Host: GitHub
- URL: https://github.com/waikato-datamining/matrix-algorithms
- Owner: waikato-datamining
- License: gpl-3.0
- Created: 2018-01-02T21:23:46.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2020-01-07T00:30:55.000Z (almost 5 years ago)
- Last Synced: 2023-07-28T10:31:39.689Z (over 1 year ago)
- Topics: java, matrix-calculations, pca, pls
- Language: Java
- Homepage:
- Size: 869 KB
- Stars: 1
- Watchers: 5
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# matrix-algorithms
Java library of 2-dimensional matrix algorithms.
## Algorithms
Unsupervised:
* [Principal Component Analysis (PCA)](https://web.archive.org/web/20160630035830/http://statmaster.sdu.dk:80/courses/ST02/module05/module.pdf)
* [Generalized Least Squares Weighting (GLSW)](http://wiki.eigenvector.com/index.php?title=Advanced_Preprocessing:_Multivariate_Filtering#GLSW_Algorithm)
* [External Parameter Orthogonalization (EPO)](http://wiki.eigenvector.com/index.php?title=Advanced_Preprocessing:_Multivariate_Filtering#External_Parameter_Orthogonalization_.28EPO.29)
* [Independent Component Analysis (FastICA)](https://www.cs.helsinki.fi/u/ahyvarin/papers/bookfinal_ICA.pdf)
Supervised:
* [Partial Least Squares (PLS1)](https://web.archive.org/web/20081001154431/http://statmaster.sdu.dk:80/courses/ST02/module07/module.pdf)
* [Simple PLS (SIMPLS)](http://www.statsoft.com/textbook/partial-least-squares/#SIMPLS)
* [Kernel PLS (KernelPLS)](http://www.plantbreeding.wzw.tum.de/fileadmin/w00bdb/www/kraemer/icml_kernelpls.pdf)
* [Orthogonal Signal Correction (OPLS)](https://www.r-bloggers.com/evaluation-of-orthogonal-signal-correction-for-pls-modeling-osc-pls-and-opls/)
* [Nonlinear Iterative PLS (NIPALS)](http://www.statsoft.com/textbook/partial-least-squares/#NIPALS)
* [Sparse PLS (SparsePLS)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2810828/)
* [Y Gradient based Generalized Least Squares Weighting (YGradientGLSW)](http://wiki.eigenvector.com/index.php?title=Advanced_Preprocessing:_Multivariate_Filtering#GLSW_Algorithm)
* [Y Gradient based External Parameter Orthogonalization (YGradientEPO)](http://wiki.eigenvector.com/index.php?title=Advanced_Preprocessing:_Multivariate_Filtering#External_Parameter_Orthogonalization_.28EPO.29)
* [Canonical Correlation Analysis (CCA)](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.16)
* [Domain Invariant PLS (DIPLS)](https://pubs.acs.org/doi/10.1021/acs.analchem.8b00498)
* [Variance Constrained PLS](http://or.nsfc.gov.cn/bitstream/00001903-5/485833/1/1000013952154.pdf)
* [Orthogonal Signal Correction (OSC)](https://www.sciencedirect.com/science/article/pii/S0169743998001099)
Planned:
* [rPLS](https://www.researchgate.net/publication/259536250_Recursive_weighted_partial_least_squares_rPLS_An_efficient_variable_selection_method_using_PLS)
* [iPLS](https://www.researchgate.net/publication/247776629_Interval_Partial_Least-Squares_Regression_iPLS_A_Comparative_Chemometric_Study_with_an_Example_from_Near-Infrared_Spectroscopy)
* [PLS2](https://web.archive.org/web/20160702070233/http://statmaster.sdu.dk/courses/ST02/module08/module.pdf)
* [mwPLS]()
* [biPLS](https://www.academia.edu/14468430/Sequential_application_of_backward_interval_partial_least_squares_and_genetic_algorithms_for_the_selection_of_relevant_spectral_regions)
* ...
## Maven
Add the following dependency to your `pom.xml`:
```xml
nz.ac.waikato.cms.adams
matrix-algorithms
0.0.16
```
## Examples
### PCA
```java
import com.github.waikatodatamining.matrix.core.matrix.Matrix;
import com.github.waikatodatamining.matrix.algorithms.PCA;
import com.github.waikatodatamining.matrix.core.matrix.MatrixHelper;
public class Main {
public static void main(String[] args) {
Matrix data = MatrixHelper.read("bolts.csv", true, ',');
// remove the class column, if present
//data = MatrixHelper.deleteCol(data, data.getColumnDimension() - 1);
System.out.println("\nInput");
System.out.println(MatrixHelper.toString(data));
PCA pca = new PCA();
Matrix transformed = pca.transform(data);
System.out.println("\nTransformed");
System.out.println(MatrixHelper.toString(transformed));
}
}
```
### SIMPLS
```java
import com.github.waikatodatamining.matrix.core.matrix.Matrix;
import com.github.waikatodatamining.matrix.algorithms.pls.SIMPLS;
import com.github.waikatodatamining.matrix.core.matrix.MatrixHelper;
public class Main {
public static void main(String[] args) {
Matrix predictors = MatrixHelper.read("bolts.csv", true, ',');
Matrix response = MatrixHelper.read("bolts_response.csv", true, ',');
System.out.println("\nPredictors");
System.out.println(MatrixHelper.toString(predictors));
System.out.println("\nResponse");
System.out.println(MatrixHelper.toString(response));
SIMPLS pls = new SIMPLS();
pls.setNumComponents(3);
try {
pls.configure(predictors, response);
} catch (Exception e) {
System.out.println("\nInitialization failed:\n" + e);
return;
}
System.out.println("\nTransformed");
System.out.println(MatrixHelper.toString(pls.transform(predictors)));
System.out.println("\nPredictions");
System.out.println(MatrixHelper.toString(pls.predict(predictors)));
System.out.println("\nLoadings");
System.out.println(MatrixHelper.toString(pls.getLoadings()));
}
}
```