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文件名: pca-master.zip |
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资料下载链接地址: https://bbs.pinggu.org/a-2057110.html
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- ml-pca
- NPM versionbuild statusDavid depsnpm download
- Principal component analysis (PCA)
- Installation
- $ npm install ml-pca
- Methods
- new PCA(dataset, options)
- Arguments
- dataset - Data to get the PCA. It must be a two-dimensional array with observations as rows and variables as columns.
- Options
- center - Center the dataset (default: true)
- scale - Standardize the dataset, i.e. divide by the standard deviation after centering (default: false)
- predict(dataset)
- Project the dataset in the PCA space
- Arguments
- dataset - A Matrix of the dataset to project.
- getExplainedVariance()
- Returns the percentage of variance explained by each component.
- getCumulativeVariance()
- Returns the cumulative explained variance.
- getStandardDeviations()
- Returns the standard deviations of each component.
- getEigenvectors()
- Get the eigenvectors of the covariance matrix.
- getEigenvalues()
- Get the eigenvalues on the diagonal.
- getLoadings()
- Get the loadings matrix (each row is a component and each column is a variable)
- License
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