Documentation
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Overview ¶
Package decomposition provides dimensionality reduction algorithms.
Index ¶
- type PCA
- func (pca *PCA) Components() [][]float64
- func (pca *PCA) ExplainedVariance() []float64
- func (pca *PCA) ExplainedVarianceRatio() []float64
- func (pca *PCA) Fit(X *dataframe.DataFrame) error
- func (pca *PCA) FitTransform(X *dataframe.DataFrame) (*dataframe.DataFrame, error)
- func (pca *PCA) Transform(X *dataframe.DataFrame) (*dataframe.DataFrame, error)
Constants ¶
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Variables ¶
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Functions ¶
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Types ¶
type PCA ¶
type PCA struct {
// NComponents is the number of principal components to keep
NComponents int
// contains filtered or unexported fields
}
PCA implements Principal Component Analysis for dimensionality reduction. PCA finds orthogonal directions of maximum variance in the data.
func (*PCA) Components ¶
Components returns the principal components (eigenvectors).
func (*PCA) ExplainedVariance ¶
ExplainedVariance returns the variance explained by each component.
func (*PCA) ExplainedVarianceRatio ¶
ExplainedVarianceRatio returns the proportion of variance explained by each component.
func (*PCA) FitTransform ¶
FitTransform fits the model and transforms the data in one step.
Source Files
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