Documentation ¶
Index ¶
- func ExplainedVarianceGraph(root *op.Scope, phs tf.Output, numSamples int) tf.Output
- func SvdFlipU(scope *op.Scope, signProdMat, u tf.Output) tf.Output
- func SvdFlipV(scope *op.Scope, signProdMat, v tf.Output) tf.Output
- func SvdGraph(root *op.Scope, x tf.Output) (tf.Output, tf.Output, tf.Output, tf.Output)
- func SvdSignU(scope *op.Scope, u tf.Output) tf.Output
- func TransGraph(root *op.Scope, x, mean, v tf.Output, comps int) tf.Output
- type PCA
- func (p *PCA) Accumulated() (*mat.VecDense, error)
- func (p *PCA) ExplainedVariance() (*mat.VecDense, error)
- func (p *PCA) ExplainedVarianceRatio() (*mat.VecDense, error)
- func (p *PCA) Fit(dense *mat.Dense) (*PCA, error)
- func (p *PCA) FitTransform(dense *mat.Dense) (*mat.Dense, error)
- func (p *PCA) NumComponents() (int, error)
- func (p *PCA) Transform(dense *mat.Dense) (*mat.Dense, error)
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
func ExplainedVarianceGraph ¶
Types ¶
type PCA ¶
type PCA struct { Mean float64 S []float64 U [][]float64 V [][]float64 // contains filtered or unexported fields }
PCA calc the PCA...
func (*PCA) ExplainedVariance ¶
ExplainedVariance is the amount of variance explained by each of the selected components.
func (*PCA) ExplainedVarianceRatio ¶
ExplainedVarianceRatio is the percentage of variance explained by each of the selected components. If “n_components“ is not set then all components are stored and the sum of explained variances is equal to 1.0.
func (*PCA) FitTransform ¶
FitTransform approximate the pca like in fit and calculate the transformed matrix.
func (*PCA) NumComponents ¶
NumComponents return the number of resulting components.
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