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PCA fit method
This commit is contained in:
Aleksei Kiselev
parent
99626ce479
commit
8e81a4b6d2
33
pca/pca.go
33
pca/pca.go
@ -8,6 +8,7 @@ import (
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type PCA struct {
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Num_components int
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svd *mat64.SVD
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}
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// Number of components. 0 - by default, use number of features as number of components
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@ -15,18 +16,21 @@ func NewPCA(num_components int) *PCA {
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return &PCA{Num_components: num_components}
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}
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//Need return is base.FixedDataGrid
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func (pca *PCA) Transform(X *mat64.Dense) *mat64.Dense {
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//Prepare before PCA
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// Fit PCA model and transform data
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// Need return is base.FixedDataGrid
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func (pca *PCA) FitTransform(X *mat64.Dense) *mat64.Dense {
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return pca.Fit(X).Transform(X)
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}
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num_samples, num_features := X.Dims()
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//Mean to input data
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// Fit PCA model
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func (pca *PCA) Fit(X *mat64.Dense) *PCA {
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// Mean to input data
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M := mean(X)
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X = matrixSubVector(X, M)
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//Get SVD decomposition from data
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var svd mat64.SVD
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ok := svd.Factorize(X, matrix.SVDThin)
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// Get SVD decomposition from data
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pca.svd = &mat64.SVD{}
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ok := pca.svd.Factorize(X, matrix.SVDThin)
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if !ok {
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panic("Unable to factorize")
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}
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@ -34,8 +38,19 @@ func (pca *PCA) Transform(X *mat64.Dense) *mat64.Dense {
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panic("Number of components can't be less than zero")
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}
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return pca
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}
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// Need return is base.FixedDataGrid
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func (pca *PCA) Transform(X *mat64.Dense) *mat64.Dense {
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if pca.svd == nil {
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panic("You should to fit PCA model first")
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}
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num_samples, num_features := X.Dims()
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vTemp := new(mat64.Dense)
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vTemp.VFromSVD(&svd)
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vTemp.VFromSVD(pca.svd)
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//Compute to full data
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if pca.Num_components == 0 || pca.Num_components > num_features {
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return compute(X, vTemp)
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@ -1,62 +1,79 @@
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package pca
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import (
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. "github.com/smartystreets/goconvey/convey"
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"testing"
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"github.com/gonum/matrix/mat64"
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"github.com/gonum/matrix/mat64"
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. "github.com/smartystreets/goconvey/convey"
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)
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func TestPCAWithZeroComponents(t *testing.T){
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Convey("Set to pca 0 components with first matrix", t, func(){
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X1 := mat64.NewDense(3,7, []float64{6,5,4,3,8,2,9,5,1,10,2,3,8,7,5,14,2,3,6,3,2})
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func TestPCAWithZeroComponents(t *testing.T) {
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Convey("Set to pca 0 components with first matrix", t, func() {
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X1 := mat64.NewDense(3, 7, []float64{6, 5, 4, 3, 8, 2, 9, 5, 1, 10, 2, 3, 8, 7, 5, 14, 2, 3, 6, 3, 2})
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pca := NewPCA(0)
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rows, cols := pca.Transform(X1).Dims()
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rows, cols := pca.FitTransform(X1).Dims()
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So(rows, ShouldEqual, 3)
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So(cols, ShouldEqual, 3)
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})
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Convey("Set to pca 0 components with second matrix", t, func(){
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X1 := mat64.NewDense(10,5, []float64{
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0.52984892, 0.1141001 , 0.91599294, 0.9574267 , 0.15361222,
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0.07057588, 0.46371013, 0.73091854, 0.84641034, 0.08122213,
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0.96221946, 0.60367214, 0.69851546, 0.91965564, 0.27040597,
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0.03152856, 0.97912403, 0.39487038, 0.12232594, 0.18474705,
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0.77061953, 0.35898551, 0.78684562, 0.11638404, 0.88908044,
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0.35828086, 0.47214831, 0.95781755, 0.74762736, 0.59850757,
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0.07806127, 0.96940955, 0.15751804, 0.00973325, 0.85041635,
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0.02663938, 0.49755131, 0.57984119, 0.12233871, 0.47967853,
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0.63903222, 0.88556565, 0.79797963, 0.13345186, 0.37415535,
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0.60605207, 0.52067165, 0.91217494, 0.57148943, 0.92210331})
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Convey("Set to pca 0 components with second matrix", t, func() {
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X1 := mat64.NewDense(10, 5, []float64{
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0.52984892, 0.1141001, 0.91599294, 0.9574267, 0.15361222,
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0.07057588, 0.46371013, 0.73091854, 0.84641034, 0.08122213,
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0.96221946, 0.60367214, 0.69851546, 0.91965564, 0.27040597,
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0.03152856, 0.97912403, 0.39487038, 0.12232594, 0.18474705,
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0.77061953, 0.35898551, 0.78684562, 0.11638404, 0.88908044,
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0.35828086, 0.47214831, 0.95781755, 0.74762736, 0.59850757,
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0.07806127, 0.96940955, 0.15751804, 0.00973325, 0.85041635,
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0.02663938, 0.49755131, 0.57984119, 0.12233871, 0.47967853,
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0.63903222, 0.88556565, 0.79797963, 0.13345186, 0.37415535,
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0.60605207, 0.52067165, 0.91217494, 0.57148943, 0.92210331})
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pca := NewPCA(0)
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rows, cols := pca.Transform(X1).Dims()
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rows, cols := pca.FitTransform(X1).Dims()
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So(rows, ShouldEqual, 10)
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So(cols, ShouldEqual, 5)
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})
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}
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func TestPCAWithNComponents(t *testing.T){
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Convey("Set to pca 3 components with 5x5 matrix", t, func(){
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X := mat64.NewDense(5,5, [] float64{
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0.23030838, 0.05669317, 0.3187813 , 0.34455114, 0.98062806,
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0.38995469, 0.2996771 , 0.99043575, 0.04443827, 0.99527955,
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0.27266308, 0.14068906, 0.46999473, 0.03296131, 0.90855405,
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0.28360708, 0.8839966 , 0.81107014, 0.52673877, 0.59432817,
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0.64107253, 0.56165215, 0.79811756, 0.48845398, 0.20506649})
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func TestPCAWithNComponents(t *testing.T) {
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Convey("Set to pca 3 components with 5x5 matrix", t, func() {
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X := mat64.NewDense(5, 5, []float64{
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0.23030838, 0.05669317, 0.3187813, 0.34455114, 0.98062806,
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0.38995469, 0.2996771, 0.99043575, 0.04443827, 0.99527955,
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0.27266308, 0.14068906, 0.46999473, 0.03296131, 0.90855405,
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0.28360708, 0.8839966, 0.81107014, 0.52673877, 0.59432817,
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0.64107253, 0.56165215, 0.79811756, 0.48845398, 0.20506649})
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pca := NewPCA(3)
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rows, cols := pca.Transform(X).Dims()
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rows, cols := pca.FitTransform(X).Dims()
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So(rows, ShouldEqual, 5)
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So(cols, ShouldEqual, 3)
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})
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Convey("Set to pca 2 components with 3x5 matrix",t, func(){
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X := mat64.NewDense(3,5, [] float64{
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0.12294845, 0.55170713, 0.67572832, 0.60615516, 0.38184551,
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0.93486821, 0.15120374, 0.89760169, 0.74715672, 0.81373931,
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0.42821569, 0.47457753, 0.18960954, 0.42466159, 0.34166049})
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Convey("Set to pca 2 components with 3x5 matrix", t, func() {
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X := mat64.NewDense(3, 5, []float64{
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0.12294845, 0.55170713, 0.67572832, 0.60615516, 0.38184551,
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0.93486821, 0.15120374, 0.89760169, 0.74715672, 0.81373931,
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0.42821569, 0.47457753, 0.18960954, 0.42466159, 0.34166049})
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pca := NewPCA(2)
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rows, cols := pca.Transform(X).Dims()
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So(rows, ShouldEqual,3)
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So(cols, ShouldEqual,2)
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rows, cols := pca.FitTransform(X).Dims()
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So(rows, ShouldEqual, 3)
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So(cols, ShouldEqual, 2)
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})
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}
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}
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func TestPCAFitAndTransformSeparately(t *testing.T) {
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Convey("Set to pca 3 components with 5x5 matrix", t, func() {
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X := mat64.NewDense(5, 5, []float64{
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0.23030838, 0.05669317, 0.3187813, 0.34455114, 0.98062806,
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0.38995469, 0.2996771, 0.99043575, 0.04443827, 0.99527955,
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0.27266308, 0.14068906, 0.46999473, 0.03296131, 0.90855405,
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0.28360708, 0.8839966, 0.81107014, 0.52673877, 0.59432817,
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0.64107253, 0.56165215, 0.79811756, 0.48845398, 0.20506649})
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pca := NewPCA(3)
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pca.Fit(X)
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rows, cols := pca.Transform(X).Dims()
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So(rows, ShouldEqual, 5)
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So(cols, ShouldEqual, 3)
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})
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}
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