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preprocess.go
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preprocess.go
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package main
import (
"math"
"gorgonia.org/tensor"
"gorgonia.org/tensor/native"
"gorgonia.org/vecf64"
)
func zca(data tensor.Tensor) (retVal tensor.Tensor, err error) {
var dataᵀ, data2, sigma tensor.Tensor
data2 = data.Clone().(tensor.Tensor)
if err := minusMean(data2); err != nil {
return nil, err
}
if dataᵀ, err = tensor.T(data2); err != nil {
return nil, err
}
if sigma, err = tensor.MatMul(dataᵀ, data2); err != nil {
return nil, err
}
cols := sigma.Shape()[1]
if _, err = tensor.Div(sigma, float64(cols), tensor.UseUnsafe()); err != nil {
return nil, err
}
s, u, _, err := sigma.(*tensor.Dense).SVD(true, true)
if err != nil {
return nil, err
}
var diag, uᵀ, tmp tensor.Tensor
if diag, err = s.Apply(invSqrt(0.08), tensor.UseUnsafe()); err != nil {
return nil, err
}
diag = tensor.New(tensor.AsDenseDiag(diag))
if uᵀ, err = tensor.T(u); err != nil {
return nil, err
}
if tmp, err = tensor.MatMul(u, diag); err != nil {
return nil, err
}
if tmp, err = tensor.MatMul(tmp, uᵀ); err != nil {
return nil, err
}
if err = tmp.T(); err != nil {
return nil, err
}
return tensor.MatMul(data, tmp)
}
func invSqrt(epsilon float64) func(float64) float64 {
return func(a float64) float64 {
return 1 / math.Sqrt(a+epsilon)
}
}
func minusMean(a tensor.Tensor) error {
nat, err := native.MatrixF64(a.(*tensor.Dense))
if err != nil {
return err
}
for _, row := range nat {
mean := avg(row)
vecf64.Trans(row, -mean)
// standardization
// var stdev float64
// for _, col := range row {
// stdev += (col - mean) * (col - mean)
// }
// stdev /= float64(len(row))
// stdev = math.Sqrt(stdev)
// for j := range row {
// row[j] = (row[j] - mean) / stdev
// }
}
rows, cols := a.Shape()[0], a.Shape()[1]
mean := make([]float64, cols)
for j := 0; j < cols; j++ {
var colMean float64
for i := 0; i < rows; i++ {
colMean += nat[i][j]
}
colMean /= float64(rows)
mean[j] = colMean
}
for _, row := range nat {
vecf64.Sub(row, mean)
}
return nil
}