kernDeepStackNet (version 2.0.2)

fourierTransPredict: Prediction based on random Fourier transformation

Description

Based on prior given weights, the estimated Fourier transformation is applied to new data.

Usage

fourierTransPredict(newx, rW)

Arguments

newx

New data design matrix.

rW

Prior drawn random weight matrix.

Value

Numeric transformed data matrix with dimension 2*Dim x n.

References

Po-Seng Huang and Li Deng and Mark Hasegawa-Johnson and Xiaodong He, (2013), Random Features for kernel deep convex network, Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

See Also

randomFourierTrans

Examples

# Generate data matrix
set.seed(50)
X <- matrix(rnorm(100*3), ncol=3)

# Apply a random Fourier transformation of higher dimension
rft <- randomFourierTrans(X=X, Dim=3, sigma=1, seedW=0)

# New data matrix
set.seed(100)
Xnew <- matrix(rnorm(100*3), ncol=3)

# Apply same Fourier transformation on new data
newZ <- fourierTransPredict(newx=Xnew, rW=rft$rW)
head(newZ)