Predict Conditional Mean Embedding Weights at New Points

Returns the row weights \(\alpha(x^*) = k(x^*, X_{\mathrm{train}}) W\) from a fitted fit_cme() object. Each row of the result is the weight vector for combining training-y quantities (kernel values or values themselves) to produce a CME prediction at x_new.

Usage

# S3 method for class 'cme_fit'
predict(object, x_new, ...)

Arguments

object

A cme_fit object.

x_new

Numeric matrix of new conditioning points.

...

Currently ignored.

Value

An n_new x n_train matrix of embedding weights.

Details

For a typical "predict Y at new X" workflow use kernel_downscale(), which combines this with the training Y matrix to return predictions directly.

Examples

set.seed(1L)
x <- matrix(rnorm(60L), ncol = 2L)
y <- matrix(x[, 1L] + rnorm(30L, sd = 0.2), ncol = 1L)
fit <- fit_cme(x, y, lambda = 1e-2)
x_new <- matrix(rnorm(6L), ncol = 2L)
w <- predict(fit, x_new)
dim(w)  # 3 new points x 30 training points
#> [1]  3 30