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Build the sparse indicator matrix Z from a leaf node matrix

Source: R/leaf_node_kernel.R
leaf_node_kernel_Z.Rd

Returns the sparse \(n \times L\) indicator matrix \(Z\) such that the proximity kernel is \(K = Z Z^\top / B\). This factored representation can be passed to kernel_balance to enable the CG solver, which avoids forming the full \(n \times n\) kernel.

Usage

leaf_node_kernel_Z(leaf_matrix)

Arguments

leaf_matrix

An integer matrix of dimension \(n \times B\), where leaf_matrix[i, b] is the leaf node ID for observation i in tree b. Typically produced by get_leaf_node_matrix.

Value

A sparse dgCMatrix of dimension \(n \times L\), where \(L = \sum_b L_b\) is the total number of leaves across all trees. Each row has exactly \(B\) nonzero entries (one per tree).

Examples

# \donttest{
library(grf)
n <- 100
p <- 5
X <- matrix(rnorm(n * p), n, p)
Y <- cbind(X[, 1] + rnorm(n), X[, 2] + rnorm(n))
forest <- multi_regression_forest(X, Y, num.trees = 50)
leaf_mat <- get_leaf_node_matrix(forest)
Z <- leaf_node_kernel_Z(leaf_mat)
# }