Updates on algorithm and test cases.

adityaxnair · Sep 5, 2019 · a7db077 · a7db077
1 parent a7b259a
commit a7db077
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Showing 2 changed files with 2 additions and 2 deletions.
diff --git a/R/liger.R b/R/liger.R
@@ -4516,7 +4516,7 @@ imputeKNN <- function(object, reference, queries = NULL, knn_k = 50, weight = FA
  for (query in queries) {
  query_cells = rownames(object@scale.data[[query]])
  #find nearest neighbors for query cell in normed ref datasets
- nn.k = get.knnx(object@H.norm[reference_cells,],object@H.norm[query_cells,],k=knn_k)
+ nn.k = get.knnx(object@H.norm[reference_cells,],object@H.norm[query_cells,], k=knn_k, algorithm='CR')
  imputed_vals = sapply(1:nrow(nn.k$nn.index),function(n){ # for each cell in the target dataset:
  weights = nn.k$nn.dist[n,]
  weights = as.matrix(exp(-weights)/sum(exp(-weights)))

diff --git a/tests/testthat/test_post_factorization.R b/tests/testthat/test_post_factorization.R
@@ -238,7 +238,7 @@ test_that("List names and dimensions correct", {
 })
 
 test_that("Imputation results correct", {
- expect_equivalent(imputation[['tenx']][1, 1:5], c(0.100934163, 0.101276286, 0.2445604407, 0.1012499851, 0.1010526404),
+ expect_equivalent(imputation[['tenx']][1, 1:5], c(0.1010272982, 0.1480327620, 0.2882094083, 0.1549154381, 0.1010290263),
  tolerance = 1e-8)
 })