Given a test dataset, the centroid coordinates of hexagonal bins in 2D and high-dimensional space, predict the 2D embeddings for each data point in the test dataset.
Arguments
- test_data
The test dataset containing high-dimensional coordinates and an unique identifier.
- df_bin_centroids
Centroid coordinates of hexagonal bins in 2D space.
- df_bin
Centroid coordinates of hexagonal bins in high dimensions.
- type_NLDR
The type of non-linear dimensionality reduction (NLDR) used. Default is "UMAP".
Examples
model <- fit_high_d_model(training_data = s_curve_noise_training,
nldr_df_with_id = s_curve_noise_umap)
df_bin_centroids <- model$df_bin_centroids
df_bin <- model$df_bin
predict_2d_embeddings(test_data = s_curve_noise_test, df_bin_centroids = df_bin_centroids,
df_bin = df_bin, type_NLDR = "UMAP")
#> # A tibble: 25 × 4
#> pred_UMAP_1 pred_UMAP_2 ID pred_hb_id
#> <dbl> <dbl> <dbl> <dbl>
#> 1 -1.84 -2.44 5 9
#> 2 1.02 0.862 10 40
#> 3 1.02 0.862 13 40
#> 4 -2.32 -1.62 18 16
#> 5 -1.36 -1.62 27 17
#> 6 -1.36 -1.62 28 17
#> 7 1.02 0.862 29 40
#> 8 1.50 1.69 30 48
#> 9 -1.84 -2.44 32 9
#> 10 0.547 0.0355 36 33
#> # ℹ 15 more rows