# predict_emb() works

    Code
      predict_emb(test_data = s_curve_noise_training, df_bin_centroids = df_bin_centroids,
        df_bin = df_bin, type_NLDR = "UMAP")
    Output
      $pred_UMAP_1
       [1] 0.1732051 0.6928203 0.8660254 0.1732051 0.1732051 0.8660254 0.6928203
       [8] 0.6928203 0.6928203 0.8660254 0.6928203 0.1732051 1.0392305 0.0000000
      [15] 0.6928203 0.6928203 0.1732051 0.6928203 0.5196152 0.1732051 0.6928203
      [22] 0.1732051 0.0000000 0.8660254 0.8660254 0.3464102 0.5196152 0.6928203
      [29] 0.3464102 0.6928203 0.8660254 0.8660254 0.6928203 0.1732051 0.8660254
      [36] 0.1732051 0.6928203 0.8660254 0.8660254 0.0000000 0.5196152 0.8660254
      [43] 0.3464102 0.8660254 0.8660254 0.6928203 0.1732051 0.0000000 0.8660254
      [50] 0.0000000 0.6928203 0.6928203 0.8660254 0.3464102 0.6928203 0.8660254
      [57] 0.8660254 1.0392305 1.0392305 0.5196152 0.8660254 1.0392305 0.5196152
      [64] 0.8660254 0.3464102 0.3464102 1.0392305 0.8660254 0.6928203 1.0392305
      [71] 0.8660254 0.5196152 0.1732051 0.3464102 1.0392305
      
      $pred_UMAP_2
       [1]  0.45  0.75  1.05 -0.15  0.45  1.65  0.75  0.75  0.75  1.65  0.75  0.45
      [13]  1.35  0.15  0.75  1.35  0.45  1.35  0.45 -0.15  0.75 -0.15  0.15  1.65
      [25]  1.05  0.15  0.45  0.75  0.15  0.75  1.05  1.65  0.75  0.45  1.65 -0.15
      [37]  1.35  1.65  1.65  0.15  0.45  1.05  0.15  1.05  1.05  1.35 -0.15  0.15
      [49]  1.65  0.15  0.75  0.75  1.65  0.15  1.35  1.65  1.05  1.35  1.35  0.45
      [61]  1.65  1.35  0.45  1.65  0.15  0.15  1.35  1.05  1.35  1.35  1.65  0.45
      [73] -0.15  0.15  1.35
      
      $ID
       [1]   1   2   3   4   6   7   8   9  11  12  14  15  16  17  19  20  21  22  23
      [20]  24  25  26  31  33  34  35  37  38  39  40  41  42  43  44  45  46  47  51
      [39]  52  54  55  56  57  59  60  62  63  64  65  66  67  69  70  71  72  73  74
      [58]  75  76  77  78  79  80  81  84  87  89  91  93  94  95  96  97  99 100
      
      $pred_hb_id
       [1] 12 18 24  2 12 34 18 18 18 34 18 12 29  6 18 28 12 28 13  2 18  2  6 34 24
      [26]  7 13 18  7 18 24 34 18 12 34  2 28 34 34  6 13 24  7 24 24 28  2  6 34  6
      [51] 18 18 34  7 28 34 24 29 29 13 34 29 13 34  7  7 29 24 28 29 34 13  2  7 29
      

---

    Code
      predict_emb(test_data = s_curve_noise_test, df_bin_centroids = df_bin_centroids,
        df_bin = df_bin, type_NLDR = "UMAP")
    Output
      $pred_UMAP_1
       [1] 0.1732051 0.8660254 0.8660254 0.3464102 0.3464102 0.3464102 0.8660254
       [8] 0.8660254 0.1732051 0.5196152 0.3464102 0.5196152 0.6928203 1.0392305
      [15] 0.5196152 0.5196152 0.0000000 0.6928203 0.1732051 0.3464102 0.6928203
      [22] 0.8660254 0.1732051 0.8660254 0.6928203
      
      $pred_UMAP_2
       [1] -0.15  1.05  1.65  0.15  0.15  0.15  1.05  1.65 -0.15  0.45  0.15  0.45
      [13]  0.75  1.35  0.45  0.45  0.15  1.35  0.45  0.15  0.75  1.05  0.45  1.65
      [25]  1.35
      
      $ID
       [1]  5 10 13 18 27 28 29 30 32 36 48 49 50 53 58 61 68 82 83 85 86 88 90 92 98
      
      $pred_hb_id
       [1]  2 24 34  7  7  7 24 34  2 13  7 13 18 29 13 13  6 28 12  7 18 24 12 34 28
      

# gen_summary() works

    Code
      gen_summary(test_data = s_curve_noise_training, prediction_df = pred_df_test,
        df_bin = df_bin, col_start = "x")
    Output
      $mse
      [1] 0.3229922
      
      $aic
      [1] -453.3167
      

---

    Code
      gen_summary(test_data = s_curve_noise_test, prediction_df = pred_df_test_n,
        df_bin = df_bin, col_start = "x")
    Output
      $mse
      [1] 0.3458999
      
      $aic
      [1] -45.78101