diff --git a/srcs/utils.py b/srcs/utils.py
index 376b949..33d07bf 100644
--- a/srcs/utils.py
+++ b/srcs/utils.py
@@ -155,32 +155,6 @@ def remove_corr_organisms_from_ref(sig_info_dict: Dict[str, Tuple[str, float, in
     manifest_df = pd.DataFrame(manifest_df, columns=['organism_name', 'md5sum', 'num_unique_kmers_in_genome_sketch', 'num_total_kmers_in_genome_sketch', 'genome_scale_factor'])
     
     return rep_remove_dict, manifest_df
-    
-# def compute_sample_vector(sample_hashes, hash_to_idx):
-#     """
-#     Helper function that computes the sample vector for a given sample signature.
-#     :param sample_hashes: hashes in the sample signature
-#     :param hash_to_idx: dictionary mapping hashes to indices in the training dictionary
-#     :return: numpy array (sample vector)
-#     """
-#     # total number of hashes in the training dictionary
-#     hash_to_idx_keys = set(hash_to_idx.keys())
-    
-#     # total number of hashes in the sample
-#     sample_hashes_keys = set(sample_hashes.keys())
-    
-#     # initialize the sample vector
-#     sample_vector = np.zeros(len(hash_to_idx_keys))
-    
-#     # get the hashes that are in both the sample and the training dictionary
-#     sample_intersect_training_hashes = hash_to_idx_keys.intersection(sample_hashes_keys)
-    
-#     # fill in the sample vector
-#     for sh in tqdm(sample_intersect_training_hashes):
-#         sample_vector[hash_to_idx[sh]] = sample_hashes[sh]
-
-#     return sample_vector
-
 
 class Prediction:
     """
diff --git a/tests/unittests.py b/tests/unittests.py
index 38e1809..10ffc96 100644
--- a/tests/unittests.py
+++ b/tests/unittests.py
@@ -1,4 +1,5 @@
 import json
+import math
 import pytest
 import pandas as pd
 import os
@@ -69,14 +70,14 @@ def test_get_cami_profile():
     prediction1 = profile[0]
     assert prediction1.rank == 'superkingdom'
     assert prediction1.taxid == '2157'
-    assert prediction1.percentage == 0.029528
+    assert math.isclose(prediction1.percentage, 0.029528, abs_tol=1e-6)
     assert prediction1.taxpath == '2157'
     assert prediction1.taxpathsn == 'Archaea'
 
     prediction2 = profile[1]
     assert prediction2.rank == 'superkingdom'
     assert prediction2.taxid == '2'
-    assert prediction2.percentage == 29.183763
+    assert math.isclose(prediction2.percentage, 29.183763, rel_tol=1e-6)
     assert prediction2.taxpath == '2'
     assert prediction2.taxpathsn == 'Bacteria'
     
@@ -87,7 +88,7 @@ def test_get_alt_mut_rate():
     significance = 0.99
     result = get_alt_mut_rate(nu, thresh, ksize, significance)
     expected_result = 0.047902071844405425
-    assert result == expected_result
+    assert math.isclose(result, expected_result, rel_tol=1e-6, abs_tol=1e-6)
     
 def test_get_alt_mut_rate_zero_nu():
     nu = 0