docs(frontend): reviews

frontends/concrete-python/examples/xor_distance/hamming_distance_to_clear.py

@@ -8,7 +8,7 @@
 
 # Hamming weight computation
 def hw(x):
-    # Hamming Weight table for 8b entries
+    """Return the Hamming Weight table for 8b entries."""
     hw_table_ref = [np.binary_repr(x).count("1") for x in range(2**8)]
 
     # fmt: off
@@ -30,17 +30,17 @@ def hw(x):
 
 
 def mapme(x):
-    # Map 0 to -1, and keep 1 as 1
+    """Map 0 to -1, and keep 1 as 1."""
     return 2 * x - 1
 
 
-# Reference function for tests
 def dist_in_clear(x, y):
+    """Compute the distance in the clear."""
     return np.sum(hw(x ^ y))
 
 
-# In FHE
 def dist_in_fhe(x_mapped, y_mapped):
+    """Compute the distance in FHE."""
 
     # x is a line tensor, whose 0's have been replaced by -1
     # y_clear is a column tensor, whose 0's have been replaced by -1
@@ -59,8 +59,8 @@ def dist_in_fhe(x_mapped, y_mapped):
     return final_result
 
 
-# Manage user args
 def manage_args():
+    """Manage user args."""
     parser = argparse.ArgumentParser(
         description="Hamming weight (aka XOR) distance in Concrete, between an encrypted vector and a clear vector."
     )
@@ -91,6 +91,7 @@ def manage_args():
 
 
 def main():
+    """Main function."""
     print()
 
     # Options by the user
@@ -125,8 +126,10 @@ def main():
 
     total_time = 0
 
+    nb_samples_for_warmup = 10
+
     # Then use
-    for _i in range(args.repeat):
+    for i in range(nb_samples_for_warmup + args.repeat):
         # Take a random input pair
         x, y = (
             np.random.randint(2**1, size=(1, nb_bits)),
@@ -144,7 +147,9 @@ def main():
         encrypted_result = circuit.run(encrypted_input)
         end_time = time.time()
 
-        total_time += end_time - begin_time
+        # Don't count the warmup samples
+        if i >= nb_samples_for_warmup:
+            total_time += end_time - begin_time
 
         # Decrypt
         result = circuit.decrypt(encrypted_result)

frontends/concrete-python/examples/xor_distance/xor_distance.md

@@ -156,7 +156,7 @@ And finally, for 12804-bit vectors, execution times should be:
     dist_in_fhe_with_multivariate_tables on 4 bits: 40.89 seconds
 ```
 
-## Distance Between Two Encrypted Tensors
+## Distance Between One Encrypted Tensor and One Clear Tensor
 
 In [this code](hamming_distance_to_clear.py), we propose a simple implementation for the special case
 where one of the vectors (here, `y`) is not encrypted. The function `dist_in_fhe` is based on the