fixed typo of 'tenary' by 'ternary'

src/liberate/fhe/ckks_engine.py

@@ -30,7 +30,7 @@ def __init__(self, devices: list[int] = None, verbose: bool = False,
             num_scales=None,
             num_special_primes=2,
             sigma=3.2,
-            uniform_tenary_secret=True,
+            uniform_ternary_secret=True,
             cache_folder='cache/',
             security_bits=128,
             quantum='post_quantum',

src/liberate/fhe/context/ckks_context.py

@@ -157,7 +157,7 @@ def __init__(
             num_scales=None,
             num_special_primes=2,
             sigma=3.2,
-            uniform_tenary_secret=True,
+            uniform_ternary_secret=True,
             cache_folder=CACHE_FOLDER,
             security_bits=128,
             quantum="post_quantum",
@@ -203,11 +203,11 @@ def __init__(
         self.distribution = distribution
         # Sampling strategy.
         self.sigma = sigma
-        self.uniform_tenary_secret = uniform_tenary_secret
-        if self.uniform_tenary_secret:
-            self.secret_key_sampling_method = "uniform tenary"
+        self.uniform_ternary_secret = uniform_ternary_secret
+        if self.uniform_ternary_secret:
+            self.secret_key_sampling_method = "uniform ternary"
         else:
-            self.secret_key_sampling_method = "sparse tenary"
+            self.secret_key_sampling_method = "sparse ternary"
 
         # dtypes.
         self.torch_dtype = {30: torch.int32, 62: torch.int64}[

src/liberate/fhe/context/security_parameters.py

@@ -15,8 +15,8 @@
 # We separate them in respective dictionaries.
 #
 # Also, there are 3 different methods of sampling the messages according to respective distributions.
-# Those are uniform, error, and (-1, 1) (tenary).
-# We differentiate the message distribution by dictionary keys: 'uniform', 'error', and 'tenary'.
+# Those are uniform, error, and (-1, 1) (ternary).
+# We differentiate the message distribution by dictionary keys: 'uniform', 'error', and 'ternary'.
 
 # This is the pre-quantum security requirements.
 logq_preq = {}
@@ -60,7 +60,7 @@
     613,
     478,
 ]
-logq_preq["tenary"] = [
+logq_preq["ternary"] = [
     27,
     19,
     14,
@@ -123,7 +123,7 @@
     573,
     445,
 ]
-logq_postq["tenary"] = [
+logq_postq["ternary"] = [
     25,
     17,
     13,
@@ -158,7 +158,7 @@ def partitq(q):
 
 # Gather up.
 logq = {}
-distributions = ["uniform", "error", "tenary"]
+distributions = ["uniform", "error", "ternary"]
 logq["pre_quantum"] = {
     distributions[disti]: partitq(logq_preq[dist])
     for disti, dist in enumerate(distributions)
@@ -176,7 +176,7 @@ def minimum_cyclotomic_order(
         "pre_quantum",
         "post_quantum",
     ], "Wrong quantum security model!!!"
-    assert distribution in ["uniform", "error", "tenary"]
+    assert distribution in ["uniform", "error", "ternary"]
     assert security_bits in [128, 192, 256]
 
     x = logq[quantum][distribution][security_bits]
@@ -192,7 +192,7 @@ def maximum_qbits(
         "pre_quantum",
         "post_quantum",
     ], "Wrong quantum security model!!!"
-    assert distribution in ["uniform", "error", "tenary"]
+    assert distribution in ["uniform", "error", "ternary"]
     assert security_bits in [128, 192, 256]
 
     x = cyclotomic_n

src/liberate/fhe/tests/test_generate_engine.py

@@ -26,7 +26,7 @@ def ckks_engine(
         "num_special_primes": 2,
         "buffer_bit_length": 62,
         "sigma": 3.2,
-        "uniform_tenary_secret": True,
+        "uniform_ternary_secret": True,
         "cache_folder": "cache/",
         "quantum": "post_quantum",
         "distribution": "uniform",