chore: fix inputsets in benchmark

benchmarks/124_minus_x.py

@@ -18,7 +18,7 @@ def main():
     engine = hnp.compile_numpy_function(
         function_to_compile,
         {"x": x},
-        [(i,) for i in range(2 ** 3)],
+        range(2 ** 3),
         compilation_configuration=BENCHMARK_CONFIGURATION,
     )
     # bench: Measure: End

benchmarks/124_minus_x_tensor.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(6), shape=(3,))
 
-    inputset = [(np.random.randint(0, 2 ** 6, size=(3,)),) for _ in range(32)]
+    inputset = [np.random.randint(0, 2 ** 6, size=(3,)) for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/c_concatenate_x.py

@@ -13,7 +13,7 @@ def main():
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(4, 5))
     c = np.arange(20).reshape((4, 5))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(4, 5)),) for _ in range(128)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(4, 5)) for _ in range(128)]
 
     inputs = []
     labels = []

benchmarks/c_matmul_x.py

@@ -14,7 +14,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(2), shape=(2, 3))
 
-    inputset = [(np.random.randint(0, 2 ** 2, size=(2, 3)),) for _ in range(128)]
+    inputset = [np.random.randint(0, 2 ** 2, size=(2, 3)) for _ in range(128)]
 
     inputs = []
     labels = []

benchmarks/linear_regression.py

@@ -168,9 +168,7 @@ def main():
     def function_to_compile(x_0):
         return table[(x_0 + zp_x) * w_0]
 
-    inputset = []
-    for x_i in x_q:
-        inputset.append((int(x_i[0]),))
+    inputset = [int(x_i[0]) for x_i in x_q]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/logistic_regression.py

@@ -240,9 +240,7 @@ def main():
     def function_to_compile(x_0, x_1):
         return table[((x_0 + zp_x) * w_0) + ((x_1 + zp_x) * w_1)]
 
-    inputset = []
-    for x_i in x_q:
-        inputset.append((int(x_i[0]), int(x_i[1])))
+    inputset = [(int(x_i[0]), int(x_i[1])) for x_i in x_q]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x-index-0-and-0.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3, 2))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(3, 2)),) for _ in range(32)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(3, 2)) for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x-index-0.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(3,)) for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x-index-1.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(3,)) for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x-index-colon-2.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(3,)) for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x-index-colon-and-1.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3, 2))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(3, 2)),) for _ in range(32)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(3, 2)) for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x-index-colon.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(3,)) for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x-index-minus-1.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(3,)) for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x-index-one-colon.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(3,)) for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x-reversed.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(3,)) for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x_concatenate_c.py

@@ -13,7 +13,7 @@ def main():
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(4, 5))
     c = np.arange(20).reshape((4, 5))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(4, 5)),) for _ in range(128)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(4, 5)) for _ in range(128)]
 
     inputs = []
     labels = []

benchmarks/x_matmul_c.py

@@ -14,7 +14,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(2), shape=(2, 3))
 
-    inputset = [(np.random.randint(0, 2 ** 2, size=(2, 3)),) for _ in range(128)]
+    inputset = [np.random.randint(0, 2 ** 2, size=(2, 3)) for _ in range(128)]
 
     inputs = []
     labels = []

benchmarks/x_minus_1_2_3.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [(np.random.randint(0, 2 ** 2, size=(3,)) + np.array([1, 2, 3]),) for _ in range(32)]
+    inputset = [np.random.randint(0, 2 ** 2, size=(3,)) + np.array([1, 2, 3]) for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x_minus_1_2_3_broadcasted.py

@@ -12,9 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(2, 3))
 
-    inputset = [
-        (np.random.randint(0, 2 ** 2, size=(2, 3)) + np.array([1, 2, 3]),) for _ in range(32)
-    ]
+    inputset = [np.random.randint(0, 2 ** 2, size=(2, 3)) + np.array([1, 2, 3]) for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x_minus_24_tensor.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(6), shape=(3,))
 
-    inputset = [(np.random.randint(0, 2 ** 5, size=(3,)) + 24,) for _ in range(32)]
+    inputset = [np.random.randint(0, 2 ** 5, size=(3,)) + 24 for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x_negative.py

@@ -13,7 +13,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(10, 6))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(10, 6)),) for _ in range(128)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(10, 6)) for _ in range(128)]
 
     inputs = []
     labels = []

benchmarks/x_plus_1_2_3.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(3,)) for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x_plus_1_2_3_broadcasted.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(2, 3))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(2, 3)),) for _ in range(32)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(2, 3)) for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x_plus_42_tensor.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(3,)) for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x_reshape.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(10, 6))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(10, 6)),) for _ in range(128)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(10, 6)) for _ in range(128)]
 
     inputs = []
     labels = []

benchmarks/x_times_1_2_3.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(3,)) for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x_times_1_2_3_broadcasted.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(2, 3))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(2, 3)),) for _ in range(32)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(2, 3)) for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x_times_7_tensor.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(3,))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(3,)),) for _ in range(32)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(3,)) for _ in range(32)]
 
     # bench: Measure: Compilation Time (ms)
     engine = hnp.compile_numpy_function(

benchmarks/x_tranpose.py

@@ -12,7 +12,7 @@ def main():
 
     x = hnp.EncryptedTensor(hnp.UnsignedInteger(3), shape=(2, 4))
 
-    inputset = [(np.random.randint(0, 2 ** 3, size=(2, 4)),) for _ in range(128)]
+    inputset = [np.random.randint(0, 2 ** 3, size=(2, 4)) for _ in range(128)]
 
     inputs = []
     labels = []