fix: fixing testing infrastructure fail

examples/linear_regression/nada-project.toml

@@ -2,6 +2,9 @@ name = "linear-regression"
 version = "0.1.0"
 authors = [""]
 
+[test_framework.nada-test]
+command = "nada-test ./tests"
+
 [[programs]]
 path = "src/linear_regression.py"
 prime_size = 64

examples/linear_regression/src/determinant.py

@@ -38,7 +38,7 @@ def nada_main():
     parties = na.parties(3)
 
     X = na.array([3, 3], parties[0], "A", SecretInteger)
-    X = X.reveal()
+    X = X.to_public()
     detX = determinant(X)
 
     return na.output(detX, parties[2], "my_output")

examples/linear_regression/src/gauss_jordan.py

@@ -51,7 +51,7 @@ def nada_main():
 
     A = na.array([3, 3], parties[0], "A", nada_type=SecretInteger)
 
-    A = A.reveal()
+    A = A.to_public()
     A_inv = gauss_jordan_zn(A, PRIME)
     outputs = na.output(A_inv, parties[2], "my_output")
 

examples/linear_regression/src/linear_regression.py

@@ -91,7 +91,7 @@ def linsol(A: NadaArray, b: NadaArray, modulo: int):
     )  # (n, n) random matrix R with inverse determinant detR_inv
 
     # Revealing matrix RA
-    RA = (R @ A).reveal()  # (n, n) revealed matrix
+    RA = (R @ A).to_public()  # (n, n) revealed matrix
 
     # Computing Rb as a secret matrix multiplication of R and b
     Rb = R @ b  # (n, n) @ (n,) = (n,)

examples/linear_regression/src/linear_regression_256.py

@@ -56,7 +56,7 @@ def private_modular_inverse(secret: SecretInteger, modulo: int) -> SecretInteger
     r = SecretInteger.random()
 
     ra = r * secret  # Masking our secret
-    ra_revealed = ra.reveal()  # Revealing the masked secret
+    ra_revealed = ra.to_public()  # Revealing the masked secret
 
     ra_inv = public_modular_inverse(
         ra_revealed, modulo
@@ -144,7 +144,7 @@ def matrix_inverse(matrix: np.ndarray, modulo: int):
     n = matrix.shape[0]
     R, detR = random_lu_matrix(n)  # n by n random matrix R with determinant detR
     # Revealing matrix RA
-    RA = (R @ matrix).reveal()
+    RA = (R @ matrix).to_public()
     # # Concatenating RA and R
     RAR = RA.hstack(R)
     # Performing Gauss-Jordan elimination
@@ -241,7 +241,7 @@ def linsol(A: NadaArray, b: NadaArray, modulo: int):
     )  # (n, n) random matrix R with inverse determinant detR_inv
 
     # Revealing matrix RA
-    RA = (R @ A).reveal()  # (n, n) revealed matrix
+    RA = (R @ A).to_public()  # (n, n) revealed matrix
 
     # Computing Rb as a secret matrix multiplication of R and b
     Rb = R @ b  # (n, n) @ (n,) = (n,)

examples/linear_regression/src/matrix_inverse.py

@@ -40,7 +40,7 @@ def public_modular_inverse(
     power = value ** Integer(
         mod - 1
     )  # value ** modulo = value ** (modulo // 2)  * modulo ** (modulo // 2)
-    power = power * power * value if rem else Integer(1)  # value ** mo
+    power = power * power * (value if rem else Integer(1))  # value ** mo
     return power
 
 
@@ -133,14 +133,7 @@ def gauss_jordan_zn(mat: na.NadaArray, modulo: int):
 
     # Forward elimination
     for i in range(rows):
-        # # Find pivot row
-        # pivot_row = i
-        # while pivot_row < rows and (mat[pivot_row][i] == Integer(0)) is Boolean(True):
-        #     pivot_row += 1
-
-        # # Swap pivot row with current row
-        # mat[[i, pivot_row]] = mat[[pivot_row, i]]
-
+
         # Scale pivot row to have leading 1
         diagonal_element = mat[i][i]
         pivot_inv = public_modular_inverse(diagonal_element, modulo)
@@ -168,7 +161,7 @@ def matrix_inverse(matrix: np.ndarray, modulo: int):
     R, detR = random_lu_matrix(n)  # n by n random matrix R with determinant detR
 
     # Revealing matrix RA
-    RA = (R @ matrix).reveal()
+    RA = (R @ matrix).to_public()
     # # Concatenating RA and R
     RAR = RA.hstack(R)
     # Performing Gauss-Jordan elimination
@@ -186,7 +179,7 @@ def matrix_inverse(matrix: np.ndarray, modulo: int):
 def nada_main():
     parties = na.parties(3)
 
-    A = na.array([4, 4], parties[0], "A", nada_type=SecretInteger)
+    A = na.array([10, 10], parties[0], "A", nada_type=SecretInteger)
     A_inv = matrix_inverse(A, PRIME)
 
     result = A @ A_inv

examples/linear_regression/src/modular_inverse.py

@@ -55,7 +55,7 @@ def private_modular_inverse(secret: SecretInteger, modulo: int) -> SecretInteger
     r = SecretInteger.random()
 
     ra = r * secret  # Masking our secret
-    ra_revealed = ra.reveal()  # Revealing the masked secret
+    ra_revealed = ra.to_public()  # Revealing the masked secret
 
     ra_inv = public_modular_inverse(
         ra_revealed, modulo

examples/linear_regression/tests/matrix_inverse.py

@@ -0,0 +1,24 @@
+from nada_test import nada_test, NadaTest
+import sys
+import nada_numpy.client as  na
+import numpy as np
+
+# Functional style test
+@nada_test(program="matrix_inverse")
+def my_test():
+    n = 10
+    m = np.random.rand(n, n)
+    mx = np.sum(np.abs(m), axis=1)
+    np.fill_diagonal(m, mx)
+    A = na.array(m * (1 << 16), "A", nada_type=int)
+    print("INPUTS:", A, file=sys.stderr)
+    outputs = yield A
+    print(outputs, file=sys.stderr)
+    for output, value in outputs.items():
+        output = output.split("_")
+        if output[-1] == output[-2]:
+            assert value == 1, f"Expected 1 {output}, got {value}"
+        else:
+            assert value == 0, f"Expected 0 {output}, got {value}"
+
+    #assert outputs["my_output"] == a + b

nada_numpy/array.py

@@ -550,14 +550,14 @@ def vstack(self, other: "NadaArray") -> "NadaArray":
         """
         return NadaArray(np.vstack((self.inner, other.inner)))
 
-    def reveal(self) -> "NadaArray":
+    def to_public(self) -> "NadaArray":
         """
-        Reveal the elements of the array.
+        Reveal the elements of the array and make them public
 
         Returns:
             NadaArray: A new NadaArray with revealed values.
         """
-        return self.apply(lambda x: x.reveal())
+        return self.apply(lambda x: x.to_public())
 
     def apply(self, func: Callable[[Any], Any]) -> "NadaArray":
         """
@@ -1516,7 +1516,7 @@ def log(x):
             f"Log is not compatible with {dtype}, only with Rational and SecretRational types."
         )
 
-    def reciprocal(  # pylint: disable=too-many-arguments
+    def reciprocal(  # pylint: disable=too-many-arguments disable=too-many-positional-arguments
         self,
         all_pos: bool = False,
         initial: Optional["Rational"] = None,

nada_numpy/client.py

@@ -61,36 +61,13 @@ def array(
         Dict: A dictionary mapping generated names to Nillion input objects.
     """
     # TODO: Use  this version when check for zero values is removed
-    # if len(arr.shape) == 1:
-    #     if nada_type == Rational:
-    #         nada_type = public_rational  # type: ignore
-    #     elif nada_type == SecretRational:
-    #         nada_type = secret_rational  # type: ignore
-    #     return {
-    #         f"{prefix}_{i}": (nada_type(int(arr[i]))) for i in range(arr.shape[0])  # type: ignore
-    #     }
-
-    # TODO: remove check for zero values when pushing zero secrets is supported
-
     if len(arr.shape) == 1:
         if nada_type == Rational:
-            return {
-                f"{prefix}_{i}": (public_rational(arr[i])) for i in range(arr.shape[0])
-            }
-        if nada_type == SecretRational:
-            return {
-                f"{prefix}_{i}": (
-                    secret_rational(arr[i]) if arr[i] != 0 else SecretInteger(1)
-                )
-                for i in range(arr.shape[0])
-            }
+            nada_type = public_rational  # type: ignore
+        elif nada_type == SecretRational:
+            nada_type = secret_rational  # type: ignore
         return {
-            f"{prefix}_{i}": (
-                nada_type(int(arr[i]))  # type: ignore
-                if (nada_type in (Integer, UnsignedInteger) or int(arr[i]) != 0)
-                else nada_type(1)  # type: ignore
-            )
-            for i in range(arr.shape[0])
+            f"{prefix}_{i}": (nada_type(int(arr[i]))) for i in range(arr.shape[0])  # type: ignore
         }
     return {
         k: v

nada_numpy/funcs.py

@@ -730,7 +730,7 @@ def log(
     )
 
 
-def reciprocal(  # pylint: disable=too-many-arguments
+def reciprocal(  # pylint: disable=too-many-arguments disable=too-many-positional-arguments
     arr: NadaArray,
     all_pos: bool = False,
     initial: Optional["Rational"] = None,

nada_numpy/types.py

@@ -24,7 +24,7 @@
 ]
 
 
-class SecretBoolean(dsl.SecretBoolean):
+class SecretBoolean(dsl.SecretBoolean): # pylint:disable=too-many-ancestors
     """SecretBoolean rational wrapper"""
 
     def __init__(self, value: dsl.SecretBoolean) -> None:
@@ -80,7 +80,7 @@ def if_else(
         return result
 
 
-class PublicBoolean(dsl.PublicBoolean):
+class PublicBoolean(dsl.PublicBoolean): # pylint:disable=too-many-ancestors
     """PublicBoolean rational wrapper"""
 
     def __init__(self, value: dsl.PublicBoolean) -> None:
@@ -169,6 +169,7 @@ def __init__(
         if not isinstance(value, (Integer, PublicInteger)):
             raise TypeError(f"Cannot instantiate Rational from type `{type(value)}`.")
 
+        self.base_type = "Rational"
         if log_scale is None:
             log_scale = get_log_scale()
         self._log_scale = log_scale
@@ -838,7 +839,7 @@ def log(
             raise TypeError("log input should be of type Rational.")
         return result
 
-    def reciprocal(  # pylint: disable=too-many-arguments
+    def reciprocal(  # pylint: disable=too-many-arguments disable=too-many-positional-arguments
         self,
         all_pos: bool = False,
         initial: Optional["Rational"] = None,
@@ -1299,6 +1300,7 @@ def __init__(
                 f"Cannot instantiate SecretRational from type `{type(value)}`."
             )
 
+        self.base_type = "Rational"
         if log_scale is None:
             log_scale = get_log_scale()
         self._log_scale = log_scale
@@ -1346,6 +1348,7 @@ def add(self, other: _NadaRational, ignore_scale: bool = False) -> "SecretRation
             SecretRational: Result of the addition.
         """
         if not isinstance(other, (Rational, SecretRational)):
+            # Lays the groundwork for broadcasting to Nada Array if it implements it
             return NotImplemented
 
         if not ignore_scale and self.log_scale != other.log_scale:
@@ -1780,14 +1783,14 @@ def public_equals(self, other: _NadaRational) -> PublicBoolean:
             raise ValueError("Cannot compare values with different scales.")
         return self.value.public_equals(other.value)
 
-    def reveal(self) -> Rational:
+    def to_public(self) -> Rational:
         """
         Reveal the SecretRational value.
 
         Returns:
             Rational: Revealed SecretRational value.
         """
-        return Rational(self.value.reveal(), self.log_scale)
+        return Rational(self.value.to_public(), self.log_scale)
 
     def trunc_pr(self, arg_0: _NadaRational) -> "SecretRational":
         """
@@ -1972,7 +1975,7 @@ def log(
             raise TypeError("log input should be of type SecretRational.")
         return result
 
-    def reciprocal(  # pylint: disable=too-many-arguments
+    def reciprocal(  # pylint: disable=too-many-arguments disable=too-many-positional-arguments
         self,
         all_pos: bool = False,
         initial: Optional["Rational"] = None,
@@ -2670,7 +2673,7 @@ def log(
     return y
 
 
-def reciprocal(  # pylint: disable=too-many-arguments
+def reciprocal(  # pylint: disable=too-many-arguments disable=too-many-positional-arguments
     x: _NadaRational,
     all_pos: bool = False,
     initial: Optional[Rational] = None,