test errors

src/qibo/quantum_info/entropies.py

@@ -1,4 +1,5 @@
 """Submodule with entropy measures."""
+from typing import Union
 
 import numpy as np
 
@@ -151,7 +152,9 @@ def classical_relative_entropy(prob_dist_p, prob_dist_q, base: float = 2, backen
     return entropy_p - relative
 
 
-def classical_renyi_entropy(prob_dist, alpha: float, base: float = 2, backend=None):
+def classical_renyi_entropy(
+    prob_dist, alpha: Union[float, int], base: float = 2, backend=None
+):
     """Calculates the classical Rényi entropy :math:`H_{\\alpha}` of a discrete probability distribution.
 
     For :math:`\\alpha \\in (0, \\, 1) \\cup (1, \\, \\infty)` and probability distribution
@@ -171,7 +174,7 @@ def classical_renyi_entropy(prob_dist, alpha: float, base: float = 2, backend=No
 
     Args:
         prob_dist (ndarray): discrete probability distribution.
-        alpha (float): order of the Rényi entropy.
+        alpha (float or int): order of the Rényi entropy.
             If :math:`\\alpha = 1`, defaults to :func:`qibo.quantum_info.entropies.shannon_entropy`.
             If :math:`\\alpha = \\infty`, defaults to the
             `min-entropy <https://en.wikipedia.org/wiki/Min-entropy>`_.
@@ -190,7 +193,7 @@ def classical_renyi_entropy(prob_dist, alpha: float, base: float = 2, backend=No
         # np.float64 is necessary instead of native float because of tensorflow
         prob_dist = backend.cast(prob_dist, dtype=np.float64)
 
-    if not isinstance(alpha, float):
+    if not isinstance(alpha, (float, int)):
         raise_error(
             TypeError, f"alpha must be type float, but it is type {type(alpha)}."
         )

tests/test_quantum_info_entropies.py

@@ -4,6 +4,7 @@
 from qibo.config import PRECISION_TOL
 from qibo.quantum_info.entropies import (
     classical_relative_entropy,
+    classical_renyi_entropy,
     entanglement_entropy,
     entropy,
     relative_entropy,
@@ -113,6 +114,48 @@ def test_classical_relative_entropy(backend, base, kind):
     backend.assert_allclose(divergence, target, atol=1e-5)
 
 
+@pytest.mark.parametrize("kind", [None, list])
+@pytest.mark.parametrize("base", [2, 10, np.e, 5])
+@pytest.mark.parametrize("alpha", [1, 2, 3, np.inf])
+def test_classical_renyi_entropy(backend, alpha, base, kind):
+    with pytest.raises(TypeError):
+        prob = np.array([1.0, 0.0])
+        prob = backend.cast(prob, dtype=prob.dtype)
+        test = classical_renyi_entropy(prob, alpha="2", backend=backend)
+    with pytest.raises(ValueError):
+        prob = np.array([1.0, 0.0])
+        prob = backend.cast(prob, dtype=prob.dtype)
+        test = classical_renyi_entropy(prob, alpha=-2, backend=backend)
+    with pytest.raises(TypeError):
+        prob = np.array([1.0, 0.0])
+        prob = backend.cast(prob, dtype=prob.dtype)
+        test = classical_renyi_entropy(prob, alpha, base="2", backend=backend)
+    with pytest.raises(ValueError):
+        prob = np.array([1.0, 0.0])
+        prob = backend.cast(prob, dtype=prob.dtype)
+        test = classical_renyi_entropy(prob, alpha, base=-2, backend=backend)
+    with pytest.raises(TypeError):
+        prob = np.array([[1.0], [0.0]])
+        prob = backend.cast(prob, dtype=prob.dtype)
+        test = classical_renyi_entropy(prob, alpha, backend=backend)
+    with pytest.raises(TypeError):
+        prob = np.array([])
+        prob = backend.cast(prob, dtype=prob.dtype)
+        test = classical_renyi_entropy(prob, alpha, backend=backend)
+    with pytest.raises(ValueError):
+        prob = np.array([1.0, -1.0])
+        prob = backend.cast(prob, dtype=prob.dtype)
+        test = classical_renyi_entropy(prob, alpha, backend=backend)
+    with pytest.raises(ValueError):
+        prob = np.array([1.1, 0.0])
+        prob = backend.cast(prob, dtype=prob.dtype)
+        test = classical_renyi_entropy(prob, alpha, backend=backend)
+    with pytest.raises(ValueError):
+        prob = np.array([0.5, 0.4999999])
+        prob = backend.cast(prob, dtype=prob.dtype)
+        test = classical_renyi_entropy(prob, alpha, backend=backend)
+
+
 @pytest.mark.parametrize("check_hermitian", [False, True])
 @pytest.mark.parametrize("base", [2, 10, np.e, 5])
 def test_entropy(backend, base, check_hermitian):