Solving #179 Simplex Domain Bug (#180)

* simplex bug solved
---------

Co-authored-by: Dario Coscia <dariocoscia@dhcp-235.eduroam.sissa.it>

pina/geometry/simplex.py

@@ -45,13 +45,13 @@ class SimplexDomain(Location):
         check_consistency(simplex_matrix, LabelTensor)
 
         # check consistency of labels
-        self._coordinates = simplex_matrix[0].labels
-        if not all(vertex.labels == self._coordinates for vertex in simplex_matrix):
+        matrix_labels = simplex_matrix[0].labels
+        if not all(vertex.labels == matrix_labels for vertex in simplex_matrix):
             raise ValueError(f"Labels don't match.")
 
         # creating vertices matrix
         self._vertices_matrix = torch.cat(simplex_matrix)
-        self._vertices_matrix.labels = self._coordinates
+        self._vertices_matrix.labels = matrix_labels
 
         # creating basis vectors for simplex
         self._vectors_shifted = (
@@ -59,15 +59,11 @@ class SimplexDomain(Location):
         )
 
         # build cartesian_bound
-        self._cartesian_bound = self._build_cartesian(self.variables)
-
-    @property
-    def coordinates(self):
-        return self._coordinates
+        self._cartesian_bound = self._build_cartesian(self._vertices_matrix)
 
     @property
     def variables(self):
-        return self._vertices_matrix
+        return self._vertices_matrix.labels
 
     def _build_cartesian(self, vertices):
         """
@@ -80,10 +76,11 @@ class SimplexDomain(Location):
 
         span_dict = {}
 
-        for i, coord in enumerate(self._coordinates):
+        for i, coord in enumerate(self.variables):
             sorted_vertices = sorted(vertices, key=lambda vertex: vertex[i])
             # respective coord bounded by the lowest and highest values
-            span_dict[coord] = [sorted_vertices[0][i], sorted_vertices[-1][i]]
+            span_dict[coord] = [float(sorted_vertices[0][i]),
+                                float(sorted_vertices[-1][i])]
 
         return CartesianDomain(span_dict)
 
@@ -105,7 +102,7 @@ class SimplexDomain(Location):
         :rtype: bool
         """
 
-        if not all([label in self.coordinates for label in point.labels]):
+        if not all(label in self.variables for label in point.labels):
             raise ValueError(
                 "Point labels different from constructor"
                 f" dictionary labels. Got {point.labels},"
@@ -182,7 +179,7 @@ class SimplexDomain(Location):
 
         while len(sampled_points) < n:
             # extract number of vertices
-            number_of_vertices = self._vertices_matrix.shape[1]
+            number_of_vertices = self._vertices_matrix.shape[0]
             # extract idx lambda to set to zero randomly
             idx_lambda = torch.randint(low=0, high=number_of_vertices, size=(1,))
             # build lambda vector
@@ -193,8 +190,7 @@ class SimplexDomain(Location):
             # 3. normalize
             lambdas /= lambdas.sum()
             # 4. compute dot product
-            sampled_points.append(self._vertices_matrix @ lambdas)
-
+            sampled_points.append(self._vertices_matrix.T @ lambdas)
         return torch.cat(sampled_points, dim=1).T
 
     def sample(self, n, mode="random", variables="all"):

tests/test_geometry/test_simplex.py

@@ -37,6 +37,33 @@ def test_constructor():
             ]
         )
 
+def test_sample():
+    # sampling inside
+    simplex = SimplexDomain(
+                            [
+                                LabelTensor(torch.tensor([[0, 0]]), labels=["x", "y"]),
+                                LabelTensor(torch.tensor([[1, 1]]), labels=["x", "y"]),
+                                LabelTensor(torch.tensor([[0, 2]]), labels=["x", "y"]),
+                            ]
+                        )
+    pts = simplex.sample(10)
+    assert isinstance(pts, LabelTensor)
+    assert pts.size() == torch.Size([10, 2])
+
+    # sampling border
+    SimplexDomain(
+        [
+            LabelTensor(torch.tensor([[0, 0]]), labels=["x", "y"]),
+            LabelTensor(torch.tensor([[1, 1]]), labels=["x", "y"]),
+            LabelTensor(torch.tensor([[0, 2]]), labels=["x", "y"]),
+        ],
+        sample_surface=True,
+    )
+
+    pts = simplex.sample(10)
+    assert isinstance(pts, LabelTensor)
+    assert pts.size() == torch.Size([10, 2])
+
 
 def test_is_inside_faulty_point():
     domain = SimplexDomain(