Fixed broken methods

cellcommunicationpf2/cc_pf2.py

@@ -12,7 +12,7 @@ def project_data(tensor: np.ndarray, proj_matrix: np.ndarray) -> np.ndarray:
     Projects a 3D tensor of C x C x LR with a projection matrix of C x CES
     along both C dimensions to form a resulting tensor of CES x CES x LR.
     """
-    return np.einsum("ab,cd,bdg->acg", proj_matrix, proj_matrix, tensor)
+    return np.einsum("ab,cd,acg->bdg", proj_matrix, proj_matrix, tensor)
 
 
 def solve_projections(
@@ -37,7 +37,7 @@ def solve_projections(
 
         @pymanopt.function.autograd(manifold)
         def objective_function(proj):
-            a_mat_recon = anp.einsum("ab,cd,bdg->acg", proj, proj, a_lhs)
+            a_mat_recon = anp.einsum("ab,cd,acg->bdg", proj.T, proj.T, a_lhs)
             return anp.sum(anp.square(a_mat - a_mat_recon))
 
         problem = Problem(manifold=manifold, cost=objective_function)

cellcommunicationpf2/tests/test_OP.py

@@ -3,7 +3,6 @@
 import pytest
 
 
-@pytest.mark.xfail(reason="The project method hasn't been completed yet")
 def test_project_data():
     """
     Tests that the dimensions are correct and that the method is able to run without errors.
@@ -22,9 +21,10 @@ def test_project_data():
     proj_matrix = np.linalg.qr(np.random.rand(cells, rank))[0]
 
     # Call the project_data method
+    print(proj_matrix.shape)
     projected_X = project_data(X_mat, proj_matrix)
 
-    assert projected_X.shape == (obs, rank, rank, LR)
+    assert projected_X.shape == (rank, rank, LR)
 
 
 @pytest.mark.xfail(reason="The project method hasn't been completed yet")
@@ -64,6 +64,6 @@ def test_project_data_output():
     for i in range(num_tensors):
         difference_sum = np.sum(np.abs(projections[i] - projections_recreated[i]))
         print(
-            f"Projection {i} difference sum: {difference_sum}. Sum of projections in absolute: {np.sum(np.abs(projections[i]))}. Sum of projections_recreated in absolute: {np.sum(np.abs(projections_recreated[i]))}"
+            f"Projection {i} difference sum: {difference_sum}. Sum of projections in absolute: {np.sum(np.abs(projections[i]))}"
         )
         assert np.allclose(projections[i], projections_recreated[i])