Added python interface to symmetric vector kernels (#83)

Author: larsbratholm

qml/kernels/wrappers.py

@@ -24,6 +24,8 @@
 
 from .fkernels import fget_vector_kernels_gaussian
 from .fkernels import fget_vector_kernels_laplacian
+from .fkernels import fget_vector_kernels_gaussian_symmetric
+from .fkernels import fget_vector_kernels_laplacian_symmetric
 from .fkernels import fget_local_kernels_gaussian
 
 from ..arad import get_local_kernels_arad
@@ -62,38 +64,52 @@ def get_atomic_kernels_laplacian(mols1, mols2, sigmas):
     return fget_vector_kernels_laplacian(x1, x2, n1, n2, sigmas, 
         nm1, nm2, nsigmas)
 
+def get_atomic_kernels_laplacian_symmetric(mols, sigmas):
 
-def get_atomic_kernels_gaussian(mols1, mols2, sigmas):
+    n = np.array([mol.natoms for mol in mols], dtype=np.int32)
 
-    n1 = np.array([mol.natoms for mol in mols1], dtype=np.int32)
-    n2 = np.array([mol.natoms for mol in mols2], dtype=np.int32)
+    max_atoms = np.max(n)
 
-    max1 = np.max(n1)
-    max2 = np.max(n2)
+    nm = n.size
 
-    nm1 = n1.size
-    nm2 = n2.size
+    cmat_size = mols[0].representation.shape[1]
 
-    cmat_size = mols1[0].representation.shape[1]
+    x = np.zeros((nm, max_atoms, cmat_size), dtype=np.float64, order="F")
 
-    x1 = np.zeros((nm1, max1, cmat_size), dtype=np.float64, order="F")
-    x2 = np.zeros((nm2, max2, cmat_size), dtype=np.float64, order="F")
+    for imol in range(nm):
+        x[imol,:n[imol],:cmat_size] = mols[imol].representation
 
-    for imol in range(nm1):
-        x1[imol,:n1[imol],:cmat_size] = mols1[imol].representation
+    # Reorder for Fortran speed
+    x = np.swapaxes(x, 0, 2)
 
-    for imol in range(nm2):
-        x2[imol,:n2[imol],:cmat_size] = mols2[imol].representation
+    sigmas = np.asarray(sigmas, dtype=np.float64)
+    nsigmas = sigmas.size
+
+    return fget_vector_kernels_laplacian(x1, n, sigmas, nm, nsigmas)
+
+
+def get_atomic_kernels_gaussian(mols, sigmas):
+
+    n = np.array([mol.natoms for mol in mols], dtype=np.int32)
+
+    max_atoms = np.max(n)
+
+    nm = n.size
+
+    cmat_size = mols[0].representation.shape[1]
+
+    x1 = np.zeros((nm, max_atoms, cmat_size), dtype=np.float64, order="F")
+
+    for imol in range(nm1):
+        x[imol,:n[imol],:cmat_size] = mols[imol].representation
 
     # Reorder for Fortran speed
-    x1 = np.swapaxes(x1, 0, 2)
-    x2 = np.swapaxes(x2, 0, 2)
+    x = np.swapaxes(x, 0, 2)
 
     sigmas = np.array(sigmas, dtype=np.float64)
     nsigmas = sigmas.size
 
-    return fget_vector_kernels_gaussian(x1, x2, n1, n2, sigmas, 
-        nm1, nm2, nsigmas)
+    return fget_vector_kernels_gaussian(x, n, sigmas, nm, nsigmas)
 
 
 def arad_local_kernels(mols1, mols2, sigmas,
@@ -125,3 +141,68 @@ def arad_local_symmetric_kernels(mols1, sigmas,
         width=width, cut_distance=cut_distance, r_width=r_width, c_width=c_width)
 
     return K
+
+def get_atomic_kernels_laplacian(mols1, mols2, sigmas):
+
+    n1 = np.array([mol.natoms for mol in mols1], dtype=np.int32)
+    n2 = np.array([mol.natoms for mol in mols2], dtype=np.int32)
+
+    max1 = np.max(n1)
+    max2 = np.max(n2)
+
+    nm1 = n1.size
+    nm2 = n2.size
+
+    cmat_size = mols1[0].representation.shape[1]
+
+    x1 = np.zeros((nm1, max1, cmat_size), dtype=np.float64, order="F")
+    x2 = np.zeros((nm2, max2, cmat_size), dtype=np.float64, order="F")
+
+    for imol in range(nm1):
+        x1[imol,:n1[imol],:cmat_size] = mols1[imol].representation
+
+    for imol in range(nm2):
+        x2[imol,:n2[imol],:cmat_size] = mols2[imol].representation
+
+    # Reorder for Fortran speed
+    x1 = np.swapaxes(x1, 0, 2)
+    x2 = np.swapaxes(x2, 0, 2)
+
+    sigmas = np.asarray(sigmas, dtype=np.float64)
+    nsigmas = sigmas.size
+
+    return fget_vector_kernels_laplacian(x1, x2, n1, n2, sigmas, 
+        nm1, nm2, nsigmas)
+
+
+def get_atomic_kernels_gaussian(mols1, mols2, sigmas):
+
+    n1 = np.array([mol.natoms for mol in mols1], dtype=np.int32)
+    n2 = np.array([mol.natoms for mol in mols2], dtype=np.int32)
+
+    max1 = np.max(n1)
+    max2 = np.max(n2)
+
+    nm1 = n1.size
+    nm2 = n2.size
+
+    cmat_size = mols1[0].representation.shape[1]
+
+    x1 = np.zeros((nm1, max1, cmat_size), dtype=np.float64, order="F")
+    x2 = np.zeros((nm2, max2, cmat_size), dtype=np.float64, order="F")
+
+    for imol in range(nm1):
+        x1[imol,:n1[imol],:cmat_size] = mols1[imol].representation
+
+    for imol in range(nm2):
+        x2[imol,:n2[imol],:cmat_size] = mols2[imol].representation
+
+    # Reorder for Fortran speed
+    x1 = np.swapaxes(x1, 0, 2)
+    x2 = np.swapaxes(x2, 0, 2)
+
+    sigmas = np.array(sigmas, dtype=np.float64)
+    nsigmas = sigmas.size
+
+    return fget_vector_kernels_gaussian(x1, x2, n1, n2, sigmas, 
+        nm1, nm2, nsigmas)