added sklearn trick

1 files changed, 5 insertions, 15 deletions

diff --git a/src/python/gudhi/representations/kernel_methods.py b/src/python/gudhi/representations/kernel_methods.py
index bbbb7c31..d89f69ab 100644
--- a/src/python/gudhi/representations/kernel_methods.py
+++ b/src/python/gudhi/representations/kernel_methods.py
@@ -62,27 +62,17 @@ def pairwise_persistence_diagram_kernels(X, Y=None, metric="sliced_wasserstein",
     :param metric: kernel to use. It can be either a string ("sliced_wasserstein", "persistence_scale_space", "persistence_weighted_gaussian", "persistence_fisher") or a function taking two numpy arrays of shape (nx2) and (mx2) as inputs.
     :returns: kernel matrix, i.e., numpy array of shape (num diagrams 1 x num diagrams 2)
     :rtype: float
-    """
-    if Y is None:
-        YY = None
-        pX = Padding(use=True).fit_transform(X)
-        diag_len = len(pX[0])
-        XX = np.reshape(np.vstack(pX), [-1, diag_len*3])
-    else:
-        nX, nY = len(X), len(Y)
-        pD = Padding(use=True).fit_transform(X + Y)
-        diag_len = len(pD[0])
-        XX = np.reshape(np.vstack(pD[:nX]), [-1, diag_len*3])
-        YY = np.reshape(np.vstack(pD[nX:]), [-1, diag_len*3])
-
+    """    
+    XX = np.reshape(np.arange(len(X)), [-1,1])
+    YY = None if Y is None else np.reshape(np.arange(len(Y)), [-1,1])
     if metric == "sliced_wasserstein":
         return np.exp(-pairwise_persistence_diagram_distances(X, Y, metric="sliced_wasserstein", num_directions=kwargs["num_directions"]) / kwargs["bandwidth"])
     elif metric == "persistence_fisher":
         return np.exp(-pairwise_persistence_diagram_distances(X, Y, metric="persistence_fisher", kernel_approx=kwargs["kernel_approx"], bandwidth=kwargs["bandwidth"]) / kwargs["bandwidth_fisher"])
     elif metric == "persistence_scale_space":
-        return pairwise_kernels(XX, YY, metric=sklearn_wrapper(persistence_scale_space_kernel, **kwargs))
+        return pairwise_kernels(XX, YY, metric=sklearn_wrapper(persistence_scale_space_kernel, X, Y, **kwargs))
     elif metric == "persistence_weighted_gaussian":
-        return pairwise_kernels(XX, YY, metric=sklearn_wrapper(persistence_weighted_gaussian_kernel, **kwargs))
+        return pairwise_kernels(XX, YY, metric=sklearn_wrapper(persistence_weighted_gaussian_kernel, X, Y, **kwargs))
     else:
         return pairwise_kernels(XX, YY, metric=sklearn_wrapper(metric, **kwargs))