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Vectorize minmax kernel #10

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Vectorize minmax kernel #10

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15 changes: 5 additions & 10 deletions pykernels/regular.py
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Original file line number Diff line number Diff line change
Expand Up @@ -465,16 +465,11 @@ def _compute(self, data_1, data_2):
if np.any(data_1 < 0) or np.any(data_2 < 0):
warnings.warn('MinMax kernel requires data to be strictly positive!')

minkernel = np.zeros((data_1.shape[0], data_2.shape[0]))
maxkernel = np.zeros((data_1.shape[0], data_2.shape[0]))

for d in range(data_1.shape[1]):
column_1 = data_1[:, d].reshape(-1, 1)
column_2 = data_2[:, d].reshape(-1, 1)
minkernel += np.minimum(column_1, column_2.T)
maxkernel += np.maximum(column_1, column_2.T)

return minkernel/maxkernel
data_1 = data_1[:, None, :]
data_2 = data_2[None, :, :]
minkernel = np.minimum(data_1, data_2).sum(axis=2) # Sum over features
maxkernel = np.maximum(data_1, data_2).sum(axis=2)
return minkernel / maxkernel

def dim(self):
return None
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