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for loop speedup in grdients X
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@ -171,7 +171,8 @@ class Stationary(Kern):
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#the lower memory way with a loop
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#the lower memory way with a loop
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ret = np.empty(X.shape, dtype=np.float64)
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ret = np.empty(X.shape, dtype=np.float64)
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[np.sum(tmp*(X[:,q][:,None]-X2[:,q][None,:]), axis=1, out=ret[:,q]) for q in xrange(self.input_dim)]
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for q in xrange(self.input_dim):
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np.sum(tmp*(X[:,q][:,None]-X2[:,q][None,:]), axis=1, out=ret[:,q])
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ret /= self.lengthscale**2
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ret /= self.lengthscale**2
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return ret
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return ret
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