From 45ede97d8536ef4f94202064b98b0ec7ba11083d Mon Sep 17 00:00:00 2001 From: Max Zwiessele Date: Fri, 28 Nov 2014 10:10:52 +0000 Subject: [PATCH] [stationary] lengthscales will be scaled by variance now --- GPy/kern/_src/stationary.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/GPy/kern/_src/stationary.py b/GPy/kern/_src/stationary.py index 443871af..06671b23 100644 --- a/GPy/kern/_src/stationary.py +++ b/GPy/kern/_src/stationary.py @@ -159,7 +159,7 @@ class Stationary(Kern): #self.lengthscale.gradient = -((dL_dr*rinv)[:,:,None]*x_xl3).sum(0).sum(0)/self.lengthscale**3 tmp = dL_dr*self._inv_dist(X, X2) if X2 is None: X2 = X - + if config.getboolean('weave', 'working'): try: @@ -261,7 +261,7 @@ class Stationary(Kern): ret(n,d) = retnd; } } - + """ if hasattr(X, 'values'):X = X.values #remove the GPy wrapping to make passing into weave safe if hasattr(X2, 'values'):X2 = X2.values @@ -278,12 +278,12 @@ class Stationary(Kern): 'extra_link_args' : ['-lgomp']} weave.inline(code, ['ret', 'N', 'D', 'M', 'tmp', 'X', 'X2'], type_converters=weave.converters.blitz, support_code=support_code, **weave_options) return ret/self.lengthscale**2 - + def gradients_X_diag(self, dL_dKdiag, X): return np.zeros(X.shape) def input_sensitivity(self, summarize=True): - return np.ones(self.input_dim)/self.lengthscale**2 + return self.variance*np.ones(self.input_dim)/self.lengthscale**2 class Exponential(Stationary): def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, active_dims=None, name='Exponential'):