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some cleaning on WarpedGP code
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2 changed files with 6 additions and 6 deletions
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@ -16,7 +16,7 @@ class WarpedGP(GP):
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if warping_function == None:
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self.warping_function = TanhWarpingFunction_d(warping_terms)
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self.warping_params = (np.random.randn(self.warping_function.n_terms * 3 + 1,) * 1)
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self.warping_params = (np.random.randn(self.warping_function.n_terms * 3 + 1) * 1)
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else:
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self.warping_function = warping_function
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@ -56,7 +56,6 @@ class WarpedGP(GP):
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self.warping_function.psi.gradient[:] = warping_grads[:, :-1]
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self.warping_function.d.gradient[:] = warping_grads[0, -1]
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def transform_data(self):
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Y = self.warping_function.f(self.Y_untransformed.copy()).copy()
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return Y
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@ -84,6 +83,9 @@ class WarpedGP(GP):
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return gh_weights.dot(self._get_warped_term(mean, std, gh_samples)) / np.sqrt(np.pi)
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def _get_warped_variance(self, mean, std, pred_init=None, deg_gauss_hermite=100):
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"""
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Calculate the warped variance by using Gauss-Hermite quadrature.
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"""
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gh_samples, gh_weights = np.polynomial.hermite.hermgauss(deg_gauss_hermite)
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gh_samples = gh_samples[:,None]
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gh_weights = gh_weights[None,:]
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@ -105,13 +107,10 @@ class WarpedGP(GP):
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if self.predict_in_warped_space:
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std = np.sqrt(var)
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if median:
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#print 'MEDIAN!'
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wmean = self.warping_function.f_inv(mean, y=pred_init)
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else:
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#print 'MEAN!'
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wmean = self._get_warped_mean(mean, std, pred_init=pred_init,
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deg_gauss_hermite=deg_gauss_hermite).T
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#var = self.warping_function.f_inv(var)
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wvar = self._get_warped_variance(mean, std, pred_init=pred_init,
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deg_gauss_hermite=deg_gauss_hermite).T
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else:
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@ -147,6 +146,7 @@ class WarpedGP(GP):
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return [new_a, new_b]
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#return self.likelihood.predictive_quantiles(m, v, quantiles, Y_metadata)
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if __name__ == '__main__':
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X = np.random.randn(100, 1)
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Y = np.sin(X) + np.random.randn(100, 1)*0.05
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@ -221,7 +221,7 @@ class MiscTests(unittest.TestCase):
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np.testing.assert_almost_equal(preds, warp_preds)
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@unittest.skip('Comment this to plot the modified sine function')
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#@unittest.skip('Comment this to plot the modified sine function')
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def test_warped_gp_sine(self):
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"""
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A test replicating the sine regression problem from
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