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Fix the bug in the prediction of full covariance matrix (#702)
* fix the bug in the prediction of full covariance matrix * Fix the test cases. * update the version requirement for matplotlib
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4 changed files with 6 additions and 5 deletions
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@ -245,6 +245,7 @@ class Posterior(object):
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for i in range(var.shape[1]):
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var[:, i] = (Kxx - (np.sum(np.dot(woodbury_inv[:, :, i].T, Kx) * Kx, 0)))
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var = var
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var = np.clip(var, 1e-15, np.inf)
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else:
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psi0_star = kern.psi0(pred_var, Xnew)
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psi1_star = kern.psi1(pred_var, Xnew)
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@ -265,7 +266,7 @@ class Posterior(object):
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var += -psi2_star.reshape(N, -1).dot(woodbury_inv.flat)[:, None]
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else:
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var += -psi2_star.reshape(N, -1).dot(woodbury_inv.reshape(-1, D))
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var = np.clip(var, 1e-15, np.inf)
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var = np.clip(var, 1e-15, np.inf)
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return mu, var
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@ -122,7 +122,7 @@ class MatplotlibPlots(AbstractPlottingLibrary):
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def barplot(self, ax, x, height, width=0.8, bottom=0, color=Tango.colorsHex['mediumBlue'], label=None, **kwargs):
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if 'align' not in kwargs:
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kwargs['align'] = 'center'
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return ax.bar(left=x, height=height, width=width,
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return ax.bar(x=x, height=height, width=width,
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bottom=bottom, label=label, color=color,
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**kwargs)
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@ -238,7 +238,7 @@ class MiscTests(unittest.TestCase):
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# Not easy to check if woodbury_inv is correct in itself as it requires a large derivation and expression
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Kinv = m.posterior.woodbury_inv
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K_hat = k.K(self.X_new) - k.K(self.X_new, Z).dot(Kinv).dot(k.K(Z, self.X_new))
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K_hat = np.clip(K_hat, 1e-15, np.inf)
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# K_hat = np.clip(K_hat, 1e-15, np.inf)
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mu, covar = m.predict_noiseless(self.X_new, full_cov=True)
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self.assertEquals(mu.shape, (self.N_new, self.D))
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4
setup.py
4
setup.py
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@ -164,7 +164,7 @@ setup(name = 'GPy',
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'optional':['mpi4py',
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'ipython>=4.0.0',
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],
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'plotting':['matplotlib >= 1.3',
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'plotting':['matplotlib >= 3.0',
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'plotly >= 1.8.6'],
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'notebook':['jupyter_client >= 4.0.6',
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'ipywidgets >= 4.0.3',
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@ -185,7 +185,7 @@ setup(name = 'GPy',
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'Intended Audience :: Developers',
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'Topic :: Software Development',
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'Topic :: Software Development :: Libraries :: Python Modules',
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]
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)
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