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Formatted README.rst
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‎README.rst‎

Lines changed: 23 additions & 24 deletions
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@@ -73,29 +73,29 @@ Benchmarking
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mae
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mean std min max median
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label
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Kriging[regr=constant,corr=gauss,thetaU=100,ARD=False] 0.017159 0.007472 0.009658 0.025359 0.014855
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Kriging[regr=constant,corr=gauss,thetaU=20,ARD=False] 0.017159 0.007472 0.009658 0.025359 0.014855
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Kriging[regr=linear,corr=gauss,thetaU=100,ARD=False] 0.018064 0.008069 0.010350 0.027456 0.014246
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Kriging[regr=linear,corr=gauss,thetaU=20,ARD=False] 0.018064 0.008069 0.010350 0.027456 0.014246
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Kriging[regr=constant,corr=gauss,thetaU=100,ARD=True] 0.021755 0.007409 0.011955 0.028896 0.025163
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Kriging[regr=constant,corr=gauss,thetaU=20,ARD=True] 0.021755 0.007409 0.011955 0.028896 0.025163
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Kriging[regr=linear,corr=gauss,thetaU=20,ARD=True] 0.025018 0.011348 0.011576 0.040585 0.022124
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Kriging[regr=linear,corr=gauss,thetaU=100,ARD=True] 0.025018 0.011348 0.011576 0.040585 0.022124
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Kriging[regr=constant,corr=exp,thetaU=100,ARD=False] 0.034493 0.009328 0.025092 0.045610 0.030661
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Kriging[regr=constant,corr=exp,thetaU=20,ARD=False] 0.034493 0.009328 0.025092 0.045610 0.030661
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Kriging[regr=linear,corr=exp,thetaU=100,ARD=False] 0.035734 0.009922 0.025611 0.047926 0.031473
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Kriging[regr=linear,corr=exp,thetaU=20,ARD=False] 0.035734 0.009922 0.025611 0.047926 0.031473
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Kriging[regr=constant,corr=exp,thetaU=100,ARD=True] 0.051527 0.010941 0.037944 0.065866 0.047440
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Kriging[regr=constant,corr=exp,thetaU=20,ARD=True] 0.051527 0.010941 0.037944 0.065866 0.047440
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Kriging[regr=linear,corr=exp,thetaU=100,ARD=True] 0.065867 0.025312 0.039058 0.104449 0.059957
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Kriging[regr=linear,corr=exp,thetaU=20,ARD=True] 0.065867 0.025312 0.039058 0.104449 0.059957
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RBF[kernel=cubic,tail=quadratic,normalized=True] 0.121947 0.033552 0.077895 0.167120 0.127345
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RBF[kernel=cubic,tail=constant,normalized=True] 0.125348 0.037982 0.072579 0.169413 0.140753
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RBF[kernel=cubic,tail=linear,normalized=True] 0.125474 0.038609 0.071268 0.169843 0.137987
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RBF[kernel=cubic,tail=linear+quadratic,normalized=True] 0.126070 0.039773 0.071279 0.171862 0.135489
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mae
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mean std min max median
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label
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Kriging[regr=constant,corr=gauss,thetaU=100,ARD=False] 0.017159 0.007472 0.009658 0.025359 0.014855
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Kriging[regr=constant,corr=gauss,thetaU=20,ARD=False] 0.017159 0.007472 0.009658 0.025359 0.014855
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Kriging[regr=linear,corr=gauss,thetaU=100,ARD=False] 0.018064 0.008069 0.010350 0.027456 0.014246
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Kriging[regr=linear,corr=gauss,thetaU=20,ARD=False] 0.018064 0.008069 0.010350 0.027456 0.014246
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Kriging[regr=constant,corr=gauss,thetaU=100,ARD=True] 0.021755 0.007409 0.011955 0.028896 0.025163
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Kriging[regr=constant,corr=gauss,thetaU=20,ARD=True] 0.021755 0.007409 0.011955 0.028896 0.025163
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Kriging[regr=linear,corr=gauss,thetaU=20,ARD=True] 0.025018 0.011348 0.011576 0.040585 0.022124
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Kriging[regr=linear,corr=gauss,thetaU=100,ARD=True] 0.025018 0.011348 0.011576 0.040585 0.022124
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Kriging[regr=constant,corr=exp,thetaU=100,ARD=False] 0.034493 0.009328 0.025092 0.045610 0.030661
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Kriging[regr=constant,corr=exp,thetaU=20,ARD=False] 0.034493 0.009328 0.025092 0.045610 0.030661
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Kriging[regr=linear,corr=exp,thetaU=100,ARD=False] 0.035734 0.009922 0.025611 0.047926 0.031473
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Kriging[regr=linear,corr=exp,thetaU=20,ARD=False] 0.035734 0.009922 0.025611 0.047926 0.031473
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Kriging[regr=constant,corr=exp,thetaU=100,ARD=True] 0.051527 0.010941 0.037944 0.065866 0.047440
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Kriging[regr=constant,corr=exp,thetaU=20,ARD=True] 0.051527 0.010941 0.037944 0.065866 0.047440
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Kriging[regr=linear,corr=exp,thetaU=100,ARD=True] 0.065867 0.025312 0.039058 0.104449 0.059957
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Kriging[regr=linear,corr=exp,thetaU=20,ARD=True] 0.065867 0.025312 0.039058 0.104449 0.059957
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RBF[kernel=cubic,tail=quadratic,normalized=True] 0.121947 0.033552 0.077895 0.167120 0.127345
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RBF[kernel=cubic,tail=constant,normalized=True] 0.125348 0.037982 0.072579 0.169413 0.140753
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RBF[kernel=cubic,tail=linear,normalized=True] 0.125474 0.038609 0.071268 0.169843 0.137987
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RBF[kernel=cubic,tail=linear+quadratic,normalized=True] 0.126070 0.039773 0.071279 0.171862 0.135489
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@@ -155,7 +155,6 @@ Kriging
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# create some data to test this model on
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X, y, _X, _y = sine_function(100, 20)
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# let the model fit the data
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model.fit(X, y)
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