OoDACE:ooDACE toolbox - Revision history

Icouckuy at 10:32, 26 March 2012

2012-03-26T10:32:00Z

← Older revision		Revision as of 10:32, 26 March 2012
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	== Introduction ==		== Introduction ==
	[[Image:ooDACE.~~gif~~\|250 px\|right\|ooDACE Toolbox]]		[[Image:ooDACE.png\|250 px\|right\|ooDACE Toolbox]]

	The ooDACE toolbox is a versatile Matlab toolbox that implements the popular Gaussian Process based kriging surrogate models. Kriging is in particular popular for approximating (and optimizing) deterministic computer experiments. Given a dataset the toolbox automatically fits a kriging surrogate model to it. Afterwards the kriging surrogate can be fully exploited instead of the (probably more expensive) simulation code.		The ooDACE toolbox is a versatile Matlab toolbox that implements the popular Gaussian Process based kriging surrogate models. Kriging is in particular popular for approximating (and optimizing) deterministic computer experiments. Given a dataset the toolbox automatically fits a kriging surrogate model to it. Afterwards the kriging surrogate can be fully exploited instead of the (probably more expensive) simulation code.

Icouckuy at 10:55, 25 March 2012

2012-03-25T10:55:46Z

← Older revision		Revision as of 10:55, 25 March 2012
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	== Introduction ==		== Introduction ==
	[[Image:~~blindDACE~~.gif\|250 px\|right\|~~blindDACE~~ Toolbox]]		[[Image:ooDACE.gif\|250 px\|right\|ooDACE Toolbox]]

	The ~~blindDACE~~ toolbox is a versatile Matlab toolbox that implements the popular Gaussian Process based kriging surrogate models. Kriging is in particular popular for approximating (and optimizing) deterministic computer experiments. Given a dataset the toolbox automatically fits a kriging surrogate model to it. Afterwards the kriging surrogate can be fully exploited instead of the (probably more expensive) simulation code.		The ooDACE toolbox is a versatile Matlab toolbox that implements the popular Gaussian Process based kriging surrogate models. Kriging is in particular popular for approximating (and optimizing) deterministic computer experiments. Given a dataset the toolbox automatically fits a kriging surrogate model to it. Afterwards the kriging surrogate can be fully exploited instead of the (probably more expensive) simulation code.

	The toolbox is aimed for solving complex applications (expensive simulation codes, physical experiments, ...) and for researching new kriging extensions and techniques.		The toolbox is aimed for solving complex applications (expensive simulation codes, physical experiments, ...) and for researching new kriging extensions and techniques.
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	== Download ==		== Download ==

	See: [http://sumo.intec.ugent.be/?q=~~blindDACE~~ download page]		See: [http://sumo.intec.ugent.be/?q=ooDACE download page]

	== Quick start guide ==		== Quick start guide ==
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	</source>		</source>

	Now the toolbox is ready to be used. The ~~blindDACE~~ toolbox is designed in an object oriented (OO) fashion.		Now the toolbox is ready to be used. The ooDACE toolbox is designed in an object oriented (OO) fashion.
	It is strongly recommended to exploit the OO design directly, i.e., use the Kriging and Optimizer matlab classes.		It is strongly recommended to exploit the OO design directly, i.e., use the Kriging and Optimizer matlab classes.
	However, for convenience wrapper scripts (dacefit, predictor) are provided that emulate the DACE toolbox interface (see [[#DACE toolbox interface\|wrapper scripts]] for more information).		However, for convenience wrapper scripts (dacefit, predictor) are provided that emulate the DACE toolbox interface (see [[#DACE toolbox interface\|wrapper scripts]] for more information).
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	<b>lb</b> and <b>ub</b> are 1-by-d arrays defining the lower bounds and upper bounds, respectively, needed to optimize the hyperparameters (<b>theta</b>). In addition, a set of starting values for <b>theta</b> has to be specified (i.e., <b>theta0</b> is also an 1-by-d array)		<b>lb</b> and <b>ub</b> are 1-by-d arrays defining the lower bounds and upper bounds, respectively, needed to optimize the hyperparameters (<b>theta</b>). In addition, a set of starting values for <b>theta</b> has to be specified (i.e., <b>theta0</b> is also an 1-by-d array)

	As of version 0.2 of the ~~blindDACE~~ toolbox a script is provided, ~~blinddacefit~~, that just takes your dataset (a <b>samples</b> and <b>values</b> matrix) and returns a fitted kriging object, all other parameters (<b>theta0</b>, etc.) are set to some sensible defaults.		As of version 0.2 of the ooDACE toolbox a script is provided, oodacefit, that just takes your dataset (a <b>samples</b> and <b>values</b> matrix) and returns a fitted kriging object, all other parameters (<b>theta0</b>, etc.) are set to some sensible defaults.

	For more flexibility the full example code to fit the dataset is:		For more flexibility the full example code to fit the dataset is:
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	</source>		</source>

	See the included demo.m script for more example code on how to use the ~~blindDACE~~ toolbox (including more advanced features such as using blind kriging or how to use regression instead of interpolation). For more information on the classes and their methods please refer to the source files.		See the included demo.m script for more example code on how to use the ooDACE toolbox (including more advanced features such as using blind kriging or how to use regression instead of interpolation). For more information on the classes and their methods please refer to the source files.

	== DACE toolbox interface ==		== DACE toolbox interface ==

	The ~~blindDACE~~ toolbox provides two scripts dacefit.m and predictor.m that emulate the behavior of the DACE toolbox ([http://www2.imm.dtu.dk/~hbn/dace/]). Note, that full compatibility between ~~blindDACE~~ and the DACE toolbox is not provided. The scripts merely aim to ease the transition from the DACE toolbox to the ~~blindDACE~~ toolbox.		The ooDACE toolbox provides two scripts dacefit.m and predictor.m that emulate the behavior of the DACE toolbox ([http://www2.imm.dtu.dk/~hbn/dace/]). Note, that full compatibility between ooDACE and the DACE toolbox is not provided. The scripts merely aim to ease the transition from the DACE toolbox to the ooDACE toolbox.

	Example code:		Example code:
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	</source>		</source>

	Obviously, a lot less code is used to copy the setup described above. However, less code means less flexibility (e.g., blind kriging and regression kriging are not available using the wrapper scripts). Hence, it is suggested to learn the object oriented interface of ~~blindDACE~~ and use it instead.		Obviously, a lot less code is used to copy the setup described above. However, less code means less flexibility (e.g., blind kriging and regression kriging are not available using the wrapper scripts). Hence, it is suggested to learn the object oriented interface of ooDACE and use it instead.

	== Contribute ==		== Contribute ==

	Suggestions on how to improve the ~~blindDACE~~ toolbox are always welcome. For more information please see the [[Feedback \| feedback]] page.		Suggestions on how to improve the ooDACE toolbox are always welcome. For more information please see the [[Feedback \| feedback]] page.

Icouckuy: BlindDACE:BlindDACE toolbox moved to OoDACE:ooDACE toolbox: renamed toolbox

2012-03-25T10:55:07Z

BlindDACE:BlindDACE toolbox moved to OoDACE:ooDACE toolbox: renamed toolbox

← Older revision	Revision as of 10:55, 25 March 2012
(No difference)

Tdhaene at 15:40, 19 November 2010

2010-11-19T15:40:52Z

← Older revision		Revision as of 15:40, 19 November 2010
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	== Introduction ==		== Introduction ==
			[[Image:blindDACE.gif\|250 px\|right\|blindDACE Toolbox]]

	The blindDACE toolbox is a versatile Matlab toolbox that implements the popular Gaussian Process based kriging surrogate models. Kriging is in particular popular for approximating (and optimizing) deterministic computer experiments. Given a dataset the toolbox automatically fits a kriging surrogate model to it. Afterwards the kriging surrogate can be fully exploited instead of the (probably more expensive) simulation code.		The blindDACE toolbox is a versatile Matlab toolbox that implements the popular Gaussian Process based kriging surrogate models. Kriging is in particular popular for approximating (and optimizing) deterministic computer experiments. Given a dataset the toolbox automatically fits a kriging surrogate model to it. Afterwards the kriging surrogate can be fully exploited instead of the (probably more expensive) simulation code.

Icouckuy: /* Quick start guide */

2010-10-08T08:59:52Z

Quick start guide

← Older revision		Revision as of 08:59, 8 October 2010
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	<b>samples</b> holds the input parameters n-by-d array (each row is one observation) and <b>values</b> is the corresponding n-by-1 array containing the output values.		<b>samples</b> holds the input parameters n-by-d array (each row is one observation) and <b>values</b> is the corresponding n-by-1 array containing the output values.
	<b>lb</b> and <b>ub</b> are 1-by-d arrays defining the lower bounds and upper bounds, respectively, needed to optimize the hyperparameters. In addition, a set of starting values has to be specified (e.g., <b>theta0</b> is also an 1-by-d array)		<b>lb</b> and <b>ub</b> are 1-by-d arrays defining the lower bounds and upper bounds, respectively, needed to optimize the hyperparameters (<b>theta</b>). In addition, a set of starting values for <b>theta</b> has to be specified (i.e., <b>theta0</b> is also an 1-by-d array)

	As of version 0.2 of the blindDACE toolbox a script is provided, blinddacefit, that just takes your dataset (a <b>samples</b> and <b>values</b> matrix) and returns a fitted kriging object, all other parameters (<b>theta0</b>, etc.) are set to some sensible defaults.		As of version 0.2 of the blindDACE toolbox a script is provided, blinddacefit, that just takes your dataset (a <b>samples</b> and <b>values</b> matrix) and returns a fitted kriging object, all other parameters (<b>theta0</b>, etc.) are set to some sensible defaults.

Icouckuy: /* Quick start guide */

2010-10-08T08:56:43Z

Quick start guide

← Older revision		Revision as of 08:56, 8 October 2010
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	<b>lb</b> and <b>ub</b> are 1-by-d arrays defining the lower bounds and upper bounds, respectively, needed to optimize the hyperparameters. In addition, a set of starting values has to be specified (e.g., <b>theta0</b> is also an 1-by-d array)		<b>lb</b> and <b>ub</b> are 1-by-d arrays defining the lower bounds and upper bounds, respectively, needed to optimize the hyperparameters. In addition, a set of starting values has to be specified (e.g., <b>theta0</b> is also an 1-by-d array)

	~~The~~ example code to fit the dataset is ~~then~~:		As of version 0.2 of the blindDACE toolbox a script is provided, blinddacefit, that just takes your dataset (a <b>samples</b> and <b>values</b> matrix) and returns a fitted kriging object, all other parameters (<b>theta0</b>, etc.) are set to some sensible defaults.

			For more flexibility the full example code to fit the dataset is:
	<source lang="matlab">		<source lang="matlab">
	...		...

Tdhaene: /* DACE toolbox interface */

2010-03-19T14:28:21Z

DACE toolbox interface

← Older revision		Revision as of 14:28, 19 March 2010
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	</source>		</source>

	Obviously, a lot less code is used to copy the setup described above. However, less code means less flexibility (e.g., blind kriging and regression kriging are not available using the wrapper scripts). Hence, it is suggested to learn the object oriented interface of blindDACE and use ~~that~~ instead.		Obviously, a lot less code is used to copy the setup described above. However, less code means less flexibility (e.g., blind kriging and regression kriging are not available using the wrapper scripts). Hence, it is suggested to learn the object oriented interface of blindDACE and use it instead.

	== Contribute ==		== Contribute ==

	Suggestions on how to improve the blindDACE toolbox are always welcome. For more information please see the [[Feedback \| feedback]] page.		Suggestions on how to improve the blindDACE toolbox are always welcome. For more information please see the [[Feedback \| feedback]] page.

Tdhaene: /* DACE toolbox interface */

2010-03-19T14:27:22Z

DACE toolbox interface

← Older revision		Revision as of 14:27, 19 March 2010
Line 54:		Line 54:
	== DACE toolbox interface ==		== DACE toolbox interface ==

	The blindDACE toolbox provides two scripts dacefit.m and predictor.m that emulate the behavior of the DACE toolbox ([http://www2.imm.dtu.dk/~hbn/dace/]). Note, that full compatibility between blindDACE and the DACE toolbox is not provided. The scripts merely aim to ease the transition from the DACE toolbox to blindDACE.		The blindDACE toolbox provides two scripts dacefit.m and predictor.m that emulate the behavior of the DACE toolbox ([http://www2.imm.dtu.dk/~hbn/dace/]). Note, that full compatibility between blindDACE and the DACE toolbox is not provided. The scripts merely aim to ease the transition from the DACE toolbox to the blindDACE toolbox.

	Example code:		Example code:

Tdhaene: /* DACE toolbox interface */

2010-03-19T14:26:38Z

DACE toolbox interface

← Older revision		Revision as of 14:26, 19 March 2010
Line 54:		Line 54:
	== DACE toolbox interface ==		== DACE toolbox interface ==

	The blindDACE toolbox provides two scripts dacefit.m and predictor.m that ~~simulate~~ the behavior of the DACE toolbox ([http://www2.imm.dtu.dk/~hbn/dace/]). Note, that full compatibility between blindDACE and the DACE toolbox is not provided. The scripts merely aim to ease the transition from the DACE toolbox to blindDACE.		The blindDACE toolbox provides two scripts dacefit.m and predictor.m that emulate the behavior of the DACE toolbox ([http://www2.imm.dtu.dk/~hbn/dace/]). Note, that full compatibility between blindDACE and the DACE toolbox is not provided. The scripts merely aim to ease the transition from the DACE toolbox to blindDACE.

	Example code:		Example code:

Tdhaene: /* Quick start guide */

2010-03-19T14:25:39Z

Quick start guide

← Older revision		Revision as of 14:25, 19 March 2010
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	However, for convenience wrapper scripts (dacefit, predictor) are provided that emulate the DACE toolbox interface (see [[#DACE toolbox interface\|wrapper scripts]] for more information).		However, for convenience wrapper scripts (dacefit, predictor) are provided that emulate the DACE toolbox interface (see [[#DACE toolbox interface\|wrapper scripts]] for more information).

	~~Assuming we~~ want to fit a dataset of n samples in d dimensions.		Assume you want to fit a dataset of n samples in d dimensions.
	<b>samples</b> holds the input parameters ~~nXd~~ array (each row is one observation) and <b>values</b> is the corresponding ~~nX1~~ array containing the output values.
	<b>lb</b> and <b>ub</b> are ~~1Xd~~ arrays defining the lower bounds and upper bounds, respectively, needed to optimize the hyperparameters. In addition, a ~~start~~ values has to be specified (e.g., <b>theta0</b> is also an ~~1Xd~~ array)		<b>samples</b> holds the input parameters n-by-d array (each row is one observation) and <b>values</b> is the corresponding n-by-1 array containing the output values.
			<b>lb</b> and <b>ub</b> are 1-by-d arrays defining the lower bounds and upper bounds, respectively, needed to optimize the hyperparameters. In addition, a set of starting values has to be specified (e.g., <b>theta0</b> is also an 1-by-d array)

	The example code to fit the dataset is then:		The example code to fit the dataset is then: