From b891dcb26a14c45405bb4cad1883bcd7b6d1e77c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Nicol=C3=B2=20Fusi?= Date: Mon, 11 Mar 2013 04:01:13 -0700 Subject: [PATCH] Create gh-pages branch via GitHub --- images/arrow-down.png | Bin 0 -> 423 bytes images/octocat-small.png | Bin 0 -> 570 bytes index.html | 55 +++++ javascripts/scale.fix.js | 20 ++ params.json | 1 + stylesheets/pygment_trac.css | 69 ++++++ stylesheets/styles.css | 413 +++++++++++++++++++++++++++++++++++ 7 files changed, 558 insertions(+) create mode 100644 images/arrow-down.png create mode 100644 images/octocat-small.png create mode 100644 index.html create mode 100644 javascripts/scale.fix.js create mode 100644 params.json create mode 100644 stylesheets/pygment_trac.css create mode 100644 stylesheets/styles.css diff --git a/images/arrow-down.png b/images/arrow-down.png new file mode 100644 index 0000000000000000000000000000000000000000..585b0bddba878b95acc961fc5c0c55c3ea2e75db GIT binary patch literal 423 zcmeAS@N?(olHy`uVBq!ia0vp^JU}eW!3HFke0{SLNU;<^miKrK zR4D>d>0FeWSdy8arx22vo62CJZ=!E#Q zuUEhHFKq}eVcf!fOXk2252o^`A0Pfx?pl-`?tS^o=dZa}n)o-rOj#-_e`l7jy7u+r zhj0Hd|L1+)z+f_A|GJISy4qrPpME*#k;S{#M4)d)HS2xXctpM~nN-ejV$=G%1)}zh z6_&;B=NTB*Rz&o)EN^QNw)=Nauj9or^J@~D*Th}TXM8@bL&NZxPHmc!SKl9%dr^gl zqh@m|zG6sNdzVY`kauM4^m$&|dG9N?-~61nSXA6TxH5R=Pxc4Kow9qMI-LOq1%s!n KpUXO@geCw=gQOS$ literal 0 HcmV?d00001 diff --git a/images/octocat-small.png b/images/octocat-small.png new file mode 100644 index 0000000000000000000000000000000000000000..66c25398dd9090905e37aa2d48bb2d77a0ac6255 GIT binary patch literal 570 zcmV-A0>%A_P)V>IRB3Hx05~r+FEKJgdgKHE00ELo zL_t(Ijg^xed`v89bwq|>- zBcAI>wNjQ~{V)H%tlWtR3ZPgl_WI*s7zQut?CiWv)3hNC$Q&OX?xs?y7lFshPE4M* zWwYbCv9ZzqmMOqA&6xTyFpvlX0a(^MlwlaPupofy>3N$E3)SoOTg`Kw1aKY(ER{+J z8i40IIbbma`(6R7dL#-k0u){W3czrO-f82x&g$~grz@IyWqNvQc6)p4Nm@33tb3m8 zqya>Phsa&m{#?w>&fMT<@_Edvm9hja12DK_rqO6@Dy6#qH=`bjY5@o|v#Lj;wod}b>JrdoE#olbS0=7RdB$LTJ@R`4#*!KNI_tZAjO+JC<^Z)<=07*qo IM6N<$f|}m$2LJ#7 literal 0 HcmV?d00001 diff --git a/index.html b/index.html new file mode 100644 index 00000000..1e3ed180 --- /dev/null +++ b/index.html @@ -0,0 +1,55 @@ + + + + + + GPy by SheffieldML + + + + + + + + +
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+

GPy

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Gaussian processes framework in python

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This project is maintained by SheffieldML

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+

GPy

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In probability theory and statistics, a Gaussian process is a stochastic process whose realizations consist of random values associated with every point in a range of times (or of space) such that each such random variable has a normal distribution. Moreover, every finite collection of those random variables has a multivariate normal distribution. The concept of Gaussian processes is named after Carl Friedrich Gauss because it is based on the notion of the normal distribution which is often called the Gaussian distribution. In fact, one way of thinking of a Gaussian process is as an infinite-dimensional generalization of the multivariate normal distribution. +A Gaussian processes framework in python

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A key fact of Gaussian processes is that they can be completely defined by their second-order statistics.[2] Thus, if a Gaussian process is assumed to have mean zero, defining the covariance function completely defines the process' behaviour. The covariance matrix K between all the pair of points x and x' specifies a distribution on functions and is known as the Gram matrix. Importantly, because every valid covariance function is a scalar product of vectors, by construction the matrix K is a non-negative definite matrix. Equivalently, the covariance function K is a non-negative definite function in the sense that for every pair x and x' , K(x,x')≥ 0, if K(,) >0 then K is called positive definite. Importantly the non-negative definiteness of K enables its spectral decomposition using the Karhunen-Loeve expansion. Basic aspects that can be defined through the covariance function are the process' stationarity, isotropy, smoothness and periodicity.[6][7] +Stationarity refers to the process' behaviour regarding the separation of any two points x and x' . If the process is stationary, it depends on their separation, x - x' , while if non-stationary it depends on the actual position of the points x and x'; an example of a stationary process is the Ornstein–Uhlenbeck process. On the contrary, the special case of an Ornstein–Uhlenbeck process, a Brownian motion process, is non-stationary. +If the process depends only on |x - x'|, the Euclidean distance (not the direction) between x and x' then the process is considered isotropic. A process that is concurrently stationary and isotropic is considered to be homogeneous;[8] in practice these properties reflect the differences (or rather the lack of them) in the behaviour of the process given the location of the observer. +Ultimately Gaussian processes translate as taking priors on functions and the smoothness of these priors can be induced by the covariance function.[6] If we expect that for "near-by" input points x and x' their corresponding output points y and y' to be "near-by" also, then the assumption of smoothness is present. If we wish to allow for significant displacement then we might choose a rougher covariance function. Extreme examples of the behaviour is the Ornstein–Uhlenbeck covariance function and the squared exponential where the former is never differentiable and the latter infinitely differentiable.

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+ + + + \ No newline at end of file diff --git a/javascripts/scale.fix.js b/javascripts/scale.fix.js new file mode 100644 index 00000000..08716c00 --- /dev/null +++ b/javascripts/scale.fix.js @@ -0,0 +1,20 @@ +fixScale = function(doc) { + + var addEvent = 'addEventListener', + type = 'gesturestart', + qsa = 'querySelectorAll', + scales = [1, 1], + meta = qsa in doc ? doc[qsa]('meta[name=viewport]') : []; + + function fix() { + meta.content = 'width=device-width,minimum-scale=' + scales[0] + ',maximum-scale=' + scales[1]; + doc.removeEventListener(type, fix, true); + } + + if ((meta = meta[meta.length - 1]) && addEvent in doc) { + fix(); + scales = [.25, 1.6]; + doc[addEvent](type, fix, true); + } + +}; \ No newline at end of file diff --git a/params.json b/params.json new file mode 100644 index 00000000..1b048756 --- /dev/null +++ b/params.json @@ -0,0 +1 @@ +{"name":"GPy","tagline":"Gaussian processes framework in python","body":"GPy\r\n===\r\nIn probability theory and statistics, a Gaussian process is a stochastic process whose realizations consist of random values associated with every point in a range of times (or of space) such that each such random variable has a normal distribution. Moreover, every finite collection of those random variables has a multivariate normal distribution. The concept of Gaussian processes is named after Carl Friedrich Gauss because it is based on the notion of the normal distribution which is often called the Gaussian distribution. In fact, one way of thinking of a Gaussian process is as an infinite-dimensional generalization of the multivariate normal distribution.\r\nA Gaussian processes framework in python\r\n\r\nA key fact of Gaussian processes is that they can be completely defined by their second-order statistics.[2] Thus, if a Gaussian process is assumed to have mean zero, defining the covariance function completely defines the process' behaviour. The covariance matrix K between all the pair of points x and x' specifies a distribution on functions and is known as the Gram matrix. Importantly, because every valid covariance function is a scalar product of vectors, by construction the matrix K is a non-negative definite matrix. Equivalently, the covariance function K is a non-negative definite function in the sense that for every pair x and x' , K(x,x')≥ 0, if K(,) >0 then K is called positive definite. Importantly the non-negative definiteness of K enables its spectral decomposition using the Karhunen-Loeve expansion. Basic aspects that can be defined through the covariance function are the process' stationarity, isotropy, smoothness and periodicity.[6][7]\r\nStationarity refers to the process' behaviour regarding the separation of any two points x and x' . If the process is stationary, it depends on their separation, x - x' , while if non-stationary it depends on the actual position of the points x and x'; an example of a stationary process is the Ornstein–Uhlenbeck process. On the contrary, the special case of an Ornstein–Uhlenbeck process, a Brownian motion process, is non-stationary.\r\nIf the process depends only on |x - x'|, the Euclidean distance (not the direction) between x and x' then the process is considered isotropic. A process that is concurrently stationary and isotropic is considered to be homogeneous;[8] in practice these properties reflect the differences (or rather the lack of them) in the behaviour of the process given the location of the observer.\r\nUltimately Gaussian processes translate as taking priors on functions and the smoothness of these priors can be induced by the covariance function.[6] If we expect that for \"near-by\" input points x and x' their corresponding output points y and y' to be \"near-by\" also, then the assumption of smoothness is present. If we wish to allow for significant displacement then we might choose a rougher covariance function. Extreme examples of the behaviour is the Ornstein–Uhlenbeck covariance function and the squared exponential where the former is never differentiable and the latter infinitely differentiable.\r\n\r\n\r\n* [Online documentation](https://gpy.readthedocs.org/en/latest/)\r\n* [Unit tests (Travis-CI)](https://travis-ci.org/SheffieldML/GPy)","google":"","note":"Don't delete this file! 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