GitHub Actions build openturns/otsvm 6733810987

otsvm/master/_downloads/2421e198bfdefccbcbfa5427d1acaa31/plot_example1.ipynb

@@ -22,7 +22,7 @@
       },
       "outputs": [],
       "source": [
-        "import openturns as ot\nimport otsvm\n\n# create a function, here we create the Sobol function\ndimension = 3\nmeanTh = 1.0\na = ot.Point(dimension)\ninputVariables = ot.Description(dimension)\nformula = \"1.0\"\ncovTh = 1.0\nfor i in range(dimension):\n    a[i] = 0.5 * i\n    covTh = covTh * (1.0 + 1.0 / (3.0 * (1.0 + a[i]) ** 2))\n    inputVariables[i] = \"xi\" + str(i)\n    formula += (\n        \" * ((abs(4.0 * xi\"\n        + str(i)\n        + \" -2.0) + \"\n        + str(a[i])\n        + \") / (1.0 + \"\n        + str(a[i])\n        + \"))\"\n    )\ncovTh = covTh - 1.0\nmodel = ot.SymbolicFunction(inputVariables, ot.Description(1, formula))\n\n# create the input distribution\not.RandomGenerator.SetSeed(0)\nmarginals = ot.DistributionCollection(dimension)\nfor i in range(dimension):\n    marginals[i] = ot.Uniform(0.0, 1.0)\ndistribution = ot.ComposedDistribution(marginals)\n\n# create lists of kernel parameters and tradeoff factors\ntradeoff = [0.01, 0.1, 1, 10, 100, 1000]\nkernel = [0.001, 0.01, 0.1, 1, 10, 100]\n\n# first example : create the problem with an input and output samples:\n# first, we create samples\ndataIn = distribution.getSample(250)\ndataOut = model(dataIn)\n# second, we create our svm regression object, we must select the third parameter\n# in an enumerate in the list { NormalRBF, Linear, Sigmoid, Polynomial }\nalgo = otsvm.SVMRegression(dataIn, dataOut, otsvm.LibSVM.NormalRbf)\n# third, we set kernel parameter and tradeoff factor\nalgo.setTradeoffFactor(tradeoff)\nalgo.setKernelParameter(kernel)\n# Perform the algorithm\nalgo.run()\n# Stream out the results\nresult = algo.getResult()\n# get the residual error\nresidual = result.getResiduals()\n# get the relative error\nrelativeError = result.getRelativeErrors()\nprint(f\"residual={residual} error={relativeError}\")\n\n# second example : create the problem with an experiment plane:\n# first, we create the plane\nmyExperiment = ot.MonteCarloExperiment(distribution, 250)\ndataIn = myExperiment.generate()\ndataOut = model(dataIn)\n# second, we create our svm regression object, the first parameter is the\n# function\nalgo2 = otsvm.SVMRegression(dataIn, dataOut, otsvm.LibSVM.Linear)\n# third, we set kernel parameter and tradeoff factor\nalgo2.setTradeoffFactor(tradeoff)\nalgo2.setKernelParameter(kernel)\n# Perform the algorithm\nalgo2.run()\n# Stream out the results\nresult = algo2.getResult()\n# get the residual error\nresidual = result.getResiduals()\n# get the relative error\nrelativeError = result.getRelativeErrors()\nprint(f\"residual={residual} error={relativeError}\")\n\n# third example is here to present you the SVMResourceMap class.\n# Users can fix others parameters like the degree and the constant of the\n# Polynomial Kernel,the cacheSize, the number of folds or the epsilon\n# first, we create samples\ndataIn = distribution.getSample(250)\ndataOut = model(dataIn)\n# second, we create our svm regression object\n# here, we select the Polynomial Kernel but by default his degree is 3. We want a\n# degree of 2\not.ResourceMap.Set(\"LibSVM-DegreePolynomialKernel\", \"2\")\n# now the degree of the Polynomial kernel is 2\nalgo = otsvm.SVMRegression(dataIn, dataOut, otsvm.LibSVM.Polynomial)\n# third, we set kernel parameter and tradeoff factor\nalgo.setTradeoffFactor(tradeoff)\nalgo.setKernelParameter(kernel)\n# Perform the algorithm\n#algo.run()\n## Stream out the results\n#result = algo.getResult()\n#print(result)\n## get the residual error\n#residual = result.getResiduals()\n## get the relative error\n#relativeError = result.getRelativeErrors()"
+        "import openturns as ot\nimport otsvm\n\n# create a function, here we create the Sobol function\ndimension = 3\nmeanTh = 1.0\na = ot.Point(dimension)\ninputVariables = ot.Description(dimension)\nformula = \"1.0\"\ncovTh = 1.0\nfor i in range(dimension):\n    a[i] = 0.5 * i\n    covTh = covTh * (1.0 + 1.0 / (3.0 * (1.0 + a[i]) ** 2))\n    inputVariables[i] = \"xi\" + str(i)\n    formula += (\n        \" * ((abs(4.0 * xi\"\n        + str(i)\n        + \" -2.0) + \"\n        + str(a[i])\n        + \") / (1.0 + \"\n        + str(a[i])\n        + \"))\"\n    )\ncovTh = covTh - 1.0\nmodel = ot.SymbolicFunction(inputVariables, ot.Description(1, formula))\n\n# create the input distribution\not.RandomGenerator.SetSeed(0)\nmarginals = ot.DistributionCollection(dimension)\nfor i in range(dimension):\n    marginals[i] = ot.Uniform(0.0, 1.0)\ndistribution = ot.ComposedDistribution(marginals)\n\n# create lists of kernel parameters and tradeoff factors\ntradeoff = [0.01, 0.1, 1, 10, 100, 1000]\nkernel = [0.001, 0.01, 0.1, 1, 10, 100]\n\n# first example : create the problem with an input and output samples:\n# first, we create samples\ndataIn = distribution.getSample(250)\ndataOut = model(dataIn)\n# second, we create our svm regression object, we must select the third parameter\n# in an enumerate in the list { NormalRBF, Linear, Sigmoid, Polynomial }\nalgo = otsvm.LibSVMRegression(dataIn, dataOut, otsvm.LibSVM.NormalRbf)\n# third, we set kernel parameter and tradeoff factor\nalgo.setTradeoffFactor(tradeoff)\nalgo.setKernelParameter(kernel)\n# Perform the algorithm\nalgo.run()\n# Stream out the results\nresult = algo.getResult()\n# get the residual error\nresidual = result.getResiduals()\n# get the relative error\nrelativeError = result.getRelativeErrors()\nprint(f\"residual={residual} error={relativeError}\")\n\n# second example : create the problem with an experiment plane:\n# first, we create the plane\nmyExperiment = ot.MonteCarloExperiment(distribution, 250)\ndataIn = myExperiment.generate()\ndataOut = model(dataIn)\n# second, we create our svm regression object, the first parameter is the\n# function\nalgo2 = otsvm.LibSVMRegression(dataIn, dataOut, otsvm.LibSVM.Linear)\n# third, we set kernel parameter and tradeoff factor\nalgo2.setTradeoffFactor(tradeoff)\nalgo2.setKernelParameter(kernel)\n# Perform the algorithm\nalgo2.run()\n# Stream out the results\nresult = algo2.getResult()\n# get the residual error\nresidual = result.getResiduals()\n# get the relative error\nrelativeError = result.getRelativeErrors()\nprint(f\"residual={residual} error={relativeError}\")\n\n# third example is here to present you the SVMResourceMap class.\n# Users can fix others parameters like the degree and the constant of the\n# Polynomial Kernel,the cacheSize, the number of folds or the epsilon\n# first, we create samples\ndataIn = distribution.getSample(250)\ndataOut = model(dataIn)\n# second, we create our svm regression object\n# here, we select the Polynomial Kernel but by default his degree is 3. We want a\n# degree of 2\not.ResourceMap.Set(\"LibSVM-DegreePolynomialKernel\", \"2\")\n# now the degree of the Polynomial kernel is 2\nalgo = otsvm.LibSVMRegression(dataIn, dataOut, otsvm.LibSVM.Polynomial)\n# third, we set kernel parameter and tradeoff factor\nalgo.setTradeoffFactor(tradeoff)\nalgo.setKernelParameter(kernel)\n# Perform the algorithm\n#algo.run()\n## Stream out the results\n#result = algo.getResult()\n#print(result)\n## get the residual error\n#residual = result.getResiduals()\n## get the relative error\n#relativeError = result.getRelativeErrors()"
       ]
     }
   ],
@@ -42,7 +42,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.11.3"
+      "version": "3.11.5"
     }
   },
   "nbformat": 4,

otsvm/master/_downloads/cd55f92c8d819b776c6a1e904e5efb23/plot_example2.ipynb

@@ -42,7 +42,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.11.3"
+      "version": "3.11.5"
     }
   },
   "nbformat": 4,

otsvm/master/_downloads/ce615eeeaf183d728a05b647cb915a04/plot_example1.py

@@ -64,7 +64,7 @@
 dataOut = model(dataIn)
 # second, we create our svm regression object, we must select the third parameter
 # in an enumerate in the list { NormalRBF, Linear, Sigmoid, Polynomial }
-algo = otsvm.SVMRegression(dataIn, dataOut, otsvm.LibSVM.NormalRbf)
+algo = otsvm.LibSVMRegression(dataIn, dataOut, otsvm.LibSVM.NormalRbf)
 # third, we set kernel parameter and tradeoff factor
 algo.setTradeoffFactor(tradeoff)
 algo.setKernelParameter(kernel)
@@ -85,7 +85,7 @@
 dataOut = model(dataIn)
 # second, we create our svm regression object, the first parameter is the
 # function
-algo2 = otsvm.SVMRegression(dataIn, dataOut, otsvm.LibSVM.Linear)
+algo2 = otsvm.LibSVMRegression(dataIn, dataOut, otsvm.LibSVM.Linear)
 # third, we set kernel parameter and tradeoff factor
 algo2.setTradeoffFactor(tradeoff)
 algo2.setKernelParameter(kernel)
@@ -110,7 +110,7 @@
 # degree of 2
 ot.ResourceMap.Set("LibSVM-DegreePolynomialKernel", "2")
 # now the degree of the Polynomial kernel is 2
-algo = otsvm.SVMRegression(dataIn, dataOut, otsvm.LibSVM.Polynomial)
+algo = otsvm.LibSVMRegression(dataIn, dataOut, otsvm.LibSVM.Polynomial)
 # third, we set kernel parameter and tradeoff factor
 algo.setTradeoffFactor(tradeoff)
 algo.setKernelParameter(kernel)

otsvm/master/_sources/auto_examples/plot_example1.rst.txt

@@ -86,7 +86,7 @@ Usually, the algorithm always converges, but this can take a long while, mostly
     dataOut = model(dataIn)
     # second, we create our svm regression object, we must select the third parameter
     # in an enumerate in the list { NormalRBF, Linear, Sigmoid, Polynomial }
-    algo = otsvm.SVMRegression(dataIn, dataOut, otsvm.LibSVM.NormalRbf)
+    algo = otsvm.LibSVMRegression(dataIn, dataOut, otsvm.LibSVM.NormalRbf)
     # third, we set kernel parameter and tradeoff factor
     algo.setTradeoffFactor(tradeoff)
     algo.setKernelParameter(kernel)
@@ -107,7 +107,7 @@ Usually, the algorithm always converges, but this can take a long while, mostly
     dataOut = model(dataIn)
     # second, we create our svm regression object, the first parameter is the
     # function
-    algo2 = otsvm.SVMRegression(dataIn, dataOut, otsvm.LibSVM.Linear)
+    algo2 = otsvm.LibSVMRegression(dataIn, dataOut, otsvm.LibSVM.Linear)
     # third, we set kernel parameter and tradeoff factor
     algo2.setTradeoffFactor(tradeoff)
     algo2.setKernelParameter(kernel)
@@ -132,7 +132,7 @@ Usually, the algorithm always converges, but this can take a long while, mostly
     # degree of 2
     ot.ResourceMap.Set("LibSVM-DegreePolynomialKernel", "2")
     # now the degree of the Polynomial kernel is 2
-    algo = otsvm.SVMRegression(dataIn, dataOut, otsvm.LibSVM.Polynomial)
+    algo = otsvm.LibSVMRegression(dataIn, dataOut, otsvm.LibSVM.Polynomial)
     # third, we set kernel parameter and tradeoff factor
     algo.setTradeoffFactor(tradeoff)
     algo.setKernelParameter(kernel)
@@ -162,7 +162,7 @@ Usually, the algorithm always converges, but this can take a long while, mostly
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  6.452 seconds)
+   **Total running time of the script:** (0 minutes 4.452 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_example1.py:

otsvm/master/_sources/auto_examples/plot_example2.rst.txt

@@ -90,7 +90,7 @@ otsvm enables to:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  2.537 seconds)
+   **Total running time of the script:** (0 minutes 1.962 seconds)
 
 
 .. _sphx_glr_download_auto_examples_plot_example2.py:

otsvm/master/_sources/auto_examples/sg_execution_times.rst.txt

@@ -6,10 +6,10 @@
 
 Computation times
 =================
-**00:08.990** total execution time for **auto_examples** files:
+**00:06.414** total execution time for **auto_examples** files:
 
 +-----------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_example1.py` (``plot_example1.py``) | 00:06.452 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_example1.py` (``plot_example1.py``) | 00:04.452 | 0.0 MB |
 +-----------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_plot_example2.py` (``plot_example2.py``) | 00:02.537 | 0.0 MB |
+| :ref:`sphx_glr_auto_examples_plot_example2.py` (``plot_example2.py``) | 00:01.962 | 0.0 MB |
 +-----------------------------------------------------------------------+-----------+--------+

otsvm/master/_static/basic.css

@@ -237,6 +237,10 @@ a.headerlink {
     visibility: hidden;
 }
 
+a:visited {
+    color: #551A8B;
+}
+
 h1:hover > a.headerlink,
 h2:hover > a.headerlink,
 h3:hover > a.headerlink,
@@ -670,6 +674,16 @@ dd {
     margin-left: 30px;
 }
 
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
 dl > dd:last-child,
 dl > dd:last-child > :last-child {
     margin-bottom: 0;
@@ -738,6 +752,14 @@ abbr, acronym {
     cursor: help;
 }
 
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
 /* -- code displays --------------------------------------------------------- */
 
 pre {

otsvm/master/_static/classic.css

@@ -129,7 +129,7 @@ a {
 }
 
 a:visited {
-    color: #355f7c;
+    color: #551a8b;
     text-decoration: none;
 }
 

otsvm/master/_static/documentation_options.js

@@ -1,5 +1,4 @@
-var DOCUMENTATION_OPTIONS = {
-    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
+const DOCUMENTATION_OPTIONS = {
     VERSION: '0.12',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,

otsvm/master/_static/pygments.css

@@ -17,6 +17,7 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
 .highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
 .highlight .gd { color: #A00000 } /* Generic.Deleted */
 .highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
 .highlight .gr { color: #FF0000 } /* Generic.Error */
 .highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
 .highlight .gi { color: #00A000 } /* Generic.Inserted */

otsvm/master/_static/searchtools.js

@@ -57,12 +57,12 @@ const _removeChildren = (element) => {
 const _escapeRegExp = (string) =>
   string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
 
-const _displayItem = (item, searchTerms) => {
+const _displayItem = (item, searchTerms, highlightTerms) => {
   const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
-  const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
   const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
   const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
   const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
 
   const [docName, title, anchor, descr, score, _filename] = item;
 
@@ -75,20 +75,24 @@ const _displayItem = (item, searchTerms) => {
     if (dirname.match(/\/index\/$/))
       dirname = dirname.substring(0, dirname.length - 6);
     else if (dirname === "index/") dirname = "";
-    requestUrl = docUrlRoot + dirname;
+    requestUrl = contentRoot + dirname;
     linkUrl = requestUrl;
   } else {
     // normal html builders
-    requestUrl = docUrlRoot + docName + docFileSuffix;
+    requestUrl = contentRoot + docName + docFileSuffix;
     linkUrl = docName + docLinkSuffix;
   }
   let linkEl = listItem.appendChild(document.createElement("a"));
   linkEl.href = linkUrl + anchor;
   linkEl.dataset.score = score;
   linkEl.innerHTML = title;
-  if (descr)
+  if (descr) {
     listItem.appendChild(document.createElement("span")).innerHTML =
       " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
   else if (showSearchSummary)
     fetch(requestUrl)
       .then((responseData) => responseData.text())
@@ -97,6 +101,9 @@ const _displayItem = (item, searchTerms) => {
           listItem.appendChild(
             Search.makeSearchSummary(data, searchTerms)
           );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
       });
   Search.output.appendChild(listItem);
 };
@@ -115,14 +122,15 @@ const _finishSearch = (resultCount) => {
 const _displayNextItem = (
   results,
   resultCount,
-  searchTerms
+  searchTerms,
+  highlightTerms,
 ) => {
   // results left, load the summary and display it
   // this is intended to be dynamic (don't sub resultsCount)
   if (results.length) {
-    _displayItem(results.pop(), searchTerms);
+    _displayItem(results.pop(), searchTerms, highlightTerms);
     setTimeout(
-      () => _displayNextItem(results, resultCount, searchTerms),
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
       5
     );
   }
@@ -360,7 +368,7 @@ const Search = {
     // console.info("search results:", Search.lastresults);
 
     // print the results
-    _displayNextItem(results, results.length, searchTerms);
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
   },
 
   /**

otsvm/master/_static/sg_gallery-dataframe.css

@@ -19,6 +19,7 @@ table.dataframe {
   color: var(--sg-text-color);
   font-size: 12px;
   table-layout: fixed;
+  width: auto;
 }
 table.dataframe thead {
   border-bottom: 1px solid var(--sg-text-color);

otsvm/master/_static/sphinx_highlight.js

@@ -29,14 +29,19 @@ const _highlight = (node, addItems, text, className) => {
       }
 
       span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
       parent.insertBefore(
         span,
         parent.insertBefore(
-          document.createTextNode(val.substr(pos + text.length)),
+          rest,
           node.nextSibling
         )
       );
       node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
 
       if (isInSVG) {
         const rect = document.createElementNS(
@@ -140,5 +145,10 @@ const SphinxHighlight = {
   },
 };
 
-_ready(SphinxHighlight.highlightSearchWords);
-_ready(SphinxHighlight.initEscapeListener);
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});