Changeset 8956:0abe1bb4e3ee in orange


Ignore:
Timestamp:
09/12/11 17:30:05 (3 years ago)
Author:
mitar
Branch:
default
Convert:
a3962dea6f64861a1edb335cc01c384e77b88385
Message:

Use new Orange website URL.

Location:
orange
Files:
17 edited

Legend:

Unmodified
Added
Removed
  • orange/OrangeCanvas/orngCanvas.pyw

    r8888 r8956  
    728728    def menuOpenOnlineOrangeHelp(self): 
    729729        import webbrowser 
    730         webbrowser.open("http://www.ailab.si/orange/doc/catalog") 
     730        webbrowser.open("http://orange.biolab.si/doc/catalog") 
    731731 
    732732    def menuOpenOnlineCanvasHelp(self): 
    733733        import webbrowser 
    734         #webbrowser.open("http://www.ailab.si/orange/orangeCanvas") # to be added on the web 
    735         webbrowser.open("http://www.ailab.si/orange") 
     734        #webbrowser.open("http://orange.biolab.si/orangeCanvas") # to be added on the web 
     735        webbrowser.open("http://orange.biolab.si") 
    736736 
    737737    def menuCheckForUpdates(self): 
  • orange/OrangeWidgets/Classify/OWC45Tree.py

    r8042 r8956  
    113113            import orngDebugging 
    114114            if not orngDebugging.orngDebuggingEnabled and getattr(self, "__showMessageBox", True):  # Dont show the message box when running debugging scripts 
    115                 QMessageBox.warning( None, "C4.5 plug-in", 'File c45.dll not found. See http://www.ailab.si/orange/doc/reference/C45Learner.htm', QMessageBox.Ok) 
     115                QMessageBox.warning( None, "C4.5 plug-in", 'File c45.dll not found. See http://orange.biolab.si/doc/reference/C45Learner.htm', QMessageBox.Ok) 
    116116                setattr(self, "__showMessageBox", False) 
    117117            return 
  • orange/addOnServer.py

    r8042 r8956  
    108108#              </head> 
    109109#              <body><h1>Orange Add-on Repository %s</h1> 
    110 #                <p>This is an <a href="http://www.ailab.si/orange">Orange</a> add-on repository. Would you like to <a href="upload.html">upload</a> new add-ons?</p> 
     110#                <p>This is an <a href="http://orange.biolab.si">Orange</a> add-on repository. Would you like to <a href="upload.html">upload</a> new add-ons?</p> 
    111111#              </body> 
    112112#            </html>""" % req.uri 
  • orange/doc/INSTALL.linux.txt

    r8042 r8956  
    66 
    77You can download the Orange's nightly packed sources from   
    8 http://www.ailab.si/orange/nightly_builds.html and extract the archive. 
     8http://orange.biolab.si/nightly_builds.html and extract the archive. 
    99 
    1010An alternative is to check out the newest revision directly from SVN  
  • orange/doc/catalog-rst/rst/orange_theme/header.html

    r8118 r8956  
    1212            <div id="orangeimg"><a href="/"><img src="/orange-logo-w.png"></a></div> 
    1313 
    14             <form action="http://www.ailab.si/orange/searchRes.html" id="cse-search-box"> 
     14            <form action="http://orange.biolab.si/searchRes.html" id="cse-search-box"> 
    1515                <input type="hidden" name="cx" value="004435948024671398314:koge-dvl9sc" /> 
    1616                <input type="hidden" name="cof" value="FORID:10" /> 
  • orange/doc/catalog-rst/rst/orange_theme/static/header.html

    r8118 r8956  
    1212            <div id="orangeimg"><a href="/"><img src="/orange-logo-w.png"></a></div> 
    1313 
    14             <form action="http://www.ailab.si/orange/searchRes.html" id="cse-search-box"> 
     14            <form action="http://orange.biolab.si/searchRes.html" id="cse-search-box"> 
    1515                <input type="hidden" name="cx" value="004435948024671398314:koge-dvl9sc" /> 
    1616                <input type="hidden" name="cof" value="FORID:10" /> 
  • orange/doc/catalog/Visualize/ParallelCoordinates.htm

    r6538 r8956  
    4343<p><img class="screenshot" src="ParallelCoordinates-Iris.png" alt="Parallel Coordinates widget"></p> 
    4444 
    45 <p>The <span class="option">Main</span> tab allows the user to choose the subset of attributes to be displayed in the visualization. In case of a class-labeled data set, only one class (vs. the others) may be exposed by selecting it from <span class="option">Target class</span>. Especially with data sets that include many attributes, <span class="option">Optimization Dialog</span> may help to find interesting projections. Currently, this is decided based on a correlation between neighboring attributes in the visualization, where the target s to find visualizations with the highest sum of the absolute value of correlations between neighboring attributes. Snapshot below shows such a visualization which uses five attributes and plots the data set from functional genomics (<a href="http://www.ailab.si/orange/doc/datasets/brown-selected.tab">brown-selected.tab</a>). 
     45<p>The <span class="option">Main</span> tab allows the user to choose the subset of attributes to be displayed in the visualization. In case of a class-labeled data set, only one class (vs. the others) may be exposed by selecting it from <span class="option">Target class</span>. Especially with data sets that include many attributes, <span class="option">Optimization Dialog</span> may help to find interesting projections. Currently, this is decided based on a correlation between neighboring attributes in the visualization, where the target s to find visualizations with the highest sum of the absolute value of correlations between neighboring attributes. Snapshot below shows such a visualization which uses five attributes and plots the data set from functional genomics (<a href="http://orange.biolab.si/doc/datasets/brown-selected.tab">brown-selected.tab</a>). 
    4646 
    4747<p><img class="screenshot" src="ParallelCoordinates-Optimization.png" alt="Axis optimization in parallel coordinates"></p> 
  • orange/doc/catalog/Visualize/Polyviz.htm

    r5811 r8956  
    4343<p><img class="screenshot" src="Polyviz-Iris.png" alt="Polyviz widget"></p> 
    4444 
    45 <p>Just like other point-based visualizations, Polyviz provides support for explorative data analysis and search for interesting visualizations. For further details on both, see the documentation on   <a href="Scatterplot.htm">Scatterplot</a> widget. See the documentation on <a href="Radviz.htm">Radviz</a> for details on various aspects controlled by the <span class="option">Settings</span> tab. The utility of VizRank, an intelligent visualization technique, using <a href="http://www.ailab.si/orange/doc/datasets/brown-selected.tab">brown-selected.tab</a> data set is illustrated with a snapshot below.</p> 
     45<p>Just like other point-based visualizations, Polyviz provides support for explorative data analysis and search for interesting visualizations. For further details on both, see the documentation on   <a href="Scatterplot.htm">Scatterplot</a> widget. See the documentation on <a href="Radviz.htm">Radviz</a> for details on various aspects controlled by the <span class="option">Settings</span> tab. The utility of VizRank, an intelligent visualization technique, using <a href="http://orange.biolab.si/doc/datasets/brown-selected.tab">brown-selected.tab</a> data set is illustrated with a snapshot below.</p> 
    4646 
    4747<p><img class="screenshot" src="Polyviz-VizRank.png" alt="VizRank in Polyviz"></p> 
  • orange/doc/catalog/Visualize/Scatterplot.htm

    r5811 r8956  
    4848<h3>Intelligent Data Visualization</h2> 
    4949 
    50 <p>If a data set has many (many!) attributes, it is impossible to manually scan through all the pairs of attributes to find interesting scatterplots. Intelligent data visualizations techniques are about finding such visualizations automatically. Orange's Scatterplot includes one such tool called VizRank <a href="#Leban2006" title="">(Leban et al., 2006)</a>, that can be in current implementation used only with classification data sets, that is, data sets where instances are labeled with a discrete class. The task of optimization is to find those scatterplot projections, where instances with different class labels are well separated. For example, for a data set <a href="http://www.ailab.si/orange/doc/datasets/brown-selected.tab">brown-selected.tab</a> (comes with Orange installation) the two attributes that best separate instances of different class are displayed in the snapshot below, where we have also switched on the <span class="option">Show Probabilities</span> option from Scatterplot's <span class="option">Settings</span> pane. Notice that this projection appears at the top of <span class="option">Projection list, most interesting first</span>, followed by a list of other potentially interesting projections. Selecting each of these would change the projection displayed in the scatterplot, so the list and associated projections can be inspected in this way.</p> 
     50<p>If a data set has many (many!) attributes, it is impossible to manually scan through all the pairs of attributes to find interesting scatterplots. Intelligent data visualizations techniques are about finding such visualizations automatically. Orange's Scatterplot includes one such tool called VizRank <a href="#Leban2006" title="">(Leban et al., 2006)</a>, that can be in current implementation used only with classification data sets, that is, data sets where instances are labeled with a discrete class. The task of optimization is to find those scatterplot projections, where instances with different class labels are well separated. For example, for a data set <a href="http://orange.biolab.si/doc/datasets/brown-selected.tab">brown-selected.tab</a> (comes with Orange installation) the two attributes that best separate instances of different class are displayed in the snapshot below, where we have also switched on the <span class="option">Show Probabilities</span> option from Scatterplot's <span class="option">Settings</span> pane. Notice that this projection appears at the top of <span class="option">Projection list, most interesting first</span>, followed by a list of other potentially interesting projections. Selecting each of these would change the projection displayed in the scatterplot, so the list and associated projections can be inspected in this way.</p> 
    5151 
    5252<p><img class="screenshot" src="Scatterplot-VizRank-Brown.png" alt="VizRank and scatterplot"></p> 
     
    7373<p><img class="screenshot" src="Scatterplot-VizRank-Scores.png" alt="VizRank and attribute scoring"></p> 
    7474 
    75 <p>List of best-rated projections may also be used for the search and analysis of outliers. The idea is that the outliers are those data instances, which are incorrectly classified in many of the top visualizations. For example, the class of the 33-rd instance in <a href="http://www.ailab.si/orange/doc/datasets/brown-selected.tab">brown-selected.tab</a> should be Resp, but this instance is quite often misclassified as Ribo. The snapshot below shows one particular visualization displaying why such misclassification occurs. Perhaps the most important part of the <span class="option">Outlier Identification</span> window is a list in the lower left (<span class="option">Show predictions for all examples</span>) with a list of candidates for outliers sorted by the probabilities of classification to the right class. In our case, the most likely outlier is the instance 171, followed by an instance 33, both with probabilities of classification to the right class below 0.5.<p> 
     75<p>List of best-rated projections may also be used for the search and analysis of outliers. The idea is that the outliers are those data instances, which are incorrectly classified in many of the top visualizations. For example, the class of the 33-rd instance in <a href="http://orange.biolab.si/doc/datasets/brown-selected.tab">brown-selected.tab</a> should be Resp, but this instance is quite often misclassified as Ribo. The snapshot below shows one particular visualization displaying why such misclassification occurs. Perhaps the most important part of the <span class="option">Outlier Identification</span> window is a list in the lower left (<span class="option">Show predictions for all examples</span>) with a list of candidates for outliers sorted by the probabilities of classification to the right class. In our case, the most likely outlier is the instance 171, followed by an instance 33, both with probabilities of classification to the right class below 0.5.<p> 
    7676 
    7777<p><img class="screenshot" src="Scatterplot-VizRank-Outliers.png" alt="Outliers"></p> 
  • orange/doc/ofb-rst/rst/orange_theme/header.html

    r8118 r8956  
    1212            <div id="orangeimg"><a href="/"><img src="/orange-logo-w.png"></a></div> 
    1313 
    14             <form action="http://www.ailab.si/orange/searchRes.html" id="cse-search-box"> 
     14            <form action="http://orange.biolab.si/searchRes.html" id="cse-search-box"> 
    1515                <input type="hidden" name="cx" value="004435948024671398314:koge-dvl9sc" /> 
    1616                <input type="hidden" name="cof" value="FORID:10" /> 
  • orange/doc/ofb-rst/rst/orange_theme/static/header.html

    r8118 r8956  
    1212            <div id="orangeimg"><a href="/"><img src="/orange-logo-w.png"></a></div> 
    1313 
    14             <form action="http://www.ailab.si/orange/searchRes.html" id="cse-search-box"> 
     14            <form action="http://orange.biolab.si/searchRes.html" id="cse-search-box"> 
    1515                <input type="hidden" name="cx" value="004435948024671398314:koge-dvl9sc" /> 
    1616                <input type="hidden" name="cof" value="FORID:10" /> 
  • orange/doc/ofb-rst/rst/start.rst

    r7010 r8956  
    33 
    44To start Orange scripting, you will need to `download 
    5 <http://www.ailab.si/orange/download.html>`_ and install Orange. Python can 
     5<http://orange.biolab.si/download.html>`_ and install Orange. Python can 
    66be run in a window with a terminal, special integrated environments 
    77(like PythonWin), or shells like `iPython 
  • orange/doc/reference/DomainDepot.htm

    r6538 r8956  
    1010<h1>Domain Depots</h1> 
    1111 
    12 <p>The domain depot mechanism has been partially hidden and mostly replaced by a more efficient, simpler and safer alternative. Please see the page on <a href="http://www.ailab.si/orange/doc/reference/Variable.htm#getExisting">Reuse of attribute descriptors</a>.</p> 
     12<p>The domain depot mechanism has been partially hidden and mostly replaced by a more efficient, simpler and safer alternative. Please see the page on <a href="http://orange.biolab.si/doc/reference/Variable.htm#getExisting">Reuse of attribute descriptors</a>.</p> 
    1313 
    1414</BODY> 
  • orange/doc/sphinx-ext/themes/orange_theme/header.html

    r8118 r8956  
    1212            <div id="orangeimg"><a href="/"><img src="/orange-logo-w.png"></a></div> 
    1313 
    14             <form action="http://www.ailab.si/orange/searchRes.html" id="cse-search-box"> 
     14            <form action="http://orange.biolab.si/searchRes.html" id="cse-search-box"> 
    1515                <input type="hidden" name="cx" value="004435948024671398314:koge-dvl9sc" /> 
    1616                <input type="hidden" name="cof" value="FORID:10" /> 
  • orange/doc/sphinx-ext/themes/orange_theme/static/header.html

    r8118 r8956  
    1212            <div id="orangeimg"><a href="/"><img src="/orange-logo-w.png"></a></div> 
    1313 
    14             <form action="http://www.ailab.si/orange/searchRes.html" id="cse-search-box"> 
     14            <form action="http://orange.biolab.si/searchRes.html" id="cse-search-box"> 
    1515                <input type="hidden" name="cx" value="004435948024671398314:koge-dvl9sc" /> 
    1616                <input type="hidden" name="cof" value="FORID:10" /> 
  • orange/downloadPyd.py

    r8808 r8956  
    88    files = files + ("orangeqt",) 
    99     
    10 baseurl = "http://www.ailab.si/orange/download/binaries/%i%i/" % sys.version_info[:2] 
     10baseurl = "http://orange.biolab.si/download/binaries/%i%i/" % sys.version_info[:2] 
    1111fleurl = baseurl + "%s.pyd" 
    1212 
  • orange/updateOrange.py

    r8042 r8956  
    165165        self.downfile = os.path.join(self.orangeDir, "whatsdown.txt") 
    166166 
    167         self.updateUrl = "http://www.ailab.si/orange/download/update/" 
    168         self.binaryUrl = "http://www.ailab.si/orange/download/binaries/%i%i/" % sys.version_info[:2] 
    169         self.whatsupUrl = "http://www.ailab.si/orange/download/whatsup.txt" 
     167        self.updateUrl = "http://orange.biolab.si/download/update/" 
     168        self.binaryUrl = "http://orange.biolab.si/download/binaries/%i%i/" % sys.version_info[:2] 
     169        self.whatsupUrl = "http://orange.biolab.si/download/whatsup.txt" 
    170170 
    171171        self.updateGroups = [] 
     
    417417        files = "orange", "corn", "statc", "orangeom", "orangene", "_orngCRS" 
    418418 
    419         baseurl = "http://www.ailab.si/orange/download/binaries/%i%i/" % sys.version_info[:2] 
     419        baseurl = "http://orange.biolab.si/download/binaries/%i%i/" % sys.version_info[:2] 
    420420        repository_stamps = dict([tuple(x.split()) for x in urllib.urlopen(baseurl + "stamps_pyd.txt") if x.strip()]) 
    421421        updated = 0 
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