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This item is really a Device for Understanding to detect objects in images based on a list of labeled pictures. The teaching course of action generates an object_detector which may be used to predict the spots of objects in new images. It learns the parameter vector by formulating the issue as a structural SVM issue.
This perform computes the modularity of a specific graph clustering. This is the selection that lets you know how very good the clustering is. Especially, it is the evaluate optimized because of the newman_cluster routine.
EXCLUDE = # The EXCLUDE_SYMLINKS tag can be employed choose if files or # directories which are symbolic links (a Unix filesystem aspect) are excluded # from the enter.
EXTRA_PACKAGES = # The LATEX_HEADER tag can be used to specify a private LaTeX header for # the created latex document.
RECURSIVE = NO # The EXCLUDE tag may be used to specify information and/or directories That ought to # excluded from the Enter resource information. By doing this you can certainly exclude a # subdirectory from the Listing tree whose root is specified with the Enter tag.
This object is a Instrument for tagging layers in the deep neural community. These tags allow it to be simple to confer with the tagged layer in other parts of your code. Precisely, this object provides a brand new layer onto a deep neural community. Nevertheless, this layer simply performs the identification remodel.
If established to NO, the find out JavaDoc # opinions will behave just like the Qt-fashion responses (Therefore demanding an # explicit @brief command for a quick description.
It truly is object oriented. That basically signifies which you can divide This system into scaled-down sections and perform.
GENERATE_HTMLHELP = NO # If your GENERATE_HTMLHELP tag Get More Information is about to YES, the CHM_FILE tag can # be accustomed to specify the file identify in the ensuing .chm file. You # can include a path before the file if The end result shouldn't be # created to your html output directory.
This object is really a Resource for Mastering the burden vector required to use a sequence_labeler item. It learns the parameter vector by formulating the trouble as a structural SVM trouble. The general method is mentioned inside the paper: Hidden Markov Aid Vector Machines by Y.
Split up very long Boolean expressions and parameter lists onto individual traces with acceptable indentation for alignment. if( longVariableNameAbc < longVariableNameDef && longVariableNameIjk < longVariableNameXyz) Braces: The above case in point also exhibits using "unsnuggled braces" which I preffer (but some Other folks don't). Decide 1 style and follow it on your challenge.
As soon as the max dictionary sizing is arrived at each new position kicks out a former point. This is performed by taking away the dictionary vector that has the smallest projection distance on to the Some others. Which is, the "minimum linearly impartial" vector is removed to create place for the new 1.
GRAPHICAL_HIERARCHY find more info = Certainly # In case the DIRECTORY_GRAPH, SHOW_DIRECTORIES and HAVE_DOT tags are established to YES # then doxygen will demonstrate the dependencies a directory has on other directories # within a graphical way. The dependency relations are based on the #contain
around the command in curly braces: if ( grep -s junk $1 ) then echo "We identified junk in file $1" endif Notice that if (`grep junk $one`) would not do the job for the reason that This could lead to grep's output to generally be substituted into your expression, but in silent method, there