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package { import flash.display.*; import flash.filters.BevelFilter; import flash.events.MouseEvent; public class ch25ex2 extends Sprite { protected var button:Sprite; public function ch25ex2() { button = new Sprite(); button.graphics.beginFill(0xaaaaaa); button.graphics.drawRoundRect(-50, -10, 100, 20, 6, 6); button.graphics.endFill(); button.filters = [new BevelFilter(2)]; button.buttonMode = true; button.x = stage.stageWidth / 2; button.y = stage.stageHeight / 2; addChild(button); button.addEventListener(MouseEvent.CLICK, onButtonClick); } protected function onButtonClick(event:MouseEvent):void { doSomethingTricky(event.localX, event.localY); } protected function doSomethingTricky(x:Number, y:Number):void { var distance:Number = Math.sqrt(x * x + y * y); trace("Distance:", distance); } } }



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The debugger is halted at a breakpoint set at the line presented in bold in the preceding code. The Variables panel in Figure 25-5 displays all the variables in scope. This includes local variables bound to the arguments of a method, such as x and y; local variables created with var, such as distance; and instance variables of the class the code is executing in the scope of in other words, everything under this. If you were to expand the view of this, you would nd the instance variable button, but also all the properties of a Sprite such as scaleX and visible, because the example extends Sprite. The variables in scope depend on where in the code you re looking, and they represent the variables state at the moment in time you are looking at. The line that your program is about to run is marked

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Jun 10, 2014 · How to configure a Shared Network Printer in Windows 7, 8, or 10 - Duration: 45:​12. Rohit ...Duration: 1:38 Posted: Jun 10, 2014

In subsection 2.5, we outlined the conformal geometry Gn+1 1 , which includes spheres. This is a potential geometric entity which can be used for encapsulating clusters. In this section, we present a new way to compress data in order to facilitate the learning of support multivector machines. First we use a simple self-organizing network for the preprocessing of the clusters. Each one of them is modeled in the conformal space using spheres, see Figure 21.6(c). Note the interesting point: our data have been reduced to a small bunch of spheres, which in conformal geometry are represented simply as multivectors s Gn+1 1 of (n + 2)th dimension. Thereafter we supply these spheres to an SMVM for obtaining the optimal hyperplane, see Figure 21.6(d). In the case of nonlinear separable problems, we can apply the SMVM with Gaussian kernels. You can see now the difference between the McCulloch Pitts neuron in Figure 21.6(a) and the conformal neuron in Figure 21.6(e), the conformal neuron gets as input geometric entities of the conformal geometric algebra. The separating hyperplane in Figure 21.6(d) denoted by w (one element of the version space) can be found by an SMVM. However, we cannot guarantee that the point Figure 21.6(c) of the version space is a Tshebysheff or Bayes point. The design of an SMVM which will yield a hyperplane which approaches the Bayes point is one of our current research concerns.

with an arrow, which in Figure 25-5 is superimposed over the breakpoint symbol because the program is halted exactly at the breakpoint. You can see that the distance variable is unde ned. That s because the current line calculates distance, and it has not run yet. You can see the values of x and y, and you can see the type of this, which veri es the class that is currently in context. You can con gure the Variables panel to show all the elds of a class, including its private instance variables, implicit getter functions and derived properties, constants, and static variables, by using the Variables panel menu (the icon with the downward facing arrow on the top right corner of the panel).

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