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The ReplicatorActivity normally processes all elements in the InitialChildData collection, creating an instance of the child activity for each one. However, you can optionally define a condition that is capable of interrupting the normal execution of the child activities. The ReplicatorActivity includes an UntilCondition property that you can define using a code or rule condition. This condition is checked when the ReplicatorActivity first begins execution, prior to the creation of any child activities. It is also checked again after each child activity completes. If this condition evaluates to true, no further processing of activities takes place. No new child activities are created, and any that might be executing are canceled. Think of this as a C# break statement that interrupts the normal operation of the foreach. Because it is possible that not all child instances completed, the ReplicatorActivity provides the AllChildrenComplete property. A value of true for this property indicates that all child instances completed.

In this task, we will attempt to predict the probability of a batter in a baseball league to attain a certain number of hits in a given day. We will predict this information only knowing how many hits he had in the past season. Therefore, our predictive analytics model will use a single variable. This will

month29, month30, month31 ) + @"/\d{4}$"); public void Run(string fileName) { String line; int lineNbr = 0; using (StreamReader sr = new StreamReader(fileName)) { while(null != (line = sr.ReadLine())) { lineNbr++; if (_Regex.IsMatch(line)) { Console.WriteLine("Found match '{0}' at line {1}", line, lineNbr); } } } }

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The ConditionedActivityGroup (sometimes called the CAG) is a composite activity that enables the construction of fairly complex workflows. It is really a hybrid activity, exhibiting some of the behavior of a ParallelActivity and a WhileActivity. You use the ConditionedActivityGroup by adding one or more child activities that you wish to execute. Like the ParallelActivity, child activities are executed from left to right, and each child activity is given a turn at execution. If the child is a composite, only one of its children is executed during any given turn. Each child activity can optionally declare a WhenCondition. This can be a code or rule condition that determines whether the child activity should execute. If a condition is defined and it evaluates to true, the child is executed once during its next turn. The child activity will continue to execute once during its turn until the WhenCondition evaluates to false. If no WhenCondition is defined, the child activity is executed only once. The WhenCondition is evaluated when the CAG first begins execution, and also each time one of its direct child activities completes. Because the conditions are evaluated over and over again, it is possible for the actions of one child to have an impact on the condition for another child. For example, the condition for the first child may initially evaluate to false. But a subsequent child may modify a

CHAPTER 9 PREDICTIVE ANALYTICS (WHAT-IF MODELING)

workflow variable or property that is used in the first child s condition. When the conditions are evaluated again, the change in variable or property value may cause the first child to execute. The ConditionedActivityGroup also supports an optional UntilCondition. Like the other condition properties, this can be a code or a rule condition. If defined, this property specifies a condition that causes the CAG to end execution early. If the UntilCondition is not defined, the CAG completes when all of its children have completed and no WhenCondition evaluates to true. With the UntilCondition defined, the CAG will end when the UntilCondition is true, or when all child activities have completed normally.

make the model very simple to comprehend. This is a classical example of using recent historic data to predict the outcome of a future event. There are many statistical algorithms that we can use in order to predict this event. Some are simple and some are more complex and provide varying degrees of good insight. In order to forecast the future, this coding scenario will use the Poisson probability distribution in order to provide a good researched guess of a certain event happening. This statistical distribution is a great way to predict simple future events having known the average number of times these events happened in a past time frame. In our scenario, we will know how many hits the batter has had in the past season. The Poisson distribution function is defined in Figure 9-10. If you are not familiar with the formula, it might look scary, but it is actually pretty simple.

public static void Main( string[] args ) { Recipe r = new Recipe(); r.Run(args[0]); } }

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