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function -> Num 1 -> Num 0 -> simpSum (simpDeriv e1, simpDeriv e2) -> simpSum (simpProd (e1, simpDeriv e2), simpProd (e2, simpDeriv e1))

See Table 6-2 for an example of showing only data records that match these advanced filter criteria: sales dates after January 2, product names beginning with the letter G, and units sold in excess of 100. Table 6-2. Advanced Filter Criteria for Data Records

These measures produce significant improvement over the previous naive approach, but they don t place the result in a normal form, as the following shows: > simpDeriv e3;; val it : Expr = x*2+x*2

However, you can t implement all simplifications using local rules; for example, collecting like terms across a polynomial involves looking at every term of the polynomial.

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Remember you have two layouts to edit here. The basic search interface is the primary one, which should contain the most often used search fields for the module, while the advanced search page is the secondary one and is used for more customized searching of the module fields. The second metadata file used to search for a module is the SearchFields.php file, shown in Listing 10-9. You ll remember from 3 that you use this file to define how to do this search against the module, such as if the display field in the Search form interface is different from the field you are actually searching against. Listing 10-9. SearchFields.php File for the Parts Module

(fun x -> match x with pattern-rules -> expression) and is especially convenient as a way to define

Table 6-3 shows an example of data records that match these advanced filter criteria: sales dates on January 1 or January 3, and number of units sold less than 150. Table 6-3. Advanced Filter Criteria and Results for Data Records

This section goes beyond the approach presented so far and shows how to develop a UI application that can accept algebraic expressions as input and simplify, differentiate, and render them graphically. Figure 12-2 shows the project structure. Note in particular that you need to add library references to FSharp.PowerPack and System.Windows.Forms. Furthermore, you add the lexer ExprLexer.fsl and the parser ExprParser.fsy to the project so you can quickly edit them if necessary. You can manually include a reference to the PowerPack targets in the project file to automatically generate code for the parser and lexer definitions, or you can manually generate these files. You do the latter for this example, but in both cases you need to add the generated files (ExprParser.fs and ExprLexer.fs) to the project. The main Expr type that represents algebraic expressions is contained in Expr.fs. Although you can use the expression constructors defined in this type to create expression values on the fly, the most convenient method for embedding complex expressions into this representation is by parsing them.

< php $searchFields['Parts'] = array ( 'name' => array( 'query_type'=>'default'), 'part_reference_number' => array( 'query_type'=>'default'), 'current_user_only'=> array('query_type'=>'default','db_field'=>array ('assigned_user_id'),'my_items'=>true, 'vname' => 'LBL_CURRENT_USER_FILTER', 'type' => 'bool'), 'assigned_user_id'=> array('query_type'=>'default'), ); >

Figure 12-2. The SymbolicDifferentiation project in Visual Studio 2010 Armed with the ability to encode and parse algebraic expressions, you place the derivation and simplification logic in its own module and file (ExprUtil.fs). The last piece is rendering (in VisualExpr.fs). Finally, a simple UI client in Main.fs completes the application.

Table 6-4 shows an example of showing only data records that match these advanced filter criteria: sales dates of January 2, units sold of less than 150, and product names ending in the letters ets. Table 6-4. Advanced Filter Criteria and Results for Data Records

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