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PUBLICATIONS SA Chatterjee Ordinary differential equations, partial differential equations, and applied mathematics Transactions of the American Mathematical Society, 62 (1994), 172 194 SA Chatterjee and Gifford von Edsel A UNIX tool for software development in determining executive compensation packages The Computer Journal 27 (1995), 200 239 SA Chatterjee and Mignon E Delacroix Linear and multilinear algebra: some matrix theories Studies in Applied Mathematics, 95 (1993), 6 18

"The model configuration used to open the store is incompatible with the one that was used to create the store."

= argrnax P(vj)

vj~(yes,no]

While alarming, this error message is easily sorted out. By default, Core Data stores its data in an XML file (there are other storage options and you will cover those later). If you have changed the data model, the version stored the first time you ran the program may be different to the current storage structure. Hence this message your program doesn t understand the structure of the data store. The solution is simply to find the persistent store (by default this will be ~/Library/Application Support/[your application name]/storedata and just delete the file. Go ahead it will be regenerated the next time you run the program (of course, you will lose any data in the store, sadly). There is an alternative, which you might like to investigate if the data is too important to lose. Xcode provides migration tools to allow you to migrate data from one version of a data model to another. Search the Developer Documentation for Core Data Migration or look here: http://developer.apple.com/mac/library/documentation/Cocoa/Conceptual/CoreDataV ersioning/Introduction/Introduction.html. This is a quite advanced topic and you should aim to be comfortable with Core Data programming before investigating it indepth.

P(0utlook = sunny)v,)P(Temperature = coolIvj)

Notice in the final expression that ai has been instantiated using the particular attribute values of the new instance To calculate VNB we now require 10 probabilities that can be estimated from the training data First, the probabilities of the different target values can easily be estimated based on their frequencies over the 14 training examples P(P1ayTennis = yes) = 9/14 = 64 P(P1ayTennis = no) = 5/14 = 36

You may be wondering where your database is located. Nothing in this example so far has indicated how or where your data is being stored. In fact, Core Data gives you a degree of choice about this. By default, your data is stored as an XML file in the location ~/Library/Application Support/[your application name]/storedata as mentioned in the previous section, A Potential Gotcha. However, you also have the option to store it in SQLite or binary formats. Here s how. Look in the file MUG_AppDelegate.m and find the method called persistentStoreCoordinator. In Listing 5 1 you will see the significant code fragment:

P(Wind = stronglPlayTennis = yes) = 319 = 33 P(Wind = strongl PlayTennis = no) = 315 = 60

GRANTS An empirical study of the impact of perceived environmental uncertainty and perceived agent effectiveness on the composition of compensation contracts Research supported by a $50,000 grant from the National Science Foundation Some advanced technological and organizational implications for change in human resources management Research supported by a $60,000 grant from AT&T Bell Laboratories

Using these probability estimates and similar estimates for the remaining attribute values, we calculate V N B according to Equation (621) as follows (now omitting attribute names for brevity)

Listing 5 1. Setting the Storage Options for a Core Data Application NSURL *url = [NSURL fileURLWithPath: [applicationSupportDirectory stringByAppendingPathComponent: @"storedata"]]; persistentStoreCoordinator = [[NSPersistentStoreCoordinator alloc] initWithManagedObjectModel: mom]; if (![persistentStoreCoordinator addPersistentStoreWithType:NSXMLStoreType configuration:nil URL:url

Thus, the naive Bayes classifier assigns the target value PlayTennis = no to this new instance, based on the probability estimates learned from the training data Furthermore, by normalizing the above quantities to sum to one we can calculate the conditional probability that the target value is no, given the observed attribute values For the current example, this probability is = -795

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options:nil error:&error]){ [[NSApplication sharedApplication] presentError:error]; [persistentStoreCoordinator release], persistentStoreCoordinator = nil; return nil; }