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It lists all the attributes of My_Class. It also displays two special class attributes __doc__ and __module__. __doc__ contains the documentation string for the class, which is the first unassigned string that comes after the header line. It is similar to the document strings used for functions and modules. The documentation string for a class is specific to that class. __module__ contains the name of the module in which the class is defined. It is part of the fully qualified class name. The fully qualified name of the class, My_Class, is __main__.My_Class, in which __main__ is the module name. You can refer to these special class attributes as you refer to any other attribute. Now let s use the __dict__ special class attribute to show the attributes of My_Class.

where, expression can be any function, statement, or series of JavaScript statements. The expression must be enclosed in double quotation marks. ID is an identifier that is used with the clearTimeout() method to cancel the setTimeout() method before the expression is evaluated. The amount of time the Web browser should wait before executing the code argument of the setTimeout() method is specified in milliseconds. The clearTimeout() method is used to cancel the setTimeout() method call. If the setTimeout() method to which the clearTimeout() method refers was given a timerID, the clearTimeout() method would clear the time out so that the related procedure would not run again at the lapse of the specified interval. The syntax of the clearTimeout() method is:

The Naive Bayes tab allows for the generation of a naive Bayes model (see Section 4.6 for more details). Under this implementation, models can only be built for binary response variables with any categorical variable used as an independent variable. These should be set as shown in Fig. B.64. In addition to the cross-validated percentage to set aside, a number of predicted values can be optionally generated by selecting the Generate results option: Prediction, Prediction prob, CV Prediction, and CV Prediction prob, as discussed in Section B.6.4. Once a model is built, the results are displayed in the results area, as shown in Fig. B.64. The independent variables and the response variable are initially summarized, along with a summary of the cross-validated results. Any model generated can be saved for use with other data sets, using the Save model button, as shown in Fig. B.64.

In the cap market, forward rates are mostly semiannual, whereas those used in forward-swap-rate formulas are usually annual rates. To jointly calibrate to both the cap and swaption markets, the volatilities of semiannual forward rates needs to be reconciled with the volatilities of annual forward rates. Suppose we have three time instants 0 < S < T < U that are each six months spaced. Furthermore, suppose we have an S 1 swaption and both S and T-expiry six-month caplets. For example, S = 4 years, T = 4.5 years, and U = 5 years. We will derive a relationship between the Black swaption volatility and the two Black caplet volatilities. Consider three forward rates F1(t) = F(t; S, T), F2(t) = F(t; T, U), and F(t) = F(t; S, U), where the rst two forward rates are semiannual while the third rate is annual and composed of the two inner rates. Thus, the tenor for F1 and F2 is 0.5 and it is 1 for F. Using the relationships between forward rates and zero-coupon bond prices, we get F1 (t ) =

On the negative side, the proliferation of messaging services, such as voicemail on PBXs, for PSTN phones, for mobile phones, e-mail, fax, instant messaging, and paging, creates challenges for: