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#include handle.h typdef size_t Size; typdef double Time; namespace QuantLib { namespace MonteCarlo { // General purpose Monte Carlo model for path samples /* Any Monte Carlo which uses path samples has three main components, namely, - S, a sample accumulator, - PG, a path generator, - PP, a path pricer. MonteCarloModel<S, PG, PP> puts together these three elements. The constructor accepts two safe references, i.e. two smart pointers, one to a path generator and the other to a path pricer. In case of control variate technique the user should provide the additional control option, namely the option path pricer and the option value. The minimal interfaces for the classes S, PG, and PP are: class S { void add(VALUE_TYPE sample, double weight) const; }; class PG { Sample<PATH_TYPE> next() const; }; class PP :: unary_function<PATH_TYPE, VALUE_TYPE> { VALUE_TYPE operator()(PATH_TYPE &) const; }; */ template<class S, class PG, class PP> class MonteCarloModel { public: typedef typename PG::sample_type sample_type; typedef typename PP::result_type result_type; MonteCarloModel(const Handle<PG>& pathGenerator, const Handle<PP>& pathPricer,

Field 6 The number in this field indicates whether the indicated file system needs to be checked with fsck. 1 means it needs to be checked, and 2 means it doesn t. If you want to add an additional local disk or partition, you can create an entry for it in the /etc/fstab file. See 27 for information on mounting Samba, NFS, and other remount file systems from /etc/fstab.

Sound maker or whistle speci c to the animal. Lots of outdoor stores have bird or dog whistles or sound makers that attract a wide variety of wildlife. Use sparingly to attract animals, and then put them away so as not to confuse your subject into thinking there is food or a mate close by. Much of the same gear I use for travel and landscape photography goes with me on wildlife shoots or when photographing pet portraits for friends. Keep these ideas in mind the next time you re stalking big game photographically, of course!

The bandwidth reservation model works under the assumption that the reservations on a link accurately reflect the traffic that is crossing the link. This assumption can break in two cases: 1. Traffic is not kept within the limits of the reservation. The implications of not keeping the traffic within the reservation limits and the use of policers for doing so are discussed in more detail in Section 4.4.7 of the DiffServ-TE chapter ( 4).

Dozens of bootable Linux distributions have appeared in the past few years. Those distributions can contain anywhere from 1.4MB of data on a firewall bootable Linux to many gigabytes of data on a bootable DVD. Without needing to touch the computer s hard disk, these distributions can offer full-featured systems that are tailored to be desktop systems, multimedia players, rescue systems, or many other types of systems. Many bootable Linuxes are based on KNOPPIX (described in 11), so they feature very fine hardware detection and strong network connectivity. If you want to try out a mini-bootable Linux distribution, try Damn Small Linux or Puppy Linux. For a Linux distribution that fits on a floppy disk, try Coyote Linux (described in 18). Nearly all bootable Linux distributions offer ways to access data from the hard disks of the computers on which they are running. While many bootable Linuxes are still experimental in nature, you can have lots of fun playing with them. Also, with the extraordinary improvements in custom features, you can create your own customized bootable Linux distribution to take with you on a floppy, CD, DVD, or USB flash drive (also referred to as pen drives, thumb drives, or other names).

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Percentage Sampling: Splits a data ow by randomly sampling the rows for a given percentage For example, this could be used to separate a data set into training and testing sets for data mining Within the editor, specify the approximate percentage of rows to allocate to the selected output, while the remaining rows are sent to the unselected output If a sampling seed is provided, the transform will always select the same rows from a given data set Pivot: Denormalizes a data ow, similar to the way an Excel pivot table operates, making attribute values into columns For example, a data ow with three columns, Quarter, Region, and Revenue, could be transformed into a data ow with columns for Quarter, Western Region, and Eastern Region, thus pivoting on Region Row Count: Counts the number of rows in a data ow and places the result into a variable.