The goal of this library is to make ODBC recordsets look just like an STL container. As a user, you can move through our containers using standard STL iterators and if you insert(), erase() or replace() records in our containers changes can be automatically committed to the database for you. The library s compliance with the STL iterator and container standards means you can plug our abstractions into a wide variety of STL algorithms for data storage, searching and manipulation. In addition, the C++ reflection mechanism used by our library to bind to database tables allows us to add generic indexing and lookup properties to our containers with No special code required from the end-user. Because our code takes full advantage of the template mechanism, it adds minimal overhead compared with using raw ODBC calls to access a database.
THE SYNTAX OF A PROGRAMMING LANGUAGE tells you what code it is possible
to write—what the machine will understand. Style tells you what you ought to write—
what the humans reading the code will understand. Code written with a consistent, simple
style will be maintainable, robust, and contain fewer bugs. Code written with No regard to
style will contain more bugs. It may simply be thrown away and rewritten rather than
maintained.
acm HDOJ 1051WoodenSticks
Description:
There is a pile of n wooden sticks. The length and weight of each stick are kNown in advance. The sticks are to be processed by a woodworking machine in one by one fashion. It needs some time, called setup time, for the machine to prepare processing a stick. The setup times are associated with cleaning operations and changing tools and shapes in the machine. The setup times of the woodworking machine are given as follows:
(a) The setup time for the first wooden stick is 1 minute.
(b) Right after processing a stick of length l and weight w , the machine will need No setup time for a stick of length l and weight w if l<=l and w<=w . Otherwise, it will need 1 minute for setup.
Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as No surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.
By building a Nonlinear function relationship between an d the error signal，this paper presents a No—
vel variable step size LMS(Least Mean Square)adaptive filtering algorithm．
The purpose of this lab is to introduce the concept of FSMs with a datapath, and to
study the usage of more complex test benches. Also, we enforce a rudimentary design
methodology by assuming that the students are part of a bigger project, and have No
kNowledge of VHDL-implementation of the datapath (made by a hypothetical other
group) other than its predefined Entity Interface until they come to the lab.
The rest of this document is structured as follows: Section 2 describes some prelimi-
nary reading and exercises that should be done before the lab. Section 3 details the
design tasks that should be carried out to pass this lab.
