Exploring C++ uses a series of self–directed lessons to divide C++ into bite–sized chunks that you can digest as rapidly as you can swallow them. The book assumes only a basic understanding of fundamental programming concepts (variables, functions, expressions, statements) and requires no prior knowledge of C or any other particular language. It reduces the usually considerable complexity of C++.
The included lessons allow you to learn by doing, as a participant of an interactive education session. You’ll master each step in a one sitting before you proceed to the next. Author Ray Lischner has designed questions to promote learning new material. And by responding to questions throughout the text, youll be engaged every step of the way.
I implement Dijkstra s Single Source Shortest Path, say SSP, algorithm for directed graphs using a simple data structure, say simple scheme, Fibonacci heaps, say F-heap scheme, and Pairing heaps, say P-heap scheme, and measure the relative performance of the three implementations.
On the LPC13xx, programming, erasure and re-programming of the on-chip flash can be performed using In-System Programming (ISP) via the UART serial port, and also, can be performed using In-Application Programming (IAP) calls directed by the end-user code. For In-System Programming (ISP) via the UART serial port, the ISP command handler (resides in the bootloader) allows erasure of one or more sector (s) of the on-chip flash memory.
LVQ學習矢量化算法源程序
This directory contains code implementing the Learning vector quantization
network. Source code may be found in LVQ.CPP. Sample training data is found
in LVQ1.PAT. Sample test data is found in LVQTEST1.TST and LVQTEST2.TST. The
LVQ program accepts input consisting of vectors and calculates the LVQ
network weights. If a test set is specified, the winning neuron (class) for
each neuron is identified and the Euclidean distance between the pattern and
each neuron is reported. Output is directed to the screen.
Hidden_Markov_model_for_automatic_speech_recognition
This code implements in C++ a basic left-right hidden Markov model
and corresponding Baum-Welch (ML) training algorithm. It is meant as
an example of the HMM algorithms described by L.Rabiner (1) and
others. Serious students are directed to the sources listed below for
a theoretical description of the algorithm. KF Lee (2) offers an
especially good tutorial of how to build a speech recognition system
using hidden Markov models.
This directory contains code implementing the K-means algorithm. Source code
may be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANS
program accepts input consisting of vectors and calculates the given
number of cluster centers using the K-means algorithm. Output is
directed to the screen.
k-meansy算法源代碼。This directory contains code implementing the K-means algorithm. Source code
may be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANS
program accepts input consisting of vectors and calculates the given
number of cluster centers using the K-means algorithm. Output is
directed to the screen.
Recent advances in experimental methods have resulted in the generation
of enormous volumes of data across the life sciences. Hence clustering and
classification techniques that were once predominantly the domain of ecologists
are now being used more widely. This book provides an overview of these
important data analysis methods, from long-established statistical methods
to more recent machine learning techniques. It aims to provide a framework
that will enable the reader to recognise the assumptions and constraints that
are implicit in all such techniques. Important generic issues are discussed first
and then the major families of algorithms are described. Throughout the focus
is on explanation and understanding and readers are directed to other resources
that provide additional mathematical rigour when it is required. Examples
taken from across the whole of biology, including bioinformatics, are provided
throughout the book to illustrate the key concepts and each technique’s
potential.
c pgm to find redundant paths in a graph.Many fault-tolerant network algorithms rely on an underlying assumption that there are possibly distinct network paths between a source-destination pair. Given a directed graph as input, write a program that uses depth-first search to determine all such paths. Note that, these paths are not vertex-disjoint i.e., the vertices may repeat but they are all edge-disjoint i.e., no two paths have the same edges. The input is the adjacency matrix of a directed acyclic graph and a pair(s) of source and destination vertices and the output should be the number of such disjoint paths and the paths themselves on separate lines. In case of multiple paths the output should be in order of paths with minimum vertices first. In case of tie the vertex number should be taken in consideration for ordering.
