Description: Design Patterns
by Christopher G. Lasater 一书配套资源文件：the example code for
C# .NET 2.0 Example Projects Platform: |
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Description: Design Patterns
by Christopher G. Lasater 一书配套资源文件：the example code for
C# .NET 2.0 Example Projects-Design Patterns by Christopher G. Lasater book documents matching resources: the example code for C#. NET 2.0 Example Projects Platform: |
Size: 45056 |
Author:1212131 |
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Description: GoF（“四人帮”Gang of Four，指Erich Gamma, Richard Helm, Ralph Johnson & John Vlissides四人）的《设计模式》（1995年出版）是第一次将设计模式提升到理论高度，并将之规范化。本书提出了23种基本设计模式，自此，在可复用面向对象软件的发展过程中，新的大量的设计模式不断出现。-GoF (" Gang of Four" Gang of Four, that Erich Gamma, Richard Helm, Ralph Johnson & John Vlissides four) of " design patterns" (published in 1995) is the first time raised to the level of theory design patterns, and the standardization. Book presents 23 kinds of basic design patterns, since, in the reusable object-oriented software development process, a large number of new design patterns emerging. Platform: |
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Author:youwei |
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Description: LR分析器设计
给定说明语句的拓广文法G[S ]如下：
(0) S ->S (1) S->v I:T (2) I->I,i (3) I->i (4) T->r
其中v代表终结符var,r代表real。
其识别规范句型活前缀的DFA及LR(0)分析表如下:
输入
状态 ACTION表 GOTO表
v i , : r # S I T
0 S2 1
1 acc
2 S4 3
3 S6 S5
4 r3 r3 r3 r3 r3 r3
5 S9 8
6 S7
7 r2 r2 r2 r2 r2 r2
8 r1 r1 r1 r1 r1 r1
9 r4 r4 r4 r4 r4 r4
编程实现此文法的LR分析器，并设输入的文法的句子为：
var i , i , i : real
给出输出结果
-LR parser design
Given that statement, The Extension of the grammar G [S ] as follows:
(0) S -> S (1) S-> v I: T (2) I-> I, i (3) I-> i (4) T-> r
One representative of terminator v var, r representative of real.
Living patterns of its identification Specification prefix DFA and LR (0) analysis as follows:
Input
GOTO table table status ACTION
v i,: r# S I T
0 S2 1
1 acc
2 S4 3
3 S6 S5
4 r3 r3 r3 r3 r3 r3
5 S9 8
6 S7
7 r2 r2 r2 r2 r2 r2
8 r1 r1 r1 r1 r1 r1
9 r4 r4 r4 r4 r4 r4
Programming LR parser article law, and set the input sentence is the grammar:
var i, i, i: real
Given output Platform: |
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Description: 通过例子学习VC++，作者：Stefan Bjö rnander-S. G. Ganesh is currently working as a research engineer in Siemens Corporate Technology, Bangalore. He works in the area of Code Quality Management (CQM). He has good experience in system software development having worked for around five years in Hewlett-Packard s C++ compiler team in Bangalore. He also represented the ANSI/ISO C++ standardization committee (JTC1/SC22/WG21) 2005 to 2007. He has authored several books. The latest one is 60 Tips for Object Oriented Programming (Tata-McGraw Hill/ISBN-13 978-0-07-065670-3). He has a master s degree in computer science. His research interests include programming languages, compiler design and design patterns. If you re a student or a novice developer, you might find his website www.joyofprogramming.com to be interesting. You can reach him at sgganesh@gmail.com. Platform: |
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Author:jiang |
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Description: Using MATLAB tools for MLP NNs (e.g., newff, …), design a two-layer feed-forward neural network as a classifier to categorize the input geometric shapes.
- The snapshot and bitmap of shapes are given:
- Training shapes: shkt.bmp
- Training patterns: trn.txt (each shape is in a 125*140 matrix)
- Test shapes: shks.bmp
- Test patterns: tsn.txt (each shape is in a 125*140 matrix)
- Since the dimension of inputs is too high (17500-dimensional), it is not possible to apply them directly to the net. So, … .
- Try the number of hidden neurons to be at least.
- Do training of NN until all training patterns are truly classified.
- To examine the generalization ability of your NN after training,
a) Apply it to the test patterns and report the accuracies.
b) Add p noise (p=5, 10, …, 75) to the training shapes (only degrade the
black pixels of the shapes) and report in a plot the accuracy versus p.-Using MATLAB tools for MLP NNs (e.g., newff, …), design a two-layer feed-forward neural network as a classifier to categorize the input geometric shapes.
- The snapshot and bitmap of shapes are given:
- Training shapes: shkt.bmp
- Training patterns: trn.txt (each shape is in a 125*140 matrix)
- Test shapes: shks.bmp
- Test patterns: tsn.txt (each shape is in a 125*140 matrix)
- Since the dimension of inputs is too high (17500-dimensional), it is not possible to apply them directly to the net. So, … .
- Try the number of hidden neurons to be at least.
- Do training of NN until all training patterns are truly classified.
- To examine the generalization ability of your NN after training,
a) Apply it to the test patterns and report the accuracies.
b) Add p noise (p=5, 10, …, 75) to the training shapes (only degrade the
black pixels of the shapes) and report in a plot the accuracy versus p. Platform: |
Size: 3072 |
Author:fatemeh |
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