[...]... Contents x 11 .3 11 .4 11 .5 11 .6 11 .2 .1 Permutations 11 .2.2 Combinations 11 .2.3 Binomial Probability Measures of Central Tendency 11 .3 .1 Mean 11 .3.2 Median 11 .3.3 Midrange 11 .3.4 Mode 11 .3.5 Weighted Measures of Central Tendency Measures of Dispersion 11 .4 .1 Range 11 .4.2 Variance 11 .4.3 Standard Deviation Normal Distribution 11 .5 .1 Normal Curve Standard Normal Curve 11 .5.2 Calculating f(x) 11 .5.3 Probability... Models 12 .1 Numerical Differentiation 12 .2 Numerical Integration 12 .2 .1 Integration by the Trapezoidal Rule 12 .2.2 Integration by Simpson's Rule 311 311 313 316 319 319 3 21 3 21 323 327 332 333 336 Chapter 13 — Linear Systems Chapter Summary 13 .0 Linear Equations 13 .0 .1 Systems of Linear Equations 13 .0.2 Matrix Representations of Linear Systems 13 .1 Numeric Data in Matrix Form 13 .1. 1 Matrices in C++ 13 .1. 2... 20.5 .1 Selecting a Font 20.5.2 Drawing with Text 511 515 518 518 523 Chapter 21 — Keyboard and Mouse Programming Chapter Summary 21. 0 Keyboard Input 21. 1 Input Focus 21. 1 .1 Keystroke Processing 21. 1.2 Determining the Key State 21. 1.3 Character Code Processing 21. 1.4 Keyboard Demonstration Program 21. 2 The Caret 21. 2 .1 Caret Processing 21. 2.2 Caret Demonstration Program 21. 3 Mouse Programming 21. 3 .1 Mouse... PolyBezier() 23.4 .10 Drawing with PolyBezierTo() 23.4 .11 Drawing with PolyDraw() 23.4 .12 Pixel and Line Demonstration Program 597 597 598 599 600 600 602 603 607 610 611 611 613 615 617 617 618 618 619 620 6 21 6 21 622 623 624 624 626 629 629 633 Chapter 24 — Drawing Solid Figures Chapter Summary 24.0 Closed Figures 24 .1 Closed Figure Elements 24 .1. 1 Brush Origin 24 .1. 2 Object Selection Macros 24 .1. 3 Polygon... 3 81 382 382 386 Chapter 15 — Neural Networks Chapter Summary 15 .0 Reverse-Engineering the Brain 15 .0 .1 The Biological Neuron 15 .0.2 The Artificial Neuron 15 .0.3 Artificial Neural Networks 15 .1 The Network as a Classifier 15 .1. 1 Multiple-Node Networks 15 .1. 2 Software Model for Neural Nets 15 .2 The Perceptron 15 .2 .1 Perceptron as a Classifier 15 .2.2 Perceptron Learning 15 .2.3 Training the Perceptron 15 .2.4... Files 17 .3.3 Resource Files 17 .3.4 Make Files 17 .3.5 Object Files 17 .3.6 Executable Files 17 .3.7 Dynamic Linking 4 31 4 31 432 432 434 434 434 436 436 436 436 437 437 437 438 438 439 440 440 4 41 442 444 Chapter 18 — The Window Program Components Chapter Summary 18 .0 “Hello, World” 18 .1 Naming Conventions 18 .2 Constants and Handles 18 .2 .1 Windows Handles 18 .3 Visual Elements 18 .3 .1 The Main Window 18 .3.2... Chapter 17 — Event-Driven Programming Chapter Summary 17 .0 Graphical Operating Systems 17 .1 Enter Windows 17 .1. 1 Text-based and Graphical Programs 17 .1. 2 Graphics Services 17 .2 Programming Models 17 .2 .1 Event-Driven Programs The Event Manager The Event Handler 17 .2.2 Event Types System Events Control Events Program Events 17 .2.3 Event Modeling 17 .3 File Structure of a Windows Program 17 .3 .1 Source Files 17 .3.2... 400 402 403 405 405 406 409 409 410 412 413 414 415 417 418 Table of Contents xii PART II — APPLICATION DEVELOPMENT Chapter 16 — The C++ Language on the PC Chapter Summary 16 .0 Introducing C++ 16 .0 .1 Evolution of C++ 16 .0.2 Advantages of the C++ Language 16 .0.3 Disadvantages of the C++ Language 16 .1 PC Implementations of C and C++ 16 .2 Flowcharts and Software Design 16 .3 The C++ Console Application... Function 15 .3 The Adaline 15 .3 .1 Widrow-Hoff Learning 15 .3.2 A Neuron for Adaline 15 .4 Improving the Classification Function 15 .4 .1 Calculating the Error Sum 15 .4.2 Improving Perceptron Results 15 .5 Backpropagation Networks 15 .5 .1 Nonlinear Neurons 15 .5.2 Backpropagation Algorithm 15 .5.3 Software Model for Backpropagation 15 .5.4 Executing the Network 15 .5.5 A Backpropagation Trainer 389 389 389 3 91 394... Elimination 13 .4 A Gauss-Jordan Algorithm 13 .5 Solution of a Linear System xi 359 360 363 368 368 370 3 71 372 Chapter 14 — Solving and Parsing Equations Chapter Summary 14 .0 Function Mapping 14 .1 Developing a Parser 14 .2 Evaluating User Equations 14 .2 .1 Equation Grammar 14 .2.2 Equation Syntax 14 .2.3 Symbol Table and Numeric Data 14 .3 An Equation-Solving Algorithm 14 .3 .1 The _EVALUATE Procedure 14 .3.2 _CALCULATE_Y . 278 11 .1 Data Manipulation Primitives 278 11 .1. 1 Common Summations 278 11 .1. 2 Sor ting 280 11 .2 Counting Techniques 2 81 Table of Contents ix 11 .2 .1 Per mutations 282 11 .2.2 Combinations 283 11 .2.3. 285 11 .3 Measures of Central Tendency 286 11 .3 .1 Mean 287 11 .3.2 Median 287 11 .3.3 Midrange 288 11 .3.4 Mode 289 11 .3.5 Weighted Measures of Central Tendency 292 11 .4 Measures of Dispersion 294 11 .4 .1. 11 0 5.3.5 Not a Number (NaN) 11 2 Signaling and Quiet NaNs 11 2 5.3.6 Exceptions 11 3 Invalid Operation Exception 11 3 Division by Zero Exception 11 4 Overflow Exception 11 4 Underflow Exception 11 5 Inexact