14 Pages

l27jd

Course: CPS 004, Fall 2009
School: Duke
Rating:
 
 
 
 
 

Word Count: 629

Document Preview

4 Javadoc 27jd.1 The Javadoc CompSci Plan What is Javadoc? Writing Javadoc comments Using the Javadoc tool Practice CompSci 4 Javadoc 27jd.2 What is Javadoc? Javadoc is a way to comment your code that enables automatic generation of web pages that document your code. Why use Javadoc? It's much faster than generating webpages documenting your code. It's standard documentation which means it's easy to...

Register Now

Unformatted Document Excerpt

Coursehero >> North Carolina >> Duke >> CPS 004

Course Hero has millions of student submitted documents similar to the one
below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.

Course Hero has millions of student submitted documents similar to the one below including study guides, practice problems, reference materials, practice exams, textbook help and tutor support.
4 Javadoc 27jd.1 The Javadoc CompSci Plan What is Javadoc? Writing Javadoc comments Using the Javadoc tool Practice CompSci 4 Javadoc 27jd.2 What is Javadoc? Javadoc is a way to comment your code that enables automatic generation of web pages that document your code. Why use Javadoc? It's much faster than generating webpages documenting your code. It's standard documentation which means it's easy to use and the structure is given. CompSci 4 Javadoc 27jd.3 Writing Javadoc Comment Javadoc comments start with /** and end with */ The placement of the comment is important. The following can be commented: classes methods instance variables static variables CompSci 4 Javadoc 27jd.4 Writing Javadoc Comment package tipgame; /** * Used to enable timed events. * @author Jam Jenkins */ public interface Alarm { /** creates alarm */ public void alarm(); } Javadoc Comments CompSci 4 Javadoc 27jd.5 Commenting a Class Put the comment immediately before the class declaration. Briefly describe the purpose of the class in 2-3 sentences. Optionally include @author tag @version tag others CompSci 4 Javadoc 27jd.6 Commenting a Class /** * This class uses polling rather * than events for keyboard input. * * @author Jam Jenkins */ public class Keyboard implements CompSci 4 Javadoc 27jd.7 Commenting a Method Put the comment immediately before the method declaration. Briefly describe the purpose of the method in a short phrase or 2-3 sentences. Include more detail if necessary Include these tags if needed @param name describes parameter @return describes the return value CompSci 4 Javadoc 27jd.8 Commenting a Method /** Simulates the surface normal used for * bouncing the moving object off of the * stationary object. Normal is in the * direction from the surface of the * stationary object to the center of the * moving shape's bounding box. * @param stationary the object not in motion * @param moving the object that will bounce * of the stationary object * @return the radians of the normal vector * public static double getNormalVector(Shape stationary, Shape moving) CompSci 4 Javadoc 27jd.9 Commenting and Instance Static Variables Put the comment immediately before the variable declaration. Briefly describe the purpose of the variable in a short phrase. Include more detail only if absolutely necessary. No tags needed. CompSci 4 Javadoc 27jd.10 Commenting Instance and Static Variables /** shape should initially be centered at (0, 0) */ private GeneralPath shape; /** transformed shape */ private GeneralPath shapeTransformed; /** applied to the shape prior to drawing it */ private AffineTransform transform; /** the fill color of the shape, black by default */ protected Color color; CompSci 4 Javadoc 27jd.11 For more information... Visit the article: How to Write Doc Comments for the Javadoc Tool http://java.sun.com/j2se/javadoc/writingdoccomments/index.html CompSci 4 Javadoc 27jd.12 Generating HTML using the Javadoc Tool in Eclipse 1. 2. 3. 4. 5. 6. 7. 8. Highlight the project you want to javadoc in the Project Explorer Select File->Export->Javadoc Under the Javadoc command: enter the location of javadoc if it is not already there. The location should be something like: C:\Program Files\Java\jdk1.5.0\bin\javadoc.exe For the visibility select Private Select Use Standard Doclet For the Destination, enter where you want the html code generated to go. The html in the location you choose will be overwritten with the javadoc generated HTML, so make sure not to choose a place which already has an index.html youd like to keep. Click on Finish If you get the source files out of sync with file system error then say okay, highlight your project, right click and select refresh. This will resync your files. Repeat the instructions above. CompSci 4 Javadoc 27jd.13 Practice Put Javadoc comments in one of the previous homework assignment's source code. Generate the javadoc HTML files Post the HTML files to your web site. When transferring the files, be sure to transport them into an empty directory. DO NOT transfer them directly into your public_html page because this will overwrite your index.hml. Instead transfer them into a subdirectory of public_html. CompSci 4 Javadoc 27jd.14
Textbooks related to the document above:
Find millions of documents on Course Hero - Study Guides, Lecture Notes, Reference Materials, Practice Exams and more. Course Hero has millions of course specific materials providing students with the best way to expand their education.

Below is a small sample set of documents:

Duke - CPS - 004
Computer Science 4: Java for Video Gameswww.cs.duke.edu/education/courses/spring06/cps004/ Instructors Dietolf (Dee) Ramm D226 LSRC dr@cs.duke.edu Robert Duvall D228 LSRC rcd@cs.duke.eduIntroduction Administrative material Introduction thinki
Duke - CPS - 004
The Plan Graphics Hardware Coordinate System Built-in Shapes User-defined Shapes Sprites and Shapes Making a SpriteCompSci 45.1CompSci 45.2HardwareCoordinate Systems Monitor Resolutions (640x480, 800x600, 1280x1024) Bit
Duke - CPS - 001
Todays topics!Write Your Names(or just exercise your curiosity)!Complexity " Notes from Mason Matthews " Great Ideas Ch. 13 Computability " Great Ideas Ch. 15, Brookshear Ch. 11CPS 00111.1CPS 00111.2What is Computer Science?What c
Duke - CPS - 001
KeyEscrowasaSecurity andRecoveryDevicePresented by: Kevin Ji, Maura Tresch, Eyan Townsend, Whitney Anderson, Turner Rooney, Dan Tulley WhatisaKeyEscrowSystemKey Escrow is the use of a third party (the escrow service) to save and catalog private
UC Davis - LOG - 0503
A Homage toAlfred SchnittkeIrina Schnittke, piano Tatyana Grindenko, violin Alexander Ivashkin, cello Wednesday 30 March 2005, 7.30pmConcert in aid of the Alfred Schnittke Archive in London Sponsored by the Centre for Russian Music, Goldsmiths C
UC Davis - ATT - 0503
A Homage toAlfred SchnittkeIrina Schnittke, piano Tatyana Grindenko, violin Alexander Ivashkin, cello Wednesday 30 March 2005, 7.30pmConcert in aid of the Alfred Schnittke Archive in London Sponsored by the Centre for Russian Music, Goldsmiths C
Duke - STA - 205
Midterm ExaminationSTA 205: Probability and Measure Theory Wednesday, 2005 Mar 9, 2:50-4:05 pmThis is a closed-book examination. You may use a single one-sided sheet of prepared notes, if you wish, but you may not share materials. If a question se
Duke - STA - 216
Bayesian Hypothesis Testing in GLMs: One-Sided and Ordered Alternatives Often interest may focus on comparing a null hypothesis of no dierence between groups to an ordered restricted alternative. For example, we may have a k level ordered categoric
Duke - STA - 103
STA103Probability/Statistical InferenceJenises contact infoInstructor: Jenise Swall Office: 221 Old Chem Bldg. Phone: 684-4608 Office hours: Wed. 9:30PM-10:30PM, Thu. 1:30PM-2:30PM jenise@stat.duke.eduTA contact infoChristine KohnenMickel
Duke - STA - 242
Sta242/Env255, Week 3, 1/23/001Sta242/Env255, Week 3, 1/23/002Week 3: Simple Linear Regression1-way ANOVA: Week 2 Plant ExampleLast class: One-way ANOVA modelsfertilizer levels.Week 3 Reading: Chapter 7, Statistical Sleuth, plus all c
Duke - STA - 293
STA 293B/BGT 08 Expression Analysis Aymetrix expression data See Aymetrix tutorial Expression summaries: AD and ALR, and other information Array normalisation; Hybridisation problems - low levels of intensity One gene: Sample statistics, summari
Duke - STA - 242
6.63 15.04 14.26 15.56 16.83 16.98 14.88 14.37 14.23 15.15 15.74 26.15 26.52 25.84 25.69 26.08 25.32 25.95 26.95 25.57 29.57 36.99 38.77 39.59 37.07 37.7 37.22 37.45 37.89 38.99 38.79 49.35 48.21 48.63 410.33 48.51
Duke - STA - 242
STA242/ENV255 Quiz, 3/7/01 Name:_Page 1 of 3 Total Points (out of 15):_The questions below refer to the bird energy data described on this page. The results below summarize a study of the amount of energy metabolized (in calories) by two similar
Duke - STA - 244
STA2441/22/2001Homework 2Due 1/29/2001 1. (From CB 11.40) Consider the standard simple linear model with normal errors that has been re-parameterized as Yt = Y + t + where t = (X X) so that Y = Y and are independent. Extend Sches procedure
Duke - STA - 242
Week 5, Lec. 1, 2/5/02Week 5, Lec. 1, 2/5/02Regression Parameter EstimatesResiduals: Min 1Q Median 3Q Max -0.339 -0.1071 -0.01023 0.1361 0.3588 Coefficients: (Intercept) log(time) Value Std. Error 6.8115 0.1113 -0.5350 0.0609 t value Pr(>|t|) 6
Duke - STA - 242
W X b i C2f6"egvCvrdvh6iCvY2yvtrr2vCvyr0egvYCgEl2lv ie b u b u b u x w q i x x e b u b n q i ie e e e X X be u b s i u s u b b u v V Cgvvd&v2!add6Y{ V|Xf CYElevCbhCvuYxfwvrCgr)vCy2v02)V|V 2egv26$vEvd b b b
Duke - STA - 103
( {~ m { zyG}~ 0 G ~ t 0y k m| { 0~ p~m { 0y k { Y ym d dy ~ k { ym d k 2X B T4 RXaa22 D h 0c`C0bbSSIpap4h D2 B q { T4 2 B4 h 2 8 T4 2 22 D 2R Ta H F h h H 2 B4 2 42 1 rW) }0y YbICwD#Q`CSSYSPpHpD`AICQ8 k 5@8 ( Y}pm Y0Y
Duke - STA - 244
APMAM APSAB APSLAKE OPBPC OPRC OPSLAKE Y Year 9.13 3.58 3.91 4.1 7.43 6.47 54235 1948 5.28 4.82 5.2 7.55 11.11 10.26 67567 1949 4.2 3.77 3.67 9.52 12.2 11.35 66161 1950 4.6 4.46 3.93 11.14 15.15 11.13 68094 1951 7.15 4.99 4.88 16.34 20.05 22.81
Duke - STA - 244
BigMac Bread BusFare EngSal EngTax Service TeachSal TeachTax VacDays WorkHrs 31 9 1.27 44.3 44.1 280 21.8 28.2 31.9 1714 33 9 0.27 19.4 23.7 170 9.4 14.8 23.5 1792 98 23 0.09 15.4 20.3 100 2.2 4.3 17.4 2152 131 27 0.09 4.7 37.6 70 1.1 11.7 30.6 2
Duke - STA - 244
Exotic Sire Total Trt 9 1 9 1 5 1 8 2 5 1 8 3 6 1 8 4 3 2 9 1 0 2 9 2 5 2 9 3 5 2 8 4 5 3 8 1 5 3 8 2 6 3 9 3 5 3 6 4 7 4 7 1 7 4 8 2 3 4 6 3 4 4 8 4 8 5 9 1 4 5 8 2 4 5 7 3 6 5 9 4 5 6 9 1 5 6 9 2 4 6 9 3 2 6 7 4 8 7 9 1 4
Duke - STA - 244
Height Length Type 75 502 0 80 522 0 68 425 0 64 344 0 83 407 0 80 451 0 70 551 0 76 530 0 74 547 0 100 519 1 75 225 1 52 300 1 62 418 1 68 409 1 86 425 1 57 370 1 82 506 1 82 506 1 88 295 1 55 273 1 67 415 1 45 182 1 103 530 1
Duke - STA - 244
D F S W 7.2 0 0 10.404 8.2 0 0 18.161 10.3 0 0 25.778 10.1 0 0 20.511 10.7 0 0 21.87 13.3 0 0 47.186 5.1 1 0 4.447 7.2 1 0 8.682 10.2 1 0 19.511 11.3 1 0 37.682 12.6 1 0 25.775 17.1 1 0 67.363 5.1 0 1 4.02 6.5 0 1 7.504 8.4 0 1 13.391
Duke - STA - 244
BodyWt Dose LiverWt y 176 0.88 6.5 0.42 176 0.88 9.5 0.25 190 1 9 0.56 176 0.88 8.9 0.23 200 1 7.2 0.23 167 0.83 8.9 0.32 188 0.94 8 0.37 195 0.98 10 0.41 176 0.88 8 0.33 165 0.84 7.9 0.38 158 0.8 6.9 0.27 148 0.74 7.3 0.36 149 0.75 5.2
Duke - STA - 244
Age Score 15 95 26 71 10 83 9 91 15 102 20 87 18 93 11 100 8 104 20 94 7 113 9 96 10 83 11 84 11 102 10 100 12 105 42 57 17 121 11 86 10 100
Duke - STA - 102
smoke disease sex 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Duke - STA - 216
CASE STUDY: Bayesian Incidence Analyses from Cross-Sectional Data with Multiple Markers of Disease Severity Outline: 1. NIEHS Uterine Fibroid Study Design of Study Scientific Questions Difficulties 2. General Problem and Earlier Approaches 3. Baye
Duke - STA - 216
Discrete Time Survival Modelsj = P (Ti = j | Ti j, xi) = h(j + xi), where j is the discrete hazard, = (1, . . . , k ) are parameters characterizing the baseline hazard xi are time-independent covariates are regression coecients1Proportional
Duke - STA - 240
Fall 2003'$1Fall 2003'Exploratory Data AnalysisNematodes 0$2One-way ANOVA: Example X10.650 10.425 5.600 5.450s2.053 1.486 1.244 1.771How do nematodes (microscopic worms) affect plant growth? A botanist prepares 16 identical p
Duke - STA - 240
www.stat.duke.edu/courses/Fall02/sta240/quiz4/quiz4data.htmlQuiz 4: Lab Exercise, 11/11/02 I will follow the NSEES Honor Code.Name:_ Signature:_1.[3 points] Circle the terms that describe the meadowfoam study: (a) (b) (c) (d) (e) completely r
Duke - STA - 278
STA 278/BGT 208 GENE EXPRESSION ANALYSISStatistical Models, Methods & Computation Mike West Institute of Statistics & Decision Sciences www.isds.duke.edu Computational & Applied Genomics Program www.cagp.duke.eduSTA 278/BGT 208January 12, 2004
Duke - STA - 113
STA 113 Spring 2004 I. H. DinwoodieAssignment 1Due January 29 1. Consider the data in arsenic.txt explained in Arsenic.txt. a. Do a scatter plot of the amount of arsenic in the drinking water in ppm versus theamount in a toenail.b. Find
Duke - STA - 113
0 49 376 726 736 990 2008 2574 2718 2857 2920 3423 3678 3739 4465 4879 5056 5217 6027
Duke - STA - 205
Midterm Examination #1STA 205: Probability and Measure Theory Thursday, 2004 Feb 16, 2:20-3:35 pmThis is a closed-book examination. You may use a single one-sided sheet of prepared notes, if you wish, but you may not share materials. You may use a
Duke - STA - 205
Final ExaminationSTA 205: Probability and Measure Theory Due Monday, 2002 Apr 29, 5:00 pmThis is an open-book take-home examination. You must do your own work- collaboration is not permitted. If a questions seems ambiguous or confusing please ask
Duke - STA - 113
This is from the same paper as the etchratedata.txt file.The 490 measurements in etchratedata.txt were used tocompute a measure of nonuniformity for each of the ten wafers. The nonuniformity is actually the standard deviation of the 49etch ra
Duke - CH - 113
"x1""x2""x3""x4""y"8410011.42418072.27418014.610712054.97418054.67718014.771314014.65416074.54714034.85110071.481014034.72410031.641018034.56712074.7101318034.84101605
Duke - CH - 113
"C1""C2""C3"1.2"pH 3""Diseased"1.4"pH 3""Diseased"1"pH 3""Diseased"1.2"pH 3""Diseased"1.4"pH 3""Diseased".8"pH 5.5""Diseased".6"pH 5.5""Diseased".8"pH 5.5""Diseased"1"pH 5.5""Diseased".8"pH 5.5""Diseased"1"pH 7""Dis
Duke - CH - 113
"response""type""subject"12111022733744815926837748919152114321443111411251136124711181329123113421313102483511461217828103910411212923934745101611
Duke - CH - 113
"Obs:""x:""y:"1.41.022.421.213.48.884.51.985.571.526.61.837.71.58.751.89.751.7410.781.6311.84212.952.813.992.48141.032.47151.123.05161.153.18171.23.76181.253.68191.253.82201.283.21211.3
Duke - CH - 113
"Linoleic""Kerosene""Antiox""Betacaro"303010.7303010.63303018.41.01340405.049303010.713.183010.120405.04204015.006540205.202303010.6330301.59.04402015.132404015.15303010.73046.8210.34630
Duke - CH - 113
"C1""C2""C3""C4""C1""C2""C3""C4""Carbon""Sand""Fiber""Addition""Addition""Casting""Wet-Mold""(%)""(%)""Hardness""Strength""0""0""61""34""0""0""63""16""15""0""67""36""15""0""69""19""30""0""65""28""30""0""7
Duke - CH - 113
"stiffness""plate lengths"309.24409.543114326.54316.84349.84309.74402.16347.263616404.563316348.96381.76392.48366.283518357.18409.98367.383828346.710452.910461.410433.110410.610384.210362.6104
Duke - CH - 113
"temp""removal%"7.6898.096.5198.256.4397.825.4897.826.5797.8210.2297.9315.6998.3816.7798.8917.1398.9617.6398.916.7298.6815.4598.6912.0698.5111.4498.0910.1798.259.6498.368.5598.277.57986.9498.098.3298.2510.59
Duke - CH - 113
"C1"212401320533132470230421311341232284513150232106421603336123
Duke - CH - 113
"c1"20.919.620.420.320.820.620.520.419.919.819.520.216.518.318.719.6202019.519.619.118.818.317.617.217.818.7191918.618.81918.518.317.516.91717.818.118.818.918.919.118.818.417.81716.817.918.41919.41
Duke - STA - 113
w 88y #zu XQy u w w f8|y zu i a gb i a i j ap r i g r c i r e a n r s tQeA5rqptefet$eAbIA`Xqp$ilm8 y e A8q|w q v u y w y 2 w G8G u v|w o8w u s p w e q|X2w2f|w y y y y w y G8G u VG8G u GQtG u G$G u Ev|w
Duke - STA - 113
p " 8 " " 8 " 1 " U54BA05ih 547#25& " T &U T(0S R)0@&PIHGF )(E&" $#! D D Q 8 ' % " g F d c F F W a72 2a f@8 &6eR!b2 2a !0`Y&00`Y XV U) 547#25& " 1 " " T &U T(0S R)0@&PIHGF )(E&" $#! D D Q
Duke - STA - 113
jhi Eu0ihf Q VIr60 ERqCvv U S p I hh p p t U p he i hU Q p i h w Qe h Q i h q h p w s U U p h q p i VIT@uQ (Ro R(h w@9uT(x)40vu44t V(h VI | Q W Q h Q p q p i Q h t W p j pU Q U p I tI @p xt mt' mVxh I4mu Vi k 'x6Sw4e VIrm H V hq Q h q
Duke - STA - 244
STA2441/08/2003Homework 1Due 1/15/2003.Please provide concise, neatly written or typed solutions. All work should be your own and not copied from other texts or sources. Do feel free to discuss questions with me, the TA, others in class, or po
Duke - STA - 113
Students0510152025Range: 69.38% - 96.08%, 84 Students Median = 82.07, Quantiles = [76.36, 86.09] Mean = 81.4, Std Dev = 6.255060708090100Course Averages for STA113
Duke - STA - 205
z V g # " 4 w a F " $ & B $ g 0 $ Q # rSw P6B SiGo1%S1s6E" D E%GEU%651%S1$ i # Q D $ # "4 & 2 # 0F & B B # " & B # W B c i # Q w i B W Q # " i B # W B i $4 B $ " 0 ( " Q B i4 # "4 $ # B & @ B B y & $ B " i CS'S%bU3i G%kfC86EqS
Duke - STA - 103
The data come from http:/www.econstats.com/eq_d1.htm. After the date and day of week they are open high low close return(%)
Duke - STA - 103
The wins (1) and losses (0) of the Philadelphia Phillies in the 2001 season.
Duke - STA - 103
Review of key points about estimators Populations can be at least partially described by population parameters Population parameters include: mean, proportion, variance, etc. Because populations are often very large (maybe innite, like the output
Duke - STA - 216
Frequentist Logistic Regression & ExtensionsReturning to the DDE & Pre-Term Birth Example, recall: yi = 1 for pre-term birth & yi = 0 otherwise di = dose of DDE for woman i zi = vector of covariatesLogistic Regression: logitPr(yi = 1 | xi) =
Duke - STA - 101
21.0 Paired Dierences Answer Questions Paired Dierences Signicance Tests121.1 Paired DierencesExample 1: You want to show that men spend less on Valentines Day than women. You could draw some random men and some random women, ask them what th
Duke - STA - 290
Introduction to Statistical Data AnalysisGiven a new set of data to analyze, how should we proceed? Faced with uncertainty, statistics provides answers to questions and addresses uncertainties p. 1/15Model BuildingWhere should we start? 1. What
Duke - STA - 104
Midterm Examination # 2Mth 135 = Sta 104 Thursday, 2000 November 16, 2:15 3:30 pmIf you dont understand something in one of the questions, please 1 ask me. You may use your own one-sided, 8 2 11 sheet of notes and calculator, but do not share m
Duke - STA - 113
3.14 (d) check whether3.37 P (X = k) = p(k) = 1=6, where k = 1; 2; :; 6. Calculate E(1=X). If it bigger than (1=3:5), gamble; otherwise, accept the guaranteed amount. s 3.48 Let X = number of drivers who will come to a complete stop among 20 random
Duke - STA - 216
Extending GLMs for Correlated DataGLMs assume that the observations y1, . . . , yn are independent draws from an exponential family distribution However, in many applications, there may be dependency in the outcome data For example, in longitudinal
Duke - STA - 216
Standard Errors & Confidence Intervals - N (0, I()-1), where 2l(, ; y) I() = ij=asyWe can obtain asymptotic 100(1 - )% confidence intervals for j using: j Z1-/2se(j ) j 1.96se(j ) for = 0.05, where Zp denotes the pth percentile of the N