Get all perfect squares in a given range - java

I have 2 numbers M and N of range, I want to count all perfect squares in this range and that perfect square should have below property.
lets say the perfect square has digits n1,n2,n3,n4, n5 etc then the digits should be like this:
n1 < n2 > n3 < n4 > n5 etc
Example:
Input : M = 40, N = 70
Output : 1
Explanation:
The perfect squares in the range 40 to 70 are 49 and 64. But only 49 has the proerty where 4 < 9. But for 64, 6 < 4 is false. So output is 1.
Constraints:
1 <= M <= N <= 1011.
This was asked during an interview and I tried the below code that works for some input and fails for some hidden test cases which I don't know.
I have taken code references from this link to find out perfect squares first.
static int perfectSquares(long M, long N) {
int total = 0;
// Getting the very first number
long number = (long) Math.ceil(Math.sqrt(M));
// First number's square
long n2 = number * number;
// Next number is at the difference of
number = (number * 2) + 1;
// While the perfect squares
// are from the range
while ((n2 >= M && n2 <= N)) {
/** Logic to check if digits of form d1 < d2 > d3 < d4 > d5 etc **/
long n = n2;
String s = n + "";
int a = s.charAt(0) - '0';
boolean isless = true;
boolean valid = true;
for (int i = 1; i < s.length(); i++) {
int b = s.charAt(i) - '0';
if (isless && a > b) {
valid = false;
break;
}
if (!isless && a < b) {
valid = false;
break;
}
isless = !isless;
a = b;
}
// Count the valid the perfect square
if (valid) {
System.out.println("sq:" + n2);
total++;
}
// Get the next perfect square
n2 = n2 + number;
// Next odd number to be added
number += 2;
}
return total;
}
Is there any bug in my code or otherwise can we reduce the time complexity of this code?


Issue is with the checks for "valid" where simply using >= and <= will fix it.
I ran your code against the full range (1..100000000000) and found an issue early with the series result (your code prints the valid numbers (i got rid of the newline)):
1 4 9 16 25 36 49 121 144 196 361 441 484 576 676 784 1225 1444 1600 1849 1936 2209 2304 2401 2500 2601 2704 2809 2916 3600 3844 3969 4624 4900 5625 5929 6724 6889 7744 8836 14161 15376 16384 17161 18496 19044 19881 25281 26244 28561 29241 29584 35344 36481 49284 57121 58081 58564 66564 67081 68121 69696 77284 110224 110889 111556 120409 121104 121801 122500 131769 133956 140625 144400 150544 154449 160000 160801 161604 162409 164836 165649 172225 173889 174724 180625 182329 184900 186624 190969 191844 193600 198916 220900 230400 232324 240100 250000 260100 270400 273529 275625 277729 280900 291600 294849 295936 330625 332929 342225 351649 352836 360000 361201 362404 363609 364816 374544 375769 381924 384400 390625 396900 442225 443556 444889 452929 455625 462400 471969 481636 484416 485809 490000 491401 492804 495616 553536 562500 565504 571536 580644 586756 592900 660969 665856 672400 680625 683929 688900 692224 693889 695556 774400 777924 786769 883600 894916 896809 1119364 1227664 1229881 1304164 1406596 1418481 1503076 1515361 1527696 1535121 1615441 1623076 1628176 1633284 1666681 1669264 1726596 1745041 1747684 1758276 1766241 1844164 1855044 1907161 1937664 1999396 2208196 2214144 2217121 2223081 2226064 2307361 2316484 2328676 2427364 2446096 2515396 2534464 2637376 2768896 2802276 2819041 2849344 2859481 2917264 2937796 2979076 2989441 3316041 3319684 3334276 3504384 3519376 3538161 3644281 3709476 3717184 3748096 3767481 3837681 3849444 3857296 3888784 3916441 3924361 3944196 3956121 3968064 4528384 4713241 4717584 4726276 4748041 4778596 4879681 4937284 4946176 4955076 4968441 4977361 4999696 5612161 5626384 5645376 5669161 5803281 5827396 5866084 5900041 5934096 5958481 6615184 6625476 6646084 6713281 6718464 6859161 6906384 6922161 6948496 6959044 7706176 7717284 7907344 7918596 7958041 8856576 8868484 8916196 8928144 8934121 8946081 9979281 11022400 11042329 11055625 11088900 11122225 11155600 11175649 11182336 11195716 12040900 12061729 12075625 12110400 12180100 12250000 13010449 13053769 13140625 13176900 13191424 13271449 13293316 13344409 13351716 13373649 13395600 14032516 14040009 14062500 14070001 14092516 14152644 14182756 14197824 14250625 14265729 14386849 14394436 14440000 14462809 14470416 14485636 15054400 15186609 15194404 15233409 15241216 15264649 15342889 15350724 15444900 15460624 15499969 15570916 15586704 15594601 16000000 16080100 16120225 16160400 16184529 16240900 16281225 16353936 16370116 16394401 16483600 16564900 16662724 16670889 17073424 17164449 17172736 17222500 17230801 17255716 17272336 17280649 17355556 17363889 17372224 17388900 17472400 17673616 17690436 17698849 17774656 17791524 18011536 18062500 18164644 18190225 18232900 18275625 18292729 18352656 18361225 18386944 18455616 18472804 18481401 18490000 18662400 18774889 18783556 18792225 18887716 19061956 19096900 19140625 19184400 19254544 19333609 19342404 19351201 19360000 19465744 19492225 19562929 19580625 19686969 19695844 19775809 19784704 19793601 19891600 22033636 22052416 22061809 22071204 22080601 22090000 22155849 22231225 22240656 23011209 23020804 23030401 23040000 23164969 23193856 23232400 23261329 23280625 23299929 24000201 24010000 24176889 24186724 24255625 24373969 24383844 24462916 24472809 24482704 24492601 25000000 25010001 25020004 25030009 25150225 25180324 25230529 25250625 25270729 25351225 25381444 25441936 25472209 25482304 25492401 26010000 26020201 26030404 26040609 26050816 26081449 26132544 26142769 26193924 26265625 26450449 26460736 26481316 26491609 26553409 26563716 26594649 26697889 27040000 27050401 27060804 27071209 27081616 27164944 27352900 27373824 27394756 27562500 27583504 27772900 28090000 28111204 28121809 28132416 28153636 28355625 28376929 28440889 28451556 28462225 28590409 28686736 28793956 28890625 29030544 29084449 29160000 29170801 29181604 29192409 29343889 29354724 29452329 29484900 29560969 29571844 29593600 29680704 29691601 33062500 33085504 33131536 33292900 33350625 33373729 34140649 34152336 34175716 34222500 34292736 34374769 35010889 35022724 35081929 35164900 35283600 35354916 35366809 35390601 35450116 35473936 35485849 35581225 36000000 36120100 36180225 36240400 36360900 36481600 36590401 37063744 37161216 37173409 37185604 37197801 37222201 37454400 37576900 37662769 37773316 37785609 37797904 38031889 38130625 38192400 38241856 38365636 38390416 38440000 38452401 38464804 38588944 38663524 38775529 39050001 39062500 39250225 39262756 39287824 39350529 39375625 39463524 39664804 39690000 39891856 44142736 44182609 44195904 44222500 44262409 44275716 44355600 44462224 44488900 45050944 45091225 45131524 45252529 45292900 45360225 45481536 45562500 46022656 46076944 46144849 46185616 46240000 46253601 46280809 46294416 46553329 46580625 47141956 47196900 47265625 47361924 47444544 47554816 47582404 47692836 47775744 48052624 48121969 48163600 48260809 48274704 48330304 48385936 48441600 48580900 48790225 49000000 49140100 49252324 49280400 49350625 49491225 49561600 49660209 49885969 55011889 55130625 55160329 55353600 55442916 55472704 55591936 56220004 56250000 56280004 56550400 56595529 57032704 57062916 57153600 57183844 57198969 57274624 57350329 57380625 57486724 57577744 57790404 58033924 58064400 58140625 58277956 58461316 58476609 58491904 58583716 58675600 58782889 59043856 59243809 59274601 59290000 59351616 59382436 59397849 59474944 59675625 59783824 66096900 66161956 66373609 66471409 66487716 66585600 66683556 67141636 67174416 67190809 67240000 67272804 67354849 67683529 68062500 68095504 68161536 68260644 68392900 68442529 68475625 68591524 68690944 68773849 68790436 68890000 69155856 69222400 69272329 69388900 69455556 69472225 69555600 69672409 69772609 77000625 77440000 77492809 77792400 78021889 78251716 78340201 78375609 78393316 78552769 78676900 79174404 79192201 79263409 79281216 79691329 79780624 79887844 88021924 88284816 88341201 88360000 88472836 88491649 88585744 88792929 89075844 89264704 89283601 89340304 89491600 89680900 89775625 99022401 99042304 99062209 99121936 99141849 99241444 99460729 110418064 110439081 110502144 110544196 110649361 110838784 111746041 111767184 111809476 111915241 111957561 120209296 120428676 120516484 120538441 120736144 120758121 120802081 120824064 121308196 121418361 121528576 121837444 122412096 122722084 122744241 130439241 130507776 130622041 130919364 131308681 131423296 131744484 131767441 131813361 131928196 132434064 132503121 132526144 132756484 132848676 133726096 133888041 133911184 133957476 140209281 140612164 140707044 140944384 141419664 141824281 142229476 142301041 143328784 143544361 143712144 143736121 143808064 144504441 144528484 150528361 150749284 151807041 151979584 152226244 152819044 152868496 153958464 154405476 154778481 154803364 155725441 160326244 160757041 161518681 161849284 162333081 162766564 162919696 163609681 163635264 164506276 164557584 164814244 165611161 166616464 166978084 170537481 170668096 170877184 170929476 171819664 172423161 172607044 172712164 173659684 173923344 174609796 174927076 175801081 177715561 177848896 177902244 180311184 180338041 180526096 181225444 181548676 181656484 181737361 181926144 182547121 182628196 182817441 182844484 183819364 183846481 184715281 185558884 185613376 185804161 186978276 187909264 187936681 188815081 190219264 190826596 192959881 193738561 193822084 193877776 193989184 194937444 195608196 195748081 196616484 196728676 220879044 220938496 221444161 222427396 222636241 222666084 222845184 230402041 230766481 231709284 232227121 232318564 232806564 232837081 232989696 233417284 233845264 240002064 240529081 240622144 240839361 241118784 241429444 242549476 242705241 242767561 242923396 243609664 244547044 250304041 250525584 250557241 250778896 250937281 251603044 251666496 251825161 252206161 253637476 253701184 253733041 253956096 254849296 255616144 255648121 255712081 255744064 260209161 260435044 261113281 261436561 261727684 263445361 263607696 263705121 264517696 264745441 264908176 266636241 270306481 270339364 271524484 271557441 271623361 272514064 272547081 272613121 272646144 272712196 272877361 273439296 273505444 274929561 275858881 276756496 276823044 276956164 277855561 277922241 277955584 281434176 281769796 281937681 282946041 282979684 283518244 283619281 284968161 285914281 286828096 287709444 287777296 290225296 291419041 291658084 293711044 293848164 294534244 294877584 294946276 296666176 296735076 296838441 296907361 297838564 298978681 330439684 330803344 331749796 332734081 333756361 333829441 333939076 340107364 340845444 340919296 341436484 341547361 341806144 341917081 342546064 342657121 342768196 343657444 343768681 344436481 344622096 344919184 350513284 351225081 351900081 352538176 352613284 353778481 353966596 354418276 354606561 355624164 360544144 360658081 361304064 361418121 361836484 361988676 362445444 362559681 363436096 363703041 363817476 364504464 365536161 365727376 370216081 370909081 371949796 372837481 373301041 373339684 373958244 375623161 376709281 376903396 377758096 380601081 380718144 381108484 381655296 382515364 383337241 383415561 383807281 383846464 384905161 385768881 385808164 386869561 386948241 387735481 388957284 390418081 390813361 391723264 392515344 393546244 394777161 396766561 396846241 440118441 440412196 440538121 440622081 441336064 441504144 441924484 442513296 443439364 444408561 444619396 450755361 450967696 451945081 452668176 453306681 454627684 454926241 460617444 460703296 461218576 461304484 461433361 461648196 461734144 461777121 461906064 462637081 462723121 462766144 464747364 465739561 465955396 466948881 471845284 473889361 474629796 474847681 477947044 480004281 480223396 480749476 480837184 481407481 482329444 482417296 482768784 482856676 483516121 483604081 483648064 484528144 484616196 484836361 484924441 484968484 491908041 492218596 492307344 493817284 493906176 494439696 494706564 495908361 496888681 496933264 497825344 550559296 551216484 551733121 551827081 552626064 552767121 552814144 552908196 554979364 555639184 555827776 555922084 555969241 560316241 560647684 562733284 562828176 562923076 563635081 564727696 566868481 570302161 570779881 570827664 571879396 572214241 572549184 574848576 574944484 575616064 577729296 577825444 577969681 580424464 581726161 581967376 582305161 582546496 582836164 583657281 583802244 584624041 586705284 586802176 590927481 591511041 591559684 592338244 593312164 593507044 593604496 593848161 594433161 594969664 596629476 596727184 598878784 661209796 661724176 661827076 663526081 663629121 664402176 664505284 664917796 666724041 671224464 671535396 672313041 672935481 673869681 674648676 674856484 674908441 676624144 676728196 676988361 677977444 680114241 680218561 681627664 682202161 682829161 683404164 684816561 685706596 685968481 686859264 687803076 690323076 690428176 690533284 691216681 692426596 692847684 693848281 694744164 694955044 695957161 770506564 770839696 771117361 771228441 771506176 771617284 772339681 773507344 773618596 776848384 780755364 782656576 782768484 782824441 782936361 783328144 784448064 784504081 784616121 788823396 790959376 791634496 791747044 792929281 793605241 793999684 795747681 796707076 796989361 880427584 880546276 881555481 883516176 883635076 884408121 884527081 885538564 885717121 886729284 890306244 891559881 891739044 891858496 892336384 894548281 894847396 895745041 990738576 990927441 991557121 991746064 992817081 993447361 993636484 993888676 994519296 994645444 995907364 996728041 996917476
You should see it straight away: 144.
So making the conditional changes as described yields:
1 4 9 16 25 36 49 121 196 361 484 576 676 784 1849 1936 2304 2401 2601 2704 2809 2916 3969 4624 5625 5929 6724 14161 15376 16384 17161 18496 25281 28561 29241 29584 36481 49284 57121 58081 58564 67081 68121 69696 120409 121801 131769 140625 160801 161604 162409 164836 165649 174724 180625 182329 190969 198916 232324 273529 275625 294849 295936 351649 352836 361201 362404 363609 364816 375769 381924 390625 452929 471969 481636 485809 491401 492804 495616 571536 586756 680625 683929 786769 894916 896809 1304164 1406596 1418481 1503076 1515361 1527696 1535121 1623076 1628176 1726596 1745041 1747684 1758276 1907161 2307361 2316484 2328676 2427364 2515396 2637376 2819041 2859481 2917264 2979076 3504384 3519376 3538161 3709476 3717184 3748096 3767481 3837681 3857296 3924361 3956121 3968064 4528384 4713241 4717584 4726276 4748041 4879681 4937284 4946176 5612161 5626384 5645376 5803281 5827396 5934096 5958481 6713281 6718464 6859161 6906384 6948496 7918596 7958041 8916196 8934121 8946081 12061729 12075625 13053769 13140625 13191424 14032516 14092516 14182756 14197824 14250625 14265729 14386849 15241216 15264649 15350724 15460624 16184529 16353936 17073424 17172736 17230801 17280649 17673616 17690436 18275625 18292729 18352656 18472804 18481401 19061956 19140625 19342404 19351201 19562929 19580625 19686969 19784704 19793601 23020804 23030401 23164969 23193856 23261329 23280625 24186724 24373969 25180324 25230529 25250625 25270729 25482304 25492401 26020201 26030404 26040609 26050816 26142769 26193924 26265625 26460736 26481316 26491609 26563716 26594649 27050401 27060804 27071209 27081616 27373824 27394756 27583504 28121809 28132416 28153636 28376929 28590409 28686736 28793956 29170801 29181604 29192409 29354724 29452329 29560969 29680704 29691601 34140649 34175716 34292736 34374769 35081929 35354916 35390601 35473936 35485849 36590401 37161216 37173409 37185604 37197801 38130625 38241856 38365636 38390416 38452401 38464804 39262756 39287824 39350529 39375625 39463524 39891856 45131524 45252529 45481536 46185616 46253601 46280809 46580625 47141956 47265625 47361924 47582404 47692836 48052624 48121969 48260809 48274704 48385936 49252324 49350625 57032704 57062916 57198969 57274624 57350329 57380625 57486724 58140625 58461316 58491904 58583716 59043856 59243809 59274601 59351616 59382436 59397849 59675625 59783824 67141636 67190809 67272804 67354849 67683529 68161536 68475625 68591524 68790436 69272329 69672409 78251716 78340201 78375609 79263409 79281216 79691329 79780624 89264704 89283601 89340304 120209296 120428676 120516484 120758121 120802081 120824064 121308196 121418361 121528576 130439241 130919364 131308681 131423296 131928196 132434064 132503121 132756484 132848676 140209281 140612164 141824281 142301041 143736121 143808064 150528361 150749284 151807041 151979584 152868496 153958464 160757041 161518681 161849284 162919696 163609681 163635264 164506276 170537481 170929476 172423161 172712164 173659684 174609796 174927076 175801081 180526096 181548676 181656484 181737361 182547121 182628196 183819364 183846481 184715281 185804161 186978276 187909264 190219264 190826596 193738561 193989184 195608196 195748081 196728676 230402041 231709284 232318564 232806564 232837081 232989696 240529081 240839361 242549476 242705241 242767561 250304041 250937281 251825161 253637476 253956096 254849296 260209161 261436561 261727684 263607696 263705121 264517696 264908176 270306481 272514064 272547081 272613121 272712196 273439296 274929561 276756496 276956164 281434176 281769796 281937681 282946041 282979684 283619281 284968161 285914281 286828096 291419041 291658084 293848164 294946276 296735076 296907361 297838564 298978681 340107364 340919296 341436484 341547361 341917081 342546064 342657121 342768196 343768681 350513284 352538176 352613284 354606561 360658081 361304064 361418121 361836484 363436096 363703041 363817476 365727376 370216081 370909081 371949796 372837481 375623161 376709281 380601081 382515364 383807281 383846464 384905161 385808164 386869561 386948241 390418081 391723264 396846241 450967696 451945081 454627684 454926241 460703296 461218576 461648196 461906064 462637081 462723121 464747364 465739561 471845284 474629796 474847681 480749476 480837184 481407481 482417296 482768784 483516121 483604081 483648064 484616196 484836361 484968484 491908041 493817284 493906176 494706564 495908361 560316241 560647684 562828176 562923076 563635081 564727696 570302161 571879396 572549184 574848576 575616064 581726161 581967376 582305161 582546496 582836164 583657281 584624041 586705284 586802176 590927481 593848161 596727184 671535396 672313041 672935481 673869681 674648676 674856484 676728196 680218561 682829161 683404164 684816561 685706596 685968481 686859264 687803076 690323076 690428176 692426596 692847684 693848281 695957161 782656576 782768484 782936361 784504081 784616121 790959376 792929281 793605241 795747681 796707076 796989361 891858496 894548281 894847396 895745041
Here's that section of code with the change - I suspect it doesn't need explaining ... but for n1 < n2 then the negation of that check is n1 >= n2 and similarly with n2 > n3 then the negation is n2 <= n3...
if (isless && a >= b) {
valid = false;
break;
}
if (!isless && a <= b) {
valid = false;
break;
}
Well, I didn't check every number but this correction seems promising, agreed?


Personally, I think you may have over-complicated how to get the squares. Consider the following:
// square root of lowest perfect square in range
int n = (int)(Math.ceil(Math.sqrt(N)));
// square root of highest perfect square in range
int m = (int)(Math.floor(Math.sqrt(M)));
for (int i=n;i<=m;i++)
{
// Calculate the number to test
long j = i * i;
etc...
For more readability, you might also split the order test into its own function.
Here is a different implementation that uses math, rather than using strings.
/** Test to see if the given number has all digits in increasing order
when read from Most Significant Digit to Least Significant Digit */
private boolean isOrdered(long n)
{
// Preset our current Least Significant Digit so that the first test
// won't fail.
long last = 10;
boolean isLess = false;
while (n > 0)
{
// Get the least significant digit
long digit = n % 10;
// If the digit is not in order compared to the previous LS digit, then return.
// then return false.
if (isLess ? digit <= last : digit >= last)
return false;
isLess = !isLess;
// Remember the current LS Digit.
last = digit;
// Move to the next digit.
n = n / 10;
}
return true;
}

Related

Recognition digit or math operator app

I have a problem and I can't find what it's wrong with my code.
From beginning. I have application to recognize digits and math operators, the basic one +, -, /, *. I have a model ready which creates a graph. This graph is imported by an Android application. In my opinion, it should work but I don't know why it isn't?
Model.py
import tensorflow as tf
import numpy as np
import _pickle as pickle
from matplotlib import pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
# mnist_data = input_data.read_data_sets('MNIST_data', one_hot=True)
# read CROHME_test_data
with open("test.pickle", 'rb') as f:
data = pickle.load(f)
# np.random.shuffle(data)
CROHME_test_data = {"features": np.array([d["features"] for d in data]), "labels": np.array([d["label"] for d in data])}
# read CROHME_train_data
with open("train.pickle", 'rb') as f:
data = pickle.load(f)
# np.random.shuffle(data)
CROHME_train_data = {"features": np.array([d["features"] for d in data]),
"labels": np.array([d["label"] for d in data])}
# function for pulling batches from custom data
def next_batch(num, data):
idx = np.arange(0, len(data["features"]))
np.random.shuffle(idx)
idx = idx[:num]
features_shuffle = [data["features"][i] for i in idx]
labels_shuffle = [data["labels"][i] for i in idx]
return np.asarray(features_shuffle), np.asarray(labels_shuffle)
# Function to create a weight neuron using a random number. Training will assign a real weight later
def weight_variable(shape, name):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial, name=name)
# Function to create a bias neuron. Bias of 0.1 will help to prevent any 1 neuron from being chosen too often
def biases_variable(shape, name):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial, name=name)
# Function to create a convolutional neuron. Convolutes input from 4d to 2d. This helps streamline inputs
def conv_2d(x, W, name):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME', name=name)
# Function to create a neuron to represent the max input. Helps to make the best prediction for what comes next
def max_pool(x, name):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name=name)
labels_number = 14
# A way to input images (as 784 element arrays of pixel values 0 - 1)
x_input = tf.placeholder(dtype=tf.float32, shape=[None, 784], name='x_input')
# A way to input labels to show model what the correct answer is during training
y_input = tf.placeholder(dtype=tf.float32, shape=[None, labels_number], name='y_input')
# First convolutional layer - reshape/resize images
# A weight variable that examines batches of 5x5 pixels, returns 32 features (1 feature per bit value in 32 bit float)
W_conv1 = weight_variable([5, 5, 1, 32], 'W_conv1')
# Bias variable to add to each of the 32 features
b_conv1 = biases_variable([32], 'b_conv1')
# Reshape each input image into a 28 x 28 x 1 pixel matrix
x_image = tf.reshape(x_input, [-1, 28, 28, 1], name='x_image')
# Flattens filter (W_conv1) to [5 * 5 * 1, 32], multiplies by [None, 28, 28, 1] to associate each 5x5 batch with the
# 32 features, and adds biases
h_conv1 = tf.nn.relu(conv_2d(x_image, W_conv1, name='conv1') + b_conv1, name='h_conv1')
# Takes windows of size 2x2 and computes a reduction on the output of h_conv1 (computes max, used for better prediction)
# Images are reduced to size 14 x 14 for analysis
h_pool1 = max_pool(h_conv1, name='h_pool1')
# Second convolutional layer, reshape/resize images
# Does mostly the same as above but converts each 32 unit output tensor from layer 1 to a 64 feature tensor
W_conv2 = weight_variable([5, 5, 32, 64], 'W_conv2')
b_conv2 = biases_variable([64], 'b_conv2')
h_conv2 = tf.nn.relu(conv_2d(h_pool1, W_conv2, name='conv2') + b_conv2, name='h_conv2')
# Images at this point are reduced to size 7 x 7 for analysis
h_pool2 = max_pool(h_conv2, name='h_pool2')
# First dense layer, performing calculation based on previous layer output
# Each image is 7 x 7 at the end of the previous section and outputs 64 features, we want 32 x 32 neurons = 1024
W_dense1 = weight_variable([7 * 7 * 64, 1024], name='W_dense1')
# bias variable added to each output feature
b_dense1 = biases_variable([1024], name='b_dense1')
# Flatten each of the images into size [None, 7 x 7 x 64]
h_pool_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64], name='h_pool_flat')
# Multiply weights by the outputs of the flatten neuron and add biases
h_dense1 = tf.nn.relu(tf.matmul(h_pool_flat, W_dense1, name='matmul_dense1') + b_dense1, name='h_dense1')
# Dropout layer prevents overfitting or recognizing patterns where none exist
# Depending on what value we enter into keep_prob, it will apply or not apply dropout layer
keep_prob = tf.placeholder(dtype=tf.float32, name='keep_prob')
# Dropout layer will be applied during training but not testing or predicting
h_drop1 = tf.nn.dropout(h_dense1, keep_prob, name='h_drop1')
# Readout layer used to format output
# Weight variable takes inputs from each of the 1024 neurons from before and outputs an array of 14 elements
W_readout1 = weight_variable([1024, labels_number], name='W_readout1')
# Apply bias to each of the 14 outputs
b_readout1 = biases_variable([labels_number], name='b_readout1')
# Perform final calculation by multiplying each of the neurons from dropout layer by weights and adding biases
y_readout1 = tf.add(tf.matmul(h_drop1, W_readout1, name='matmul_readout1'), b_readout1, name='y_readout1')
# Softmax cross entropy loss function compares expected answers (labels) vs actual answers (logits)
cross_entropy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_input, logits=y_readout1))
# Adam optimizer aims to minimize loss
train_step = tf.train.AdamOptimizer(0.0001).minimize(cross_entropy_loss)
# Compare actual vs expected outputs to see if highest number is at the same index, true if they match and false if not
correct_prediction = tf.equal(tf.argmax(y_input, 1), tf.argmax(y_readout1, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# Used to save the graph and weights
saver = tf.train.Saver()
# Run in with statement so session only exists within it
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# Save the graph shape and node names to pbtxt file
tf.train.write_graph(sess.graph_def, '.', 'advanced_mnist.pbtxt', False)
# Train the model, running through data 20000 times in batches of 50
# Print out step # and accuracy every 100 steps and final accuracy at the end of training
# Train by running train_step and apply dropout by setting keep_prob to 0.5
for i in range(1000):
batch = next_batch(50, CROHME_train_data)
if i % 100 == 0:
train_accuracy = accuracy.eval(feed_dict={x_input: batch[0], y_input: batch[1], keep_prob: 1.0})
print("step %d, training accuracy %g" % (i, train_accuracy))
train_step.run(feed_dict={x_input: batch[0], y_input: batch[1], keep_prob: 0.5})
print("test accuracy %g" % accuracy.eval(feed_dict={x_input: CROHME_test_data["features"],
y_input: CROHME_test_data["labels"], keep_prob: 1.0}))
# Save the session with graph shape and node weights
saver.save(sess, './advanced_mnist.ckpt')
# Make a prediction of random image
img_num = np.random.randint(0, len(CROHME_test_data["features"]))
print("expected :", CROHME_test_data["labels"][img_num])
print("predicted :",
sess.run(y_readout1, feed_dict={x_input: [CROHME_test_data["features"][img_num]], keep_prob: 1.0}))
for j in range(28):
print(CROHME_test_data["features"][img_num][j * 28:(j + 1) * 28])
# this code can be used for image displaying
first_image = CROHME_test_data["features"][img_num]
first_image = np.array(first_image, dtype='float')
pixels = first_image.reshape((28, 28))
plt.imshow(pixels, cmap='gray')
plt.show()
Graph.py
import tensorflow as tf
from tensorflow.python.tools import freeze_graph, optimize_for_inference_lib
# Saving the graph as a pb file taking data from pbtxt and ckpt files and providing a few operations
freeze_graph.freeze_graph('advanced_mnist.pbtxt',
'',
True,
'advanced_mnist.ckpt',
'y_readout1',
'save/restore_all',
'save/Const:0',
'frozen_advanced_mnist.pb',
True,
'')
# Read the data form the frozen graph pb file
input_graph_def = tf.GraphDef()
with tf.gfile.Open('frozen_advanced_mnist.pb', 'rb') as f:
data = f.read()
input_graph_def.ParseFromString(data)
# Optimize the graph with input and output nodes
output_graph_def = optimize_for_inference_lib.optimize_for_inference(
input_graph_def,
['x_input', 'keep_prob'],
['y_readout1'],
tf.float32.as_datatype_enum)
# Save the optimized graph to the optimized pb file
f = tf.gfile.FastGFile('optimized_advanced_mnist.pb', 'w')
f.write(output_graph_def.SerializeToString())
MainActivity
package com.example.owl.advanced_mnist_android;
import android.graphics.Bitmap;
import android.graphics.BitmapFactory;
import android.support.v7.app.AppCompatActivity;
import android.os.Bundle;
import android.view.View;
import android.widget.ImageView;
import android.widget.TextView;
import org.tensorflow.contrib.android.TensorFlowInferenceInterface;
public class MainActivity extends AppCompatActivity {
ImageView imageView;
TextView resultsTextView;
static {
System.loadLibrary("tensorflow_inference");
}
private static final String MODEL_FILE = "file:///android_asset/optimized_advanced_mnist.pb";
private static final String INPUT_NODE = "x_input";
private static final int[] INPUT_SIZE = {1, 784};
private static final String KEEP_PROB = "keep_prob";
private static final int[] KEEP_PROB_SIZE = {1};
private static final String OUTPUT_NODE = "y_readout1";
private TensorFlowInferenceInterface inferenceInterface;
private int imageIndex = 10;
private int[] imageResourceIDs = {
R.drawable.digit0,
R.drawable.digit1,
R.drawable.digit2,
R.drawable.digit3,
R.drawable.digit4,
R.drawable.digit5,
R.drawable.digit6,
R.drawable.digit7,
R.drawable.digit8,
R.drawable.digit9,
R.drawable.rsz_przechwytywanie,
};
#Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
imageView = (ImageView) findViewById(R.id.image_view);
resultsTextView = (TextView) findViewById(R.id.results_text_view);
inferenceInterface = new TensorFlowInferenceInterface();
inferenceInterface.initializeTensorFlow(getAssets(), MODEL_FILE);
}
public void loadImageAction(View view) {
imageIndex = (imageIndex == 10) ? 0 : imageIndex + 1 ;
imageView.setImageResource(imageResourceIDs[imageIndex]);
}
public void predictDigitAction(View view) {
float[] pixelBuffer = convertImage();
float[] results = predictDigit(pixelBuffer);
formatResults(results);
}
private void formatResults(float[] results) {
float max = 0;
float secondMax = 0;
int maxIndex = 0;
int secondMaxIndex = 0;
for (int i = 0; i < 15; i++) {
if (results[i] > max) {
secondMax = max;
secondMaxIndex = maxIndex;
max = results[i];
maxIndex = i;
} else if (results[i] < max && results[i] > secondMax) {
secondMax = results[i];
secondMaxIndex = i;
}
}
String output = "Model predicts: " + String.valueOf(maxIndex) +
", second choice: " + String.valueOf(secondMaxIndex);
resultsTextView.setText(output);
}
private float[] predictDigit(float[] pixelBuffer) {
inferenceInterface.fillNodeFloat(INPUT_NODE, INPUT_SIZE, pixelBuffer);
inferenceInterface.fillNodeFloat(KEEP_PROB, KEEP_PROB_SIZE, new float[] {0.5f});
inferenceInterface.runInference(new String[] {OUTPUT_NODE});
float[] outputs = new float[14];
inferenceInterface.readNodeFloat(OUTPUT_NODE, outputs);
return outputs;
}
private float[] convertImage() {
Bitmap bitmap = BitmapFactory.decodeResource(getResources(), imageResourceIDs[imageIndex]);
Bitmap scaledBitmap = Bitmap.createScaledBitmap(bitmap, 28, 28, true);
imageView.setImageBitmap(scaledBitmap);
int[] intArray = new int[784];
float[] floatArray = new float[784];
scaledBitmap.getPixels(intArray, 0, 28, 0, 0, 28, 28);
for (int i = 0; i < 784; i++) {
floatArray[i] = intArray[i] / -16777216;
}
return floatArray;
}
}
Everything looks good, but I have an error:
E/AndroidRuntime: FATAL EXCEPTION: main
Process: com.example.owl.advanced_mnist_android, PID: 6855
java.lang.IllegalStateException: Could not execute method for android:onClick
at android.support.v7.app.AppCompatViewInflater$DeclaredOnClickListener.onClick(AppCompatViewInflater.java:293)
at android.view.View.performClick(View.java:6891)
at android.widget.TextView.performClick(TextView.java:12651)
at android.view.View$PerformClick.run(View.java:26083)
at android.os.Handler.handleCallback(Handler.java:789)
at android.os.Handler.dispatchMessage(Handler.java:98)
at android.os.Looper.loop(Looper.java:164)
at android.app.ActivityThread.main(ActivityThread.java:6938)
at java.lang.reflect.Method.invoke(Native Method)
at com.android.internal.os.Zygote$MethodAndArgsCaller.run(Zygote.java:327)
at com.android.internal.os.ZygoteInit.main(ZygoteInit.java:1374)
Caused by: java.lang.reflect.InvocationTargetException
at java.lang.reflect.Method.invoke(Native Method)
at android.support.v7.app.AppCompatViewInflater$DeclaredOnClickListener.onClick(AppCompatViewInflater.java:288)
at android.view.View.performClick(View.java:6891) 
at android.widget.TextView.performClick(TextView.java:12651) 
at android.view.View$PerformClick.run(View.java:26083) 
at android.os.Handler.handleCallback(Handler.java:789) 
at android.os.Handler.dispatchMessage(Handler.java:98) 
at android.os.Looper.loop(Looper.java:164) 
at android.app.ActivityThread.main(ActivityThread.java:6938) 
at java.lang.reflect.Method.invoke(Native Method) 
at com.android.internal.os.Zygote$MethodAndArgsCaller.run(Zygote.java:327) 
at com.android.internal.os.ZygoteInit.main(ZygoteInit.java:1374) 
Caused by: java.lang.IllegalArgumentException: No Operation named [x_input] in the Graph
at org.tensorflow.Session$Runner.operationByName(Session.java:297)
at org.tensorflow.Session$Runner.feed(Session.java:115)
at org.tensorflow.contrib.android.TensorFlowInferenceInterface.addFeed(TensorFlowInferenceInterface.java:437)
at org.tensorflow.contrib.android.TensorFlowInferenceInterface.fillNodeFloat(TensorFlowInferenceInterface.java:186)
at com.example.owl.advanced_mnist_android.MainActivity.predictDigit(MainActivity.java:89)
at com.example.owl.advanced_mnist_android.MainActivity.predictDigitAction(MainActivity.java:63)
at java.lang.reflect.Method.invoke(Native Method) 
at android.support.v7.app.AppCompatViewInflater$DeclaredOnClickListener.onClick(AppCompatViewInflater.java:288) 
at android.view.View.performClick(View.java:6891) 
at android.widget.TextView.performClick(TextView.java:12651) 
at android.view.View$PerformClick.run(View.java:26083) 
at android.os.Handler.handleCallback(Handler.java:789) 
at android.os.Handler.dispatchMessage(Handler.java:98) 
at android.os.Looper.loop(Looper.java:164) 
at android.app.ActivityThread.main(ActivityThread.java:6938) 
at java.lang.reflect.Method.invoke(Native Method) 
at com.android.internal.os.Zygote$MethodAndArgsCaller.run(Zygote.java:327) 
at com.android.internal.os.ZygoteInit.main(ZygoteInit.java:1374) 

Is this the most efficient way to initialize an array

I am doing an exercise, which requires to create 20 long arrays with 10^18 numbers in 10^5 cells. Every time I am always using the same array simply overwriting it in this way:
array = new long [b];
The program stops working at around 19th iteration, because of overused provided resources by the 'Hackerrank' website. What are the ways to make this code more efficient? I was thinking that maybe I am not deleting previously used array and it overuses system parameters. Is there a better way to do this? This is done in www.hackerrank.com and it only shows that "Runtime time" error occurs, but doesn't show which one exactly. It runs fine without printing the answer, so it probably requires to use slightly less resources and I think most resources are required for array.
My code:
import java.io.*;
import java.util.*;
import java.util.stream.*;
public class Solution {
public static void main(String[] args) {
/* Enter your code here. Read input from STDIN. Print output to STDOUT. Your class should be named Solution. */
Scanner sc = new Scanner(System.in);
int t = sc.nextInt();
int b;
long n;
long k;
long []array;
int count;
long sum = 0;
for(int i = 0; i < t; i++){
n = sc.nextLong();
k = sc.nextLong();
b = sc.nextInt();
count = 0;
sum = 0;
array = new long [b];
for (int j = 0; j < b; j++){
array[b - 1 - j]=j+1;
sum +=(j+1);
}
while (true){
while (sum < n && count < b && k > b){
if ((sum + (k - count - array[count]) )< n) {
sum += k - count - array[count];
array[count]+=k - count - array[count];
}
else {
System.out.println (n - sum);
array[count]+= n - sum;
sum += n - sum;
}
if (array[count] == k - count) count++;
}
if (sum == n) {
for (int x = 0; x < b; x++){
System.out.print (array[x]);
if (x < b - 1) System.out.print(" ");
else System.out.print("\n");
}
break;
}
else {
System.out.println ("-1");
break;
}
}
}
}
}
Input:
20
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
999999995000050000 10000000000000 100000
All cases work separately.
The output is:
10000000000000 9999999999999 9999999999998 9999999999997 9999999999996 9999999999995 9999999999994 9999999999993 9999999999992 9999999999991 9999999999990 9999999999989 9999999999988 9999999999987 9999999999986 9999999999985 9999999999984 9999999999983 9999999999982 9999999999981 9999999999980 9999999999979 9999999999978 9999999999977 9999999999976 9999999999975 9999999999974 9999999999973 9999999999972 9999999999971 9999999999970 9999999999969 9999999999968 9999999999967 9999999999966 9999999999965 9999999999964 9999999999963 9999999999962 9999999999961 9999999999960 9999999999959 9999999999958 9999999999957 9999999999956 9999999999955 9999999999954 9999999999953 9999999999952 9999999999951 9999999999950 9999999999949 9999999999948 9999999999947 9999999999946 9999999999945 9999999999944 9999999999943 9999999999942 9999999999941 9999999999940 9999999999939 9999999999938 9999999999937 9999999999936 9999999999935 9999999999934 9999999999933 9999999999932 9999999999931 9999999999930 9999999999929 9999999999928 9999999999927 9999999999926 9999999999925 9999999999924 9999999999923 9999999999922 9999999999921 9999999999920 9999999999919 9999999999918 9999999999917 9999999999916 9999999999915 9999999999914 9999999999913 9999999999912 9999999999911 9999999999910 9999999999909 9999999999908 9999999999907 9999999999906 9999999999905 9999999999904 9999999999903 9999999999902 9999999999901 9999999999900 9999999999899 9999999999898 9999999999897 9999999999896 9999999999895 9999999999894 9999999999893 9999999999892 9999999999891 9999999999890 9999999999889 9999999999888 9999999999887 9999999999886 9999999999885 9999999999884 9999999999883 9999999999882 9999999999881 9999999999880 9999999999879 9999999999878 9999999999877 9999999999876 9999999999875 9999999999874 9999999999873 9999999999872 9999999999871 9999999999870 9999999999869 9999999999868 9999999999867 9999999999866 9999999999865 9999999999864 9999999999863 9999999999862 9999999999861 9999999999860 9999999999859 9999999999858 9999999999857 9999999999856 9999999999855 9999999999854 9999999999853 9999999999852 9999999999851 9999999999850 9999999999849 9999999999848 9999999999847 9999999999846 9999999999845 9999999999844 9999999999843 9999999999842 9999999999841 9999999999840 9999999999839 9999999999838 9999999999837 9999999999836 9999999999835 9999999999834 9999999999833 9999999999832 9999999999831 9999999999830 9999999999829 9999999999828 9999999999827 9999999999826 9999999999825 9999999999824 9999999999823 9999999999822 9999999999821 9999999999820 9999999999819 9999999999818 9999999999817 9999999999816 9999999999815 9999999999814 9999999999813 9999999999812 9999999999811 9999999999810 9999999999809 9999999999808 9999999999807 9999999999806 9999999999805 9999999999804 9999999999803 9999999999802 9999999999801 9999999999800 9999999999799 9999999999798 9999999999797 9999999999796 9999999999190 9999999999189 9999999999188 9999999999187 9999999999186 9999999999185 9999999999184 9999999999183 9999999999182 9999999999181 9999999999180 9999999999179 9999999999178 9999999999177 9999999999176 9999999999175 9999999999174 9999999999173 9999999999172 9999999999171 9999999999170 9999999999169 9999999999168 9999999999167 9999999999166 9999999999165 9999999999164 9999999999163 9999999999162 9999999999161 9999999999160 9999999999159 9999999999158 9999999999157 9999999999156 9999999999155 9999999999154 9999999999153 9999999999152 9999999999151 9999999999150 9999999999149 9999999999148 9999999999147 9999999999146 9999999999145 9999999999144 9999999999143 9999999999142 9999999999141 9999999999140 9999999999139 9999999999138 9999999999137 9999999999136 9999999999135 9999999999134 9999999999133 9999999999132 9999999999131 9999999999130 9999999999129 9999999999128 9999999999127 9999999999126 9999999999125 9999999999124 9999999999123 9999999999122 9999999999121 9999999999120 9999999999119 9999999999118 9999999999117 9999999999116 9999999999115 9999999999114 9999999999113 9999999999112 9999999999111 9999999999110 9999999999109 9999999999108 9999999999107 9999999999106 9999999999105 9999999999104 9999999999103 9999999999102 9999999999101 9999999999100 9999999999099 9999999999098 9999999999097 9999999999096 9999999999095 9999999999094 9999999999093 9999999999092 9999999999091 9999999999090 9999999999089 9999999999088 9999999999087 9999999999086 9999999999085 9999999999084 9999999999083 9999999999082 9999999999081 9999999999080 9999999999079 9999999999078 9999999999077 9999999999076 9999999999075 9999999999074 9999999999073 9999999999072 9999999999071 9999999999070 9999999999069 9999999999068 9999999999067 9999999999066 9999999999065 9999999999064 9999999999063 9999999999062 9999999999061 9999999999060 9999999999059 9999999999058 9999999999057 9999999999056 9999999999055 9999999999054 9999999999053 9999999999052 9999999999051 9999999999050 9999999999049 9999999999048 9999999999047 9999999999046 9999999999045 9999999999044 9999999999043 9999999999042 9999999999041 9999999999040 9999999999039 9999999999038 9999999999037 9999999999036 9999999999035 9999999999034 9999999999033 9999999999032 9999999999031 9999999999030 9999999999029 9999999999028 9999999999027 9999999999026 9999999999025 9999999999024 9999999999023 9999999999022 9999999999021 9999999999020 9999999999019 9999999999018 9999999999017 9999999999016 9999999999015 9999999999014 9999999999013 9999999999012 9999999999011 9999999999010 9999999999009 9999999999008 9999999999007 9999999999006 9999999999005 9999999999004 9999999999003 9999999999002 9999999999001 9999999999000 9999999998999 9999999998998 9999999998997 9999999998996 9999999998995 9999999998994 9999999998993 9999999998992 9999999998991 9999999998990 9999999998989 9999999998988 9999999998987 9999999998986 9999999998985 9999999998984 9999999998983 9999999998982 9999999998981 9999999998980 9999999998979 9999999998978 9999999998977 9999999998976 9999999998975 9999999998974 9999999998973 9999999998972 9999999998971 9999999998970 9999999998969 9999999998968 9999999998967 9999999998966 9999999998965 9999999998964 9999999998963 9999999998962 9999999998961 9999999998960 9999999998959 9999999998958 9999999998957 9999999998956 9999999998955 9999999998954 9999999998953 9999999998952 9999999998951 9999999998950 9999999998949 9999999998948 9999999998947 9999999998946 9999999998945 9999999998944 9999999998943 9999999998942 9999999998941 9999999998940 9999999998939 9999999998938 9999999998937 9999999998936 9999999998935 9999999998934 9999999998933 9999999998932 9999999998931 9999999998930 9999999998929 9999999998928 9999999998927 9999999998926 9999999998925 9999999998924 9999999998923 9999999998922 9999999998921 9999999998920 9999999998919 9999999998918 9999999998917 9999999998916 9999999998915 9999999998914 9999999998913 9999999998912 9999999998911 9999999998910 9999999998909 9999999998908 9999999998907 9999999998906 9999999998905 9999999998904 9999999998903 9999999998902 9999999998901 9999999998900 9999999998899 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You are reusing the same array pointer. But not really re using the exact same memory space. Its not really the best practice in the case you mentioned.
You can create the array (alloccate memory) only once in the beginning of your code and re use it in all further cases.
import java.io.*;
import java.util.*;
import java.util.stream.*;
public class Solution {
public static void main(String[] args) {
/* Enter your code here. Read input from STDIN. Print output to STDOUT. Your class should be named Solution. */
Scanner sc = new Scanner(System.in);
int t = sc.nextInt();
int b;
long n;
long k;
long []array;
array = new long [100005]; // biggest size that can be possible
int count;
long sum = 0;
for(int i = 0; i < t; i++){
n = sc.nextLong();
k = sc.nextLong();
b = sc.nextInt();
count = 0;
sum = 0;
//no need to create it again and again, just create it once but create it as the biggest size
for (int j = 0; j < b; j++){
array[b - 1 - j]=j+1;
sum +=(j+1);
}
while (true){
while (sum < n && count < b && k > b){
if ((sum + (k - count - array[count]) )< n) {
sum += k - count - array[count];
array[count]+=k - count - array[count];
}
else {
System.out.println (n - sum);
array[count]+= n - sum;
sum += n - sum;
}
if (array[count] == k - count) count++;
}
if (sum == n) {
for (int x = 0; x < b; x++){
System.out.print (array[x]);
if (x < b - 1) System.out.print(" ");
else System.out.print("\n");
}
break;
}
else {
System.out.println ("-1");
break;
}
}
}
}
}

Unexpected output of InfluxDB batch write

I am using batch processing to write into InfluxDB and below is my code for doing that.
String dbName = "test";
influxDB.query(new Query("CREATE DATABASE " + dbName, dbName));
Stopwatch watch = Stopwatch.createStarted();
influxDB.enableBatch(2000, 100, TimeUnit.MILLISECONDS);
for (int j = 0; j < 100000; j++) {
Point point = Point.measurement("cpu")
.addField("idle", (double) j)
.addField("system", 3.0 * j).build();
influxDB.write(dbName, "autogen", point);
}
influxDB.disableBatch();
System.out.println("Write for " + 100000 + " Points took:" + watch);
}
Here i am writing 100000 points and which is taking very reasonable time to write, however only few records are written into DB instead of expected 100000 records.
select count(idle) from cpu gives me only "89" i am expecting it to be "100000"
While select * from cpu gives me following:
cpu
time idle system
2016-10-06T23:57:41.184Z 8 24
2016-10-06T23:57:41.185Z 196 588
2016-10-06T23:57:41.186Z 436 1308
2016-10-06T23:57:41.187Z 660 1980
2016-10-06T23:57:41.188Z 916 2748
2016-10-06T23:57:41.189Z 1278 3834
2016-10-06T23:57:41.19Z 1405 4215
2016-10-06T23:57:41.191Z 1409 4227
2016-10-06T23:57:41.192Z 1802 5406
2016-10-06T23:57:41.193Z 1999 5997
2016-10-06T23:57:41.456Z 3757 11271
2016-10-06T23:57:41.457Z 3999 11997
2016-10-06T23:57:41.858Z 4826 14478 and so on.....
Here my question is why the values of idle are missing, for example, after 8 it should 9, 10, 11, and so on but these values were not persisted and comes directly 196 and then missing in between and then 436. Any idea how to persist all value of loop variable "j" in this situation?

This line
influxDB.enableBatch(2000, 100, TimeUnit.MILLISECONDS);
says that it will flush input data if there are more than 2000 samples per 100 ms period. Since you are trying to write 100k samples then logically most of them get flushed.
Instead, write less samples in a single batch. My recommendation would be to write 5000 samples in a single batch, and make multiple batches until all your data is in the db.
// Batch 1
influxDB.enableBatch(5000, 100, TimeUnit.MILLISECONDS);
for (int j = 0; j < 5000; j++) {
Point point = Point.measurement("cpu")
.addField("idle", (double) j)
.addField("system", 3.0 * j).build();
influxDB.write(dbName, "autogen", point);
}
influxDB.disableBatch();
// Batch 2
// ...

Base64 encoding vs Ascii85 encoding

My project at work is using the Jackson JSON serializer to convert a bunch of Java objects into Strings in order to send them to REST services.
Some of these objects contain sensitive data, so I've written custom serializers to serialize these objects to JSON strings, then gzip them, then encrypt them using AES;
This turns the strings into byte arrays, so I use the Base64 encoder in Apache commons codec to convert the byte arrays into strings. The custom deserializers behind the REST interfaces reverse this process:
base64 decode -> decrypt -> decompress -> deserialize using default Jackson deserializer.
Base64 encoding increases the size of the output (the gzip step in serialization is meant to help ameliorate this increase), so I checked Google to see if there was a more efficient alternative, which led me to this previous stackoverflow thread that brought up Ascii85 encoding as a more efficient alternative -
Base64 adds 33% to the size of the output, Ascii85 adds 25% to the size of the output.
I found a few Java Ascii85 implementations e.g. Apache pdfbox, but I'm a bit leery to use the encoding - it seems like hardly anybody is using or implementing it, which might just mean that Base64 has more inertia, or which may instead mean that there's some wonky problem with Ascii85.
Does anybody know more on this subject? Are there any problems with Ascii85 that mean that I should use Base64 instead?

Base64 is way more common. The difference in size really isn't that significant in most cases, and if you add at the HTTP level (which will compress the base64) instead of within your payload, you may well find the difference goes away entirely.
Are there any problems with Ascii85 that mean that I should use Base64 instead?
I would strongly advise using base64 just because it's so much more widespread. It's pretty much the canonical way of representing binary data as text (unless you want to use hex, of course).

ASCII85 is a nice encoding to use to save that extra bit of space. But it outputs many characters that would need to be escaped if naively sent over HTTP. Base64 encoding has a variant that can be sent over HTTP without any escaping.
Here's a javascript ASCII85 encoder in case anyone needs to try:
// By Steve Hanov. Released to the public domain.
function encodeAscii85(input) {
var output = "<~";
var chr1, chr2, chr3, chr4, chr, enc1, enc2, enc3, enc4, enc5;
var i = 0;
while (i < input.length) {
// Access past the end of the string is intentional.
chr1 = input.charCodeAt(i++);
chr2 = input.charCodeAt(i++);
chr3 = input.charCodeAt(i++);
chr4 = input.charCodeAt(i++);
chr = ((chr1 << 24) | (chr2 << 16) | (chr3 << 8) | chr4) >>> 0;
enc1 = (chr / (85 * 85 * 85 * 85) | 0) % 85 + 33;
enc2 = (chr / (85 * 85 * 85) | 0) % 85 + 33;
enc3 = (chr / (85 * 85) | 0 ) % 85 + 33;
enc4 = (chr / 85 | 0) % 85 + 33;
enc5 = chr % 85 + 33;
output += String.fromCharCode(enc1) +
String.fromCharCode(enc2);
if (!isNaN(chr2)) {
output += String.fromCharCode(enc3);
if (!isNaN(chr3)) {
output += String.fromCharCode(enc4);
if (!isNaN(chr4)) {
output += String.fromCharCode(enc5);
}
}
}
}
output += "~>";
return output;
}
<input onKeyUp="result.innerHTML = encodeAscii85(this.value)" placeholder="write text here" type="text">
<p id="result"></p>

Here is matching ASCII85 AKA Base85 decoder (for user Qwerty) in JavaScript:
function decode_ascii85(a) {
var c, d, e, f, g, h = String, l = "length", w = 255, x = "charCodeAt", y = "slice", z = "replace";
for ("<~" === a[y](0, 2) && "~>" === a[y](-2), a = a[y](2, -2)[z](/\s/g, "")[z]("z", "!!!!!"),
c = "uuuuu"[y](a[l] % 5 || 5), a += c, e = [], f = 0, g = a[l]; g > f; f += 5) d = 52200625 * (a[x](f) - 33) + 614125 * (a[x](f + 1) - 33) + 7225 * (a[x](f + 2) - 33) + 85 * (a[x](f + 3) - 33) + (a[x](f + 4) - 33),
e.push(w & d >> 24, w & d >> 16, w & d >> 8, w & d);
return function(a, b) {
for (var c = b; c > 0; c--) a.pop();
}(e, c[l]), h.fromCharCode.apply(h, e);
}
<input onKeyUp="result.innerHTML = decode_ascii85(this.value)" placeholder="insert encoded string here" type="text">
<p id="result"></p>
example: <xmp><~<+oue+DGm>#3BW*D/a<&+EV19F<L~></xmp>

Search & display results [java]

I run a small online gaming community and deal with a database of accounts.
The setup is this:
Folder named Accounts
Inside the Accounts directory, there is 200,000+ text files organized by player name. Access to this folder manually is a pain because of the needed RAM to get in and search files. I find this very inconvenient.
I access this directory to send password reminders or for highscores on who has been playing the longest.
Here is an example of an account file. This file is named Falcon.txt
[ACCOUNT]
character-username = Falcon
character-password = falconpassword
[INFO]
character-coordx = 3252
character-coordy = 3432
character-active = yes
character-ismember = 1
character-messages = 5
character-lastconnection = [removed]
character-lastlogin = 2009-11-29
character-energy = 100
character-gametime = 193
character-gamecount = 183
[EQUIPMENT]
character-equip = 0 4724 0
character-equip = 1 1052 0
character-equip = 2 6585 0
character-equip = 3 4151 0
character-equip = 4 4720 0
character-equip = 5 1215 0
character-equip = 6 -1 0
character-equip = 7 4722 0
character-equip = 8 -1 0
character-equip = 9 775 0
character-equip = 10 1837 0
character-equip = 11 -1 0
character-equip = 12 6735 0
character-equip = 13 -1 0
[APPEARANCE]
character-look = 0 1
character-look = 1 1
character-look = 2 2
character-look = 3 3
character-look = 4 5
character-look = 5 2
[STATS]
character-skill = 0 1 0
character-skill = 1 1 0
character-skill = 2 1 0
character-skill = 3 1 0
character-skill = 4 1 0
character-skill = 5 1 0
character-skill = 6 1 0
character-skill = 7 1 0
character-skill = 8 1 0
character-skill = 9 1 0
character-skill = 10 1 0
character-skill = 11 1 0
character-skill = 12 1 0
character-skill = 13 1 0
character-skill = 14 1 0
character-skill = 15 1 0
character-skill = 16 1 0
character-skill = 17 1 0
character-skill = 18 1 0
character-skill = 19 1 0
character-skill = 20 1 0
[ITEMS]
[BANK]
[FRIENDS]
[IGNORES]
[END]
There is a huge database of these and search through the directory in the files for values.
Values I mean by item ID's or IP addresses to find and track other accounts.
However I have a new problem and my development for this is crashing.
As you can see in the file the lines are organized by tabs.
character-equip = 0 4724 1
If I put the value 4724 in my search application, I want it to print out the value 1 tab to the right of the found search result. I want it to print out the value for the found results only, not extra results.
So the search could look like this:
1 "Enter item to find:"
2 "Enter item to find: 4724"
3 "Account Falcon.txt has 1!"
press any key to continue...
Or if there was more quantity of that equipped item
character-equip = 5 1239 102
1. "Enter item to find:"
2. "Enter item to find: 1239"
3. "Account Falcon2.txt has 102!"
press any key to continue...
I simply want to input an item ID, and have it display the value after the found value. The white space is a tab. I have tried doing this and the only successful way of getting any result is to put a tab in between the search term. So if I want to find item 1239 id type this in the cmd line:
Enter item to find:<tab>1239<tab>
It would then search for that and will display the accounts with that item in it. However I still have to individually open up the accounts to find out the quantity of that item. I want the search results to display the quantity of the item if the value is found. However if the value is a quantity and it trys to search one tab over, I want it to either skip it or say zero.
This is what I mean.
character-equip = 0 1024 1239
Enter item to find: 1239
If it hits this account I want to make the search results display a zero if it cannot tab over or view any values in the white space. So it will display as null or zero
Account Falcon3.txt has null!
or
Account Falcon3.txt has 0!
I've attempted to do this but I am unsure how to achieve this.
Here is my code.
import java.io.*;
import java.util.*;
public class ItemDatabase {
public static void main(String args[]) {
System.out.print("Enter item to find: ");
Scanner sc = new Scanner(System.in);
find(sc.nextLine());
}
public static void find(String delim) {
File dir = new File("accounts");
if (dir.exists()) {
String read;
try {
File files[] = dir.listFiles();
for (int i = 0; i < files.length; i++) {
File loaded = files[i];
if (loaded.getName().endsWith(".txt")) {
BufferedReader in = new BufferedReader(new FileReader(loaded));
StringBuffer load = new StringBuffer();
while ((read = in.readLine()) != null) {
load.append(read + "\n");
}
String delimiter[] = new String(load).split(delim);
if(delimiter.length > 1) {
System.out.println("Account " + loaded.getName() + "has " + delimiter[1] + "!");
}
}
}
} catch (Exception e) {
e.printStackTrace();
}
} else {
System.out.println("error: dir wasn't found!");
}
}
}
Thanks guys I hope you can help me.

This is simply crying out for a database. If your entire back end is running in a single java process I'd recommend going with something like Apache Derby or H2.
If you'd rather not move to a database, and the main problem is the act of listing all the entries in a single directory, could you split it into a heirarchy. Your Falcon account could then be located in F/FA/Falcon.txt - each directory would then contain a more manageable number of files.

Aside from the need for a database, you could probably implement your solution more intuitively and easily using commandline utilities such as find, grep, etc. or a text-processing language such as perl.
Maybe
grep '[0-9]+\t[0-9]+\t1239' Account_Falcon3.txt
Would return
character-equip = 0 1024 1239
You could then easily see that the value is 0 for that item.
I cannot emphasize enough the need to not write a Java program to do this - you won't do as good a job as the authors of the standard shell utilities. Let's face it, the fact that you are asking this question indicates that you are a newb! :) (We are all newbs depending on the topic).

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