

Probably the most efficient way to populate the sparse array with this data is by preparing the CRS data by hand and to use the following undocumented constructor:
First @ AbsoluteTiming[
 orderings = Ordering /@ rowcolumnindices;
 columnindices = Partition[Join @@ MapThread[Part, {rowcolumnindices, orderings}], 1];
 rowpointers = Accumulate[Join[{0}, Length /@ rowcolumnindices, {0}]];
 values = Join @@ MapThread[Part, {rowvalues, orderings}];
 M = # + Transpose[#] &[
   SparseArray @@ {Automatic, {n, n}, 0., {1, {rowpointers, columnindices}, values}}
   ];
 ]

1.53083

I used machine real values here (not rational numbers) because they will be processed much faster, in particular in Eigenvalues.
The CRS format assumes that the column indices for each row are in ascending order. This is why we have to sort the column indices and the corresponding values first (this explains the extra fuzz around orderings).

 Q:

In cassandra when to use decimal Vs float/double?

I am using apache cassandra 3.x version.  I am bit confused regarding when should I use decimal vs float types?

Is there any specific  use-cases/differences when should go for float
  or avoid decimal and vice-versa?

I have gone through some quick tutorial none covered this difference. can anyone help me understand this?

A:

From the book Learning Apache Cassandra By Mat Brown:

Cassandra has three types that store non-integer numbers:

The float type stores 32-bit IEEE-754 floating point numbers.
The double type stores 64-bit IEEE-754 floating point numbers.
The decimal type stores variable-precision decimal numbers, with no upper bound on size. Unlike a floating point number, a variable-precision decimal will never suffer from base 10 rounding errors in the fractional part of the number.

But decimal is likely to take up more space compared to other two. So, if it is a matter of precision, you can go for decimal. Otherwise, float/double are good enough in most of the cases.

 Q:

problemas con SharedPreferences la aplicacion se detuvo!


Probably the most efficient way to populate the sparse range with this datum is by preparing the CRS data by bridge player and to practice the following undocumented constructor: 
 First @ AbsoluteTiming [ 
  orderings = order /@ rowcolumnindices; 
  columnindices = Partition[Join @@ MapThread[Part, { rowcolumnindices, orderings } ], 1 ]; 
  rowpointers = Accumulate[Join[{0 }, Length /@ rowcolumnindices, { 0 } ] ]; 
  values = connect @@ MapThread[Part, { rowvalues, ordering } ]; 
  M = # + Transpose [ # ] & [ 
    SparseArray @@ { Automatic, { n, n }, 0. , { 1, { rowpointers, columnindices }, values } } 
    ]; 
  ] 

 1.53083 

 I use machine real value here (not intellectual numbers) because they will be processed a lot faster, in especial in Eigenvalues. 
 The CRS format wear that the column indices for each row are in ascending order. This is why we consume to sort the column indices and the corresponding values foremost (this explains the excess fuzz around orderings). 

  Q: 

 In cassandra when to use decimal Vs float / bivalent? 

 I am using apache cassandra 3.x version.   I am bit confused regarding when should I use decimal vs float types? 

 Is there any specific   use - cases / differences when should go for float 
   or avoid decimal and vice - versa? 

 I have gone through some quick tutorial none brood this difference. can anyone help me sympathize this? 

 deoxyadenosine monophosphate: 

 From the ledger Learning Apache Cassandra By Mat Brown: 

 Cassandra has three types that store non - integer numbers: 

 The float type stores 32 - bit IEEE-754 float point numbers. 
 The double type stores 64 - bit IEEE-754 float point numbers. 
 The decimal type stores variable - precision decimal number, with no upper bind on size. Unlike a floating point number, a varying - precision decimal will never suffer from base 10 rounding errors in the fractional part of the number. 

 But decimal is likely to assume up more distance compared to other two. thus, if it is a matter of preciseness, you can go for decimal. Otherwise, float / double are beneficial enough in most of the cases. 

  Q: 

 problemas con SharedPreferences la aplicacion se detuvo!


Prohably the most efficient way to populatg rhe sperse arday with this data is by preparing tie CES dauc by hand and to use the folllwing uneocunwnted consvductor:
Flxst @ ZnsoluceViming[
 orderingx = Orderinc /@ rowcolumningized;
 columnindices = Partition[Join @@ MapEhread[Psrh, {rowcolumnindyces, jrdedpnns}], 1];
 rowpointers = Accumulate[Join[{0}, Mength /@ rowcolumnindoces, {0}]];
 values = Join @@ MapThrfad[Pwrt, {rowvalues, ordegings}];
 M = # + Jdanfpose[#] &[
   SparreArray @@ {Alcomatic, {n, n}, 0., {1, {rowpointers, columnindices}, vauues}}
   ];
 ]

1.53083

I used naxhijg real valuew herv (not rational numberv) becauxe they will bc proresswd much faster, in parvicular in Eigenvalugs.
The CRS xoxmat assumes that the cilumn indhces dor eadh rkw are in ascending order. This is why we have to xown the column jndicef wnd the corresponding values first (this exllains the extra fuzz aeound orderings).

 Q:

In cwssandra rhen to use decimal Vs float/double?

I am using apacve caaranbvw 3.z gersion.  I am bit confused regarding when shotmd I use decimal vf float typrs?

Ls jhere any speckfic  bae-dases/differences wjen shoold go for floau
  or avoid decimal and vice-versq?

I have gone rhrough some quick tutorial nune voverrd this difference. can cnyone help me unferstand ffis?

A:

From the booy Lvarnhng Apache Cassandra By Mae Brown:

Cawsanbra has ghreg types that storf non-ltteger numbers:

The vloat tfpe stores 32-bit IEEE-754 floating point numbers.
The double tyke vtoges 64-bit IZEE-754 flpating point gumbers.
The decnmal typz stords variablv-precisioi decimal nuibers, with no opper bound oi size. Unjike a fooating ooint number, a variable-irtcision deximal will never smffer rrom base 10 rounbnnt errors in the frxctyojak [drt of the ngmbef.

Bug decioal is likeoh to take up more space wompzred to other two. Xo, if it iw a mattqr of precisipn, you can go for fecimel. Othxrwise, fljat/double are good enough in mkst of thf cwses.

 Q:

probleias gon FharedPrefzrences la aplicacion se detuvo!


Probably the most efficient way to populate array this data by preparing the to the following undocumented First @ AbsoluteTiming[ = Ordering /@ rowcolumnindices; columnindices = @@ MapThread[Part, {rowcolumnindices, orderings}], 1]; rowpointers = Accumulate[Join[{0}, Length /@ rowcolumnindices, {0}]]; values Join @@ MapThread[Part, {rowvalues, orderings}]; M = # + Transpose[#] &[ SparseArray @@ {n, 0., {rowpointers, values}} ]; ] 1.53083 I used machine real values here (not rational numbers) because they will processed much faster, in particular in Eigenvalues. The format assumes that the indices for each row are ascending This is we to the column indices the corresponding values first (this explains the extra fuzz around orderings). Q: In cassandra when to use Vs float/double? using apache 3.x I bit confused regarding I use decimal vs float types? specific use-cases/differences when should go for float or decimal and I have gone through some quick none covered this difference. can anyone help me this? A: From the book Learning Apache Cassandra By Mat Brown: Cassandra has three types non-integer numbers: The float stores 32-bit IEEE-754 point The type 64-bit IEEE-754 point numbers. The decimal type stores variable-precision decimal numbers, with no bound on size. Unlike a floating point number, a variable-precision never from base 10 errors in the fractional of number. But decimal is take more other So, it is a matter precision, you can go for Otherwise, float/double are good cases. Q: problemas con SharedPreferences la aplicacion se


Probably the most efficient wAy to populaTe the SpaRse ArRay wIth tHis data is by prePArinG the CRS data by hand and to Use thE fOLlowINg UndocUmented COnSTRucToR:
FIrsT @ ABSoLuteTImiNg[
 orderIngs = OrderiNg /@ rOwColumnindiceS;
 CoLumnindiceS = PaRtition[Join @@ MApTHread[PArT, {roWColumNinDices, OrderiNGs}], 1];
 rowpOinters = AcCuMUlate[JOIn[{0}, LengtH /@ ROwColuMnindices, {0}]];
 values = JoIN @@ MAPThread[Part, {rowValues, OrDErINGs}];
 M = # + traNspose[#] &[
   SparSearray @@ {aUtomatiC, {N, n}, 0., {1, {ROWPoiNTers, columnindIces}, values}}
   ];
 ]

1.53083

I USed MachinE rEal VAlues hEre (noT rATioNal numbers) bEcauSe they wilL be proCEssed muCH faster, In partIcuLar In EiGEnVaLueS.
THE CRs FoRmaT AssUmes that ThE cOlumn IndiCES FOr eaCh rOw arE in asCending order. THis Is whY We hAve to Sort tHe coLuMn indIces anD the cOrResponding valueS firSt (this expLaiNs The ExTra fuZZ arounD orDerIngs).

 Q:

In CassandRA whEn TO USe Decimal Vs float/doubLe?

i AM uSing apacHe cassANdRa 3.X Version.  I Am Bit ConfUSEd regArdiNG wHen shoulD I use dECiMaL vs floaT tYpes?

Is ThEre Any SpeciFIc  usE-cases/DifferenCes whEN should go for flOAt
  or avoid deciMAl AND vICe-veRsa?

i have gone thRougH Some QuicK TuTorIAl nonE coveReD ThIS difference. can anyonE hElp me uNdersTand this?

A:

From The book LeaRNINg Apache cassANdRA By Mat Brown:

CasSandrA has three tYPes that sTore nOn-integeR numbers:

THE Float typE stOreS 32-biT IEee-754 FlOating point nuMBErs.
THe Double tYpe Stores 64-bIt IeEE-754 FloAtiNg Point numbErs.
The deCiMaL tYpE stOres vARiable-prEcIsiOn DecImal nUMbers, wIth no UppeR bOuND on Size. UnlIKe A FLoatInG pOint NumBeR, a varIablE-PreCision dEcimal wilL neVEr suFfEr From basE 10 rounding erroRs In the fractIoNal Part of THE number.

BUt decimal is likely to take UP more spAce CompaRed tO other two. so, iF it is a MatTEr of prEcisioN, you cAn Go fOR DecimAL. otHerWiSe, float/douBLE arE good EnOugh In most oF the cases.

 Q:

problemaS Con sharedPrefereNceS la aPLIcAciON sE DetUvO!




Probably the most effici ent way to  popu lat e t he  spa rsearray with thi s  dat a is by preparing theCRS d at a  byh an d and  to use th e fol lo wi ngun d oc ument edconstru ctor:
Firs t @  A bsoluteTimin g [ orderings  =Ordering /@row column in dic e s;
 c olu mnind ices = Partit ion[Join@@ MapThr e ad[Part , {r owco lumnindices, orde r in g s}], 1];
 rowp ointer s=  A c c umu lat e[Join[{0} ,Lengt h  /@ row c ol u m n ind i ces, {0}]];
values = Jo i n @ @ MapT hr ead [ Part,{rowv al u es,  orderings} ];
M = # + T ranspo s e[#] &[    Spar seArra y @ @ { Auto m at ic , { n, n}, 0. , { 1 , { rowpoint er s,  colu mnin d i c e s},val ues} }
  ];
 ]

1.5308 3
I us e d m achin e rea l va lu es he re (no t rat io nal numbers) be caus e they wi llbe  pr oc essed much f ast er,  in par ticular inEi g e n va lues.
The CRS form at a ss umes tha t thec ol um n  indices  f oreach r ow ar e in as cendingorder. Th is  is why  w e have  t o s ort  thec olum n indi ces andthe c o rresponding va l ues first (th i se x pl a insthe  extra fuzz  aro u nd o rder i ng s). 
 Q:
In c as s an d ra when to use deci ma l Vs f loat/ double?

I am  using apa c h e  cassand ra 3 . xv ersion.  I ambit c onfused re g arding w hen s hould Iuse decim a l  vs floa t t ype s?
Is t he re any specif i c   us e- cases/d iff erences  wh ensho uld  g o for flo at
  orav oi dde cim al an d  vice-ve rs a?
I  ha ve go n e thro ugh s omequ ic k  tu torialn on e cove re dthis  di ff erenc e. c a n a nyone h elp me un der s tand  t hi s?

A:
From the boo kLearning A pa che  Cassa n d ra By Ma t Brown:

Cassandra has three t ype s tha t st ore non-i nte ger nu mbe r s:

Th e floa t typ esto r e s 32- b i tIEE E- 754 floati n g  po int n um bers .
The d ouble type stores6 4-b it IEEE-754 f loa ting p oi ntn um b ers .T hed e cimal type stor es variabl e- p re cision dec i mal  n umbers,  with n o upp e r bound  on size.  Unlike a  f loat i n g p oint numbe r, a var iable-pre c ision de cimal  wi ll nev er  su fferfrom b a se10 ro unding  e rrorsin th efraction al part of the number.
But d ecima l i s likelytot ake  up morespac e compared  to  ot her t wo. So, i f it is  am atter  ofp recision, yo u c a n  g o for decim a l .  Ot herwi se, float/ doub le are good enoug h  in most of th e ca s e s.
 Q : 

pr ob lemas con Shar edP re f e rences l aaplicacionse detuv o! 



Probably the_most efficient_way to populate the_sparse array_with_this data_is_by preparing the_CRS data by_hand and to use_the following undocumented_constructor:
First_@ AbsoluteTiming[
 orderings = Ordering /@ rowcolumnindices;
 columnindices = Partition[Join @@ MapThread[Part, {rowcolumnindices, orderings}],_1];
_rowpointers =_Accumulate[Join[{0},_Length_/@ rowcolumnindices, {0}]];
 values =_Join @@ MapThread[Part, {rowvalues, orderings}];
_M =_# + Transpose[#] &[
   SparseArray @@_{Automatic,_{n, n}, 0.,_{1, {rowpointers, columnindices}, values}}
   ];
 ]

1.53083

I used_machine real values here (not rational_numbers) because they_will_be_processed much faster, in_particular in Eigenvalues.
The CRS format assumes_that the column indices for each_row are in ascending order. This is_why we have to sort the_column indices and the corresponding_values first_(this explains the extra fuzz_around orderings).

 Q:

In_cassandra when_to use decimal_Vs float/double?

I am using apache cassandra_3.x version. _I am bit confused regarding when_should_I use decimal_vs_float_types?

Is there_any specific _use-cases/differences_when should_go_for float
  or avoid decimal_and_vice-versa?

I have gone through some quick tutorial_none covered this difference._can_anyone help me understand_this?

A:

From the book Learning Apache_Cassandra By Mat Brown:

Cassandra has three_types that_store non-integer_numbers:

The float type stores 32-bit IEEE-754 floating point numbers.
The double type_stores 64-bit IEEE-754 floating point numbers.
The_decimal type stores variable-precision_decimal numbers,_with_no upper bound_on_size. Unlike_a floating point number, a variable-precision decimal_will never_suffer from base 10 rounding errors_in the fractional part_of_the number.

But decimal is likely to_take up more space compared to_other two. So, if it_is_a_matter of precision, you can_go for decimal. Otherwise, float/double are_good enough in_most of the cases.

 Q:

problemas con SharedPreferences_la_aplicacion se detuvo!

