Error: a brace-enclosed initializer is not allowed here before

WARNING: [SIM 211-51] HLS only supports CLANG compiler in Linux.
INFO: [SIM 211-4] CSIM will launch GCC as the compiler.
   Compiling ../../../../src1/main.cpp in debug mode
../../../../src1/main.cpp: In function 'void pavilion_ref(T (*)[DIM2], T (*)[DIMb], T (*)[DIMd], T (*)[DIMf])':
../../../../src1/main.cpp:199:8: error: expected unqualified-id before '=' token
../../../../src1/main.cpp: At global scope:
../../../../src1/main.cpp:212:47: error: expected ',' or '...' before 'predict_label1'
../../../../src1/main.cpp: In function 'void pavilion_hw(T (*)[DIM2], T)':
../../../../src1/main.cpp:327:8: error: expected unqualified-id before '=' token
../../../../src1/main.cpp:329:5: error: expected unqualified-id before 'return'
../../../../src1/main.cpp:333:1: error: expected ';' after struct definition
../../../../src1/main.cpp:335:1: error: a template declaration cannot appear at block scope
../../../../src1/main.cpp:363:1: error: expected ';' before 'template'
../../../../src1/main.cpp:364:29: error: 'U' was not declared in this scope
../../../../src1/main.cpp:364:31: error: 'TI' was not declared in this scope
../../../../src1/main.cpp:364:34: error: 'TD' was not declared in this scope
../../../../src1/main.cpp:364:36: error: template argument 2 is invalid
../../../../src1/main.cpp:364:36: error: template argument 3 is invalid
../../../../src1/main.cpp:364:36: error: template argument 4 is invalid
../../../../src1/main.cpp:365:1: error: a function-definition is not allowed here before '{' token
../../../../src1/main.cpp:657:2: error: expected '}' at end of input

#include <stdio.h>
#include <stdlib.h>
#include <assert.h>

#include <ap_axi_sdata.h>
#include <ap_int.h>

#include <string>
#include "rt_nonfinite.h"
#include "pavilion.h"
#include "main.h"
//#include "mat.h"
#include "pavilion_terminate.h"
#include "pavilion_initialize.h"


#include "svm.h"
#include "SVMPredict.h"
//#include " svm_predict.h"

typedef ap_axiu<32,4,5,5> AXI_VAL;
///////////////////declaration des matrices/////////////
 static double const parameters[5][1]={{1},{1},{1},{10},{0.150000000000000}};
 static  double const nr_class[1][1]={ 3 };
 static  double const totalSV[1][1]= {38};   /* total #SV */
   //double svm_node **SV=SVs=
               double rho[3][1]= {{3.85250072056665},{1.98260813354295},{-9.85381777928213}};


            static  double const label[3][1]= {{1},{2},{3}}; /* label of each class (label[k]) */
         static  double const sv_indices[38][1]= {{1},{3},{11},{18},{20},{22},{25},{26},{29},{30},{42},{43},{45},{49},{50},{51},{55},{56},{60},{63},{65},{69},{70},{71},{72},{73},{75},{82},{83},{90},{94},{98},{102},{104},{107},{110},{113},{119}};       /* sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set */


         static  double const probA[3][1]= {{-3.04020468884611},{-3.02985537320699},{-1.22838128162279}};  /* pariwise probability information */
         static  double const probB[3][1]= {{-0.143832034298135},{-0.331592706939798},{-0.317353460209991}};
      static  double const sv_coef [38][2]= {{0.0441014900184046,0},{0.115481676752995,0.0247201450973238},{0.0284525394158882,0},{0.115481676752995,0.0247201450973238},{0.115481676752995,0.0247201450973238},{0.115481676752995,0.0247201450973238},{0.0429276473187021,0.0247201450973238},{0.115481676752995,0.0247201450973238}, {0.115481676752995,0.0247201450973238}, {0.115481676752995,0.0247201450973238},{0,2.56566567302770},{-0.115481676752995,0},{-0.115481676752995,0},{-0.115481676752995,0},{-0.115481676752995,0}, {0,2.56566567302770},{0,1.19304447300938},{-0.115481676752995,0},{0,1.37262120001832},{0,2.56566567302770},{0,2.56566567302770},{-0.115481676752995,0}, {0,2.56566567302770},{0,2.56566567302770}, {0,2.56566567302770},{0,2.56566567302770},{-0.115481676752995,0},{-0.115481676752995,0},{-0.0247201450973238,-2.56566567302770},{-0.0247201450973238,-0.665006705657444},{-0.0247201450973238,-2.56566567302770},{0,-1.82845371567548},{-0.0247201450973238,-2.56566567302770},{0,-2.56566567302770},  {-0.0247201450973238,-2.56566567302770}, {-0.0247201450973238,-0.106867010440775}, {0,-1.24158009628657},  {-0.0247201450973238,-2.56566567302770}}; /* coefficients for SVs in decision functions (sv_coef[k-1][l]) */

     /* constants in decision functions (rho[k*(k-1)/2]) */
   /* for classification only */

   //double *label= [1;2;3]; /* label of each class (label[k]) */
   static  double const nSV[3][1] ={{10},{17},{11}}; /* number of SVs for each class (nSV[k]) */
      /* nSV[0] + nSV[1] + ... + nSV[k-1] = l */
      static  double const SVs[38][14]={
   {-0.579400000000000,-0.924500000000000,-0.878900000000000,-0.907400000000000,-0.902800000000000,-0.979600000000000,-0.977000000000000,-0.918200000000000,-0.904600000000000,-0.965100000000000,-0.972900000000000,-0.941700000000000,-0.961900000000000,-0.936700000000000},
  {-0.422700000000000,-0.443500000000000,-0.809000000000000,-0.880800000000000,-0.879800000000000,-0.924100000000000,-0.930800000000000,-0.978100000000000,-0.949100000000000,-0.970100000000000,-0.990400000000000,-0.975300000000000,-0.981400000000000,-0.953000000000000},
  {-0.604400000000000,-0.850700000000000,-0.851100000000000,-0.788000000000000,-0.885200000000000,-0.825500000000000,-0.921000000000000,-0.933900000000000,-0.966800000000000,-0.917600000000000,-0.965200000000000,-0.930500000000000,-0.948600000000000,-0.938700000000000},
  {-0.624000000000000,-0.648500000000000,-0.985600000000000,-0.876800000000000,-0.893100000000000,-0.938300000000000,-0.919000000000000,-0.885100000000000,-0.948400000000000,-0.899200000000000,-0.970700000000000,-0.911300000000000,-0.970000000000000,-0.944600000000000},
  {-0.373800000000000,-0.520300000000000,-0.703800000000000,-0.811600000000000,-0.843900000000000,-0.867900000000000,-0.861400000000000,-0.921700000000000,-0.848200000000000,-0.952100000000000,-0.933600000000000,-0.956300000000000,-0.972600000000000,-0.953500000000000},
  {-0.592300000000000,-0.691900000000000,-0.768000000000000,-0.894100000000000,-0.934600000000000,-0.866900000000000,-0.905700000000000,-0.945100000000000,-0.930800000000000,-0.924100000000000,-0.931800000000000,-0.954100000000000,-0.948000000000000,-0.924400000000000},
  {-0.654400000000000,-0.672400000000000,-0.835300000000000,-0.712100000000000,-0.832100000000000,-0.886900000000000,-0.937600000000000,-0.940200000000000,-0.886500000000000,-0.936000000000000,-0.931100000000000,-0.939500000000000,-0.956700000000000,-0.919200000000000},
  {-0.561800000000000,-0.692600000000000,-0.856300000000000,-0.844700000000000,-0.909900000000000,-0.966400000000000,-0.960200000000000,-0.930600000000000,-0.941100000000000,-0.954500000000000,-0.974700000000000,-0.970400000000000,-0.965500000000000,-0.955400000000000},
  {-0.391300000000000,-0.480500000000000,-0.932100000000000,-0.764200000000000,-0.972300000000000,-0.867300000000000,-0.848900000000000,-0.905100000000000,-0.848600000000000,-0.914100000000000,-0.981400000000000,-0.982400000000000,-0.964400000000000,-0.932900000000000},
  {-0.396100000000000,-0.744400000000000,-0.801300000000000,-0.727400000000000,-0.803800000000000,-0.792200000000000,-0.971400000000000,-0.910600000000000,-0.871400000000000,-0.888500000000000,-0.925600000000000,-0.908700000000000,-0.957600000000000,-0.940900000000000},
  {0.222900000000000,0.123500000000000,-0.748800000000000,-0.749800000000000,-0.844400000000000,-0.844300000000000,-0.850000000000000,-0.773300000000000,-0.812600000000000,-0.917000000000000,-0.845500000000000,-0.868700000000000,-0.912900000000000,-0.848100000000000},
  {-0.192100000000000,-0.604500000000000,-0.759700000000000,-0.777900000000000,-0.893400000000000,-0.894400000000000,-0.937800000000000,-0.854600000000000,-0.925500000000000,-0.921700000000000,-0.944000000000000,-0.920000000000000,-0.892500000000000,-0.813400000000000},
  {-0.204200000000000,-0.572400000000000,-0.856900000000000,-0.844600000000000,-0.792400000000000,-0.915600000000000,-0.910700000000000,-0.895800000000000,-0.926600000000000,-0.936200000000000,-0.953000000000000,-0.942300000000000,-0.898400000000000,-0.758500000000000},
  {-0.0876000000000000,-0.390600000000000,-0.908600000000000,-0.877600000000000,-0.892700000000000,-0.909400000000000,-0.921300000000000,-0.925000000000000,-0.963700000000000,-0.942500000000000,-0.960700000000000,-0.956600000000000,-0.893900000000000,-0.812300000000000},
  {-0.0936000000000000,-0.458900000000000,-0.854300000000000,-0.889600000000000,-0.812200000000000,-0.891600000000000,-0.936300000000000,-0.905800000000000,-0.948900000000000,-0.921800000000000,-0.946200000000000,-0.916800000000000,-0.906800000000000,-0.812800000000000},
  {0.0935000000000000,-0.146800000000000,-0.573100000000000,-0.545100000000000,-0.589000000000000,-0.881400000000000,-0.871000000000000,-0.753100000000000,-0.849100000000000,-0.841400000000000,-0.978900000000000,-0.876200000000000,-0.948100000000000,-0.873700000000000},
  {0.180900000000000,-0.187700000000000,-0.706100000000000,-0.642400000000000,-0.545500000000000,-0.655700000000000,-0.468800000000000,-0.694200000000000,-0.728000000000000,-0.824000000000000,-0.861800000000000,-0.882100000000000,-0.917600000000000,-0.742600000000000},
  {-0.0212000000000000,-0.778900000000000,-0.772300000000000,-0.947900000000000,-0.912400000000000,-0.921000000000000,-0.940000000000000,-0.950700000000000,-0.937100000000000,-0.972200000000000,-0.937400000000000,-0.963200000000000,-0.848000000000000,-0.821400000000000},
  {0.0572000000000000,-0.277800000000000,-0.757100000000000,-0.712300000000000,-0.838600000000000,-0.938700000000000,-0.926500000000000,-0.938600000000000,-0.942300000000000,-0.965800000000000,-0.956000000000000,-0.933300000000000,-0.909400000000000,-0.826600000000000},
  {0.191300000000000,-0.332500000000000,-0.707300000000000,-0.676500000000000,-0.802500000000000,-0.789700000000000,-0.848200000000000,-0.914800000000000,-0.903900000000000,-0.898500000000000,-0.911200000000000,-0.895600000000000,-0.864300000000000,-0.800500000000000},
  {0.408700000000000,-0.243800000000000,-0.656500000000000,-0.706000000000000,-0.743500000000000,-0.782400000000000,-0.843800000000000,-0.862300000000000,-0.833800000000000,-0.904400000000000,-0.866400000000000,-0.905800000000000,-0.916900000000000,-0.829200000000000},
  {-0.323100000000000,-0.549200000000000,-0.750300000000000,-0.781800000000000,-0.849000000000000,-0.889800000000000,-0.853500000000000,-0.932100000000000,-0.871900000000000,-0.896600000000000,-0.863500000000000,-0.904000000000000,-0.925800000000000,-0.857600000000000},
  {0.209400000000000,-0.236800000000000,-0.335700000000000,-0.765000000000000,-0.785700000000000,-0.789900000000000,-0.919700000000000,-0.754400000000000,-0.844700000000000,-0.877600000000000,-0.910800000000000,-0.847100000000000,-0.941700000000000,-0.908500000000000},
  {0.242600000000000,-0.0374000000000000,-0.576700000000000,-0.593000000000000,-0.771100000000000,-0.773100000000000,-0.892300000000000,-0.868800000000000,-0.909200000000000,-0.908600000000000,-0.897400000000000,-0.914600000000000,-0.912300000000000,-0.816600000000000},
  {0.0511000000000000,-0.258800000000000,-0.668400000000000,-0.737000000000000,-0.832900000000000,-0.832400000000000,-0.887100000000000,-0.877400000000000,-0.971400000000000,-0.885400000000000,-0.963700000000000,-0.970300000000000,-0.929400000000000,-0.857400000000000},
  {-0.234300000000000,-0.486400000000000,-0.685100000000000,-0.686200000000000,-0.833600000000000,-0.909700000000000,-0.832900000000000,-0.930600000000000,-0.933100000000000,-0.916400000000000,-0.985400000000000,-0.929200000000000,-0.902100000000000,-0.841800000000000},
  {-0.262300000000000,-0.632300000000000,-0.813000000000000,-0.752200000000000,-0.927500000000000,-0.925600000000000,-0.896200000000000,-0.870100000000000,-0.942600000000000,-0.939100000000000,-0.925000000000000,-0.928300000000000,-0.903800000000000,-0.780500000000000},
  {0.230000000000000,-0.256200000000000,-0.734400000000000,-0.758100000000000,-0.817300000000000,-0.840100000000000,-0.910500000000000,-0.917400000000000,-0.879500000000000,-0.925600000000000,-0.894500000000000,-0.890100000000000,-0.941900000000000,-0.833400000000000},
  {0.0883000000000000,-0.0528000000000000,-0.489900000000000,-0.539800000000000,-0.785300000000000,-0.803000000000000,-0.736500000000000,-0.833100000000000,-0.878600000000000,-0.831800000000000,-0.867600000000000,-0.837800000000000,-0.916700000000000,-0.874400000000000},
  {0.254400000000000,-0.228500000000000,-0.674800000000000,-0.654900000000000,-0.790300000000000,-0.860700000000000,-0.852300000000000,-0.853700000000000,-0.905600000000000,-0.901800000000000,-0.971100000000000,-0.901900000000000,-0.919800000000000,-0.902300000000000},
  {0.135000000000000,-0.0175000000000000,-0.472800000000000,-0.543200000000000,-0.672200000000000,-0.677100000000000,-0.705400000000000,-0.714900000000000,-0.844500000000000,-0.943600000000000,-0.936000000000000,-0.838600000000000,-0.906000000000000,-0.890100000000000},
  {0.222300000000000,-0.114700000000000,-0.625800000000000,-0.679300000000000,-0.746200000000000,-0.826000000000000,-0.852900000000000,-0.891700000000000,-0.918300000000000,-0.882600000000000,-0.885400000000000,-0.919500000000000,-0.937700000000000,-0.847200000000000},
  {0.191200000000000,0.0347000000000000,-0.715100000000000,-0.721600000000000,-0.743500000000000,-0.814700000000000,-0.828300000000000,-0.822700000000000,-0.892300000000000,-0.877100000000000,-0.892100000000000,-0.898300000000000,-0.926600000000000,-0.804100000000000},
  {0.221700000000000,-0.0990000000000000,-0.589900000000000,-0.711300000000000,-0.716600000000000,-0.862500000000000,-0.843100000000000,-0.892000000000000,-0.849600000000000,-0.915500000000000,-0.889900000000000,-0.929300000000000,-0.964300000000000,-0.842700000000000},
  {0.154300000000000,-0.0310000000000000,-0.669200000000000,-0.740100000000000,-0.819800000000000,-0.844400000000000,-0.887100000000000,-0.943400000000000,-0.895600000000000,-0.900100000000000,-0.886500000000000,-0.923400000000000,-0.926200000000000,-0.820000000000000},
  {0.171900000000000,-0.159200000000000,-0.259400000000000,-0.510100000000000,-0.631400000000000,-0.626800000000000,-0.765700000000000,-0.787700000000000,-0.832900000000000,-0.825500000000000,-0.883200000000000,-0.859900000000000,-0.940400000000000,-0.801300000000000},
  {0.204800000000000,-0.318500000000000,-0.546800000000000,-0.742400000000000,-0.828200000000000,-0.844400000000000,-0.889100000000000,-0.891100000000000,-0.930100000000000,-0.900500000000000,-0.949100000000000,-0.896600000000000,-0.950900000000000,-0.863400000000000},
  {0.316800000000000,-0.134600000000000,-0.750300000000000,-0.711100000000000,-0.884900000000000,-0.735900000000000,-0.878500000000000,-0.868500000000000,-0.913100000000000,-0.938700000000000,-0.925300000000000,-0.932200000000000,-0.958400000000000,-0.848600000000000}};

      double  predict_label1[60][1];
        double precision1[3][1];
         double prob_estimates_t1[60][3];
//////////////////////////
#define DB_DEBUG

template <typename T, int DIM1, int DIM2, int DIMa, int DIMb, int DIMc, int DIMd, int DIMe, int DIMf>

void pavilion_ref(T LabelS1[DIM1][DIM2], T  predict_label1[DIMa][DIMb], T  precision1[DIMc][DIMd],T  prob_estimates_t1[DIMe][DIMf])
{

 double SStest1[60][14]={
 {-0.674090537952796,-0.967913560624215,-0.946607312926616,-0.949789832193271,-0.955656497278097,-0.938898706383114,-0.961972723505885,-0.943158564395126,-0.924258334652885,-0.987130021868515,-0.961224433510270,-0.979509038935985,-0.982841580894078,-0.968786461033008},
 {-0.661420074510186,-0.953582957005305,-0.976667803306075,-0.852024795662031,-0.923684241437988,-0.908602156779947,-0.961480830721390,-0.943819721336063,-0.961645084746726,-0.975901524455997,-0.964617784738062,-0.985199969033792,-0.979709442173101,-0.949066533256159},
 {-0.796228139367668,-0.761729500498894,-0.922254078572498,-0.835053436355116,-0.927209479313798,-0.955333905733235,-0.924794123637238,-0.959289492589635,-0.930944373099572,-0.938536988120820,-0.976986907828858,-0.949164123093526,-0.970737156105474,-0.947698751805312},
 {-0.760572782759851,-0.831771980500142,-0.945585944378708,-0.908125723416015,-0.942297850129234,-0.938465946137737,-0.968103420998903,-0.977193895073700,-0.969825462569338,-0.981781833938995,-0.975225320029628,-0.966562487844290,-0.979385622636404,-0.969916779098183},
 {-0.682062208785592,-0.901993333331962,-0.911439298246093,-0.995583382410982,-0.950515197199961,-0.939916724225237,-0.951278160425057,-0.951056228844938,-0.962461853366129,-0.977169060764356,-0.975068659001004,-1,-0.981383502203534,-0.960140644534363},
 {-0.764153524047345,-0.782068868778498,-0.930585958249904,-0.899522666144618,-0.961023627518624,-0.905380199817116,-0.931424494746754,-0.911851616413864,-0.955101420749475,-0.978391738284877,-0.977733347990399,-0.957980914548860,-0.987159688960536,-0.958296510208874},
 {-0.741980614232449,-0.708911988880729,-0.854515153854522,-0.844481728371788,-0.939444864580808,-0.921581100661191,-0.931215822177604,-0.924511727085001,-0.934842760732822,-0.944799003697996,-0.949046456708687,-0.969530645790348,-0.981557594662755,-0.966214670867448},
 {-0.373792542593349,-0.520278392032748,-0.703829741405612,-0.811632538345339,-0.843860131190612,-0.867918184900834,-0.861425053147186,-0.921678622630629,-0.848207919776101,-0.952149873411277,-0.933584801186523,-0.956270474264593,-0.972592914683167,-0.953500986248213},
 {-0.661229877813482,-0.663476191336492,-0.875946105955396,-0.813599831284167,-0.927616269612180,-0.947770900356774,-0.944605151964964,-0.949965011099178,-0.951558296918110,-0.959706992151603,-0.934147669647518,-0.959913517244191,-0.976789712492956,-0.957643754265247},
 {-0.518554977026420,-0.602825724193184,-0.883181911007378,-0.866976656576412,-0.881653303448664,-0.834429119509439,-0.938506509068625,-0.792206968065827,-0.884123330616924,-0.917397025318987,-0.938798625979835,-0.925986787255497,-0.986438030341222,-0.961106108508390},
 {-0.530140301878371,-0.883934580851869,-0.929208213377834,-0.981337861458516,-0.942509396789582,-0.918370801892608,-0.946368904151541,-0.968727717519249,-0.965415345904235,-0.992420840751352,-0.960290441628204,-0.987263434783806,-0.980777852550863,-0.961027269770681},
 {-0.614065935999483,-0.643092868497660,-0.849915771303324,-0.923684372369931,-0.899325977629591,-0.940079152274114,-0.943645330258078,-0.966640084204524,-0.954120749867146,-0.992492691029133,-0.942907389517172,-0.986160598733704,-0.961571311375914,-0.944437607583134},
 {-0.586207974039630,-0.655562550483140,-0.848822046603218,-0.828032863687348,-0.979545766504300,-0.954849783433112,-0.933180508531547,-0.901952187183437,-0.935767611336519,-0.947717892133932,-0.963983017763248,-0.936129485635805,-0.962853677964312,-0.939892729210301},
 {-0.698562423991206,-0.828928876308795,-0.930529603172751,-0.973583105511949,-0.922780081115147,-0.950411689148607,-0.922089545604029,-0.902482494918462,-0.919077025971580,-0.938762961947185,-0.946683851734027,-0.943899220923818,-0.970751027958274,-0.962890874229321},
 {-0.792352413710960,-0.846807561570503,-0.962463796461496,-0.938280210895608,-0.970676035685350,-0.950577966455638,-0.982653505409229,-0.964264032678215,-0.961530079570332,-0.984175640092749,-0.990259222509500,-0.957261271696928,-0.982531823910805,-0.975962025172037},
 {-0.794841618144870,-0.829566960381545,-0.965210501288073,-0.932270663074643,-0.967556626906079,-0.946620792181095,-0.972488769216231,-0.962127134647906,-0.957393614634659,-0.981727556097064,-0.987503639619946,-0.953217441593240,-0.981375073603447,-0.973405792323495},
 {-0.795020194095386,-0.806888733270744,-0.960369889000445,-0.915515767489692,-0.957828103336507,-0.939683270081732,-0.951942785566158,-0.957422184175696,-0.947345455495148,-0.975149131137862,-0.978221174145014,-0.944545398095925,-0.978853699190608,-0.968099161607423},
 {-0.698839070385112,-0.854584769884228,-0.879448228263050,-0.898303458884004,-0.970834545509510,-0.997729382049760,-0.938912886553759,-0.968614408138659,-0.974484461898617,-0.985571704652593,-0.967328388395865,-0.972452715835938,-0.983660906019555,-0.963564537278949},
 {-0.673302416738159,-0.879272351929407,-0.968223106643735,-0.972084171937861,-0.959896101364386,-0.961700573227355,-0.969790464510721,-0.954916739185306,-0.945985863949909,-0.975300691304921,-0.968364403832397,-0.977561535571141,-0.980589365529592,-0.954447517556441},
 {-0.637045925923046,-0.710061083800907,-0.936826613792305,-0.859638718802183,-0.912322897550303,-0.981683944934587,-0.890113781116241,-0.913234535285272,-0.894395484221828,-0.922010414470385,-0.957815677576098,-0.941998783974119,-0.969077948566397,-0.955456380822368},
 {-0.0464553899974760,-0.662925986991806,-0.833828153044336,-0.929089542429105,-0.837654390767010,-0.945247889228093,-0.957871446601941,-0.960270138903518,-0.965707812000886,-0.958450094695355,-0.968893174805499,-0.975892896458684,-0.882317284020699,-0.784076824928733},
 {0.168450404584060,-0.438611610872580,-0.739305838353202,-0.753469051869701,-0.872053346050208,-0.902328666401733,-0.875296732403390,-0.891944157498292,-0.875131611862177,-0.876570070893988,-0.916492079359415,-0.892474870559755,-0.880173712101710,-0.818840278283343},
 {0.214956796965889,-0.463100108992908,-0.795731268208176,-0.892232485332773,-0.796178277611433,-0.876064110052591,-0.917887824420658,-0.933514902536318,-0.906487752914575,-0.906161558178618,-0.963182144556198,-0.927398731670949,-0.880132272769419,-0.776628206791729},
 {-0.0355148259693545,-0.103832635363957,-0.642826483479963,-0.675170193621003,-0.726663088806811,-0.743780040787059,-0.831794948409108,-0.812381272649148,-0.818233308995815,-0.936764748241986,-0.927791786953338,-0.946337910236447,-0.865122830148352,-0.803850525738294},
 {-0.0375370422771995,-0.714446611429583,-0.817139932455997,-0.861863433965497,-0.801308409717709,-0.912671943149429,-0.902056680948667,-0.924358383893747,-0.930365403512554,-0.933505439717915,-0.907344668404472,-0.962516466348439,-0.888667439601752,-0.759379503762919},
 {-0.0996422526625035,-0.569498829531724,-0.785394421709639,-0.775105653598853,-0.896482238814758,-0.792471179311656,-0.893821172610422,-0.919322542815255,-0.929090887568931,-0.947222968505341,-0.941521326987315,-0.939564066437628,-0.886581473928985,-0.854622228969523},
 {-0.157947246037774,-0.746922906626790,-0.755491702134737,-0.884559880628624,-0.926822302167037,-0.892694279049245,-0.880997501268835,-0.960323416980551,-0.931254655744972,-0.929470986910981,-0.910748517978109,-0.956965233219925,-0.892806218692971,-0.824323609583577},
 {0.0143100845225228,-0.296971046115063,-0.756037633627588,-0.763254487870567,-0.853796813421072,-0.903855283273275,-0.932880202365020,-0.920300138069262,-0.937750780107648,-0.931802164909103,-0.938127043278968,-0.936648786420969,-0.897396787074019,-0.841443684643823},
 {0.126406089554415,-0.553436242221630,-0.878529200298623,-0.811481080953497,-0.858597903016569,-0.858187238247150,-0.929425420879273,-0.898856593831970,-0.954706925783214,-0.905919892252836,-0.955668439276137,-0.938058984422267,-0.859062344153360,-0.781233364834173},
 {-0.126055537585803,-0.545552869682677,-0.731986017810449,-0.886554196997008,-0.843484118770908,-0.933727925913333,-0.929494256217181,-0.949857908445841,-0.933022166519305,-0.955371701911967,-0.945336532486260,-0.958137376491658,-0.907276661798547,-0.823729901317309},
 {0.0145717851732077,-0.533090337648005,-0.839692084485777,-0.895485971838661,-0.928205663722173,-0.925860301792440,-0.929578060336248,-0.960552985114785,-0.949903531077579,-0.934632681448160,-0.968632836944857,-0.950189964823872,-0.879233119136866,-0.785002723370355},
 {-0.178891784381271,-0.399532717894635,-0.669385253177598,-0.727133395461031,-0.751773861739512,-0.852940528963974,-0.888222736607932,-0.865518628517857,-0.904806207358640,-0.896746617989777,-0.933151592845165,-0.903824933251927,-0.892900090292400,-0.850301284686282},
 {0.339342751347667,-0.168658832220281,-0.701126807517342,-0.705317306144173,-0.766727960954685,-0.805058424745359,-0.908411767678567,-0.850936664511331,-0.938429790986132,-0.897160305989233,-0.966173046779846,-0.901648919962718,-0.923592942739536,-0.812147836977765},
 {-0.165273390468733,-0.202594882499135,-0.685271591575949,-0.641279617027603,-0.810903427104329,-0.721277680480091,-0.878931073742658,-0.770692553521093,-0.915436309034622,-0.845750173546187,-0.920759281404426,-0.849039794164389,-0.883592352877391,-0.786060658867459},
 {-0.174398260956424,-0.628964902893240,-0.851345814848046,-0.929633727074964,-0.912288496191548,-0.952393561588859,-0.957556620703674,-0.966293462428000,-0.967979236795410,-0.967761734377952,-0.968514139800528,-0.966000184471383,-0.873900925904829,-0.804602844673793},
 {0.0912987881509080,-0.562348503567656,-0.544977227135293,-0.496953610164791,-0.665280698891481,-0.825516237438271,-0.764714736496212,-0.825266477368957,-0.778124049781793,-0.889578890948835,-0.860510945808230,-0.889449590530369,-0.899510852733607,-0.817813916256618},
 {-0.159278240258134,-0.562350349373472,-0.672010009362750,-0.702971077603283,-0.791579089506330,-0.773580188614902,-0.868797839368800,-0.901404564649279,-0.883359902797464,-0.882538284811406,-0.902504051837194,-0.888858485690590,-0.907802404057984,-0.860390254926969},
 {-0.115571783442574,-0.364989767126751,-0.715693202021139,-0.720794054845607,-0.734368283253484,-0.821024371768553,-0.864273592837283,-0.884637763632675,-0.905128610959545,-0.911052662127927,-0.934668657036883,-0.959498348008793,-0.948659165025090,-0.857302848890063},
 {-0.119022287702128,-0.435263305220441,-0.674278713268148,-0.717147325691312,-0.786355896498330,-0.803730904718690,-0.918533634832367,-0.871846660926452,-0.930289987345790,-0.945177344298286,-0.948345725996483,-0.900852637961356,-0.927660822404464,-0.829129813555718},
 {-0.243582392888872,-0.456156540815826,-0.882025256314901,-0.878826799838538,-0.875702181196543,-0.952037299706992,-0.922147237385639,-0.953195599137228,-0.988774455168753,-0.956906724025646,-0.990803722658199,-0.962527534612586,-0.917165742920258,-0.857395436225837},
 {0.399888144165083,0.187596602167405,-0.571206951482556,-0.630021295932159,-0.732918080832751,-0.782042578842550,-0.884501484522094,-0.862443525574920,-0.907782413800503,-0.879582251780713,-0.875536974905776,-0.912451877893607,-0.922455195733535,-0.902359364047133},
 {0.556284386426115,0.314036089555063,-0.519960698533138,-0.646049325780951,-0.741233326065650,-0.712137290320001,-0.795024298435714,-0.727879833200862,-0.853933696711804,-0.789936654005493,-0.895739890390449,-0.834987182855880,-0.931084212245965,-0.785223030050557},
 {0.144327924856347,-0.00175945025183155,-0.764123925530958,-0.788116589181975,-0.815023410153421,-0.765757055388867,-0.819440340310641,-0.832834974075190,-0.889065353427949,-0.879626121154264,-0.955003504819721,-0.886941582940570,-0.951457072822665,-0.838292308039416},
 {0.423517043395152,0.207103229391809,-0.537337037899986,-0.719082296323729,-0.730712289172267,-0.803970667494681,-0.776968241650566,-0.770243671002982,-0.870732435883177,-0.790184354464107,-0.849528556769068,-0.857417938148147,-0.914235935637580,-0.862839599386109},
 {0.673400833910405,0.263687282928186,-0.468684958192597,-0.555004829882917,-0.597759466034923,-0.879943048249649,-0.759647494885421,-0.842498114781129,-0.795476315407791,-0.874613706944541,-0.825569610343889,-0.906032284945689,-0.959418030250979,-0.845363247476606},
 {0.183271855620944,0.0211381514220783,-0.502683851137181,-0.636454841163429,-0.786437592022666,-0.805282205840683,-0.813868765137396,-0.807502478689115,-0.789136059855138,-0.787535977735564,-0.823588573107538,-0.833053461976117,-0.925414212506851,-0.879916140090418},
 {0.295845138886807,-0.00395006804240183,-0.548896710154673,-0.673532260911150,-0.743809959983556,-0.766709087110313,-0.792579121413211,-0.812789970935862,-0.888809512385073,-0.865512371317576,-0.866483384552372,-0.910365914309237,-0.922859275559095,-0.805741791608307},
 {0.382230322025496,-0.100544388585754,-0.288045792305930,-0.462878731933967,-0.733635653289924,-0.810774775566528,-0.784311635942323,-0.824278283823241,-0.837674642235770,-0.820294633519681,-0.930289366057359,-0.880333683094064,-0.914809932792345,-0.733615286641321},
 {0.515086084868621,-0.0118463200641755,-0.681312282108688,-0.721728766135035,-0.896943193457623,-0.892492731672196,-0.900895034581712,-0.958202884169137,-0.890317876836114,-0.925894485203484,-0.887102514104963,-0.925150645540907,-0.951009285936294,-0.828633953324099},
 {0.218939003616403,-0.219908089866804,-0.554543937993146,-0.697744593495449,-0.817644527591940,-0.815044931936388,-0.835611927342840,-0.863014952910201,-0.870015506961933,-0.924498109016286,-0.861738324469650,-0.888798266919434,-0.963003761418040,-0.752728018190114},
 {0.379801716860667,0.0803934635748880,-0.348489273860588,-0.443364836987716,-0.686210071710231,-0.749012364568597,-0.868160795860281,-0.905931730794798,-0.827560530714372,-0.843130513494941,-0.874882850617447,-0.898079375435337,-0.958923408320119,-0.826200545619004},
 {0.499909795739691,0.0624123264063490,-0.257162655088657,-0.474302401472459,-0.717398057495245,-0.792006768091720,-0.765814288840469,-0.795160131332406,-0.817978992797693,-0.871336925999567,-0.907076913192798,-0.851666096922091,-0.932313438565331,-0.778005802680850},
 {0.269368264824844,0.0215786140194887,-0.476977706904142,-0.647680815543785,-0.700462589073132,-0.788128770460292,-0.835354852471012,-0.862240914525829,-0.860676230010876,-0.845117177334758,-0.912670838989494,-0.867405287746421,-0.973060275185485,-0.823505423592852},
 {0.179006934566610,0.0585770863310706,-0.729516278647376,-0.736020711155540,-0.833340421570685,-0.863888272177564,-0.858242670121925,-0.908051906398737,-0.888570941463165,-0.900872325708206,-0.872080362074863,-0.886372519170446,-0.948853071328778,-0.841226487206630},
 {0.255490817526987,0.0983271005342115,-0.506833291130341,-0.651349940443935,-0.756337911242788,-0.786870364249337,-0.799083430523079,-0.798476951216588,-0.930360503515967,-0.961047836742245,-0.950977568316625,-0.966198529200948,-0.944896563690946,-0.877086968426192},
 {0.244696633497264,0.124742610239186,-0.00974949294211636,-0.527185835802341,-0.669863996792797,-0.660506846176490,-0.777882938739871,-0.741441779984189,-0.863181488070075,-0.798643774891844,-0.919225101825890,-0.838533769615936,-0.900125302373132,-0.798067298811813},
 {0.154383004639343,-0.135587455441821,-0.474282157787074,-0.545456066636011,-0.647593724862076,-0.755085065020264,-0.761876996351167,-0.835705857964857,-0.887714636576169,-0.869061999970453,-0.882416185426200,-0.923607732584544,-0.959373168720827,-0.838276040824778},
 {0.504849989457597,0.214254376963237,-0.486090671351638,-0.601583503221537,-0.696568826469689,-0.752801035520086,-0.811002254524322,-0.825807713677726,-0.833769846610573,-0.855476282988971,-0.883160491267595,-0.962853439329057,-0.902581371883451,-0.743974014417838},
 {0.445145614467157,-0.0889240542286259,-0.251341749130795,-0.325574329282160,-0.661040242281816,-0.652164091731617,-0.813620922276726,-0.807485140580725,-0.856971911757818,-0.756592971519269,-0.864973897574650,-0.840655456848447,-0.920416268675231,-0.746278678613040},
 {0.434793037813971,-0.0492388032158161,-0.365397844714972,-0.528993051702485,-0.685064834479619,-0.737413635953856,-0.799290876511762,-0.850728865215625,-0.825002878399477,-0.891118614677671,-0.870372516784281,-0.902812350877063,-0.922480285663246,-0.885180878798811}};




 struct model
 {
  double parameters[5][1]; //={{1},{1},{1},{10},{0.150000000000000}};

  double nr_class[1][1];//={ { new double(3) } }; /* number of classes, = 2 in regression/one class svm */
  double totalSV[1][1];//= { { new double(38) } };   /* total #SV */
  //double svm_node **SV=SVs=
              double rho[3][1];//= {{3.85250072056665},{1.98260813354295},{-9.85381777928213}};


           double label[3][1];//= {{1},{2},{3}}; /* label of each class (label[k]) */
        double sv_indices[38][1];//= {{1},{3},{11},{18},{20},{22},{25},{26},{29},{30},{42},{43},{45},{49},{50},{51},{55},{56},{60},{63},{65},{69},{70},{71},{72},{73},{75},{82},{83},{90},{94},{98},{102},{104},{107},{110},{113},{119}};       /* sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set */


        double probA[3][1];//:= {{-3.04020468884611},{-3.02985537320699},{-1.22838128162279}};  /* pariwise probability information */
        double probB[3][1];//= {{-0.143832034298135},{-0.331592706939798},{-0.317353460209991}};
               double sv_coef [38][2];//= {
     /* coefficients for SVs in decision functions (sv_coef[k-1][l]) */

    /* constants in decision functions (rho[k*(k-1)/2]) */
  /* for classification only */

  //double *label= [1;2;3]; /* label of each class (label[k]) */
  double nSV[3][1];// ={{10},{17},{11}}; /* number of SVs for each class (nSV[k]) */
     /* nSV[0] + nSV[1] + ... + nSV[k-1] = l */
     double SVs[38][14];//={

  /* XXX */
  //int free_sv;  /* 1 if svm_model is created by svm_load_model*/
     /* 0 if svm_model is created by svm_train */
 };
 struct res
  {
  double predict_label1[3][1];
  double precision1[60][1];
  double prob_estimates_t1[60][3];

  };
  //(predict_label1,precision1,prob_estimates_t1)=res;
   res = SVMPredict(LabelS1,SStest1, model);


   return;

   }


// --------------------------------------------------------
// actual multiplication algorithm


template <typename T, int DIM1,int DIM2,int DIMa,int DIMb,int DIMc,int DIMd,int DIMe,int DIMf>
void pavilion_hw(T LabelS1[DIM1][DIM2], T out predict_label1[DIMa][DIMb], T precision1[DIMc][DIMd],T prob_estimates_t1[DIMe][DIMf])
{

 // partition with half dimension size b/c BRAM has two ports

#pragma HLS INLINE off
#pragma HLS array_partition variable=LabelS1 dim=1

 double SStest1[60][14]={
  {-0.674090537952796,-0.967913560624215,-0.946607312926616,-0.949789832193271,-0.955656497278097,-0.938898706383114,-0.961972723505885,-0.943158564395126,-0.924258334652885,-0.987130021868515,-0.961224433510270,-0.979509038935985,-0.982841580894078,-0.968786461033008},
  {-0.661420074510186,-0.953582957005305,-0.976667803306075,-0.852024795662031,-0.923684241437988,-0.908602156779947,-0.961480830721390,-0.943819721336063,-0.961645084746726,-0.975901524455997,-0.964617784738062,-0.985199969033792,-0.979709442173101,-0.949066533256159},
  {-0.796228139367668,-0.761729500498894,-0.922254078572498,-0.835053436355116,-0.927209479313798,-0.955333905733235,-0.924794123637238,-0.959289492589635,-0.930944373099572,-0.938536988120820,-0.976986907828858,-0.949164123093526,-0.970737156105474,-0.947698751805312},
  {-0.760572782759851,-0.831771980500142,-0.945585944378708,-0.908125723416015,-0.942297850129234,-0.938465946137737,-0.968103420998903,-0.977193895073700,-0.969825462569338,-0.981781833938995,-0.975225320029628,-0.966562487844290,-0.979385622636404,-0.969916779098183},
  {-0.682062208785592,-0.901993333331962,-0.911439298246093,-0.995583382410982,-0.950515197199961,-0.939916724225237,-0.951278160425057,-0.951056228844938,-0.962461853366129,-0.977169060764356,-0.975068659001004,-1,-0.981383502203534,-0.960140644534363},
  {-0.764153524047345,-0.782068868778498,-0.930585958249904,-0.899522666144618,-0.961023627518624,-0.905380199817116,-0.931424494746754,-0.911851616413864,-0.955101420749475,-0.978391738284877,-0.977733347990399,-0.957980914548860,-0.987159688960536,-0.958296510208874},
  {-0.741980614232449,-0.708911988880729,-0.854515153854522,-0.844481728371788,-0.939444864580808,-0.921581100661191,-0.931215822177604,-0.924511727085001,-0.934842760732822,-0.944799003697996,-0.949046456708687,-0.969530645790348,-0.981557594662755,-0.966214670867448},
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  struct model
  {
   double parameters[5][1] ;//={{1},{1},{1},{10},{0.150000000000000}};



   double nr_class[1][1];//={3}; /* number of classes, = 2 in regression/one class svm */
   double totalSV[1][1];//;;= {38};   /* total #SV */
   //double svm_node **SV=SVs=
               double rho[3][1];//= {

            double label[3][1];//= {{1},{2},{3}}; /* label of each class (label[k]) */
         double sv_indices[38][1];//= {{1},{3},{11},{18},{20},{22},{25},{26},{29},{30},{42},{43},{45},{49},{50},{51},{55},{56},{60},{63},{65},{69},{70},{71},{72},{73},{75},{82},{83},{90},{94},{98},{102},{104},{107},{110},{113},{119}};       /* sv_indices[0,...,nSV-1] are values in [1,...,num_traning_data] to indicate SVs in the training set */


         double probA[3][1];//= {{-3.04020468884611},{-3.02985537320699},{-1.22838128162279}};  /* pariwise probability information */
         double probB[3][1];//= {{-0.143832034298135},{-0.331592706939798},{-0.317353460209991}};
                double sv_coef [38][2];//= {
       /* coefficients for SVs in decision functions (sv_coef[k-1][l]) */

     /* constants in decision functions (rho[k*(k-1)/2]) */
   /* for classification only */

   //double *label= [1;2;3]; /* label of each class (label[k]) */
   double nSV[3][1] ;//={{10},{17},{11}}; /* number of SVs for each class (nSV[k]) */
      /* nSV[0] + nSV[1] + ... + nSV[k-1] = l */
      double SVs[38][14];//={

   /* XXX */
   //int free_sv;  /* 1 if svm_model is created by svm_load_model*/
      /* 0 if svm_model is created by svm_train */
;

  //[predict_label1,precision1,prob_estimates_t1]=SVMPredict(LabelS1,SStest1, model);
struct res
  {
  double predict_label1[3][1];
  double precision1[60][1];
  double prob_estimates_t1[60][3];

  };
  //(predict_label1,precision1,prob_estimates_t1)=res;
   res = SVMPredict(LabelS1,SStest1, model);

    return;



}
/////////////////////////////////
template <typename T, int U, int TI, int TD>
inline T pop_stream(ap_axiu <sizeof(T)*8,U,TI,TD> const &e)
{
#pragma HLS INLINE off
 // does not work b/c of:
 //  unsupported pointer reinterpretation
 //T ret = *reinterpret_cast<T const *> (&e.data);

 // instead use a union
 assert(sizeof(T) == sizeof(int));
 union
 {
  int ival;
  T oval;
 } converter;
 converter.ival = e.data;
 T ret = converter.oval;

 volatile ap_uint<sizeof(T)> strb = e.strb;
 volatile ap_uint<sizeof(T)> keep = e.keep;
 volatile ap_uint<U> user = e.user;
 volatile ap_uint<1> last = e.last;
 volatile ap_uint<TI> id = e.id;
 volatile ap_uint<TD> dest = e.dest;

 return ret;
}

template <typename T, int U, int TI, int TD>;
inline ap_axiu <sizeof(T)*8,U,TI,TD> push_stream(T const &v, bool last = false)
{
#pragma HLS INLINE off
 ap_axiu<sizeof(T)*8,U,TI,TD> e;

 // use a union, b/c reinterpret_cast not supported
 assert(sizeof(T) == sizeof(int));
 union
 {
  int oval;
  T ival;
 } converter;
 converter.ival = v;
 e.data = converter.oval;

 // set it to sizeof(T) ones
 e.strb = -1;
 e.keep = 15; //e.strb;
 e.user = 0;
 e.last = last ? 1 : 0;
 e.id = 0;
 e.dest = 0;
 return e;
}
///////////////////////////////////////////////////
template <typename T, int DIM1,int DIM2,int DIMa,int DIMb,int DIMc,int DIMd,int DIMe,int DIMf, int SIZE, int U, int TI, int TD>
void dut_mmult_accel_core (
 AXI_VAL in_stream[SIZE],
 AXI_VAL out_stream[3*SIZE],
 //volatile ap_uint<1> *hw_trig)
  volatile   ap_uint<1> *predict_label1_hw_trig;
  volatile ap_uint<1> *precision1_hw_trig;
  volatile ap_uint<1> *prob_estimates_t1_hw;
{

// Map ports to Vivado HLS interfaces
#pragma HLS INTERFACE ap_fifo port=in_stream
#pragma HLS INTERFACE ap_fifo port=out_stream

 // Map HLS ports to AXI interfaces
#pragma HLS RESOURCE variable=in_stream  core=AXIS metadata="-bus_bundle INPUT_STREAM"
#pragma HLS RESOURCE variable=out_stream core=AXIS metadata="-bus_bundle OUTPUT_STREAM"
#pragma HLS RESOURCE variable=return core=AXI4LiteS metadata="-bus_bundle CONTROL_BUS"

 ap_uint<1> logic_zero = 0;
 ap_uint<1> logic_one = 1;
 T LabelS [DIM1][DIM2];
    T predict_label1[DIMa][DIMb];
 T precision1[DIMc][DIMd];
 T prob_estimates_t1[DIMe][DIMf];


*predict_label1_trig = logic_zero; *precision1_trig = logic_zero; *prob_estimates_t1_trig = logic_zero;  assert(sizeof(T)*8 == 32);  *predict_label1_trig = logic_one; *precision1_trig = logic_one; *prob_estimates_t1_trig = logic_one; // stream in first matrix for(int i=0; i<DIM1; i++)  for(int j=0; j<DIM2; j++)  {#pragma HLS PIPELINE II=1   int k = i*DIM+j;   //a[i][j] = pop_stream<T,1,1,1>(in_stream[k]);   LabelS[i][j] = pop_stream<T,U,TI,TD>(in_stream[k]);  }     // do multiplicationpredict   //matrix_multiply_hw<T, DIM>(a,b,out);   pavilion_hw < T,  DIM1, DIM2, DIMa, DIMb, DIMc, DIMd, DIMe, DIMf>(LabelS1[,  out predict_label1,  precision1, prob_estimates_t1);   // stream out1 result matrix   for(int i=0; i<DIMa; i++)    for(int j=0; j<DIMb; j++)    {#pragma HLS PIPELINE II=1     int k = i*DIM+j;     //out_stream[k] = push_stream<T,1,1,1>(out[i][j],k == (SIZE-1));     out_stream[k] = push_stream<T,U,TI,TD>(out[i][j],k == (SIZE-1));    }   *predict_label1_trig = logic_zero; //*hw_trig = false;//*(bool *) hw_trig = false;   return ;      // stream out2 result matrix   for(int i=0; i<DIMc; i++)    for(int j=0; j<DIMd; j++)    {#pragma HLS PIPELINE II=1     int k = i*DIM+j;     //out_stream[k] = push_stream<T,1,1,1>(out[i][j],k == (SIZE-1));     out_stream[k] = push_stream<T,U,TI,TD>(out[i][j],k == (SIZE-2));    }   *precision1_trig = logic_zero; //*hw_trig = false;//*(bool *) hw_trig = false;   return ;            // stream out3 result matrix   for(int i=0; i<DIMe; i++)    for(int j=0; j<DIMf; j++)    {#pragma HLS PIPELINE II=1     int k = i*DIM+j;     //out_stream[k] = push_stream<T,1,1,1>(out[i][j],k == (SIZE-1));     out_stream[k] = push_stream<T,U,TI,TD>(out[i][j],k == (SIZE-3));    }   *prob_estimates_t1_trig = logic_zero; //*hw_trig = false;//*(bool *) hw_trig = false;   return ;}/////////////////////// should be template parameters at top level function#include <ap_int.h>template <typename T, int DIM1,int DIM2,int DIMa,int DIMb,int DIMc,int DIMd,int DIMe,int DIMf, int SIZE, int U, int TI, int TD>int test_pavilion(void){ int i,j, err;    T LabelS [DIM1][DIM2]; T predict_label1_sw[DIMa][DIMb]; T precision1_sw[DIMc][DIMd]; T prob_estimates_t1_sw[DIMe][DIMf]; T predict_label1_hw[DIMa][DIMb]; T precision1_hw[DIMc][DIMd]; T prob_estimates_t1_hw[DIMe][DIMf]; /** Matrix Initiation */ for(i = 0; i<DIM1; i++)  for(j = 0; j<DIM2; j++)   LabelS[i][j] = (float)(i+j);  /** End of Initiation */#ifdef DB_DEBUGo printf("DEBUGGING AXI4 STREAMING DATA TYPES!\r\n"); // prepare data for the DUT AXI_VAL inp_stream[SIZE]; AXI_VAL out_stream[3*SIZE]; assert(sizeof(T)*8 == 32); // stream in the first input  matrix for(int i=0; i<DIM1; i++)  for(int j=0; j<DIM2; j++)  {   int k = i*DIM+j;   inp_stream[k]      = push_stream<T,U,TI,TD>(LabelS[i][j],0);  }     }   //call the DUT   ap_uint<1> predict_label1_hw_trig;   ap_uint<1> precision1_hw_trig;   ap_uint<1> prob_estimates_t1_hw;      //dut_mmult_accel_core<T, DIM, SIZE, U, TI, TD>(inp_stream, out_stream, &hw_trig);   mmult_accel_core (inp_stream, out_stream,&predict_label1_hw_trig,&precision1_hw_trig,&prob_estimates_t1_hw);   // extract the output matrix 1 from the out stream    for(int i=0; i<DIMa; i++)    for(int j=0; j<DIMb; j++)    {     int k = i*DIM+j;     predict_label1_hw[i][j] = pop_stream<T,U,TI,TD>(out_stream[k]);    }        // extract the output matrix 2 from the out stream    for(int i=0; i<DIMc; i++)    for(int j=0; j<DIMd; j++)    {     int k = i*DIM+j;     precision1_hw[i][j] = pop_stream<T,U,TI,TD>(out_stream[k]);    }        // extract the output matrix 3 from the out stream    for(int i=0; i<DIMe; i++)    for(int j=0; j<DIMf; j++)    {     int k = i*DIM+j;     prob_estimates_t1_hw[i][j] = pop_stream<T,U,TI,TD>(out_stream[k]);    }#else printf("NORMAL MODE\r\n"); pavilion_hw<T,  DIM1, DIM2, DIMa, DIMb, DIMc, DIMd, DIMe, DIMf>(LabelS, predict_label1_hw,precision1_hw,prob_estimates_t1_hw);#endif /* reference Matrix Multiplication */ pavilion_ref<T,  DIM1, DIM2, DIMa, DIMb, DIMc, DIMd, DIMe, DIMf>(LabelS, predict_label1_sw,precision1_sw,prob_estimates_t1_sw); /** Matrix 1 comparison */ err = 0; for (i = 0; (i<DIMa && !err); i++)  for (j = 0; (j<DIMb && !err); j++)   if (predict_label1_sw[i][j] != predict_label1_hw[i][j])     err++; if (err == 0)  printf("Matrixes 1 identical ... Test successful!\r\n"); else  printf("Test 1 failed!\r\n"); return err;  /** Matrix 2 comparison */ err = 0; for (i = 0; (i<DIMc && !err); i++)  for (j = 0; (j<DIMd && !err); j++)   if (precision1_sw[i][j] != precision1_hw[i][j])     err++; if (err == 0)  printf("Matrixes 2 identical ... Test successful!\r\n"); else  printf("Test  2 failed!\r\n"); return err;  /** Matrix 3 comparison */ err = 0; for (i = 0; (i<DIMe && !err); i++)  for (j = 0; (j<DIMf && !err); j++)   if (prob_estimates_t1_sw[i][j] != prob_estimates_t1_hw[i][j])     err++; if (err == 0)  printf("Matrixes 3 identical ... Test successful!\r\n"); else  printf("Test 3 failed!\r\n"); return err;}////////////////////////// THIS IS THE TOP LEVEL DESIGN THAT WILL BE SYNTHESIZED#define MCR_SIZE 1024void mmult_accel_core(AXI_VAL in_stream[MCR_SIZE],AXI_VAL out_stream[3 * MCR_SIZE],volatile ap_uint<1>* predict_label1_hw_trig,volatile ap_uint<1>* precision1_hw_trig,volatile ap_uint<1>* prob_estimates_t1_hw_trig){ // Map ports to Vivado HLS interfaces #pragma HLS INTERFACE ap_fifo port=in_stream #pragma HLS INTERFACE ap_fifo port=out_stream // Map HLS ports to AXI interfaces #pragma HLS RESOURCE variable=in_stream  core=AXIS metadata="-bus_bundle INPUT_STREAM" #pragma HLS RESOURCE variable=out_stream core=AXIS metadata="-bus_bundle OUTPUT_STREAM" #pragma HLS RESOURCE variable=return core=AXI4LiteS metadata="-bus_bundle CONTROL_BUS" #pragma HLS INTERFACE ap_none register port=predict_label1_hw_trig #pragma HLS INTERFACE ap_none register port=precision1_hw_trig #pragma HLS INTERFACE ap_none register port=prob_estimates_t1_hw_trig dut_mmult_accel_core <float, 60, 1*60, 4, 5, 5>(in_stream, out_stream, predict_label1_hw_trig,precision1_hw_trig,prob_estimates_t1_hw_trig); return;}/////////////int main(const double LabelS[60]){ typedef float T; int const DIM1 = 1; int const DIM2 = 60; int const SIZE = DIM1*DIM2; int ret_val = 0; ret_val = test_pavilion<T, DIM1, DIM2, DIMa, DIMb, DIMc, DIMd, DIMe, DIMf, SIZE, 4,5,5>;pavilion_terminate();//main_pavilion();pavilion_initialize(); return ret_val;};