d3/d89/base-example_8cpp-example.html

#include "boonnano.h"

#include "boonconfig.h"

#include "clusterstats.h"

#include "autotune.h"


#include <fstream>

#include <sstream>

// Full pipeline for nano core usage


int main() {


    CBoonNano Nano;

//  Set up configuration (boonconfig.h)

    CNanoConfigParameters NCP;


//  init parameters

    int SWS = 1;

    int NUM_FEATURES = 15;

    float MinVal = -400.0;

    float MaxVal = 1100.0;

    int Weight = 1;

    float PV = 0.05;


//  set parameters in config object

    NCP.SetParameters(NF_FLOAT, PV, SWS);

    NCP.AddFeature(MinVal, MaxVal, Weight, "column", NUM_FEATURES);


//  set config

    Nano.Setup(NCP);


//  EXAMPLE OUTPUT

    std::cout << NCP.GetStatusString() << std::endl;

// CNanoConfigParameters

//     Numeric Type: Float  PV: 0.050000  SWS: 1

//     Feature 0:

//         Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//     Feature 1:

//         Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//     Feature 2:

//         Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//     Feature 3:

//         Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//     Feature 4:

//         Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//     Feature 5:

//         Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//     Feature 6:

//         Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//     Feature 7:

//         Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//     Feature 8:

//         Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//     Feature 9:

//         Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//     Feature 10:

//         Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//     Feature 11:

//         Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//     Feature 12:

//         Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//     Feature 13:

//         Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//     Feature 14:

//         Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//     Error Message:


//  Clustering (boonnano.h)


//  Load data

    std::string line;

    std::ifstream myFile("./docs/examples/Planets.csv");

    int length = 14400;  // hardcoded for the specific file

    float *Data = new float[NUM_FEATURES * length];

    float val;

    int i = 0;

    while (std::getline(myFile, line)) {

      std::stringstream ss(line);


      while (ss >> val) {

        Data[i] = val;

        i++;


        if (ss.peek() == ',')

          ss.ignore();

      }

    }

    myFile.close();


//  Cluster

    int *ClusterResults = new int[length];

    Nano.ClusterData(Data, length, ClusterResults);


//  EXAMPLE OUTPUT

    std::cout << Nano.GetStatusString() << std::endl;

// CBoonNano

//     CNanoConfigParameters

//         Numeric Type: Float  PV: 0.050000  SWS: 1

//         Feature 0:

//             Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//         Feature 1:

//             Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//         Feature 2:

//             Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//         Feature 3:

//             Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//         Feature 4:

//             Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//         Feature 5:

//             Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//         Feature 6:

//             Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//         Feature 7:

//             Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//         Feature 8:

//             Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//         Feature 9:

//             Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//         Feature 10:

//             Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//         Feature 11:

//             Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//         Feature 12:

//             Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//         Feature 13:

//             Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//         Feature 14:

//             Label: column MinVal: -400.000000 MaxVal: 1100.000000 Weight: 1

//         Error Message:

//     IsNanoBackend: true

//     BackendManager is Non-nullptr

//     CNanoBackendManager

//         SGXManager is nullptr

//            BackendInUse 0: true

//         NumKernels: 1  InitComplete: true

//         Error Message:

//         NanoBackend 0

//     NanoBackend is Non-nullptr

//     nano_sw_backend Ptr = 5502959616

//         InitComplete: true   PatternLength: 15   NumOfPatternsClustered: 14400   Learning is ON

//         Error Message:

//             Cluster 0:

//             Size: 0  Created at Inference: 0  RI: 1000  FI: 0  DI: 0  PI: 1000  NN: 0  PCA: (0, 0, 0) RCA: ()

//             Cluster 1:

//             Size: 1200  Created at Inference: 1  RI: 0  FI: 1042  DI: 409  PI: 0  NN: 0  PCA: (83, 689, 116) RCA: (0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000)

//             Cluster 2:

//             Size: 417  Created at Inference: 1201  RI: 149  FI: 886  DI: 363  PI: 320  NN: 0  PCA: (135, 734, 203) RCA: (0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000)

//             Cluster 3:

//             Size: 783  Created at Inference: 1225  RI: 60  FI: 979  DI: 380  PI: 176  NN: 0  PCA: (135, 704, 199) RCA: (0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000)

//             Cluster 4:

//             Size: 1200  Created at Inference: 2401  RI: 0  FI: 1042  DI: 407  PI: 0  NN: 0  PCA: (0, 1000, 8) RCA: (0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000)

//             Cluster 5:

//             Size: 1200  Created at Inference: 3601  RI: 0  FI: 1042  DI: 398  PI: 0  NN: 0  PCA: (78, 724, 124) RCA: (0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000)

//             Cluster 6:

//             Size: 948  Created at Inference: 4801  RI: 33  FI: 1007  DI: 313  PI: 144  NN: 0  PCA: (697, 40, 249) RCA: (0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000)

//             Cluster 7:

//             Size: 252  Created at Inference: 5328  RI: 220  FI: 812  DI: 304  PI: 422  NN: 0  PCA: (710, 60, 253) RCA: (0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000)

//             Cluster 8:

//             Size: 1200  Created at Inference: 6001  RI: 0  FI: 1042  DI: 299  PI: 0  NN: 0  PCA: (684, 151, 298) RCA: (0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000)

//             Cluster 9:

//             Size: 1200  Created at Inference: 7201  RI: 0  FI: 1042  DI: 317  PI: 0  NN: 0  PCA: (1000, 0, 223) RCA: (0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000)

//             Cluster 10:

//             Size: 1200  Created at Inference: 8401  RI: 0  FI: 1042  DI: 299  PI: 0  NN: 0  PCA: (702, 42, 265) RCA: (0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000)

//             Cluster 11:

//             Size: 1200  Created at Inference: 9601  RI: 0  FI: 1042  DI: 302  PI: 0  NN: 0  PCA: (258, 99, 606) RCA: (0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000)

//             Cluster 12:

//             Size: 1200  Created at Inference: 10801  RI: 0  FI: 1042  DI: 323  PI: 0  NN: 0  PCA: (251, 21, 718) RCA: (0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000)

//             Cluster 13:

//             Size: 1200  Created at Inference: 12001  RI: 0  FI: 1042  DI: 347  PI: 0  NN: 0  PCA: (223, 8, 1000) RCA: (0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000)

//             Cluster 14:

//             Size: 645  Created at Inference: 13201  RI: 87  FI: 951  DI: 309  PI: 213  NN: 0  PCA: (281, 31, 662) RCA: (0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000)

//             Cluster 15:

//             Size: 555  Created at Inference: 13633  RI: 108  FI: 928  DI: 303  PI: 257  NN: 0  PCA: (321, 1, 649) RCA: (0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000)

//     Error Message:


//  Clean up

    Nano.Reset();

    delete[] ClusterResults;

    delete[] Data;


    return 0;

}

autotune.h

boonconfig.h

NF_FLOAT
const uint16_t NF_FLOAT
Numeric code for float data type.
Definition boonconfig.h:74

boonnano.h

CBoonNano::Setup
bool Setup(CNanoConfigParameters &NanoConfig)
Set configuration parameters for model clustering.

CBoonNano::GetStatusString
std::string GetStatusString(int CurrentIndent=0, int AdditionalIndent=4)
Print string of object variables.

CBoonNano::ClusterData
int ClusterData(float *Pattern, CClusterStats *AdvancedAnalytics=nullptr)
Cluster float pattern.

CBoonNano::Reset
void Reset(bool BackendReset=true)
Clear object variables.

CBoonNano
Main class of processing data.
Definition boonnano.h:110

CNanoConfigParameters::GetStatusString
std::string GetStatusString(int CurrentIndent=0, int AdditionalIndent=4) const
Print string of object.

CNanoConfigParameters::AddFeature
bool AddFeature(float MinVal, float MaxVal, uint16_t Weight, const char *Label=nullptr, uint16_t NumInstances=1)
Add feature values to overall config.

CNanoConfigParameters::SetParameters
bool SetParameters(uint16_t NumericFormat, float PercentVariation, uint16_t StreamingWindowSize)
Initialize base config values.

CNanoConfigParameters
Main object for configuration.
Definition boonconfig.h:86

clusterstats.h