int main(int argc, char** argv) {
Cvector v(6, Random());
Vectorv v1(v);
Vectorl v2(v);
Vectorh v3(v);
cout << v.str(Dense()) << endl;
cout << v1.str(Dense()) << endl;
cout << v2.str(Dense()) << endl;
cout << v3.str(Dense()) << endl;
cout << Vectorv(v1) << endl;
for (auto& p : v1)
cout << p.first << " " << p.second << endl;
cout << endl;
for (auto& p : v2)
cout << p.first << " " << p.second << endl;
cout << endl;
for (auto& p : v3)
cout << p.first << " " << p.second << endl;
cout << endl;
v1.insert(3, 74);
v1(2) = 0;
cout << v1 << endl;
v1.tidy();
cout << v1 << endl;
v1.foreach([](int i, FIELD& v) {cout<<"ff:"<<i<<" "<<v<<endl;});
}
int main(int argc, char* argv[])
{
int input_size = 10;
int dense_size1 = 5;
int dense_size2 = 3;
Container model;
model.add(Dense(dense_size1, input_size));
model.add(Dense(dense_size2));
auto layer = model.get_layer();
for (unsigned int i = 0; i < layer.size(); ++i) {
auto dense = layer[i];
printf("layer : %d\tdense_size : %d\t prev_dense_size : %d\n", i, dense.dense_size(), dense.get_cell(0).edge_size());
for(unsigned int j = 0; j < dense.dense_size(); ++j) {
auto cell = dense.get_cell(j);
auto weight = cell.get_weight();
auto bias = cell.get_bias();
for(unsigned int k = 0; k < weight.size(); ++k) {
printf("(%d,%d) : %lf\t%lf\n", j, k, weight[k], bias);
}
}
}
return 0;
}
int main(int argc, char** argv) {
int nrows = 6;
if (argc >= 2)
sscanf(argv[1], "%d", &nrows);
int ncols = 6;
if (argc >= 3)
sscanf(argv[2], "%d", &ncols);
Cmatrix Md = Cmatrix::Random(nrows, ncols);
MatrixXv Mv(Md);
MatrixXl Ml(Md);
MatrixXh Mh(Md);
cout << Md.str(Dense()) << endl;
cout << Mv.str(Dense()) << endl;
cout << Ml.str(Dense()) << endl;
cout << Mh.str(Dense()) << endl;
cout << Mh.nnz() << endl;
}
string str(const Dense dummy) const {
return Vector::str(Dense());
}
#include "TFile.h"
#include "TTree.h"
#include "TSystem.h"
#include "TMVA/Factory.h"
#include "TMVA/Reader.h"
#include "TMVA/DataLoader.h"
#include "TMVA/PyMethodBase.h"
TString pythonSrc = "\
from keras.models import Sequential\n\
from keras.layers.core import Dense, Activation\n\
from keras import initializations\n\
from keras.optimizers import SGD\n\
\n\
model = Sequential()\n\
model.add(Dense(64, init=\"normal\", activation=\"tanh\", input_dim=2))\n\
model.add(Dense(1, init=\"normal\", activation=\"linear\"))\n\
model.compile(loss=\"mean_squared_error\", optimizer=SGD(lr=0.01))\n\
model.save(\"kerasModelRegression.h5\")\n";
int testPyKerasRegression(){
// Get data file
std::cout << "Get test data..." << std::endl;
TString fname = "./tmva_reg_example.root";
if (gSystem->AccessPathName(fname)) // file does not exist in local directory
gSystem->Exec("curl -O http://root.cern.ch/files/tmva_reg_example.root");
TFile *input = TFile::Open(fname);
// Build model from python file
std::cout << "Generate keras model..." << std::endl;
UInt_t ret;
