void testDatasetEquality(LabeledData<int, int> const& set1, LabeledData<int, int> const& set2){
BOOST_REQUIRE_EQUAL(set1.numberOfBatches(),set2.numberOfBatches());
BOOST_REQUIRE_EQUAL(set1.numberOfElements(),set2.numberOfElements());
for(std::size_t i = 0; i != set1.numberOfBatches(); ++i){
BOOST_REQUIRE_EQUAL(set1.batch(i).input.size(),set1.batch(i).label.size());
BOOST_REQUIRE_EQUAL(set2.batch(i).input.size(),set2.batch(i).label.size());
}
testSetEquality(set1.inputs(),set2.inputs());
testSetEquality(set1.labels(),set2.labels());
}
TEST(Csv, ReadCsv) {
LabeledData<RealVector, uint32_t> data;
EXPECT_EQ(0U, data.size());
EXPECT_FALSE(ReadCsv("null/null", &data));
ASSERT_TRUE(ReadCsv("testdata/data/cls.10.csv", &data, LAST_COLUMN));
const std::size_t kN = 10;
EXPECT_EQ(kN, data.size());
uint32_t expected_labels_arr[kN] = {0, 1, 0, 1, 0, 1, 0, 1, 0, 1};
Data<uint32_t> expected_labels;
ToData(expected_labels_arr, kN, &expected_labels);
EXPECT_EQ(expected_labels, data.labels());
EXPECT_EQ(2U, data.input(0).size());
EXPECT_DOUBLE_EQ(2.4114, data.input(0)(0));
EXPECT_DOUBLE_EQ(-3.8901, data.input(0)(1));
ASSERT_TRUE(ReadCsv("testdata/data/cls.csv", &data));
EXPECT_EQ(1000U, data.size());
LabeledData<RealVector, double> data2;
ASSERT_TRUE(ReadCsv("testdata/data/cls.10.csv", &data2, FIRST_COLUMN));
EXPECT_EQ(10U, data2.size());
EXPECT_DOUBLE_EQ(2.4114, data2.label(0));
EXPECT_EQ(2U, data2.input(0).size());
EXPECT_DOUBLE_EQ(-3.8901, data2.input(0)(0));
EXPECT_DOUBLE_EQ(0, data2.input(0)(1));
}
int main(){
//get problem data
Problem problem(1.0);
LabeledData<RealVector,unsigned int> training = problem.generateDataset(1000);
LabeledData<RealVector,unsigned int> test = problem.generateDataset(100);
std::size_t inputs=inputDimension(training);
std::size_t outputs = numberOfClasses(training);
std::size_t hiddens = 10;
unsigned numberOfSteps = 1000;
//create network and initialize weights random uniform
FFNet<LogisticNeuron,LinearNeuron> network;
network.setStructure(inputs,hiddens,outputs);
initRandomUniform(network,-0.1,0.1);
//create error function
CrossEntropy loss;
ErrorFunction error(training,&network,&loss);
// loss for evaluation
// The zeroOneLoss for multiclass problems assigns the class to the highest output
ZeroOneLoss<unsigned int, RealVector> loss01;
// evaluate initial network
Data<RealVector> prediction = network(training.inputs());
cout << "classification error before learning:\t" << loss01.eval(training.labels(), prediction) << endl;
//initialize Rprop
IRpropPlus optimizer;
optimizer.init(error);
for(unsigned step = 0; step != numberOfSteps; ++step)
optimizer.step(error);
// evaluate solution found by training
network.setParameterVector(optimizer.solution().point); // set weights to weights found by learning
prediction = network(training.inputs());
cout << "classification error after learning:\t" << loss01(training.labels(), prediction) << endl;
}
