コード例 #1
0
ファイル: Dataset.cpp プロジェクト: inglada/Shark
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());
}
コード例 #2
0
ファイル: csv_test.cpp プロジェクト: bin3/toyml
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));
}
コード例 #3
0
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;
}