private void ButtonViewEmployee_Click(object sender, System.Windows.RoutedEventArgs e)
{
if (string.IsNullOrEmpty(selectedEmployeeId))
{
MessageBox.Show("Please select employee");
}
else
{
EducationCollection selectedEduList = new EducationCollection();
ExperienceCollection selectedExpList = new ExperienceCollection();
TrainingCollection selectedTrainList = new TrainingCollection();
var vEmpItem = new EmployeeItem();
var vEduList = new List <EducationItem>();
var vExpList = new List <ExperienceItem>();
var vTrainList = new List <TrainingItem>();
vEmpItem = empCollection.Where(t => t._EMP_NO.Equals(selectedEmployeeId)).FirstOrDefault();
vEduList = selectedEduList.RetreiveEmpEducation(vEmpItem._EMP_NO);
vExpList = selectedExpList.RetreiveEmpExperience(vEmpItem._EMP_NO);
vTrainList = selectedTrainList.RetreiveEmpTraining(vEmpItem._EMP_NO);
ViewEmployeeDetails viewEmp = new ViewEmployeeDetails(vEmpItem, vEduList, vExpList, vTrainList);
EmployeeManagement.mEmpTransitioner.Items.Add(viewEmp);
EmployeeManagement.mEmpTransitioner.SelectedIndex = 1;
GetAllEmployees();
}
}
public void CanMakeAndAdd()
{
TrainingCollection tc = new TrainingCollection();
tc.Add(new VectorPair(
new NetworkVector(1),
new NetworkVector(2)
));
Assert.IsNotNull(tc);
Assert.AreEqual(1, tc.Count);
}
public void CanRunOnline_WC()
{
WeightsMatrix matrix = new WeightsMatrix(new double[, ] {
{ 1, 1 }
});
WeightedCombiner wc = new WeightedCombiner(matrix);
TrainingCollection trainingVectors = new TrainingCollection
{
new VectorPair(
new NetworkVector(new double[] { 0, 0 }),
new NetworkVector(new double[] { 1 })
)
};
}
public void TrainOnline_WC_CorrectThreePasses()
{
WeightsMatrix matrix = new WeightsMatrix(new double[, ] {
{ 1, 1 }
});
WeightedCombiner wc = new WeightedCombiner(matrix);
TrainingCollection trainingVectors = new TrainingCollection
{
new VectorPair(
new NetworkVector(new double[] { 0, 0 }),
new NetworkVector(new double[] { 1 })
),
new VectorPair(
new NetworkVector(new double[] { 1, 0 }),
new NetworkVector(new double[] { 0 })
),
new VectorPair(
new NetworkVector(new double[] { 0, 1 }),
new NetworkVector(new double[] { 0 })
),
new VectorPair(
new NetworkVector(new double[] { 1, 1 }),
new NetworkVector(new double[] { 1 })
)
};
Trainer trainer = new Trainer(wc, new SquaredError(), new GradientDescent());
foreach (TrainingCollection tc in trainingVectors.AsSingletons())
{
trainer.Train(tc);
}
foreach (TrainingCollection tc in trainingVectors.AsSingletons())
{
trainer.Train(tc);
}
foreach (TrainingCollection tc in trainingVectors.AsSingletons())
{
trainer.Train(tc);
}
WeightsMatrix weightsCheck = new WeightsMatrix(new double[, ] {
{ 3, 7 }
});
NetworkVector biasesCheck = new NetworkVector(new double[] { -1 });
Assert.AreEqual(biasesCheck, wc.Biases);
Assert.AreEqual(weightsCheck, wc.Weights);
}
public void CanRunOnline_LogisticLayer()
{
WeightsMatrix matrix = new WeightsMatrix(new double[, ] {
{ 1, 1 }
});
Layer layer = Layer.CreateLogisticLayer(matrix);
TrainingCollection trainingVectors = new TrainingCollection
{
new VectorPair(
new NetworkVector(new double[] { 0, 0 }),
new NetworkVector(new double[] { 1 })
)
};
Trainer trainer = new Trainer(layer, new SquaredError(), new GradientDescent());
trainer.Train(trainingVectors);
}
public void TrainOnline_LinearLayer_CorrectOnePass()
{
WeightsMatrix matrix = new WeightsMatrix(new double[, ] {
{ 1, 1 }
});
Layer layer = Layer.CreateLinearLayer(matrix);
TrainingCollection trainingVectors = new TrainingCollection
{
new VectorPair(
new NetworkVector(new double[] { 0, 0 }),
new NetworkVector(new double[] { 1 })
),
new VectorPair(
new NetworkVector(new double[] { 1, 0 }),
new NetworkVector(new double[] { 0 })
),
new VectorPair(
new NetworkVector(new double[] { 0, 1 }),
new NetworkVector(new double[] { 0 })
),
new VectorPair(
new NetworkVector(new double[] { 1, 1 }),
new NetworkVector(new double[] { 1 })
)
};
Trainer trainer = new Trainer(layer, new SquaredError(), new GradientDescent());
foreach (TrainingCollection tc in trainingVectors.AsSingletons())
{
trainer.Train(tc);
}
WeightsMatrix weightsCheck = new WeightsMatrix(new double[, ] {
{ 1, 3 }
});
NetworkVector biasesCheck = new NetworkVector(new double[] { 1 });
Assert.AreEqual(biasesCheck, layer.Biases);
Assert.AreEqual(weightsCheck, layer.Weights);
}
public void CanIterate()
{
TrainingCollection tc = new TrainingCollection();
tc.Add(new VectorPair(
new NetworkVector(1),
new NetworkVector(2)
));
tc.Add(new VectorPair(
new NetworkVector(1),
new NetworkVector(2)
));
int count = 0;
foreach (VectorPair pair in tc)
{
count++;
}
Assert.IsNotNull(tc);
Assert.AreEqual(count, tc.Count);
}
public void BatchTrainCorrectThreePasses_WC()
{
WeightsMatrix matrix = new WeightsMatrix(new double[, ] {
{ 1, 1 }
});
WeightedCombiner wc = new WeightedCombiner(matrix);
TrainingCollection trainingVectors = new TrainingCollection
{
new VectorPair(
new NetworkVector(new double[] { 0, 0 }),
new NetworkVector(new double[] { 1 })
),
new VectorPair(
new NetworkVector(new double[] { 1, 0 }),
new NetworkVector(new double[] { 0 })
),
new VectorPair(
new NetworkVector(new double[] { 0, 1 }),
new NetworkVector(new double[] { 0 })
),
new VectorPair(
new NetworkVector(new double[] { 1, 1 }),
new NetworkVector(new double[] { 1 })
)
};
Trainer trainer = new Trainer(wc, new SquaredError(), new GradientDescent());
trainer.Train(trainingVectors);
trainer.Train(trainingVectors);
trainer.Train(trainingVectors);
WeightsMatrix weightsCheck = new WeightsMatrix(new double[, ] {
{ -37, -37 }
});
NetworkVector biasesCheck = new NetworkVector(new double[] { -62 });
Assert.AreEqual(biasesCheck, wc.Biases);
Assert.AreEqual(weightsCheck, wc.Weights);
}
public void BatchTrainCorrectOnePass_LinearLayer()
{
WeightsMatrix matrix = new WeightsMatrix(new double[, ] {
{ 1, 1 }
});
Layer layer = Layer.CreateLinearLayer(matrix);
TrainingCollection trainingVectors = new TrainingCollection
{
new VectorPair(
new NetworkVector(new double[] { 0, 0 }),
new NetworkVector(new double[] { 1 })
),
new VectorPair(
new NetworkVector(new double[] { 1, 0 }),
new NetworkVector(new double[] { 0 })
),
new VectorPair(
new NetworkVector(new double[] { 0, 1 }),
new NetworkVector(new double[] { 0 })
),
new VectorPair(
new NetworkVector(new double[] { 1, 1 }),
new NetworkVector(new double[] { 1 })
)
};
Trainer trainer = new Trainer(layer, new SquaredError(), new GradientDescent());
trainer.Train(trainingVectors);
WeightsMatrix weightsCheck = new WeightsMatrix(new double[, ] {
{ -1, -1 }
});
NetworkVector biasesCheck = new NetworkVector(new double[] { -2 });
Assert.AreEqual(biasesCheck, layer.Biases);
Assert.AreEqual(weightsCheck, layer.Weights);
}
public void CanMakeGetAsBatches()
{
TrainingCollection tc = new TrainingCollection();
for (int i = 0; i < 100; i++)
{
tc.Add(new VectorPair(
new NetworkVector(1),
new NetworkVector(2)
));
}
Assert.IsNotNull(tc);
Assert.AreEqual(100, tc.Count);
int count = 0;
foreach (TrainingCollection singleton in tc.AsBatches(10))
{
Assert.AreEqual(10, singleton.Count);
count++;
}
Assert.AreEqual(10, count);
}
public void TrainBatch_SmallChain_CorrectOnePass()
{
int inputs = 3;
int inputneurons = 2;
int outputneurons = 1;
double[,] inputWeights = new double[inputneurons, inputs];
double[,] outputWeights = new double[outputneurons, inputneurons];
for (int i = 0; i < inputneurons; i++)
{
for (int j = 0; j < inputs; j++)
{
inputWeights[i, j] = 1;
}
}
for (int i = 0; i < outputneurons; i++)
{
for (int j = 0; j < inputneurons; j++)
{
outputWeights[i, j] = 1;
}
}
Layer InputLayer = Layer.CreateLinearLayer(new WeightsMatrix(inputWeights), new NetworkVector(inputneurons));
Layer OutputLayer = Layer.CreateLinearLayer(new WeightsMatrix(outputWeights), new NetworkVector(outputneurons));
NetComponentChain network = new NetComponentChain();
network.AddTrainable(InputLayer);
network.AddTrainable(OutputLayer);
TrainingCollection trainingVectors = new TrainingCollection
{
new VectorPair(
new NetworkVector(new double[] { 0, 0, 0 }),
new NetworkVector(new double[] { 1 })
),
new VectorPair(
new NetworkVector(new double[] { 1, 0, 0 }),
new NetworkVector(new double[] { 0 })
),
new VectorPair(
new NetworkVector(new double[] { 0, 1, 0 }),
new NetworkVector(new double[] { 0 })
),
new VectorPair(
new NetworkVector(new double[] { 1, 1, 0 }),
new NetworkVector(new double[] { 1 })
)
};
Trainer trainer = new Trainer(network, new SquaredError(), new GradientDescent());
trainer.Train(trainingVectors);
WeightsMatrix inputWeightsCheck = new WeightsMatrix(new double[, ] {
{ -4, -4, 1 }, { -4, -4, 1 }
});
NetworkVector inputBiasesCheck = new NetworkVector(new double[] { -6, -6 });
WeightsMatrix outputWeightsCheck = new WeightsMatrix(new double[, ] {
{ -9, -9 }
});
NetworkVector outputBiasesCheck = new NetworkVector(new double[] { -6 });
Assert.AreEqual(inputWeightsCheck, InputLayer.Weights);
Assert.AreEqual(inputBiasesCheck, InputLayer.Biases);
Assert.AreEqual(outputWeightsCheck, OutputLayer.Weights);
Assert.AreEqual(outputBiasesCheck, OutputLayer.Biases);
}
