public void TrainMiniBatch_XORGateTest_SingleBatch()
{
// Arrange
ANeuralNetwork ANN = new ANeuralNetwork();
ANN.Create_Network(new List <int> {
2, 3, 1
});
List <List <double> > InputData = new List <List <double> > {
new List <double> {
1, 1
}, new List <double> {
0, 1
}, new List <double> {
1, 0
}, new List <double> {
0, 0
},
};
List <List <double> > OutputData = new List <List <double> > {
new List <double> {
0
}, new List <double> {
1
}, new List <double> {
1
}, new List <double> {
0
}
};
List <List <double> > ValidationData = new List <List <double> > {
new List <double> {
1, 1
}, new List <double> {
0, 1
}, new List <double> {
1, 0
}, new List <double> {
0, 0
},
};
List <double> ExpectedOutput = new List <double> {
0, 1, 1, 0
};
// Act
ANN.Train_MiniBatch(InputData, OutputData, InputData.Count, "XOR_SinleBatch.dat");
List <double> Results = new List <double> {
};
for (int i = 0; i < 4; i++)
{
List <double> result = ANN.Get_Network_Output(ValidationData[i]);
Results.Add(Math.Round(result[0], 0));
Console.Write("Output = " + Results[i] + " ");
}
// Assert
CollectionAssert.AreEqual(ExpectedOutput, Results);
}
public void SplitData()
{
// Arrange
ANeuralNetwork ANN = new ANeuralNetwork();
List <List <double> > TestData = new List <List <double> > {
new List <double> {
1, 2, 3, 4
}, new List <double> {
5, 6, 7, 8
}
};
List <List <List <double> > > ExpectedOutput = new List <List <List <double> > > {
new List <List <double> > {
new List <double> {
1, 2
}
}, new List <List <double> > {
new List <double> {
3, 4
}
}, new List <List <double> > {
new List <double> {
5, 6
}
}, new List <List <double> > {
new List <double> {
7, 8
}
}
};
// Act
ANN.Create_Network(new List <int> {
2, 1, 2
});
List <List <List <double> > > Output = ANN.SplitDataSet(TestData, 1);
// Assert
bool ItemsMatch = true;
for (int ItemInOutput = 0; ItemInOutput < Output.Count; ItemInOutput++)
{
for (int row = 0; row < Output[ItemInOutput].Count; row++)
{
for (int column = 0; column < Output[ItemInOutput][row].Count; column++)
{
Console.Write(Output[ItemInOutput][row][column] + " ");
if (ExpectedOutput[ItemInOutput][row][column] != Output[ItemInOutput][row][column])
{
ItemsMatch = false;
}
}
Console.WriteLine();
}
Console.WriteLine();
}
Assert.IsTrue(ItemsMatch);
}
public void SaveNetwork()
{
// Arrange
ANeuralNetwork ANN = new ANeuralNetwork();
ANN.Create_Network(new List <int> {
2, 3, 1
});
Exception expectedException = null;
// Act
try
{
ANN.Save_Network("TestSave.net");
}
catch (Exception ex)
{
expectedException = ex;
}
// Assert
Assert.IsNull(expectedException);
}
// Usfule functions for interacting with NeuralNet
public void CreateNetwork(List <int> Description)
{
ANN.Create_Network(Description);
}
