public void ClassifiesSmallImages()
{
var network = new PerceptronFactory().BuildPerceptron(100);
var aSet = new InputProcessor(@"..\..\trainingsets\dataset1\A");
var bSet = new InputProcessor(@"..\..\trainingsets\dataset1\B");
var trainingSet = new List <KeyValuePair <double, double[]> >
{
new KeyValuePair <double, double[]>(1.0, aSet.BmpToBinaryArr(@"1.jpg")),
new KeyValuePair <double, double[]>(1.0, aSet.BmpToBinaryArr(@"2.jpg")),
new KeyValuePair <double, double[]>(1.0, aSet.BmpToBinaryArr(@"3.jpg")),
new KeyValuePair <double, double[]>(1.0, aSet.BmpToBinaryArr(@"4.jpg")),
new KeyValuePair <double, double[]>(1.0, aSet.BmpToBinaryArr(@"5.jpg")),
new KeyValuePair <double, double[]>(0.0, bSet.BmpToBinaryArr(@"1.jpg")),
new KeyValuePair <double, double[]>(0.0, bSet.BmpToBinaryArr(@"2.jpg")),
new KeyValuePair <double, double[]>(0.0, bSet.BmpToBinaryArr(@"3.jpg")),
new KeyValuePair <double, double[]>(0.0, bSet.BmpToBinaryArr(@"4.jpg")),
new KeyValuePair <double, double[]>(0.0, bSet.BmpToBinaryArr(@"5.jpg")),
};
network.Train(trainingSet);
trainingSet.ForEach(kvp => Assert.AreEqual(kvp.Key, network.Classify(kvp.Value)));
}
public void CanReadBitmapsIntoArrs()
{
var aSet = new InputProcessor(@"..\..\trainingsets\dataset1\A");
double[] arr = aSet.BmpToBinaryArr(@"1.jpg");
Assert.AreEqual(100, arr.Length);
}
public void CanReadBitmapsIntoArrs()
{
var aSet = new InputProcessor(@"..\..\trainingsets\dataset1\A");
double[] arr = aSet.BmpToBinaryArr(@"1.jpg");
Assert.AreEqual(100, arr.Length);
}
public void GeneralisesSmallImages()
{
var network = new PerceptronFactory().BuildPerceptron(100);
var aSet = new InputProcessor(@"..\..\trainingsets\dataset1\A");
var bSet = new InputProcessor(@"..\..\trainingsets\dataset1\B");
var trainingSet = new List<KeyValuePair<double, double[]>>
{
new KeyValuePair<double, double[]>( 1.0, aSet.BmpToBinaryArr(@"1.jpg") ),
new KeyValuePair<double, double[]>( 1.0, aSet.BmpToBinaryArr(@"2.jpg") ),
new KeyValuePair<double, double[]>( 1.0, aSet.BmpToBinaryArr(@"3.jpg") ),
new KeyValuePair<double, double[]>( 1.0, aSet.BmpToBinaryArr(@"4.jpg") ),
new KeyValuePair<double, double[]>( 1.0, aSet.BmpToBinaryArr(@"5.jpg") ),
new KeyValuePair<double, double[]>( 0.0, bSet.BmpToBinaryArr(@"1.jpg") ),
new KeyValuePair<double, double[]>( 0.0, bSet.BmpToBinaryArr(@"2.jpg") ),
new KeyValuePair<double, double[]>( 0.0, bSet.BmpToBinaryArr(@"3.jpg") ),
new KeyValuePair<double, double[]>( 0.0, bSet.BmpToBinaryArr(@"4.jpg") ),
new KeyValuePair<double, double[]>( 0.0, bSet.BmpToBinaryArr(@"5.jpg") ),
};
network.Train(trainingSet);
Assert.AreEqual(1.0, network.Classify(aSet.BmpToBinaryArr(@"7.jpg")));
Assert.AreEqual(0.0, network.Classify(bSet.BmpToBinaryArr(@"7.jpg")));
}