public static void RecognizeImage()
{
var size = 10000;
var parasitizedPath = @"C:\Users\Vadim\Downloads\cell_images\Parasitized";
var uninfectedPath = @"C:\Users\Vadim\Downloads\cell_images\Uninfected";
var converter = new PictureConvector();
var testParasitizedImageInput = converter.Convert(@"C:\GithubProject\LogFileParser\ParserIntoTheMongoDB\NeuraNetworks\images\Parasitized.png");
var testUninfectedImageInput = converter.Convert(@"C:\GithubProject\LogFileParser\ParserIntoTheMongoDB\NeuraNetworks\images\Unparasitized.png");
var topology = new Topology(testParasitizedImageInput.Length, 1, 0.1, testParasitizedImageInput.Length / 2);
var neuronNetwork = new NeuronNetworks(topology);
double[,] parasitizedInputs = GetData(parasitizedPath, converter, testParasitizedImageInput, size);
neuronNetwork.Learning(new double[] { 1 }, parasitizedInputs, 1);
double[,] uninfectedInputs = GetData(parasitizedPath, converter, testUninfectedImageInput, size);
neuronNetwork.Learning(new double[] { 0 }, uninfectedInputs, 1);
var par = neuronNetwork.FeedForward(testParasitizedImageInput.Select(t => (double)t).ToArray());
var unpar = neuronNetwork.FeedForward(testUninfectedImageInput.Select(t => (double)t).ToArray());
Console.WriteLine("1" + " " + Math.Round(par.Output, 2));
Console.WriteLine("0" + " " + Math.Round(unpar.Output, 2));
}
public void FeedForwardTest()
{
var topology = new Topology(4, 1, 2);
var neuronNetwork = new NeuronNetworks(topology);
neuronNetwork.Layers[1].Neurons[0].SetWeights(0.5, -0.1, 0.3, -0.1);
neuronNetwork.Layers[1].Neurons[1].SetWeights(0.1, -0.3, 0.7, -0.3);
neuronNetwork.Layers[2].Neurons[0].SetWeights(1.2, 0.8);
var result = neuronNetwork.FeedForward(new List <double> {
1, 0, 0, 0
});
}
public static void RunNeuronNetworks1()
{
Console.WriteLine("Human is sick-1");
Console.WriteLine("Human is helthi-0");
//Result-- Human is sick - 1
// Human is helthi - 0
//Temprache T
//Good year A
//Smoking S
//Food helthi F
var dataset = new List <Tuple <double, double[]> > {
// T A S F
new Tuple <double, double[]>(0, new double[] { 0, 0, 0, 0 }),
new Tuple <double, double[]>(0, new double[] { 0, 0, 0, 1 }),
new Tuple <double, double[]>(1, new double[] { 0, 0, 1, 0 }),
new Tuple <double, double[]>(0, new double[] { 0, 0, 1, 1 }),
new Tuple <double, double[]>(0, new double[] { 0, 1, 0, 0 }),
new Tuple <double, double[]>(0, new double[] { 0, 1, 0, 1 }),
new Tuple <double, double[]>(1, new double[] { 0, 1, 1, 0 }),
new Tuple <double, double[]>(0, new double[] { 0, 1, 1, 1 }),
new Tuple <double, double[]>(1, new double[] { 1, 0, 0, 0 }),
new Tuple <double, double[]>(1, new double[] { 1, 0, 0, 1 }),
new Tuple <double, double[]>(1, new double[] { 1, 0, 1, 0 }),
new Tuple <double, double[]>(1, new double[] { 1, 0, 1, 1 }),
new Tuple <double, double[]>(1, new double[] { 1, 1, 0, 0 }),
new Tuple <double, double[]>(0, new double[] { 1, 1, 0, 1 }),
new Tuple <double, double[]>(1, new double[] { 1, 1, 1, 0 }),
new Tuple <double, double[]>(1, new double[] { 1, 1, 1, 1 }),
};
var topology = new Topology(4, 1, 0.1, 2);
var neuronNetwork = new NeuronNetworks(topology);
var difference = neuronNetwork.Learning(dataset, 100000);
var result = new List <double>();
foreach (var data in dataset)
{
result.Add(neuronNetwork.FeedForward(data.Item2).Output);
}
for (int i = 0; i < result.Count; i++)
{
var expecte = Math.Round(dataset[i].Item1, 3);
var actual = Math.Round(result[i], 3);
Console.WriteLine(expecte + " -- " + actual);
}
}
public static void RunNeuronNetworks3()
{
var outputs = new List <double>();
var inputs = new List <double[]>();
using (var str = new StreamReader(@"C:\GithubProject\LogFileParser\ParserIntoTheMongoDB\NeuraNetworks\heart.csv"))
{
var header = str.ReadLine();
while (!str.EndOfStream)
{
var row = str.ReadLine();
var values = row.Split(',').Select(v => Convert.ToDouble(v.Replace('.', ','))).ToList();
var output = values.Last();
var input = values.Take(values.Count - 1).ToArray();
outputs.Add(output);
inputs.Add(input);
}
}
var inputSignals = new double[inputs.Count, inputs[0].Length];
for (int i = 0; i < inputSignals.GetLength(0); i++)
{
for (int j = 0; j < inputSignals.GetLength(1); j++)
{
inputSignals[i, j] = inputs[i][j];
}
}
var topology = new Topology(outputs.Count, 1, 0.1, outputs.Count / 2);
var neuronNetwork = new NeuronNetworks(topology);
var difference = neuronNetwork.Learning(outputs.ToArray(), inputSignals, 10);
var result = new List <double>();
for (int i = 0; i < outputs.Count; i++)
{
result.Add(neuronNetwork.FeedForward(inputs[i]).Output);
}
for (int i = 0; i < result.Count; i++)
{
var expecte = Math.Round(outputs[i], 3);
var actual = Math.Round(result[i], 3);
Console.WriteLine(expecte + " -- " + actual);
}
}
public static void RunNeuronNetworks2()
{
//Result-- Human is sick - 1
// Human is helthi - 0
var outputs = new double[] { 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1 };
var inputs = new double[, ] {
{ 0, 0, 0, 0 },
{ 0, 0, 0, 1 },
{ 0, 0, 1, 0 },
{ 0, 0, 1, 1 },
{ 0, 1, 0, 0 },
{ 0, 1, 0, 1 },
{ 0, 1, 1, 0 },
{ 0, 1, 1, 1 },
{ 1, 0, 0, 0 },
{ 1, 0, 0, 1 },
{ 1, 0, 1, 0 },
{ 1, 0, 1, 1 },
{ 1, 1, 0, 0 },
{ 1, 1, 0, 1 },
{ 1, 1, 1, 0 },
{ 1, 1, 1, 1 }
};
var topology = new Topology(4, 1, 0.1, 2);
var neuronNetwork = new NeuronNetworks(topology);
var difference = neuronNetwork.Learning(outputs, inputs, 100000);
var result = new List <double>();
for (int i = 0; i < outputs.Length; i++)
{
var row = NeuronNetworks.GetRow(inputs, i);
result.Add(neuronNetwork.FeedForward(row).Output);
}
for (int i = 0; i < result.Count; i++)
{
var expecte = Math.Round(outputs[i], 3);
var actual = Math.Round(result[i], 3);
Console.WriteLine(expecte + " -- " + actual);
}
}