用 C# 实现神经网络

Question

I'm following the tutorial at this link: http://www.c-sharpcorner.com/UploadFile/rmcochran/AI_OOP_NeuralNet06192006090112AM/AI_OOP_NeuralNet.aspx

I'm new to neural networking and I'm trying to edit the example in the above tutorial to match my problem. I'm using multiple regression to find coefficients for 3 different sets of data and I then calculate the rsquared value for each set of data. I'm trying to create a neural network that will change the coefficient value to get the rsquared value as close to 100 as possible.

This is how I establish the coefficient and find the rsquared value for that coefficient. All 3 coefficients use these same methods:

Calculations calc = new Calculations();
Vector<double> lowRiskCoefficient = MultipleRegression.QR(                                            Matrix<double>.Build.DenseOfColumnArrays(lowRiskShortRatingList.ToArray(), lowRiskMediumRatingList.ToArray(), lowRiskLongRatingList.ToArray()),                                            Vector<double>.Build.Dense(lowRiskWeekReturnList.ToArray()));
                decimal lowRiskShortCoefficient = Convert.ToDecimal(lowRiskCoefficient[0]);
                decimal lowRiskMediumCoefficient = Convert.ToDecimal(lowRiskCoefficient[1]);
                decimal lowRiskLongCoefficient = Convert.ToDecimal(lowRiskCoefficient[2]);
                List<decimal> lowRiskWeekReturnDecimalList = new List<decimal>(lowRiskWeekReturnList.Count);
                lowRiskWeekReturnList.ForEach(i => lowRiskWeekReturnDecimalList.Add(Convert.ToDecimal(i)));
                List<decimal> lowRiskPredictedReturnList = new List<decimal>(lowRiskWeekReturnList.Count);
                List<decimal> lowRiskResidualValueList = new List<decimal>(lowRiskWeekReturnList.Count);
                for (int i = 0; i < lowRiskWeekReturnList.Count; i++)
                {
                    decimal lowRiskPredictedValue = (Convert.ToDecimal(lowRiskShortRatingList.ElementAtOrDefault(i)) * lowRiskShortCoefficient) + (Convert.ToDecimal(lowRiskMediumRatingList.ElementAtOrDefault(i)) * lowRiskMediumCoefficient) +
                        (Convert.ToDecimal(lowRiskLongRatingList.ElementAtOrDefault(i)) * lowRiskLongCoefficient);
                    lowRiskPredictedReturnList.Add(lowRiskPredictedValue);
                    lowRiskResidualValueList.Add(calc.calculateResidual(lowRiskWeekReturnDecimalList.ElementAtOrDefault(i), lowRiskPredictedValue));
                }
                decimal lowRiskTotalSumofSquares = calc.calculateTotalSumofSquares(lowRiskWeekReturnDecimalList, lowRiskWeekReturnDecimalList.Average());
                decimal lowRiskTotalSumofRegression = calc.calculateTotalSumofRegression(lowRiskPredictedReturnList, lowRiskWeekReturnDecimalList.Average());
                decimal lowRiskTotalSumofErrors = calc.calculateTotalSumofErrors(lowRiskResidualValueList);
                decimal lowRiskRSquared = lowRiskTotalSumofRegression / lowRiskTotalSumofSquares;

This is the example that performs the training and I'm currently stuck on how to change this example to match what I'm trying to do.

private void button1_Click(object sender, EventArgs e)
{
net = new NeuralNet();
double high, mid, low;
high = .9;
low = .1;
mid = .5; 
// initialize with
//   2 perception neurons
//   2 hidden layer neurons
//   1 output neuron
net.Initialize(1, 2, 2, 1);  
double[][] input = new double[4][];
input[0] = new double[] {high, high};
input[1] = new double[] {low, high};
input[2] = new double[] {high, low};
input[3] = new double[] {low, low};
double[][] output = new double[4][];
output[0] = new double[] { low };
output[1] = new double[] { high };
output[2] = new double[] { high };
output[3] = new double[] { low };
double ll, lh, hl, hh;
int count;
count = 0;
do
{
    count++;
    for (int i = 0; i < 100; i++)
        net.Train(input, output);
    net.ApplyLearning();
    net.PerceptionLayer[0].Output = low;
    net.PerceptionLayer[1].Output = low;
    net.Pulse();
    ll = net.OutputLayer[0].Output;
    net.PerceptionLayer[0].Output = high;
    net.PerceptionLayer[1].Output = low;
    net.Pulse();
    hl = net.OutputLayer[0].Output;
    net.PerceptionLayer[0].Output = low;
    net.PerceptionLayer[1].Output = high;
    net.Pulse();
    lh = net.OutputLayer[0].Output;
    net.PerceptionLayer[0].Output = high;
    net.PerceptionLayer[1].Output = high;
    net.Pulse();
    hh = net.OutputLayer[0].Output;
}
while (hh > mid || lh < mid || hl < mid || ll > mid);
MessageBox.Show((count*100).ToString() + " iterations required for training");
}

How do I use this information to create a neural network to find the coefficient that will in turn have a rsquared value as close to 100 as possible?

Answer 1

Instead of building one, you can use Neuroph framework built in the .NET by using the Neuroph.NET from here https://github.com/starhash/Neuroph.NET/releases/tag/v1.0-beta

It is a light conversion of the original Neuroph they did for the JAVA platform.

Hope this helps you.

Answer 2

You can check out some github implementations online. I have a simple one that uses sigmoid to classify data, if that's what anyone else wanted. Neural Network Implementation