/// <summary>
/// Classify our data using Logistic Regression classifer and save the model.
/// </summary>
/// <param name="train_data">Frame objects that we will use to train classifers.</param>
/// <param name="test_data">Frame objects that we will use to test classifers.</param>
/// <param name="train_label">Labels of the train data.</param>
/// <param name="test_label">Labels of the test data.</param>
/// <param name="Classifier_Path">Path where we want to save the classifer on the disk.</param>
/// <param name="Classifier_Name">Name of the classifer we wnat to save.</param>
/// <returns></returns>
public void LogisticRegression(double[][] train_data, double[][] test_data, int[] train_label, int[] test_label, String Classifier_Path, String Classifier_Name)
{
var learner = new IterativeReweightedLeastSquares <LogisticRegression>()
{
Tolerance = 1e-4,
MaxIterations = 100,
Regularization = 0
};
LogisticRegression regression = learner.Learn(train_data, train_label);
double ageOdds = regression.GetOddsRatio(0);
double smokeOdds = regression.GetOddsRatio(1);
double[] scores = regression.Probability(test_data);
//bool[] pre = regression.Decide(test_data);
var cm = GeneralConfusionMatrix.Estimate(regression, test_data, test_label);
double error = cm.Error;
Console.WriteLine(error);
regression.Save(Path.Combine(Classifier_Path, Classifier_Name));
}
static public int [] IterativeLeastSquares(double[][] input1, int[] output1, string fName)
{
double[] labels = System.Array.ConvertAll <int, double>(output1, x => x);
var learner = new IterativeReweightedLeastSquares <LogisticRegression>()
{
// Gets or sets the tolerance value used to determine whether the algorithm has converged.
Tolerance = 1e-4, // Let's set some convergence parameters
MaxIterations = 10,
//MaxIterations = 100, // maximum number of iterations to perform
Regularization = 0
};
// Now, we can use the learner to finally estimate our model:
LogisticRegression regression = learner.Learn(input1, output1);
double [] coefficients = learner.Solution;
double[] scores = regression.Probability(input1);
regression.Save(fName.Replace(".csv", ".IRLS.save"), compression: SerializerCompression.None);
// Finally, if we would like to arrive at a conclusion regarding
// each sample, we can use the Decide method, which will transform
// the probabilities (from 0 to 1) into actual true/false values:
return(Funcs.Utility.BoolToInt(regression.Decide(input1)));
// mean(double(p == y)) * 100);
}
public double regressionProbability()
{
double probablity = regression.Probability(new double[] { lam.Age, lam.Gender, lam.Total_Bilirubin, lam.Direct_Bilirubin
, lam.Alkaline_Phosphotase, lam.Alamine_Aminotransferase
, lam.Aspartate_Aminotransferase, lam.Total_Protiens
, lam.Albumin, lam.Albumin_and_Globulin_Ratio });
return(probablity);
}
/// <summary>
/// Evaluates a model by calculating it's loss from a set.
/// </summary>
/// <returns>An array of measurements. (SME, False Positive Rate, Accuracy)</returns>
/// <param name="crunchingScores">The scores for crunching studios.</param>
/// <param name="nonCrunchingScores">The scores for non-crunching studios.</param>
/// <param name="modelName">The file name of the model.</param>
public static float[] EvaluateModel(List <KeyValuePair <string, float[]> > crunchingScores, List <KeyValuePair <string, float[]> > nonCrunchingScores, string modelName)
{
//Get the model file and deserialize the weights.
if (!File.Exists("Logistic Regression Model/models/" + modelName + ".txt"))
{
throw new Exception("Model doesn't exist. Check your spelling and try again.");
}
string line = File.ReadAllLines("Logistic Regression Model/models/" + modelName + ".txt")[0];
double[] weights = Extensions.Extensions.LoadWeights(line);
//Create a regression model.
LogisticRegression regression = new LogisticRegression();
regression.Weights = weights.Take(weights.Length - 1).ToArray();
regression.Intercept = weights[weights.Length - 1];
//Get the actual and expected values from the scores.
double[] actual = new double[crunchingScores.Count + nonCrunchingScores.Count];
double[] expected = new double[crunchingScores.Count + nonCrunchingScores.Count];
int[] actualClass = new int[crunchingScores.Count + nonCrunchingScores.Count];
int[] expectedClass = new int[crunchingScores.Count + nonCrunchingScores.Count];
//Get expected and actual values for crunching studios.
for (int a = 0; a < crunchingScores.Count; a++)
{
actual[a] = regression.Probability(crunchingScores[a].Value.Select((arg) => (double)arg).ToArray());
expected[a] = 1;
actualClass[a] = (actual[a] > 0.5f) ? 1 : 0;
expectedClass[a] = 1;
}
//Get expected and actual values for non-crunching studios.
for (int b = 0; b < nonCrunchingScores.Count; b++)
{
actual[b + crunchingScores.Count] = regression.Probability(nonCrunchingScores[b].Value.Select((arg) => (double)arg).ToArray());
expected[b + crunchingScores.Count] = 0;
actualClass[b + crunchingScores.Count] = (actual[b + crunchingScores.Count] > 0.5f) ? 1 : 0;
expectedClass[b + crunchingScores.Count] = 0;
}
//Calculate and return the loss.
float loss = (float)new SquareLoss(expected)
{
Mean = true,
Root = true
}.Loss(actual);
//Get R-squared loss
float r2 = (float)new RSquaredLoss(crunchingScores.First().Value.Length, expected).Loss(actual);
//Calculate confusion matrix
ConfusionMatrix gcm = new ConfusionMatrix(actualClass.ToBoolArray(), expectedClass.ToBoolArray());
//Calculate false negative and positive rates
float falsePR = (float)gcm.FalsePositiveRate;
//Calculate overall accuracy
float accuracy = (float)gcm.Accuracy;
//Calculate the high confidence rate
//(how many are correct and have a probability within 0.2 of either extreme)
int correctHighConfidence = 0, highConfidence = 0;
for (int a = 0; a < actual.Length; a++)
{
//If it's in the range of 0 - 0.2 or 0.8 - 1, it's considered 'high confidence'
if (Math.Abs(actual[a] - 0.5f) > 0.3f)
{
//Is it correct?
if (actualClass[a] == expectedClass[a])
{
correctHighConfidence++;
}
highConfidence++;
}
}
float correctHC = ((float)correctHighConfidence) / highConfidence;
return(new float[] { loss, r2, falsePR, accuracy, correctHC });
}
/// <summary>
/// Uses data from <paramref name="fileName">fileName</paramref> to train a logistic regression model./>
/// </summary>
/// <param name="fileName">The name of the data file.</param>
/// <returns>A string to print giving information about the weights and odds ratios.</returns>
public static string Learn(string fileName)
{
//Read all inputs and outputs from training file.
string[] lines = File.ReadAllLines("Logistic Regression Model/data/" + fileName + ".txt");
double[][] inputs = new double[lines.Length][];
int[] outputs = new int[lines.Length];
for (int a = 0; a < lines.Length; a++)
{
string[] split = lines[a].Split(':');
//Dynamically get variables from file.
string[] scores = split[1].Split('&');
inputs[a] = new double[scores.Length];
for (int b = 0; b < scores.Length; b++)
{
inputs[a][b] = double.Parse(scores[b]);
}
outputs[a] = int.Parse(split[2]);
}
//Set up Accord.NET learner.
IterativeReweightedLeastSquares <LogisticRegression> learner = new IterativeReweightedLeastSquares <LogisticRegression>()
{
Tolerance = 1e-4,
MaxIterations = 100,
Regularization = 1e-10
};
//Shuffle the input and output pairs to eliminate some inherent bias from
//training data.
Dictionary <double[], int> map = inputs.Zip(outputs, (arg1, arg2) => new { arg1, arg2 }).ToDictionary(x => x.arg1, x => x.arg2);
map.Shuffle();
inputs = map.Keys.ToArray();
outputs = map.Values.ToArray();
//Train Regression
LogisticRegression regression = learner.Learn(inputs, outputs.ToBoolArray());
//Save to a Model file.
int counter = 0;
while (File.Exists("Logistic Regression Model/models/Model-" + counter + ".txt"))
{
counter++;
}
//Create a file writer
FileStream fs = File.Create("Logistic Regression Model/models/Model-" + counter + ".txt");
StreamWriter writer = new StreamWriter(fs);
//Print the weights
string result = "Weights: " + regression.Weights.GetString() + "\n";
//Write lines.
writer.WriteLine(regression.Weights.Append(regression.Intercept).ToArray().GetString());
for (int c = 0; c < regression.Weights.Length; c++)
{
writer.WriteLine(regression.GetOddsRatio(c));
result += "Odds Ratio " + c + ": " + regression.GetOddsRatio(c) + "\n";
}
//Get Loss values.
double[] actual = new double[inputs.Length];
double[] expected = new double[outputs.Length];
for (int a = 0; a < actual.Length; a++)
{
actual[a] = regression.Probability(inputs[a]);
expected[a] = outputs[a];
}
//Calculate and print square loss.
string loss = "Loss: " + new SquareLoss(expected)
{
Mean = true,
Root = true
}.Loss(actual);
result += loss + "\n";
writer.WriteLine(loss);
Console.WriteLine("\n\n" + loss);
//Calculate and print R-squared Loss
string r2 = "R2: " + new RSquaredLoss(inputs[0].Length, expected).Loss(actual);
result += r2;
writer.WriteLine(r2);
//Cleanup
writer.Close();
writer.Dispose();
fs.Close();
fs.Dispose();
Console.WriteLine("Model trained successfully!");
Console.WriteLine("\nEvaluating...\n");
//Get the VIFs
float[] VIFs = CalculateVIFs(inputs);
//Log it
for (int a = 0; a < VIFs.Length; a++)
{
Logger.Log("Variance Inflation Factor #" + a + ": " + VIFs[a]);
}
return(result);
}
private void GetResultButton_Click(object sender, EventArgs e)
{
Settings.Default["AlgorName"] = label3.Text;
ResultBox.Items.Clear();
if (label3.Text == "Statics")
{
if (!(PF is Istatics))
{
MessageBox.Show("Production Facade doesn't have this interface yet");
return;
}
Settings.Default["Parameter"] = textBox1.Text;
int numOfParmas = PF.GetData()[0].GetNumOfParams();
double mean, sd;
for (int i = 0; i < numOfParmas; i++)
{
mean = PF.GetMean(i);
ResultBox.Items.Add("Mean of param " + i + ": " + mean);
sd = PF.GetStandardDeviation(i);
ResultBox.Items.Add("StandardDeviation of param " + i + ": " + sd);
if (textBox1.Text != "")
{
double number = Double.Parse(textBox1.Text);
double Threshold = PF.GetThreshold(number);
ResultBox.Items.Add("Threshold of param " + i + ": " + Threshold);
ResultBox.Items.Add("");
}
}
}
else if (label3.Text == "Peak")
{
if (!(PF is Ipeek))
{
MessageBox.Show("Production Facade doesn't have this interface yet");
return;
}
if (XaxiscomboBox.Text == "" && YaxiscomboBox.Text == "")
{
return;
}
sortData();
Settings.Default["NumOfPoints"] = numBox.Text;
Settings.Default["Range"] = rangeBox.Text;
if (YaxiscomboBox.Text.Contains("#"))
{
string text = YaxiscomboBox.Text.Substring(10).Trim();
int num = Int32.Parse(text);
int numOfpoints = 3;
double percentage = 0.2;
if (numBox.Text != "")
{
numOfpoints = Int32.Parse(numBox.Text);
}
if (rangeBox.Text != "")
{
percentage = double.Parse(rangeBox.Text);
}
dataDict = PF.CalculateTimesEachX(num);
List <PeekValleyData> peaks = PF.GetPeaksWithNumOfSamePoints(num, numOfpoints, percentage);
peaksData = peaks;
/*
* for (int i = 0; i < PF.GetData().Length; i++)
* {
* ResultBox.Items.Add(PF.GetData()[i].WriteToLine());
* }*/
if (peaksData.Count() == 0)
{
ResultBox.Items.Add("No Peak found yet.");
}
foreach (var item in peaks)
{
ResultBox.Items.Add("Peak Value: " + item.value + " Times: " + item.times);
}
}
else if (YaxiscomboBox.Text != "")
{
string text = YaxiscomboBox.Text.Substring(5).Trim();
int Yindex = Int32.Parse(text);
// MessageBox.Show(Yindex.ToString());
int numOfpoints = 3;
double percentage = 0.2;
if (numBox.Text != "")
{
numOfpoints = Int32.Parse(numBox.Text);
}
if (rangeBox.Text != "")
{
percentage = double.Parse(rangeBox.Text);
}
text = XaxiscomboBox.Text.Substring(5).Trim();
int Xindex;
if (XaxiscomboBox.Text.Contains("TimeSpan"))
{
Xindex = -1;
}
else
{
Xindex = Int32.Parse(text);
}
MyData[] MD = PF.GetData();
dataPoints = new points[MD.Count()];
for (int i = 0; i < MD.Count(); i++)
{
dataPoints[i].x = MD[i].GetParameters()[Xindex];
dataPoints[i].y = MD[i].GetParameters()[Yindex];
}
List <PeekValleyData> peaks = PF.GetPeaksWithXY(Yindex, Xindex, numOfpoints, percentage);
peaksData = peaks;
/*
* for (int i = 0; i < PF.GetData().Length; i++)
* {
* ResultBox.Items.Add(PF.GetData()[i].WriteToLine());
* }
* */
foreach (var item in peaks)
{
ResultBox.Items.Add("X: " + item.x + " Y: " + item.y);
}
}
}
else if (label3.Text == "Polynomial Fit")
{
if (!(PF is Ipolyfit))
{
MessageBox.Show("Production Facade doesn't have this interface yet");
return;
}
if (XaxiscomboBox.Text == "" && YaxiscomboBox.Text == "")
{
return;
}
sortData();
int power = 1;
if (powerBox.Text != "")
{
power = Int32.Parse(powerBox.Text);
}
if (YaxiscomboBox.Text.Contains("#"))
{
string text = YaxiscomboBox.Text.Substring(10).Trim();
int num = Int32.Parse(text);
Dictionary <double, int> dict = PF.CalculateTimesEachX(num);
dataDict = dict;
double[,] x = new double[dict.Keys.Count(), power];
double[] keys = dict.Keys.ToArray();
for (int i = 0; i < dict.Keys.Count(); i++)
{
for (int j = 0; j < power; j++)
{
x[i, j] = Math.Pow(keys[i], j + 1);
}
}
double[] y = new double[keys.Length];
for (int i = 0; i < keys.Length; i++)
{
y[i] = dict[keys[i]];
}
double[,] result = PF.GetPolyFit(x, y, power);
paramsResult = result;
string r = "y=";
r = r + result[0, 0].ToString("#.000");
for (int i = 1; i <= power; i++)
{
r = r + "+ " + result[i, 0].ToString("#.000") + "x^" + i.ToString();
}
ResultBox.Items.Add(r);
}
else if (YaxiscomboBox.Text != "")
{
string text = YaxiscomboBox.Text.Substring(5).Trim();
int Yindex = Int32.Parse(text);
int Xindex;
if (XaxiscomboBox.Text.Contains("TimeSpan"))
{
MessageBox.Show("We have not support Timespan in this algorithm yet.");
return;
}
else
{
text = XaxiscomboBox.Text.Substring(5).Trim();
Xindex = Int32.Parse(text);
}
MyData[] MD = PF.GetData();
dataPoints = new points[MD.Count()];
double[,] x = new double[MD.Count(), power];
for (int i = 0; i < MD.Count(); i++)
{
dataPoints[i].x = MD[i].GetParameters()[Xindex];
for (int j = 0; j < power; j++)
{
x[i, j] = Math.Pow(MD[i].GetParameters()[Xindex], j + 1);
}
}
double[] y = new double[MD.Count()];
for (int i = 0; i < y.Length; i++)
{
y[i] = MD[i].GetParameters()[Yindex];
dataPoints[i].y = y[i];
}
double[,] result = PF.GetPolyFit(x, y, power);
paramsResult = result;
string r = "y=";
r = r + result[0, 0].ToString("#.000");
for (int i = 1; i <= power; i++)
{
r = r + "+ " + result[i, 0].ToString("#.000") + "x^" + i.ToString();
}
ResultBox.Items.Add(r);
}
if (xvalueBox.Text != "")
{
double x = Convert.ToDouble(xvalueBox.Text);
double y = 0;
for (int j = 0; j < paramsResult.GetLength(0); j++)
{
y = y + Math.Pow(x, j) * paramsResult[j, 0];
}
ResultBox.Items.Add("X-value: " + x.ToString("#.000") + " Y-value: " + y.ToString("#.000"));
Settings.Default["Xvalue"] = xvalueBox.Text;
}
Settings.Default["Power"] = powerBox.Text;
}
else if (label3.Text == "Normal Distribution Fit")
{
if (!(PF is INormalDistributionFit))
{
MessageBox.Show("Production Facade doesn't have this interface yet");
return;
}
if (XaxiscomboBox.Text == "" && YaxiscomboBox.Text == "")
{
return;
}
sortData();
/*
* for (int i = 0; i < PF.GetData().Length; i++)
* {
* ResultBox.Items.Add(PF.GetData()[i].WriteToLine());
* }*/
if (YaxiscomboBox.Text.Contains("#"))
{
string text = YaxiscomboBox.Text.Substring(10).Trim();
int num = Int32.Parse(text);
dataDict = PF.CalculateTimesEachX(num);
NormalDistributionInfo ndi = PF.GetNormalDistributionFitWithNumOfSamePoints(num);
NDinfo = ndi;
ResultBox.Items.Add("Mean: " + ndi.mean.ToString() + " SD: " + ndi.SD.ToString());
}
else
{
if (XaxiscomboBox.Text == "" || YaxiscomboBox.Text == "")
{
return;
}
string text = YaxiscomboBox.Text.Substring(5).Trim();
int Yindex = Int32.Parse(text);
int Xindex;
if (XaxiscomboBox.Text.Contains("TimeSpan"))
{
MessageBox.Show("We have not support Timespan in this algorithm yet.");
return;
}
else
{
text = XaxiscomboBox.Text.Substring(5).Trim();
Xindex = Int32.Parse(text);
}
MyData[] MD = PF.GetData();
dataPoints = new points[MD.Count()];
for (int i = 0; i < MD.Count(); i++)
{
dataPoints[i].x = MD[i].GetParameters()[Xindex];
dataPoints[i].y = MD[i].GetParameters()[Yindex];
}
int num = Int32.Parse(text);
dataDict = PF.CalculateTimesEachX(num);
NormalDistributionInfo ndi = PF.GetNormalDistributionFitWithNumOfSamePoints(num);
NDinfo = ndi;
ResultBox.Items.Add("Mean: " + ndi.mean.ToString() + " SD: " + ndi.SD.ToString());
}
if (xvalueBox.Text != "")
{
double x = Convert.ToDouble(xvalueBox.Text);
double y = 0;
y = (1 / (NDinfo.SD * Math.Sqrt(2 * Math.PI))) * Math.Exp(-Math.Pow((x - NDinfo.mean), 2) / (2 * Math.Pow(NDinfo.SD, 2)));
int numberOfdata = 0;
foreach (var item in dataDict)
{
numberOfdata = numberOfdata + item.Value;
}
y = y * numberOfdata;
ResultBox.Items.Add("X-value: " + x.ToString("#.000") + " Y-value: " + y.ToString("#.000"));
Settings.Default["Xvalue"] = xvalueBox.Text;
}
}
else if (label3.Text == "Logistic Regression")
{
if (!(PF is ILogisticRegression))
{
MessageBox.Show("Production Facade doesn't have this interface yet");
return;
}
if (comboBox2.Text != "")
{
string text = comboBox2.Text.Substring(5).Trim();
int index = Int32.Parse(text);
dataDict = PF.CalculateTimesEachX(index);
double percentage = 0.1;
PF.SortData(index);
if (rangeBox.Text != "")
{
percentage = double.Parse(rangeBox.Text);
}
LogisticRegression LR = PF.GetLogisticRegressionParams(index, percentage);
LogisticInfo LI = new LogisticInfo();
LI.LogisticParams = new double[2];
LI.LogisticParams[0] = LR.Intercept;
LI.LogisticParams[1] = LR.GetOddsRatio(1) - 1;
ResultBox.Items.Add("Param0: " + LI.LogisticParams[0] + " Parma1: " + LI.LogisticParams[1]);
if (xvalueBox.Text != "")
{
double x = Convert.ToDouble(xvalueBox.Text);
double[] valueArr = new double[] { x, 0 };
ResultBox.Items.Add("Value: " + x + " Probability: " + LR.Probability(valueArr) + " Conclusion:" + LR.Decide(valueArr));
}
MyData[] MD = PF.GetData();
double threshold = -1;
/*
* for (int i = 0; i < MD.Length; i++)
* {
* double value = MD[i].GetParameters()[index];
* double[] valueArr = new double[] { value, 0 };
* ResultBox.Items.Add("Value: " + value + " Probability: " + LR.Probability(valueArr) + " Conclusion:" + LR.Decide(valueArr));
* }*/
Dictionary <double, int> lowerDict = new Dictionary <double, int>();
Dictionary <double, int> higherDict = new Dictionary <double, int>();
for (int i = 0; i < MD.Length; i++)
{
double value = MD[i].GetParameters()[index];
double[] valueArr = new double[] { value, 0 };
if (threshold == -1 && LR.Decide(valueArr) == true)
{
threshold = value;
}
if (threshold == -1)
{
if (lowerDict.ContainsKey(value))
{
lowerDict[value]++;
}
else
{
lowerDict.Add(value, 1);
}
}
else
{
if (higherDict.ContainsKey(value))
{
higherDict[value]++;
}
else
{
higherDict.Add(value, 1);
}
}
}
ResultBox.Items.Add("Threshold: " + threshold);
}
}
else if (label3.Text == "Two Peaks")
{
if (!(PF is Ipeek))
{
MessageBox.Show("Production Facade doesn't have this interface yet");
return;
}
if (peak1Box.Text != "" && peak2Box.Text != "")
{
string text = YaxiscomboBox.Text.Substring(10).Trim();
int index = Int32.Parse(text);
double peak1 = Convert.ToDouble(peak1Box.Text);
double peak2 = Convert.ToDouble(peak2Box.Text);
int numOfpoints = 5;
if (numBox.Text != "")
{
numOfpoints = Int32.Parse(numBox.Text);
}
NormalDistributionInfo peak1Info = PF.GetPeakNormalDistribution(peak1, index, numOfpoints);
ResultBox.Items.Add("Mean: " + peak1Info.mean.ToString("#.000") + " SD: " + peak1Info.SD.ToString("#.000"));
NormalDistributionInfo peak2Info = PF.GetPeakNormalDistribution(peak2, index, numOfpoints);
ResultBox.Items.Add("Mean: " + peak2Info.mean.ToString("#.000") + " SD: " + peak2Info.SD.ToString("#.000"));
if (xvalueBox.Text != "")
{
double x = Convert.ToDouble(xvalueBox.Text);
double end = 0;
double y = (1 / (peak1Info.SD * Math.Sqrt(2 * Math.PI))) * Math.Exp(-Math.Pow((x - peak1Info.mean), 2) / (2 * Math.Pow(peak1Info.SD, 2)));
if (y < 0.01)
{
double x1 = x - 1;
double y1 = (1 / (peak1Info.SD * Math.Sqrt(2 * Math.PI))) * Math.Exp(-Math.Pow((x1 - peak1Info.mean), 2) / (2 * Math.Pow(peak1Info.SD, 2)));
if (y1 < y)
{
end = x1;
}
else
{
end = x + 1;
}
}
else
{
double x1 = x - 1;
double y1 = (1 / (peak1Info.SD * Math.Sqrt(2 * Math.PI))) * Math.Exp(-Math.Pow((x1 - peak1Info.mean), 2) / (2 * Math.Pow(peak1Info.SD, 2)));
if (y1 < y)
{
end = x1;
while (y1 > 0.01)
{
end--;
y1 = (1 / (peak1Info.SD * Math.Sqrt(2 * Math.PI))) * Math.Exp(-Math.Pow((end - peak1Info.mean), 2) / (2 * Math.Pow(peak1Info.SD, 2)));
}
}
else
{
end = x + 1;
y1 = (1 / (peak1Info.SD * Math.Sqrt(2 * Math.PI))) * Math.Exp(-Math.Pow((end - peak1Info.mean), 2) / (2 * Math.Pow(peak1Info.SD, 2)));
while (y1 > 0.01)
{
end++;
y1 = (1 / (peak1Info.SD * Math.Sqrt(2 * Math.PI))) * Math.Exp(-Math.Pow((end - peak1Info.mean), 2) / (2 * Math.Pow(peak1Info.SD, 2)));
}
}
}
Func <double, double> f1 = (a) => (1 / (peak1Info.SD * Math.Sqrt(2 * Math.PI))) * Math.Exp(-Math.Pow((a - peak1Info.mean), 2) / (2 * Math.Pow(peak1Info.SD, 2)));
double result1 = MathNet.Numerics.Integration.NewtonCotesTrapeziumRule.IntegrateTwoPoint(f1, x, end);
if (x <= peak1)
{
result1 = 1 - Math.Abs(result1);
if (result1 > 1)
{
result1 = 1;
}
}
ResultBox.Items.Add("Probablity of keeping bad chips: " + Math.Abs(result1).ToString("#.000"));
double end1 = 0;
y = (1 / (peak2Info.SD * Math.Sqrt(2 * Math.PI))) * Math.Exp(-Math.Pow((x - peak2Info.mean), 2) / (2 * Math.Pow(peak2Info.SD, 2)));
if (y < 0.01)
{
double x1 = x - 1;
double y1 = (1 / (peak2Info.SD * Math.Sqrt(2 * Math.PI))) * Math.Exp(-Math.Pow((x1 - peak2Info.mean), 2) / (2 * Math.Pow(peak2Info.SD, 2)));
if (y1 < y)
{
end1 = x1;
}
else
{
end1 = x + 1;
}
}
else
{
double x1 = x - 1;
double y1 = (1 / (peak2Info.SD * Math.Sqrt(2 * Math.PI))) * Math.Exp(-Math.Pow((x1 - peak2Info.mean), 2) / (2 * Math.Pow(peak2Info.SD, 2)));
if (y1 < y)
{
end1 = x1;
while (y1 > 0.01)
{
end1--;
y1 = (1 / (peak2Info.SD * Math.Sqrt(2 * Math.PI))) * Math.Exp(-Math.Pow((end1 - peak2Info.mean), 2) / (2 * Math.Pow(peak2Info.SD, 2)));
}
}
else
{
end1 = x + 1;
y1 = (1 / (peak2Info.SD * Math.Sqrt(2 * Math.PI))) * Math.Exp(-Math.Pow((end1 - peak2Info.mean), 2) / (2 * Math.Pow(peak2Info.SD, 2)));
while (y1 > 0.01)
{
end1++;
y1 = (1 / (peak2Info.SD * Math.Sqrt(2 * Math.PI))) * Math.Exp(-Math.Pow((end1 - peak2Info.mean), 2) / (2 * Math.Pow(peak2Info.SD, 2)));
}
}
}
Func <double, double> f2 = (a) => (1 / (peak2Info.SD * Math.Sqrt(2 * Math.PI))) * Math.Exp(-Math.Pow((a - peak2Info.mean), 2) / (2 * Math.Pow(peak2Info.SD, 2)));
double result2 = MathNet.Numerics.Integration.NewtonCotesTrapeziumRule.IntegrateTwoPoint(f2, x, end1);
if (x >= peak2)
{
result2 = 1 - Math.Abs(result2);
if (result2 > 1)
{
result2 = 1;
}
}
ResultBox.Items.Add("probability of losing good chips: " + Math.Abs(result2).ToString("#.000"));
}
dataDict = PF.CalculateTimesEachX(index);
TDinfo = new TwoDictInfo();
TDinfo.peak1Info = peak1Info;
TDinfo.peak2Info = peak2Info;
TDinfo.peak1value = peak1;
TDinfo.peak2value = peak2;
TDinfo.peak1times = dataDict[peak1];
TDinfo.peak2times = dataDict[peak2];
TDinfo.last = dataDict.Keys.ToList().Last();
//peak1Box.Text = "";
// peak2Box.Text = "";
}
else
{
if (XaxiscomboBox.Text == "" && YaxiscomboBox.Text == "")
{
return;
}
sortData();
Settings.Default["NumOfPoints"] = numBox.Text;
Settings.Default["Range"] = rangeBox.Text;
if (YaxiscomboBox.Text.Contains("#"))
{
string text = YaxiscomboBox.Text.Substring(10).Trim();
int num = Int32.Parse(text);
int numOfpoints = 3;
double percentage = 0.2;
if (numBox.Text != "")
{
numOfpoints = Int32.Parse(numBox.Text);
}
if (rangeBox.Text != "")
{
percentage = double.Parse(rangeBox.Text);
}
dataDict = PF.CalculateTimesEachX(num);
List <PeekValleyData> peaks = PF.GetPeaksWithNumOfSamePoints(num, numOfpoints, percentage);
peaksData = peaks;
/*
* for (int i = 0; i < PF.GetData().Length; i++)
* {
* ResultBox.Items.Add(PF.GetData()[i].WriteToLine());
* }*/
if (peaksData.Count() == 0)
{
ResultBox.Items.Add("No Peak found yet.");
}
foreach (var item in peaks)
{
ResultBox.Items.Add("Peak Value: " + item.value + " Times: " + item.times);
}
}
else if (YaxiscomboBox.Text != "")
{
string text = YaxiscomboBox.Text.Substring(5).Trim();
int Yindex = Int32.Parse(text);
// MessageBox.Show(Yindex.ToString());
int numOfpoints = 3;
double percentage = 0.2;
if (numBox.Text != "")
{
numOfpoints = Int32.Parse(numBox.Text);
}
if (rangeBox.Text != "")
{
percentage = double.Parse(rangeBox.Text);
}
text = XaxiscomboBox.Text.Substring(5).Trim();
int Xindex;
if (XaxiscomboBox.Text.Contains("TimeSpan"))
{
Xindex = -1;
}
else
{
Xindex = Int32.Parse(text);
}
MyData[] MD = PF.GetData();
dataPoints = new points[MD.Count()];
for (int i = 0; i < MD.Count(); i++)
{
dataPoints[i].x = MD[i].GetParameters()[Xindex];
dataPoints[i].y = MD[i].GetParameters()[Yindex];
}
List <PeekValleyData> peaks = PF.GetPeaksWithXY(Yindex, Xindex, numOfpoints, percentage);
peaksData = peaks;
/*
* for (int i = 0; i < PF.GetData().Length; i++)
* {
* ResultBox.Items.Add(PF.GetData()[i].WriteToLine());
* }
* */
foreach (var item in peaks)
{
ResultBox.Items.Add("X: " + item.x + " Y: " + item.y);
}
}
if (peaksData.Count() < 2)
{
ResultBox.Items.Add("We need at least two peaks in this algorithm");
}
else
{
peak1Box.Show();
peak2Box.Show();
label13.Show();
label12.Show();
xvalueBox.Show();
label10.Show();
peak1Box.Items.Clear();
peak2Box.Items.Clear();
points[] pts = GetPeakPoints();
foreach (var point in pts)
{
if (!peak1Box.Items.Contains(point.x))
{
peak1Box.Items.Add(point.x);
}
if (!peak2Box.Items.Contains(point.x))
{
peak2Box.Items.Add(point.x);
}
}
}
}
}
Settings.Default.Save();
}
