public void ExecuteTest()
{
string[][] words =
{
new string[] { "今日", "は", "いい", "天気", "です" },
new string[] { "明日", "も", "いい", "天気", "でしょう" }
};
var codebook = new BagOfWords()
{
//MaximumOccurance = 1 // the resulting vector will have only 0's and 1's
MaximumOccurance = int.MaxValue
};
// Compute the codebook (note: this would have to be done only for the training set)
codebook.Learn(words);
// Now, we can use the learned codebook to extract fixed-length
// representations of the different texts (paragraphs) above:
// Extract a feature vector from the text 1:
double[] bow1 = codebook.Transform(words[0]);
// Extract a feature vector from the text 2:
double[] bow2 = codebook.Transform(words[1]);
// we could also have transformed everything at once, i.e.
double[][] bow = codebook.Transform(words);
// Now, since we have finite length representations (both bow1 and bow2 should
// have the same size), we can pass them to any classifier or machine learning
// method. For example, we can pass them to a Logistic Regression Classifier to
// discern between the first and second paragraphs
// Lets create a Logistic classifier to separate the two paragraphs:
var learner = new IterativeReweightedLeastSquares <LogisticRegression>()
{
Tolerance = 1e-4, // Let's set some convergence parameters
Iterations = 100, // maximum number of iterations to perform
Regularization = 0
};
// Now, we use the learning algorithm to learn the distinction between the two:
LogisticRegression reg = learner.Learn(new[] { bow1, bow2 }, new[] { false, true });
// Finally, we can predict using the classifier:
bool c1 = reg.Decide(bow1); // Should be false
bool c2 = reg.Decide(bow2); // Should be true
Console.WriteLine(c1);
Console.WriteLine(c2);
}
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);
}
// Compute car control based on sensor readings
void ComputeControl(float sensorL, float sensorF, float sensorR, float carVelocity, float forward)
{
// Inputs
double[][] inputsT = new double[1][];
inputsT[0] = new double[4];
inputsT[0][0] = sensorL;
inputsT[0][1] = sensorF;
inputsT[0][2] = sensorR;
inputsT[0][3] = carVelocity;
double[][] inputs = new double[1][];
inputs[0] = new double[5];
inputs[0][0] = sensorL;
inputs[0][1] = sensorF;
inputs[0][2] = sensorR;
inputs[0][3] = carVelocity;
inputs[0][4] = forward;
answerThrust = treeThrust.Decide(inputsT);
answerSteer = treeSteer.Decide(inputs);
// Thrust
if (answerThrust[0] == true)
{
thrust = pod[1];
//Debug.Log("Acelera!");
}
else if (answerThrust[0] == false)
{
thrust = -pod[2];
//Debug.Log("Freia!");
}
// Steer
if (answerSteer[0] == true)
{
if (sensorLeft > sensorRight)
{
steer = pod[3];
//Debug.Log("Vire a Esquerda!");
}
else
{
steer = -pod[3];
//Debug.Log("Vire a Direita!");
}
}
else if (answerSteer[0] == false)
{
steer = 0;
//Debug.Log("Vai reto!");
}
// Command
rb.AddRelativeForce(new Vector2(0f, thrust));
rb.AddTorque(steer);
}
public bool classifierLR(data d)
{
int D = d.d;
double[] input = new double[D];
bool output;
for (int i = 0; i < D; ++i)
{
input[i] = d.msg[i];
}
output = regression.Decide(input);
return(output);
}
private static void logisticRegression(double[][] inputs, int[] outputs)
{
// Create iterative re-weighted least squares for logistic regressions
var teacher = new IterativeReweightedLeastSquares <LogisticRegression>()
{
MaxIterations = 100,
Regularization = 1e-6
};
// Use the teacher algorithm to learn the regression:
LogisticRegression lr = teacher.Learn(inputs, outputs);
// Classify the samples using the model
bool[] answers = lr.Decide(inputs);
// Convert to Int32 so we can plot:
int[] zeroOneAnswers = answers.ToZeroOne();
// Plot the results
ScatterplotBox.Show("Expected results", inputs, outputs);
ScatterplotBox.Show("Logistic Regression results", inputs, zeroOneAnswers)
.Hold();
}
private static void ShowAccordIntermediateData(IDataView input, LogisticRegression accordModel)
{
var convertedInput = IDataViewToAccord(input).inputs;
bool[] lrOutput = accordModel.Decide(convertedInput);
var lrScore = accordModel.Scores(convertedInput);
var lrProbability = accordModel.Probabilities(convertedInput);
var inputEnumerable = mlContext.Data.CreateEnumerable <IDataViewWrapper>(input, true);
sw.WriteLine("Label\tScore\tProbability\tPrediction");
int count = 0;
foreach (IDataViewWrapper pp in inputEnumerable)
{
int label = pp.Label ? 1 : 0;
int prediction = lrOutput[count] ? 1 : 0;
double score = lrScore[count][1];
double probability = lrProbability[count][1];
sw.WriteLine("{0}\t{1}\t{2}\t{3}", label, score, probability, prediction);
count++;
}
sw.Flush();
}
public void ComputeTest()
{
// Suppose we have the following data about some patients.
// The first variable is continuous and represent patient
// age. The second variable is dichotomic and give whether
// they smoke or not (This is completely fictional data).
double[][] input =
{
new double[] { 55, 0 }, // 0 - no cancer
new double[] { 28, 0 }, // 0
new double[] { 65, 1 }, // 0
new double[] { 46, 0 }, // 1 - have cancer
new double[] { 86, 1 }, // 1
new double[] { 56, 1 }, // 1
new double[] { 85, 0 }, // 0
new double[] { 33, 0 }, // 0
new double[] { 21, 1 }, // 0
new double[] { 42, 1 }, // 1
};
// We also know if they have had lung cancer or not, and
// we would like to know whether smoking has any connection
// with lung cancer (This is completely fictional data).
double[] output =
{
0, 0, 0, 1, 1, 1, 0, 0, 0, 1
};
// To verify this hypothesis, we are going to create a logistic
// regression model for those two inputs (age and smoking).
LogisticRegression regression = new LogisticRegression(inputs: 2);
// Next, we are going to estimate this model. For this, we
// will use the Iteratively Reweighted Least Squares method.
var teacher = new IterativeReweightedLeastSquares(regression);
teacher.Regularization = 0;
// Now, we will iteratively estimate our model. The Run method returns
// the maximum relative change in the model parameters and we will use
// it as the convergence criteria.
double delta = 0;
do
{
// Perform an iteration
delta = teacher.Run(input, output);
} while (delta > 0.001);
// At this point, we can compute the odds ratio of our variables.
// In the model, the variable at 0 is always the intercept term,
// with the other following in the sequence. Index 1 is the age
// and index 2 is whether the patient smokes or not.
// For the age variable, we have that individuals with
// higher age have 1.021 greater odds of getting lung
// cancer controlling for cigarette smoking.
double ageOdds = regression.GetOddsRatio(1); // 1.0208597028836701
// For the smoking/non smoking category variable, however, we
// have that individuals who smoke have 5.858 greater odds
// of developing lung cancer compared to those who do not
// smoke, controlling for age (remember, this is completely
// fictional and for demonstration purposes only).
double smokeOdds = regression.GetOddsRatio(2); // 5.8584748789881331
double[] actual = new double[output.Length];
for (int i = 0; i < input.Length; i++)
{
actual[i] = regression.Compute(input[i]);
}
double[] expected =
{
0.21044171560168326,
0.13242527535212373,
0.65747803433771812,
0.18122484822324372,
0.74755661773156912,
0.61450041841477232,
0.33116705418194975,
0.14474110902457912,
0.43627109657399382,
0.54419383282533118
};
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(expected[i], actual[i]);
}
Assert.AreEqual(1.0208597028836701, ageOdds, 1e-10);
Assert.AreEqual(5.8584748789881331, smokeOdds, 1e-8);
Assert.AreEqual(-2.4577464307294092, regression.Intercept, 1e-8);
Assert.AreEqual(-2.4577464307294092, regression.Coefficients[0], 1e-8);
Assert.AreEqual(0.020645118265359252, regression.Coefficients[1], 1e-10);
Assert.AreEqual(1.7678893101571855, regression.Coefficients[2], 1e-8);
bool[] actualOutput = regression.Decide(input);
Assert.IsFalse(actualOutput[0]);
Assert.IsFalse(actualOutput[1]);
Assert.IsTrue(actualOutput[2]);
Assert.IsFalse(actualOutput[3]);
Assert.IsTrue(actualOutput[4]);
Assert.IsTrue(actualOutput[5]);
Assert.IsFalse(actualOutput[6]);
Assert.IsFalse(actualOutput[7]);
Assert.IsFalse(actualOutput[8]);
Assert.IsTrue(actualOutput[9]);
}
public void learn_new_mechanism()
{
#region doc_log_reg_1
// Suppose we have the following data about some patients.
// The first variable is continuous and represent patient
// age. The second variable is dichotomic and give whether
// they smoke or not (This is completely fictional data).
// We also know if they have had lung cancer or not, and
// we would like to know whether smoking has any connection
// with lung cancer (This is completely fictional data).
double[][] input =
{ // age, smokes?, had cancer?
new double[] { 55, 0 }, // false - no cancer
new double[] { 28, 0 }, // false
new double[] { 65, 1 }, // false
new double[] { 46, 0 }, // true - had cancer
new double[] { 86, 1 }, // true
new double[] { 56, 1 }, // true
new double[] { 85, 0 }, // false
new double[] { 33, 0 }, // false
new double[] { 21, 1 }, // false
new double[] { 42, 1 }, // true
};
bool[] output = // Whether each patient had lung cancer or not
{
false, false, false, true, true, true, false, false, false, true
};
// To verify this hypothesis, we are going to create a logistic
// regression model for those two inputs (age and smoking), learned
// using a method called "Iteratively Reweighted Least Squares":
var learner = new IterativeReweightedLeastSquares <LogisticRegression>()
{
Tolerance = 1e-4, // Let's set some convergence parameters
Iterations = 100, // maximum number of iterations to perform
Regularization = 0
};
// Now, we can use the learner to finally estimate our model:
LogisticRegression regression = learner.Learn(input, output);
// At this point, we can compute the odds ratio of our variables.
// In the model, the variable at 0 is always the intercept term,
// with the other following in the sequence. Index 1 is the age
// and index 2 is whether the patient smokes or not.
// For the age variable, we have that individuals with
// higher age have 1.021 greater odds of getting lung
// cancer controlling for cigarette smoking.
double ageOdds = regression.GetOddsRatio(1); // 1.0208597028836701
// For the smoking/non smoking category variable, however, we
// have that individuals who smoke have 5.858 greater odds
// of developing lung cancer compared to those who do not
// smoke, controlling for age (remember, this is completely
// fictional and for demonstration purposes only).
double smokeOdds = regression.GetOddsRatio(2); // 5.8584748789881331
// If we would like to use the model to predict a probability for
// each patient regarding whether they are at risk of cancer or not,
// we can use the Probability function:
double[] scores = regression.Probability(input);
// Finally, if we would like to arrive at a conclusion regarding
// each patient, we can use the Decide method, which will transform
// the probabilities (from 0 to 1) into actual true/false values:
bool[] actual = regression.Decide(input);
#endregion
double[] expected =
{
0.21044171560168326,
0.13242527535212373,
0.65747803433771812,
0.18122484822324372,
0.74755661773156912,
0.61450041841477232,
0.33116705418194975,
0.14474110902457912,
0.43627109657399382,
0.54419383282533118
};
for (int i = 0; i < scores.Length; i++)
{
Assert.AreEqual(expected[i], scores[i], 1e-8);
}
double[] transform = regression.Transform(input, scores);
for (int i = 0; i < scores.Length; i++)
{
Assert.AreEqual(expected[i], transform[i], 1e-8);
}
Assert.AreEqual(1.0208597028836701, ageOdds, 1e-10);
Assert.AreEqual(5.8584748789881331, smokeOdds, 1e-6);
Assert.AreEqual(-2.4577464307294092, regression.Intercept, 1e-8);
Assert.AreEqual(-2.4577464307294092, regression.Coefficients[0], 1e-8);
Assert.AreEqual(0.020645118265359252, regression.Coefficients[1], 1e-10);
Assert.AreEqual(1.7678893101571855, regression.Coefficients[2], 1e-8);
Assert.IsFalse(actual[0]);
Assert.IsFalse(actual[1]);
Assert.IsTrue(actual[2]);
Assert.IsFalse(actual[3]);
Assert.IsTrue(actual[4]);
Assert.IsTrue(actual[5]);
Assert.IsFalse(actual[6]);
Assert.IsFalse(actual[7]);
Assert.IsFalse(actual[8]);
Assert.IsTrue(actual[9]);
}
public void learn_test()
{
#region doc_learn
// The Bag-Of-Words model can be used to extract finite-length feature
// vectors from sequences of arbitrary length, like for example, texts:
string[] texts =
{
@"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Maecenas molestie malesuada
nisi et placerat. Curabitur blandit porttitor suscipit. Nunc facilisis ultrices felis,
vitae luctus arcu semper in. Fusce ut felis ipsum. Sed faucibus tortor ut felis placerat
euismod. Vestibulum pharetra velit et dolor ornare quis malesuada leo aliquam. Aenean
lobortis, tortor iaculis vestibulum dictum, tellus nisi vestibulum libero, ultricies
pretium nisi ante in neque. Integer et massa lectus. Aenean ut sem quam. Mauris at nisl
augue, volutpat tempus nisl. Suspendisse luctus convallis metus, vitae pretium risus
pretium vitae. Duis tristique euismod aliquam",
@"Sed consectetur nisl et diam mattis varius. Aliquam ornare tincidunt arcu eget adipiscing.
Etiam quis augue lectus, vel sollicitudin lorem. Fusce lacinia, leo non porttitor adipiscing,
mauris purus lobortis ipsum, id scelerisque erat neque eget nunc. Suspendisse potenti. Etiam
non urna non libero pulvinar consequat ac vitae turpis. Nam urna eros, laoreet id sagittis eu,
posuere in sapien. Phasellus semper convallis faucibus. Nulla fermentum faucibus tellus in
rutrum. Maecenas quis risus augue, eu gravida massa."
};
string[][] words = texts.Tokenize();
// Create a new BoW with options:
var codebook = new BagOfWords()
{
MaximumOccurance = 1 // the resulting vector will have only 0's and 1's
};
// Compute the codebook (note: this would have to be done only for the training set)
codebook.Learn(words);
// Now, we can use the learned codebook to extract fixed-length
// representations of the different texts (paragraphs) above:
// Extract a feature vector from the text 1:
double[] bow1 = codebook.Transform(words[0]);
// Extract a feature vector from the text 2:
double[] bow2 = codebook.Transform(words[1]);
// we could also have transformed everything at once, i.e.
// double[][] bow = codebook.Transform(words);
// Now, since we have finite length representations (both bow1 and bow2 should
// have the same size), we can pass them to any classifier or machine learning
// method. For example, we can pass them to a Logistic Regression Classifier to
// discern between the first and second paragraphs
// Lets create a Logistic classifier to separate the two paragraphs:
var learner = new IterativeReweightedLeastSquares <LogisticRegression>()
{
Tolerance = 1e-4, // Let's set some convergence parameters
Iterations = 100, // maximum number of iterations to perform
Regularization = 0
};
// Now, we use the learning algorithm to learn the distinction between the two:
LogisticRegression reg = learner.Learn(new[] { bow1, bow2 }, new[] { false, true });
// Finally, we can predict using the classifier:
bool c1 = reg.Decide(bow1); // Should be false
bool c2 = reg.Decide(bow2); // Should be true
#endregion
Assert.AreEqual(bow1.Length, 99);
Assert.AreEqual(bow2.Length, 99);
Assert.AreEqual(bow1.Sum(), 67);
Assert.AreEqual(bow2.Sum(), 63);
Assert.IsFalse(c1);
Assert.IsTrue(c2);
}
static void Main(string[] args)
{
if (args.Length > 3 | args.Length < 1 | args.Length < 3)
{
Console.WriteLine("Requires a previously trained and saved Model File");
Console.WriteLine("Usage <testfile> <label file> <Model File>");
System.Environment.Exit(-1);
}
Console.WriteLine("Logisitic Regression Prediction\n");
string testFname = args[0];
string labelsFname = args[1];
string ModelFname = args[2];
double[,] Rawdata;
double[,] labeldata;
// Read in the test data, validate file existence by attempting to open the files first
try
{
FileStream fs = File.Open(testFname, FileMode.Open, FileAccess.Write, FileShare.None);
fs.Close();
// Reuse fs for validating labels
fs = File.Open(labelsFname, FileMode.Open, FileAccess.Write, FileShare.None);
fs.Close();
fs = File.Open(ModelFname, FileMode.Open, FileAccess.Read, FileShare.None);
fs.Close();
}
catch (Exception e)
{
Console.WriteLine("Error opening file{0}", e);
System.Environment.Exit(-1);
}
using (CsvReader reader = new CsvReader(testFname, hasHeaders: false))
{
Rawdata = reader.ToMatrix();
}
using (CsvReader reader = new CsvReader(labelsFname, hasHeaders: false))
{
labeldata = reader.ToMatrix();
}
// Convert Raw data to Jagged array
double[][] testdata = Rawdata.ToJagged();
int[] output1 = funcs.convetToJaggedArray(labeldata);
int [] answers = new int[labeldata.GetLength(0)];
// For Accord.net Logistic Regression the input data needs to be in Jagged Arrays
// Labels can either be int (1,0) or bools
if (ModelFname.IndexOf("bfgs", StringComparison.OrdinalIgnoreCase) >= 0)
{
// Load a BFGS regression model
try
{
MultinomialLogisticRegression mlr = Serializer.Load <MultinomialLogisticRegression>(ModelFname);
answers = mlr.Decide(testdata);
} catch (Exception e)
{
Console.WriteLine("Error opening model file: {0}", ModelFname);
Console.WriteLine("Exception {0}", e);
System.Environment.Exit(-1);
}
}
else if (ModelFname.IndexOf("pcd", StringComparison.OrdinalIgnoreCase) >= 0)
{
LogisticRegression regression = new LogisticRegression();
try
{
regression = Serializer.Load <LogisticRegression>(ModelFname);
answers = funcs.BoolToInt(regression.Decide(testdata));
}
catch (Exception e)
{
Console.WriteLine("Error opening model file: {0}", ModelFname);
Console.WriteLine("Exception {0}", e);
System.Environment.Exit(-1);
}
}
Console.WriteLine("Successfully loaded model file => {0}", ModelFname);
double subtotal = 0;
int index = 0;
foreach (var result in answers)
{
if (result == output1[index])
{
subtotal = subtotal + 1;
}
index++;
}
double accuracy = subtotal / answers.Count();
Console.WriteLine("Predicted accuracy using model:{0} is, {1}", ModelFname, Math.Round(accuracy * 100, 2));
}
private void Video1_Proccess1()
{
//if (_capture1 != null && _capture1.Ptr != IntPtr.Zero)
//{
int war_at_frame = 0;
bool warning = false;
while (camera_1.frameNum < total_frames1 - 10)
{
//Console.WriteLine(camera_1.frameNum);
if (camera_1.frameNum % 20 == 0)
{
count = 0;
}
abnormal_vote = 0;
normal_vote = 0;
try
{
double[] fe = F_E.extract(vid1, camera_1.frameNum);
if (fe[0] == null || fe[1] == null)
{
fe[0] = 240;
fe[0] = 170;
}
int[] fff = new int[] { (int)fe[0], (int)fe[1] };
//int knn_answer = knn.Decide(fe);
int RF_answer = RF.Decide(fe);
bool LR_answer = LR.Decide(fe);
//bool SVM_answer = SVM.Decide(fe);
int NB_answer = NB.Decide(fff);
double fl1 = HMM.LogLikelihood(fff);
if (chocking || lying)
{
Console.WriteLine(fl1);
if (fl1.CompareTo(-8.3) == 1)
{
hmm_count++;
}
}
else if (violence)
{
if (RF_answer == 1)
{
abnormal_vote += 0.978546619845336;
}
else
{
normal_vote += 0.978546619845336;
}
if (LR_answer)
{
abnormal_vote += 0.8428031393318365;
}
else
{
normal_vote += 0.8428031393318365;
}
if (NB_answer == 1)
{
abnormal_vote += 0.8746569953754341;
}
else
{
normal_vote += 0.8746569953754341;
}
if (abnormal_vote.CompareTo(normal_vote) == 1)
{
count++;
}
}
if (hmm_count >= 2 || count >= 4)
{
if (count >= 4)
{
count = 0;
}
if (hmm_count >= 2)
{
hmm_count = 0;
}
this.pictureBox3.Invoke((MethodInvoker) delegate
{
// Running on the UI thread
pictureBox3.Image = Properties.Resources.warning;
});
if (alarm)
{
wplayer.URL = "D:\\2\\Real-Time Abnormal Event Detection And Tracking In Video\\Alarm.mp3";
wplayer.controls.play();
}
//pictureBox3.Image = Properties.Resources.warning;
warning = true;
war_at_frame = camera_1.frameNum;
Media.Crop_video(vid1, (int)camera_1.frameNum / (fbs + 5), 30);
Media.thumbnail(vid1, (int)camera_1.frameNum / (fbs + 5));
Image image = Image.FromFile(@"D:\2\Real-Time Abnormal Event Detection And Tracking In Video\croped_videos\crop" + Media.num.ToString() + ".jpg");
dataGridView1.Rows.Add(image, @"D:\2\Real-Time Abnormal Event Detection And Tracking In Video\croped_videos\crop" + Media.num.ToString() + ".mpg");
Media.num++;
}
if (warning && camera_1.frameNum >= (war_at_frame + 10))
{
this.pictureBox3.Invoke((MethodInvoker) delegate
{
// Running on the UI thread
pictureBox3.Image = Properties.Resources._checked;
});
//pictureBox3.Image = Properties.Resources._checked;
warning = false;
}
}
catch (Exception e)
{
Console.WriteLine("1--- ", e.Message);
}
}
}
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();
}
