/// <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));
}
public void ComputeTest3()
{
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[] { 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
};
double[] output =
{
0, 0, 0, 1,
1, 1, 1, 0,
0, 0, 1
};
double[] weights =
{
1.0, 1.0, 1.0, 1.0,
0.5, 0.5, 1.0, 1.0,
1.0, 1.0, 1.0
};
LogisticRegression regression = new LogisticRegression(inputs: 2);
var teacher = new IterativeReweightedLeastSquares(regression);
teacher.Regularization = 0;
double delta = 0;
do
{
delta = teacher.Run(input, output, weights);
} while (delta > 0.001);
double ageOdds = regression.GetOddsRatio(1);
double smokeOdds = regression.GetOddsRatio(2);
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-8);
Assert.AreEqual(1.7678893101571855, regression.Coefficients[2], 1e-8);
}
public void ComputeTest3()
{
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[] { 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
};
double[] output =
{
0, 0, 0, 1,
1, 1, 1, 0,
0, 0, 1
};
double[] weights =
{
1.0, 1.0, 1.0, 1.0,
0.5, 0.5, 1.0, 1.0,
1.0, 1.0, 1.0
};
LogisticRegression regression = new LogisticRegression(inputs: 2);
var teacher = new IterativeReweightedLeastSquares(regression);
double delta = 0;
do
{
delta = teacher.Run(input, output, weights);
} while (delta > 0.001);
double ageOdds = regression.GetOddsRatio(1);
double smokeOdds = regression.GetOddsRatio(2);
Assert.AreEqual(1.0208597028836701, ageOdds, 1e-10);
Assert.AreEqual(5.8584748789881331, smokeOdds, 1e-10);
Assert.IsFalse(double.IsNaN(ageOdds));
Assert.IsFalse(double.IsNaN(smokeOdds));
}
public void BuildLR(List <train> datalist)
{
double[][] input;
int[] output;
GetData(out input, out output, datalist);
// 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:
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:
log("The LogisticRegression model has been trained");
}
private void computeInformation(double[][] inputData, double[] outputData, double[] weights)
{
// Store model information
#pragma warning disable 612, 618
result = regression.Compute(inputData);
#pragma warning restore 612, 618
if (weights == null)
{
this.deviance = regression.GetDeviance(inputData, outputData);
this.logLikelihood = regression.GetLogLikelihood(inputData, outputData);
this.chiSquare = regression.ChiSquare(inputData, outputData);
}
else
{
this.deviance = regression.GetDeviance(inputData, outputData, weights);
this.logLikelihood = regression.GetLogLikelihood(inputData, outputData, weights);
this.chiSquare = regression.ChiSquare(inputData, outputData, weights);
}
// Store coefficient information
for (int i = 0; i < regression.Coefficients.Length; i++)
{
this.standardErrors[i] = regression.StandardErrors[i];
this.waldTests[i] = regression.GetWaldTest(i);
this.coefficients[i] = regression.Coefficients[i];
this.confidences[i] = regression.GetConfidenceInterval(i);
this.oddsRatios[i] = regression.GetOddsRatio(i);
}
}
public static DTOs.Responses.LogisticRegressionResult ToDto(this LogisticRegression logisticRegression)
{
return(new DTOs.Responses.LogisticRegressionResult
{
OddsRatio = logisticRegression.GetOddsRatio(),
StandardErrors = logisticRegression.GetStandardErrors(),
Regression = logisticRegression.LinearRegressionResult.ToDto()
});
}
/// <summary>
/// Computes the Logistic Regression Analysis.
/// </summary>
/// <remarks>The likelihood surface for the
/// logistic regression learning is convex, so there will be only one
/// peak. Any local maxima will be also a global maxima.
/// </remarks>
/// <param name="limit">
/// The difference between two iterations of the regression algorithm
/// when the algorithm should stop. If not specified, the value of
/// 10e-4 will be used. The difference is calculated based on the largest
/// absolute parameter change of the regression.
/// </param>
/// <param name="maxIterations">
/// The maximum number of iterations to be performed by the regression
/// algorithm.
/// </param>
/// <returns>
/// True if the model converged, false otherwise.
/// </returns>
///
public bool Compute(double limit, int maxIterations)
{
double delta;
int iteration = 0;
do // learning iterations until convergence
{
delta = regression.Regress(inputData, outputData);
iteration++;
} while (delta > limit && iteration < maxIterations);
// Check if the full model has converged
bool converged = iteration <= maxIterations;
// Store model information
this.result = regression.Compute(inputData);
this.deviance = regression.GetDeviance(inputData, outputData);
this.logLikelihood = regression.GetLogLikelihood(inputData, outputData);
this.chiSquare = regression.ChiSquare(inputData, outputData);
// Store coefficient information
for (int i = 0; i < regression.Coefficients.Length; i++)
{
this.waldTests[i] = regression.GetWaldTest(i);
this.standardErrors[i] = regression.GetStandardError(i);
this.coefficients[i] = regression.Coefficients[i];
this.confidences[i] = regression.GetConfidenceInterval(i);
this.oddsRatios[i] = regression.GetOddsRatio(i);
}
// Perform likelihood-ratio tests against diminished nested models
for (int i = 0; i < inputCount; i++)
{
// Create a diminished inner model without the current variable
double[][] data = inputData.RemoveColumn(i);
LogisticRegression inner = new LogisticRegression(inputCount - 1);
iteration = 0;
do // learning iterations until convergence
{
delta = inner.Regress(data, outputData);
iteration++;
} while (delta > limit && iteration < maxIterations);
double ratio = 2.0 * (logLikelihood - inner.GetLogLikelihood(data, outputData));
ratioTests[i + 1] = new ChiSquareTest(ratio, 1);
}
// Returns true if the full model has converged, false otherwise.
return(converged);
}
/// <summary>
/// Constructs a new Logistic regression model.
/// </summary>
///
internal StepwiseLogisticRegressionModel(StepwiseLogisticRegressionAnalysis analysis, LogisticRegression regression,
int[] variables, ChiSquareTest chiSquare, ChiSquareTest[] tests)
{
this.Analysis = analysis;
this.Regression = regression;
int coefficientCount = regression.NumberOfInputs + 1;
this.Inputs = analysis.Inputs.Get(variables);
this.ChiSquare = chiSquare;
this.LikelihoodRatioTests = tests;
this.Variables = variables;
this.StandardErrors = new double[coefficientCount];
this.WaldTests = new WaldTest[coefficientCount];
this.CoefficientValues = new double[coefficientCount];
this.Confidences = new DoubleRange[coefficientCount];
this.OddsRatios = new double[coefficientCount];
// Store coefficient information
for (int i = 0; i < regression.NumberOfInputs + 1; i++)
{
this.StandardErrors[i] = regression.StandardErrors[i];
this.WaldTests[i] = regression.GetWaldTest(i);
this.CoefficientValues[i] = regression.GetCoefficient(i);
this.Confidences[i] = regression.GetConfidenceInterval(i);
this.OddsRatios[i] = regression.GetOddsRatio(i);
}
StringBuilder sb = new StringBuilder();
for (int i = 0; i < Inputs.Length; i++)
{
sb.Append(Inputs[i]);
if (i < Inputs.Length - 1)
{
sb.Append(", ");
}
}
this.Names = sb.ToString();
var logCoefs = new List <NestedLogisticCoefficient>(coefficientCount);
for (int i = 0; i < coefficientCount; i++)
{
logCoefs.Add(new NestedLogisticCoefficient(this, i));
}
this.Coefficients = new NestedLogisticCoefficientCollection(logCoefs);
}
public LogisticRegression GetLogisticRegressionParams(int index, double percentage)
{
LogisticInfo LI = new LogisticInfo();
LI.LogisticParams = new double[2];
int length = MD.Length;
int samplesize = (int)(length * percentage);
double [][] inputArr = new double[samplesize * 2][];
for (int i = 0; i < samplesize * 2; i++)
{
inputArr[i] = new double[2];
}
double[] outputArr = new double[samplesize * 2];
for (int i = 0; i < samplesize; i++)
{
inputArr[i][0] = MD[i].GetParameters()[index];
inputArr[i][1] = 0;
outputArr[i] = 0;
}
int j = length - samplesize;
for (int i = samplesize; i < samplesize * 2; i++)
{
inputArr[i][0] = MD[j].GetParameters()[index];
inputArr[i][1] = 0;
outputArr[i] = 1;
j++;
}
LogisticRegression LR = new LogisticRegression();
LR.NumberOfInputs = 1;
var learner = new IterativeReweightedLeastSquares <LogisticRegression>()
{
Tolerance = 1e-4, // Let's set some convergence parameters
Iterations = 100, // maximum number of iterations to perform
Regularization = 0
};
LogisticRegression regression = learner.Learn(inputArr, outputArr);
LI.LogisticParams[0] = -(regression.Intercept + 1);
LI.LogisticParams[1] = regression.GetOddsRatio(1) - 1;
return(regression);
}
private void computeInformation()
{
// Store model information
this.result = regression.Compute(inputData);
this.deviance = regression.GetDeviance(inputData, outputData);
this.logLikelihood = regression.GetLogLikelihood(inputData, outputData);
this.chiSquare = regression.ChiSquare(inputData, outputData);
// Store coefficient information
for (int i = 0; i < regression.Coefficients.Length; i++)
{
this.standardErrors[i] = regression.StandardErrors[i];
this.waldTests[i] = regression.GetWaldTest(i);
this.coefficients[i] = regression.Coefficients[i];
this.confidences[i] = regression.GetConfidenceInterval(i);
this.oddsRatios[i] = regression.GetOddsRatio(i);
}
}
public void When_Compute_Logistic_Regression()
{
double[][] inputs =
{
new double[] { 55, 0 },
new double[] { 28, 0 },
new double[] { 65, 1 },
new double[] { 46, 0 },
new double[] { 86, 1 },
new double[] { 56, 1 },
new double[] { 85, 0 },
new double[] { 33, 0 },
new double[] { 21, 1 },
new double[] { 42, 1 }
};
double[] outputs =
{
0, 0, 0, 1, 1, 1, 0, 0, 0, 1
};
var logisticRegression = new LogisticRegression();
logisticRegression.Regress(inputs, outputs);
var result = logisticRegression.Compute(new double[] { 87, 1 });
var oddsRatio = logisticRegression.GetOddsRatio();
var standardErrors = logisticRegression.GetStandardErrors();
Assert.Equal(0.75143272858390264, result);
Assert.Equal(0.085627701183141239, oddsRatio[0]);
Assert.Equal(1.0208597029292656, oddsRatio[1]);
Assert.Equal(5.8584748981778869, oddsRatio[2]);
Assert.Equal(2.1590686019476122, standardErrors[0]);
Assert.Equal(0.0337904223210436, standardErrors[1]);
Assert.Equal(1.4729903935788495, standardErrors[2]);
}
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);
// 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
Assert.AreEqual(1.0208597028836701, ageOdds, 1e-10);
Assert.AreEqual(5.8584748789881331, smokeOdds, 1e-10);
Assert.IsFalse(double.IsNaN(ageOdds));
Assert.IsFalse(double.IsNaN(smokeOdds));
}
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]);
}
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();
}
/// <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);
}
public static void Execute()
{
double[][] input =
{
new double[] { 55, 0 },
new double[] { 28, 0 },
new double[] { 65, 0 },
new double[] { 46, 0 },
new double[] { 86, 0 },
new double[] { 56, 0 },
new double[] { 85, 0 },
new double[] { 33, 0 },
new double[] { 21, 0 },
new double[] { 42, 0 },
};
double[] output =
{
0, 0, 0, 1, 1, 1, 0, 0, 0, 1
};
LogisticRegression regression = new LogisticRegression(2);
var trainer = new IterativeReweightedLeastSquares(regression);
double delta = 0;
do
{
// Perform an iteration
delta = trainer.Run(input, output);
} while (delta > 0.001);
var b1 = regression.Coefficients[1];
var b2 = regression.Coefficients[2];
var b0 = regression.Intercept;
System.Console.WriteLine(b0);
System.Console.WriteLine(b1);
System.Console.WriteLine(b2);
var func = new Func <double, double, double>((x1, x2) =>
{
var result = 1 / (1 + Math.Exp(-b0 - b1 * x1 - b2 * x2));
return(result);
});
var age = 79;
var smoking = 0;
var r = func(age, smoking);
System.Console.WriteLine("input x [age:{0}, smoking:{1}] is {2}", age, smoking, r);
LogisticRegression LR = new LogisticRegression();
LR.NumberOfInputs = 1;
var learner = new IterativeReweightedLeastSquares <LogisticRegression>()
{
Tolerance = 1e-4, // Let's set some convergence parameters
Iterations = 100, // maximum number of iterations to perform
Regularization = 0
};
LR = learner.Learn(input, output);
System.Console.WriteLine(LR.Intercept);
System.Console.WriteLine(LR.GetOddsRatio(1) - 1);
System.Console.WriteLine(LR.GetOddsRatio(2) - 1);
double [] test = new double[] { 79, 0 };
System.Console.WriteLine(LR.Probability(test));
}
