public IActionResult GetLogisticRegressionResult([FromBody] GetLogisticRegressionRequest request)
{
var logisticRegression = new LogisticRegression();
logisticRegression.Regress(request.Inputs, request.Outputs);
return(new OkObjectResult(logisticRegression.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>
/// Fits a logistic regression model to data until convergence.
/// </summary>
private bool fit(LogisticRegression regression, double[][] input, double[] output)
{
double delta;
int iteration = 0;
do // learning iterations until convergence
{
delta = regression.Regress(input, output);
iteration++;
} while (delta > limit && iteration < maxIterations);
// Check if the full model has converged
return(iteration <= maxIterations);
}
static void Main(string[] args)
{
if (args.Length != 2)
{
Console.WriteLine("TestLogisticRegression [train corpus file name] [test corpus file name]");
return;
}
double[][] inputs;
double[] outputs;
Console.WriteLine("Loading train corpus...");
//Load training corpus
LoadCorpusFromFile(args[0], out inputs, out outputs);
//Try to normalize output value
Normalize(outputs);
Console.WriteLine("Logistic regression...");
LogisticRegression lr = new LogisticRegression(inputs[0].Length);
double error = lr.Regress(inputs, outputs);
Console.WriteLine("Parameter list:");
for (int i = 0; i < lr.Coefficients.Length; i++)
{
Console.WriteLine("Coefficient {0}: {1}", i, lr.Coefficients[i]);
}
Console.WriteLine("Delta: {0}", error);
Console.WriteLine();
//Load test corpus
Console.WriteLine("Testing regress result:");
LoadCorpusFromFile(args[1], out inputs, out outputs);
//Try to normalize output value
Normalize(outputs);
for (int i = 0; i < outputs.Length; i++)
{
StringBuilder sb = new StringBuilder();
double output = lr.Compute(inputs[i]);
for (int j = 0; j < inputs[i].Length; j++)
{
sb.Append(inputs[i][j].ToString() + " ");
}
sb.Append(outputs[i] + " RV:" + output);
sb.Append(" Err:" + (output - outputs[i]).ToString());
Console.WriteLine(sb.ToString());
}
}
static void Main(string[] args)
{
if (args.Length != 2)
{
Console.WriteLine("TestLogisticRegression [train corpus file name] [test corpus file name]");
return;
}
double[][] inputs;
double[] outputs;
Console.WriteLine("Loading train corpus...");
//Load training corpus
LoadCorpusFromFile(args[0], out inputs, out outputs);
//Try to normalize output value
Normalize(outputs);
Console.WriteLine("Logistic regression...");
LogisticRegression lr = new LogisticRegression(inputs[0].Length);
double error = lr.Regress(inputs, outputs);
Console.WriteLine("Parameter list:");
for (int i = 0; i < lr.Coefficients.Length; i++)
{
Console.WriteLine("Coefficient {0}: {1}", i, lr.Coefficients[i]);
}
Console.WriteLine("Delta: {0}", error);
Console.WriteLine();
//Load test corpus
Console.WriteLine("Testing regress result:");
LoadCorpusFromFile(args[1], out inputs, out outputs);
//Try to normalize output value
Normalize(outputs);
for (int i = 0; i < outputs.Length; i++)
{
StringBuilder sb = new StringBuilder();
double output = lr.Compute(inputs[i]);
for (int j = 0; j < inputs[i].Length; j++)
{
sb.Append(inputs[i][j].ToString() + " ");
}
sb.Append(outputs[i] + " RV:" + output);
sb.Append(" Err:" + (output - outputs[i]).ToString());
Console.WriteLine(sb.ToString());
}
}
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]);
}
