public void RunTest1()
{
// 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 Stochastic Gradient Descent algorithm.
var teacher = new LogisticGradientDescent(regression)
{
Stochastic = true,
LearningRate = 1e-5,
};
// 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;
int iterations = 1;
do
{
// Perform an iteration
delta = teacher.Run(input, output);
// teacher.LearningRate *= Math.Exp(-iterations / (double)input.Length);
iterations++;
} while (delta > 1e-10);
// 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-4);
Assert.AreEqual(5.8584748789881331, smokeOdds, 0.05);
Assert.IsFalse(Double.IsNaN(ageOdds));
Assert.IsFalse(Double.IsNaN(smokeOdds));
}
private static double[] finiteDifferences(double[][] input, double[] output, bool stochastic)
{
LogisticRegression regression;
LogisticGradientDescent teacher;
regression = new LogisticRegression(inputs: 2);
teacher = new LogisticGradientDescent(regression)
{
Stochastic = stochastic,
LearningRate = 1e-4,
};
FiniteDifferences diff = new FiniteDifferences(3);
diff.Function = (x) =>
{
for (int i = 0; i < x.Length; i++)
regression.Coefficients[i] = x[i];
return regression.GetLogLikelihood(input, output);
};
return diff.Compute(regression.Coefficients);
}
private static double[] gradient(double[][] input, double[] output, bool stochastic)
{
LogisticRegression regression;
LogisticGradientDescent teacher;
regression = new LogisticRegression(inputs: 2);
teacher = new LogisticGradientDescent(regression)
{
Stochastic = stochastic,
LearningRate = 1e-4,
};
teacher.Run(input, output);
return teacher.Gradient;
}
public void run_batch_mode_new_method()
{
// 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) using
// gradient descent.
var teacher = new LogisticGradientDescent()
{
Stochastic = false,
LearningRate = 1e-4,
Iterations = 0,
Tolerance = 1e-10
};
// Now, we can proceed and estimate our model.
var regression = teacher.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
Assert.AreEqual(1.0208597028836701, ageOdds, 1e-3);
Assert.AreEqual(5.8584748789881331, smokeOdds, 1e-3);
}
