public static void TestLogisticRegression()
{
const int seed = 39;
Rand.Restart(seed);
const int d = 10;
const int n = 100;
const int epIter = 10;
Vector w = Vector.Zero(d);
Rand.Normal(Vector.Zero(d), PositiveDefiniteMatrix.Identity(d), w);
double b = Rand.Normal(0, 1);
// double b= 0;
Vector[] X;
bool[] Y;
GenData(n, w, b, out X, out Y);
Console.Write("Y: ");
StringUtils.PrintArray(Y);
VectorGaussian wPost;
Gaussian biasPost;
Type logisticOp = typeof(KEPLogisticOp);
// Type logisticOp = typeof(LogisticOp2);
string factorOpPath = Config.PathToFactorOperator(
// "serialFactorOp_fm_kgg_joint_irf500_orf1000_n400_iter5_sf1_st20_ntr5000.mat"
"serialFactorOp_fm_kgg_joint_irf500_orf1000_proj_n400_iter5_sf1_st20_ntr5000.mat"
);
KEPLogisticOpInstance opIns = KEPLogisticOpInstance.LoadLogisticOpInstance(factorOpPath);
opIns.SetPrintTrueMessages(true);
OpControl.Add(typeof(KEPLogisticOp), opIns);
InferCoefficients(X, Y, out wPost, out biasPost, epIter, logisticOp);
//print
Console.WriteLine("n: {0}", n);
Console.WriteLine("d: {0}", d);
int t = Y.Sum(o => o ? 1 : 0);
Console.WriteLine("number of true: {0}", t);
Console.WriteLine("True bias: {0}", b);
Console.WriteLine("Inferred bias: {0}", biasPost);
Console.WriteLine("True w: {0}", w);
Console.WriteLine("Inferred w: ");
Console.WriteLine(wPost);
}
public static void TestLogisticRegressionNoBias()
{
const int seed = 2;
Rand.Restart(seed);
const int d = 10;
const int n = 300;
const int epIter = 10;
Vector w = Vector.Zero(d);
Rand.Normal(Vector.Zero(d), PositiveDefiniteMatrix.Identity(d), w);
double b = 0;
// combine the bias term into W
Vector[] X;
bool[] Y;
GenData(n, w, b, out X, out Y);
Console.Write("Y: ");
StringUtils.PrintArray(Y);
VectorGaussian wPost;
// string factorOpPath = Config.PathToFactorOperator(
// // "serialFactorOp_fm_kgg_joint_irf500_orf1000_n400_iter5_sf1_st20_ntr5000.mat"
// "serialFactorOp_fm_kgg_joint_irf500_orf1000_proj_n400_iter5_sf1_st20_ntr5000.mat"
// );
// KEPLogisticOpInstance opIns = KEPLogisticOpInstance.LoadLogisticOpInstance(factorOpPath);
// opIns.SetPrintTrueMessages(true);
// OpControl.Add(typeof(KEPLogisticOp), opIns);
// Type logisticOp = typeof(KEPLogisticOp);
LogisticOpRecords records = new LogisticOpRecords();
OpControl.Add(typeof(KEPOnlineLogisticOp), new KEPOnlineISLogisticOpIns(records));
Type logisticOp = typeof(KEPOnlineLogisticOp);
// Type logisticOp = typeof(LogisticOp2);
InferCoefficientsNoBias(X, Y, out wPost, epIter, logisticOp);
//print
Console.WriteLine("n: {0}", n);
Console.WriteLine("d: {0}", d);
int t = Y.Sum(o => o ? 1 : 0);
Console.WriteLine("number of true: {0}", t);
Console.WriteLine("True bias: {0}", b);
// Vector meanW = wPost.GetMean();
Console.WriteLine("True w: {0}", w);
Console.WriteLine("Inferred w: ");
Console.WriteLine(wPost);
// write the records to a file
string fname = string.Format("rec_onlinekep_is_logistic_iter{0}_n{1}.mat",
epIter, n);
string recordPath = Config.PathToSavedFile(fname);
var extra = new Dictionary <string, object>();
// MatlabWriter cannot write int
extra.Add("d", (double)d);
extra.Add("n", (double)n);
extra.Add("epIter", (double)epIter);
extra.Add("trueW", w);
extra.Add("X", MatrixUtils.StackColumns(X));
extra.Add("Y", MatrixUtils.ToDouble(Y));
records.WriteRecords(recordPath, extra);
}
