Port "- add max Newton iter warning in newton.cpp"

Ported from cjlin1/liblinear@7b0193b

Author: bwaldvogel

src/main/java/de/bwaldvogel/liblinear/Newton.java

@@ -89,6 +89,9 @@ void newton(double[] w) {
 
             iter++;
         }
+
+        if (iter >= max_iter)
+            info("%nWARNING: reaching max number of Newton iterations%n");
     }
 
     private int pcg(double[] g, double[] M, double[] s, double[] r) {

src/main/java/de/bwaldvogel/liblinear/Parameter.java

@@ -7,7 +7,7 @@ public final class Parameter implements Cloneable {
 
     double C;
 
-    /** stopping criteria */
+    /** stopping tolerance */
     double eps;
 
     int max_iters = 1000; // maximal iterations

src/main/java/de/bwaldvogel/liblinear/Train.java

@@ -97,37 +97,41 @@ private void exit_with_help() {
             + "options:%n"
             + "-s type : set type of solver (default 1)%n"
             + "  for multi-class classification%n"
-            + "    0 -- L2-regularized logistic regression (primal)%n"
-            + "    1 -- L2-regularized L2-loss support vector classification (dual)%n"
-            + "    2 -- L2-regularized L2-loss support vector classification (primal)%n"
-            + "    3 -- L2-regularized L1-loss support vector classification (dual)%n"
-            + "    4 -- support vector classification by Crammer and Singer%n"
-            + "    5 -- L1-regularized L2-loss support vector classification%n"
-            + "    6 -- L1-regularized logistic regression%n"
-            + "    7 -- L2-regularized logistic regression (dual)%n"
+            + "        0 -- L2-regularized logistic regression (primal)%n"
+            + "        1 -- L2-regularized L2-loss support vector classification (dual)%n"
+            + "        2 -- L2-regularized L2-loss support vector classification (primal)%n"
+            + "        3 -- L2-regularized L1-loss support vector classification (dual)%n"
+            + "        4 -- support vector classification by Crammer and Singer%n"
+            + "        5 -- L1-regularized L2-loss support vector classification%n"
+            + "        6 -- L1-regularized logistic regression%n"
+            + "        7 -- L2-regularized logistic regression (dual)%n"
             + "  for regression%n"
-            + "   11 -- L2-regularized L2-loss support vector regression (primal)%n"
-            + "   12 -- L2-regularized L2-loss support vector regression (dual)%n"
-            + "   13 -- L2-regularized L1-loss support vector regression (dual)%n"
+            + "       11 -- L2-regularized L2-loss support vector regression (primal)%n"
+            + "       12 -- L2-regularized L2-loss support vector regression (dual)%n"
+            + "       13 -- L2-regularized L1-loss support vector regression (dual)%n"
+            + "  for outlier detection%n"
+            + "       21 -- one-class support vector machine (dual)%n"
             + "-c cost : set the parameter C (default 1)%n"
             + "-p epsilon : set the epsilon in loss function of SVR (default 0.1)%n"
             + "-n nu : set the parameter nu of one-class SVM (default 0.5)%n"
             + "-e epsilon : set tolerance of termination criterion%n"
-            + "   -s 0 and 2%n" + "       |f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,%n"
-            + "       where f is the primal function and pos/neg are # of%n"
-            + "       positive/negative data (default 0.01)%n" + "   -s 11%n"
-            + "       |f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.0001)%n"
-            + "   -s 1, 3, 4, 7, and 21%n"
-            + "       Dual maximal violation <= eps; similar to libsvm (default 0.1 except 0.01 for -s 21)%n"
-            + "   -s 5 and 6%n"
-            + "       |f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,%n"
-            + "       where f is the primal function (default 0.01)%n"
-            + "   -s 12 and 13%n"
-            + "       |f'(alpha)|_1 <= eps |f'(alpha0)|,%n"
-            + "       where f is the dual function (default 0.1)%n"
+            + "       -s 0 and 2%n"
+            + "               |f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,%n"
+            + "               where f is the primal function and pos/neg are # of%n"
+            + "               positive/negative data (default 0.01)%n"
+            + "       -s 11%n"
+            + "               |f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.0001)%n"
+            + "       -s 1, 3, 4, 7, and 21%n"
+            + "               Dual maximal violation <= eps; similar to libsvm (default 0.1 except 0.01 for -s 21)%n"
+            + "      -s 5 and 6%n"
+            + "               |f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,%n"
+            + "               where f is the primal function (default 0.01)%n"
+            + "       -s 12 and 13%n"
+            + "               |f'(alpha)|_1 <= eps |f'(alpha0)|,%n"
+            + "               where f is the dual function (default 0.1)%n"
             + "-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)%n"
             + "-R : not regularize the bias; must with -B 1 to have the bias; DON'T use this unless you know what it is%n"
-            + "    (for -s 0, 2, 5, 6, 11)%n"
+            + "       (for -s 0, 2, 5, 6, 11)%n"
             + "-wi weight: weights adjust the parameter C of different classes (see README for details)%n"
             + "-v n: n-fold cross validation mode%n"
             + "-C : find parameters (C for -s 0, 2 and C, p for -s 11)%n"