WRG: multi-line comment

Examples/ChangeDetection/KullbackLeiblerDistanceChDet.cxx

@@ -36,28 +36,28 @@
 //        + \frac{1}{2} \left( \log \frac{\kappa_{X_2; 2}}{\kappa_{X_1; 2}}
 //                             -1+\frac{1}{\kappa_{X_2; 2}}
 //                             \left( \kappa_{X_1; 1} - \kappa_{X_2; 1} +  \kappa_{X_1; 2}^{1/2} \right)^2
-//                        \right) \\
+//                        \right)
 //        - \left( \kappa_{X_2; 3} \frac{a_1}{6} + \kappa_{X_2; 4} \frac{a_2}{24}
 //            + \kappa_{X_2; 3}^2 \frac{a_3}{72} \right)
 //        - \frac{1}{2} \frac{ \kappa_{X_2; 3}^2}{36}
 //            \left(
 //                c_6 - 6 \frac{c_4}{\kappa_{X_1; 2}} + 9 \frac{c_2}{\kappa_{X_2; 2}^2}
-//            \right) \\
+//            \right)
 //        - 10 \frac{\kappa_{X_1; 3} \kappa_{X_2; 3}
 //                        \left( \kappa_{X_1; 1} - \kappa_{X_2; 1} \right)
 //                        \left( \kappa_{X_1; 2} - \kappa_{X_2; 2} \right)}{\kappa_{X_2; 2}^6} \qquad
 // \end{multline}
 // where
 // \begin{align*}
-//    a_1 &= c_3 - 3 \frac{\alpha}{\kappa_{X_2; 2}} \\
-//    a_2 &= c_4 - 6 \frac{c_2}{\kappa_{X_2; 2}} + \frac{3}{\kappa_{X_2; 2}^2} \\
+//    a_1 &= c_3 - 3 \frac{\alpha}{\kappa_{X_2; 2}}
+//    a_2 &= c_4 - 6 \frac{c_2}{\kappa_{X_2; 2}} + \frac{3}{\kappa_{X_2; 2}^2}
 //    a_3 &= c_6 - 15\frac{c_4}{\kappa_{X_2; 2}} + 45\frac{c_2}{\kappa_{X_2; 2}^2} -
-//            \frac{15}{\kappa_{X_2; 2}^3} \\
-//    c_2 &= \alpha^2 + \beta^2 \\
-//    c_3 &= \alpha^3 + 3 \alpha \beta^2 \\
-//    c_4 &= \alpha^4 + 6 \alpha^2 \beta^2 + 3 \beta^4 \\
-//    c_6 &= \alpha^6 + 15\alpha^4 \beta^2 + 45 \alpha^2 \beta^4 + 15 \beta^6 \\
-//    \alpha &= \frac{\kappa_{X_1; 1} - \kappa_{X_2; 1}}{\kappa_{X_2; 2}} \\
+//            \frac{15}{\kappa_{X_2; 2}^3}
+//    c_2 &= \alpha^2 + \beta^2
+//    c_3 &= \alpha^3 + 3 \alpha \beta^2
+//    c_4 &= \alpha^4 + 6 \alpha^2 \beta^2 + 3 \beta^4
+//    c_6 &= \alpha^6 + 15\alpha^4 \beta^2 + 45 \alpha^2 \beta^4 + 15 \beta^6
+//    \alpha &= \frac{\kappa_{X_1; 1} - \kappa_{X_2; 1}}{\kappa_{X_2; 2}}
 //    \beta &= \frac{ \kappa_{X_1; 2}^{1/2} }{\kappa_{X_2; 2}}.
 // \end{align*}
 // $\kappa_{X_i; 1}$, $\kappa_{X_i; 2}$, $\kappa_{X_i; 3}$ and $\kappa_{X_i; 4}$