Git commit d89494ca4facdd88989e2e1b43ac15e2d3ecfbec by Luca Tringali.
Committed on 20/06/2013 at 12:03.
Pushed by lucatringali into branch 'master'.

Errors in documentation fixed.

M  +6    -6    doc/index.docbook
M  +1    -1    doc/man-kartesio.1.docbook
M  +1    -1    src/main.cpp

http://commits.kde.org/kartesio/d89494ca4facdd88989e2e1b43ac15e2d3ecfbec

diff --git a/doc/index.docbook b/doc/index.docbook
index 3c8ee5c..211ab0c 100644
--- a/doc/index.docbook
+++ b/doc/index.docbook
@@ -66,7 +66,7 @@
         <chapter id="quick-start">
                 <title>Kartesio quick start guide</title>
 
-                <para>As soon as you open Kartesio, you will get a blank table 
and a blank plot. This is also the same screen you can get in every moment just 
clicking on (<menuchoice> <guimenu>File</guimenu> 
<guimenuitem>New</guimenuitem> </menuchoice>). You can try to best fit your 
experimental points with a regression algorithm or a neural network, using the 
tools in the appropriate tab. Please note that regression algorithm needs 
<application>maxima</application> to be installed on your computer.</para>
+                <para>As soon as you open Kartesio, you will get a blank table 
and a blank plot. This is also the same screen you can get in every moment just 
clicking on (<menuchoice> <guimenu>File</guimenu> 
<guimenuitem>New</guimenuitem> </menuchoice>). You can try to best fit your 
experimental points with a regression algorithm or a neural network, using the 
tools in the appropriate tab. Please note that regression algorithm needs 
<application>maxima</application> to be installed on your computer, while the 
neural network method needs ZorbaNeural.</para>
                 <screenshot>
                         <screeninfo>Kartesio main window</screeninfo>
                         <mediaobject>
@@ -148,7 +148,7 @@
                         </mediaobject>
                 </screenshot>
 
-                <para>Usually, back propagation training is just what you 
need. For this reason it is checked by default. Just modify the number of 
iterations (it should not be too high, or the process may end up with way too 
strange value) and then press the <guibutton>Calculate</guibutton> button. 
Please take note that the neural network, exactly as a human brain, may give 
you different results: just press the <guibutton>Calculate</guibutton> button 
more than once and you will find out that the network calculates every time a 
different best fitting curve.</para>
+                <para>Usually, back propagation training is just what you 
need. For this reason it is checked by default. Just modify the number of 
iterations (it should not be too high, or the process may end up with way too 
strange value) and then press the <guibutton>Calculate</guibutton> button. 
Please take note that the neural network, exactly as a human brain, may give 
you different results: if you press the <guibutton>Calculate</guibutton> button 
more than once and you will find out that the network calculates every time a 
different best fitting curve.</para>
 
                 <screenshot>
                         <screeninfo>Back propagation training</screeninfo>
@@ -174,7 +174,7 @@
                 <title>Other useful things</title>
                 
                 <para>
-                        Sometimes it is useful to redraw the plot. For 
example, it is if you manually changed the best fitting curve or if you edited 
some points and you don't want to recalculate the fitting function. Just use 
the <guibutton>Draw Plot</guibutton> button.
+                        Sometimes it is useful to redraw the plot. For 
example, it is if you have manually changed the best fitting curve or if you 
edited some points and you don't want to recalculate the fitting function. Just 
use the <guibutton>Draw Plot</guibutton> button.
                 </para>
 
                 <screenshot>
@@ -186,7 +186,7 @@
                 </screenshot>
                 
                 <para>
-                        To know how much the fitting curve is different from 
you experimental points, you can look at the root mean square error. To add it 
to the plot it is needed to check the checkbox <guilabel>Show RMS 
error</guilabel>. Then press the <guibutton>Draw Plot</guibutton> button to 
redraw the plot: it should contain a red label with the RMS error.
+                        To know how much the fitting curve is different from 
your experimental points, you can look at the root mean square error. To add it 
to the plot, it is needed to check the checkbox <guilabel>Show RMS 
error</guilabel>. Then press the <guibutton>Draw Plot</guibutton> button to 
redraw the plot: it should contain a red label with the RMS error.
                 </para>
 
                 <screenshot>
@@ -266,8 +266,8 @@
 
                         <para>
                                 Kartesio itself can be found on <ulink
-                                        
url="https://projects.kde.org/projects/playground/edu/kartesio";>The Kartesio 
home page</ulink> and
-                                is part of the &kde;-Edu project</para>
+                                  
url="http://www.zorbaproject.org/kartesio";>The Kartesio home page</ulink> and
+                                is part of the KDE-Edu project</para>
 
                 </sect1>
 
diff --git a/doc/man-kartesio.1.docbook b/doc/man-kartesio.1.docbook
index c4e84a5..bb8d894 100644
--- a/doc/man-kartesio.1.docbook
+++ b/doc/man-kartesio.1.docbook
@@ -62,7 +62,7 @@ url="help:/kartesio">help:/kartesio</ulink> (either enter this
 <refsect1>
 <title>Authors</title>
 
-<para>Kartesio was written by Luca Tringali - TRINGALINVENT at libero.it 
</para>
+<para>Kartesio has been written by Luca Tringali - TRINGALINVENT at libero.it 
</para>
 
 <para>This manual page was prepared by 
<personname><firstname>Luca</firstname><surname>Tringali</surname></personname></para>
 
diff --git a/src/main.cpp b/src/main.cpp
index e756dbb..a89b89a 100644
--- a/src/main.cpp
+++ b/src/main.cpp
@@ -44,7 +44,7 @@ static const char version[] = APP_VERSION;
 int main(int argc, char *argv[])
 {
   
-  KAboutData about("kartesio", 0, ki18n("Kartesio"), version, 
ki18n(description), KAboutData::License_GPL, ki18n("(C) 2011-2013 Luca 
Tringali"), KLocalizedString(), 
"https://projects.kde.org/projects/playground/edu/kartesio";);
+  KAboutData about("kartesio", 0, ki18n("Kartesio"), version, 
ki18n(description), KAboutData::License_GPL, ki18n("(C) 2011-2013 Luca 
Tringali"), KLocalizedString(), "www.zorbaproject.org/kartesio");
   about.addAuthor( ki18n("Luca Tringali"), KLocalizedString(), "TRINGALINVENT 
at libero.it" );
   //about.addCredit(ki18n("your name here"),ki18n("What you have done"));
   

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