#50-Fix erroneous documentation

Completed. Validated.

Author: schmouk

PyRandLib/fastrand32.py

@@ -59,18 +59,13 @@ class FastRand32( BaseLCG ):
       
     Furthermore this class is callable:
       rand = FastRand32()
-      print( rand() )    # prints a pseudo-random value within [0.0, 1.0)
-      print( rand(a) )   # prints a pseudo-random value within [0.0, a)
-      print( rand(a,b) ) # prints a pseudo-random value within [a  , b)
+      print( rand() )     # prints a pseudo-random value within [0.0, 1.0)
+      print( rand(a) )    # prints a pseudo-random value within [0, a) or [0.0, a) depending on the type of a
+      print( rand(a, n) ) # prints a list of n pseudo-random values each within [0, a)
 
     Notice that for simulating the roll of a dice you should program:
       diceRoll = FastRand32()
-      print( int(diceRoll(1, 7)) ) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
-
-    Such a programming is an accelerated while still robust emulation of  the 
-    inherited methods:
-      - random.Random.randint(self,1,6) and 
-      - random.Random.randrange(self,1,7,1)
+      print( int(diceRoll.randint(1, 6)) ) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
 
     Reminder:
     We give you here below a copy of the table of tests for the LCGs that have 

PyRandLib/fastrand63.py

@@ -59,13 +59,13 @@ class FastRand63( BaseLCG ):
       
     Furthermore this class is callable:
       rand = FastRand63()
-      print( rand() )    # prints a pseudo-random value within [0.0, 1.0)
-      print( rand(a) )   # prints a pseudo-random value within [0.0, a)
-      print( rand(a,b) ) # prints a pseudo-random value within [a  , b)
+      print( rand() )     # prints a pseudo-random value within [0.0, 1.0)
+      print( rand(a) )    # prints a pseudo-random value within [0, a) or [0.0, a) depending on the type of a
+      print( rand(a, n) ) # prints a list of n pseudo-random values each within [0, a)
 
     Notice that for simulating the roll of a dice you should program:
       diceRoll = FastRand63()
-      print( int(diceRoll(1, 7)) ) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
+      print( int(diceRoll.randint(1, 6)) ) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
 
     Such a programming is an accelerated while still robust emulation of  the 
     inherited methods:

PyRandLib/lfib116.py

@@ -73,14 +73,13 @@ class LFib116( BaseLFib64 ):
     Example:
     
       rand = LFib116()
-      print( rand() )    # prints a pseudo-random value within [0.0, 1.0)
-      print( rand(a) )   # prints a pseudo-random value within [0.0, a)
-      print( rand(a,b) ) # prints a pseudo-random value within [a  , b)
+      print( rand() )     # prints a pseudo-random value within [0.0, 1.0)
+      print( rand(a) )    # prints a pseudo-random value within [0, a) or [0.0, a) depending on the type of a
+      print( rand(a, n) ) # prints a list of n pseudo-random values each within [0, a)
 
     Notice that for simulating the roll of a dice you should program:
-
       diceRoll = LFib116()
-      print(int(diceRoll(1, 7))) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
+      print( int(diceRoll.randint(1, 6)) ) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
 
     Such a programming is an accelerated while still robust emulation of  the 
     inherited methods:

PyRandLib/lfib1340.py

@@ -74,14 +74,13 @@ class LFib1340( BaseLFib64 ):
     Example:
     
       rand = LFib1340()
-      print( rand() )    # prints a pseudo-random value within [0.0, 1.0)
-      print( rand(a) )   # prints a pseudo-random value within [0.0, a)
-      print( rand(a,b) ) # prints a pseudo-random value within [a  , b)
+      print( rand() )     # prints a pseudo-random value within [0.0, 1.0)
+      print( rand(a) )    # prints a pseudo-random value within [0, a) or [0.0, a) depending on the type of a
+      print( rand(a, n) ) # prints a list of n pseudo-random values each within [0, a)
 
     Notice that for simulating the roll of a dice you should program:
-
       diceRoll = LFib1340()
-      print(int(diceRoll(1, 7))) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
+      print( int(diceRoll.randint(1, 6)) ) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
 
     Such a programming is an accelerated while still robust emulation of  the 
     inherited methods:

PyRandLib/lfib668.py

@@ -73,14 +73,13 @@ class LFib668( BaseLFib64 ):
     Example:
     
       rand = LFib668()
-      print( rand() )    # prints a pseudo-random value within [0.0, 1.0)
-      print( rand(a) )   # prints a pseudo-random value within [0.0, a)
-      print( rand(a,b) ) # prints a pseudo-random value within [a  , b)
+      print( rand() )     # prints a pseudo-random value within [0.0, 1.0)
+      print( rand(a) )    # prints a pseudo-random value within [0, a) or [0.0, a) depending on the type of a
+      print( rand(a, n) ) # prints a list of n pseudo-random values each within [0, a)
 
     Notice that for simulating the roll of a dice you should program:
-
       diceRoll = LFib668()
-      print(int(diceRoll(1, 7))) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
+      print( int(diceRoll.randint(1, 6)) ) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
 
     Such a programming is an accelerated while still robust emulation of  the 
     inherited methods:

PyRandLib/lfib78.py

@@ -72,14 +72,13 @@ class LFib78( BaseLFib64 ):
     Example:
     
       rand = LFib78()
-      print( rand() )    # prints a pseudo-random value within [0.0, 1.0)
-      print( rand(a) )   # prints a pseudo-random value within [0.0, a)
-      print( rand(a,b) ) # prints a pseudo-random value within [a  , b)
+      print( rand() )     # prints a pseudo-random value within [0.0, 1.0)
+      print( rand(a) )    # prints a pseudo-random value within [0, a) or [0.0, a) depending on the type of a
+      print( rand(a, n) ) # prints a list of n pseudo-random values each within [0, a)
 
     Notice that for simulating the roll of a dice you should program:
-
       diceRoll = LFib78()
-      print(int(diceRoll(1, 7))) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
+      print( int(diceRoll.randint(1, 6)) ) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
 
     Such a programming is an accelerated while still robust emulation of  the 
     inherited methods:

PyRandLib/mrgrand1457.py

@@ -64,14 +64,14 @@ class MRGRand1457( BaseMRG ):
       random(), seed(), getstate(), and setstate().
       
     Furthermore this class is callable:
-      rand = MRGRand1457()
-      print( rand() )    # prints a pseudo-random value within [0.0, 1.0)
-      print( rand(a) )   # prints a pseudo-random value within [0.0, a)
-      print( rand(a,b) ) # prints a pseudo-random value within [a  , b)
+      print( rand() )     # prints a pseudo-random value within [0.0, 1.0)
+      print( rand(a) )    # prints a pseudo-random value within [0, a) or [0.0, a) depending on the type of a
+      print( rand(a, n) ) # prints a list of n pseudo-random values each within [0, a)
 
-    Please notice that for simulating the roll of a dice you should program:
+    Notice that for simulating the roll of a dice you should program:
       diceRoll = MRGRand1457()
-      print( int(diceRoll(1, 7)) ) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
+      print( int(diceRoll.randint(1, 6)) ) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
+
 
     Such a programming is an accelerated while still robust emulation of  the 
     inherited methods:

PyRandLib/mrgrand287.py

@@ -80,13 +80,13 @@ class MRGRand287( BaseMRG ):
       
     Furthermore, this class is callable:
       rand = MRGRand287()
-      print( rand() )    # prints a pseudo-random value within [0.0, 1.0)
-      print( rand(a) )   # prints a pseudo-random value within [0.0, a)
-      print( rand(a,b) ) # prints a pseudo-random value within [a  , b)
+      print( rand() )     # prints a pseudo-random value within [0.0, 1.0)
+      print( rand(a) )    # prints a pseudo-random value within [0, a) or [0.0, a) depending on the type of a
+      print( rand(a, n) ) # prints a list of n pseudo-random values each within [0, a)
 
     Notice that for simulating the roll of a dice you should program:
       diceRoll = MRGRand287()
-      print( int(diceRoll(1, 7)) ) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
+      print( int(diceRoll.randint(1, 6)) ) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
 
     Such a programming is an accelerated while still robust emulation of the inherited 
     methods:

PyRandLib/mrgrand49507.py

@@ -65,13 +65,14 @@ class MRGRand49507( BaseMRG ):
       
     Furthermore this class is callable:
       rand = MRGRand49507()
-      print( rand() )    # prints a pseudo-random value within [0.0, 1.0)
-      print( rand(a) )   # prints a pseudo-random value within [0.0, a)
-      print( rand(a,b) ) # prints a pseudo-random value within [a  , b)
+      print( rand() )     # prints a pseudo-random value within [0.0, 1.0)
+      print( rand(a) )    # prints a pseudo-random value within [0, a) or [0.0, a) depending on the type of a
+      print( rand(a, n) ) # prints a list of n pseudo-random values each within [0, a)
 
     Notice that for simulating the roll of a dice you should program:
       diceRoll = MRGRand49507()
-      print( int(diceRoll(1, 7)) ) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
+      print( int(diceRoll.randint(1, 6)) ) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
+
 
     Such a programming is an accelerated while still robust emulation of  the 
     inherited methods:

PyRandLib/well1024a.py

@@ -65,13 +65,14 @@ class Well1024a( BaseWELL ):
     
     Furthermore, this class is callable:
       rand = Well1024a()
-      print( rand() )    # prints a pseudo-random value within [0.0, 1.0)
-      print( rand(a) )   # prints a pseudo-random value within [0.0, a)
-      print( rand(a,b) ) # prints a pseudo-random value within [a  , b)
-    
+      print( rand() )     # prints a pseudo-random value within [0.0, 1.0)
+      print( rand(a) )    # prints a pseudo-random value within [0, a) or [0.0, a) depending on the type of a
+      print( rand(a, n) ) # prints a list of n pseudo-random values each within [0, a)
+
     Notice that for simulating the roll of a dice you should program:
       diceRoll = Well1024a()
-      print( int(diceRoll(1, 7)) ) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
+      print( int(diceRoll.randint(1, 6)) ) # prints a uniform roll within set {1, 2, 3, 4, 5, 6}
+
 
     Such a programming is an accelerated while still robust emulation of the inherited 
     methods: