diff --git a/README.md b/README.md
index 647d5d2..7e39b5c 100644
--- a/README.md
+++ b/README.md
@@ -29,9 +29,10 @@ This file is a text file, which is simply one line. It contains a list of floats
 ###ExpOutput.mat
 
 This file must be generated by the experiment and is how the learner finds out about the latest run. It must contain the following three variables::
-  cost (float)
-  uncer (float)
-  bad (boolean)
+
+    cost (float)
+    uncer (float)
+    bad (boolean)
 
 cost is the cost determined by the experiment, uncer is the uncertainty related to that cost (absolute uncertainty) and bad is a boolean indicating whether the run was fine or if it was a bad one.
 
@@ -50,13 +51,14 @@ This file gives the bounds, type and initial conditions to the learner/optimizer
 The file is parsed (reasonably) intelligently. Meaning extra whitespace, extra new lines and the order of the lines should not be an issue. Nevertheless, try and stay as close to the examples as you can.
 
 Each parameter must now be input as::
-  DescriptionOfParameter=(integer, string or array of whatever the Parameter is)
+
+    DescriptionOfParameter=(integer, string or array of whatever the Parameter is)
 
 Here are a couple of examples of formatting for integers, arrays of floats and strings::
 
-  NumberOfRuns=1
-  MinimumBoundaries= [1.0 , 3.0, 4.0 ]
-  ControllerType = 'NelderMead'
+    NumberOfRuns=1
+    MinimumBoundaries= [1.0 , 3.0, 4.0 ]
+    ControllerType = 'NelderMead'
 
 In the above examples you can again see we can be reasonably flexible with white spacing. Just be sure to put either ' or " quotations around strings on the RHS of expressions.
 
@@ -65,101 +67,113 @@ The configuration file will be very different depending on what solver is used.
 ####NelderMead Solver 
 
 The Neldermead solver has the following common settings::
-  ControllerType='NelderMead'
-  NumberOfRuns=(int)
-  NumberOfParameters=(int)  
+
+    ControllerType='NelderMead'
+    NumberOfRuns=(int)
+    NumberOfParameters=(int)  
 
 Note controller type must be 'NelderMead'. Next we have the boundary conditions which can be one of the following::
-  BoundaryConditions ='Hard'
-  MinimumBoundaries=(arrays of floats of size NumberOfParameters)
-  MaximumBoundaries=(arrays of floats of size NumberOfParameters)
+  
+    BoundaryConditions ='Hard'
+    MinimumBoundaries=(arrays of floats of size NumberOfParameters)
+    MaximumBoundaries=(arrays of floats of size NumberOfParameters)
 
 OR
 ::
-  BoundaryConditions ='None'
+
+    BoundaryConditions ='None'
 
 Next we provide the system with initial conditions. There are currently three options: Explicit, Scaled and Random described below.
 
 First Explicit::
-  InitialConditionsSetup=  'Explicit'
-  InitialParameters=(arrays of floats of size NumberOfParameters)  
-  InitialSimplexDisplacements=(arrays of floats of size NumberOfParameters)  
+
+    InitialConditionsSetup=  'Explicit'
+    InitialParameters=(arrays of floats of size NumberOfParameters)  
+    InitialSimplexDisplacements=(arrays of floats of size NumberOfParameters)  
 
 Here the initial conditions will be a simplex formed with one corner being the InitialParameters, then each of the points of the simplex being the initial point displaced by one of the numbers from the InitialSimplexDisplacements array.
 
 Next Scaled::
-  InitialConditionsSetup=  'Scaled'
-  InitialParameters=(arrays of floats of size NumberOfParameters)  
-  SimplexScaleSize=(float)  
+
+    InitialConditionsSetup=  'Scaled'
+    InitialParameters=(arrays of floats of size NumberOfParameters)  
+    SimplexScaleSize=(float)  
 
 This produces a simplex with one corner of being the InitialParameters, then each other corner being these InitialParameters plus the difference between the boundaries scaled by SimplexScaleSize. Explicitly the points of the simplex x[i] are given by::
-  x = initParameters 
-  x[i] = x[i] + ScaleOfSimplex*(MaximumBoundary - MinimumBoundary)[i]
+
+    x = initParameters 
+    x[i] = x[i] + ScaleOfSimplex*(MaximumBoundary - MinimumBoundary)[i]
 
 If you have no boundaries or want it scaled according to different values you can instead write it as::
-  InitialConditionsSetup=  'Scaled'
-  InitialParameters=(arrays of floats of size NumberOfParameters)  
-  SimplexScaleSize=(float) 
-  MinimumInitialParameters=(arrays of floats of size NumberOfParameters)
-  MaximumInitialParameters=(arrays of floats of size NumberOfParameters)
+
+    InitialConditionsSetup=  'Scaled'
+    InitialParameters=(arrays of floats of size NumberOfParameters)  
+    SimplexScaleSize=(float) 
+    MinimumInitialParameters=(arrays of floats of size NumberOfParameters)
+    MaximumInitialParameters=(arrays of floats of size NumberOfParameters)
 
 MinimumInitialParameters and MaximumInitialParameters are now used instead of the boundaries. 
 
 Lastly we have Random::
-  InitialConditionsSetup=  'Random'  
-  SimplexScaleSize=(float)  
+
+    InitialConditionsSetup=  'Random'  
+    SimplexScaleSize=(float)  
 
 Random works in a very similar fashion to Scaled except it also picks the InitialParameters from a uniform distribution, hence you don't need to explicitly provide it. Again, if you want Random to sample from a different set of intervals you can instead write::
-  InitialConditionsSetup=  'Random'  
-  SimplexScaleSize=(float)  
-  MinimumInitialParameters=(arrays of floats of size NumberOfParameters)
-  MaximumInitialParameters=(arrays of floats of size NumberOfParameters)
+
+    InitialConditionsSetup=  'Random'  
+    SimplexScaleSize=(float)  
+    MinimumInitialParameters=(arrays of floats of size NumberOfParameters)
+    MaximumInitialParameters=(arrays of floats of size NumberOfParameters)
 
 Now we give a few examples of valid Nelder Mead Controller congif files
 
 ---
 ::
-  NumberOfRuns = 100
-  NumberOfParameters=3  
-  
-  ControllerType='NelderMead'
-  
-  BoundaryConditions ='Hard'
-  MinimumBoundaries= [1.0 , 3.0 , 4.0 ]
-  MaximumBoundaries= [10.0,10.0,10.0]
-  
-  InitialConditionsSetup=  'Explicit'
-  InitialParameters  =[2.0,4.0,5.0]  
-  InitialSimplexDisplacements= [1.0,1.0,1.0]
+
+    NumberOfRuns = 100
+    NumberOfParameters=3  
+    
+    ControllerType='NelderMead'
+    
+    BoundaryConditions ='Hard'
+    MinimumBoundaries= [1.0 , 3.0 , 4.0 ]
+    MaximumBoundaries= [10.0,10.0,10.0]
+    
+    InitialConditionsSetup=  'Explicit'
+    InitialParameters  =[2.0,4.0,5.0]  
+    InitialSimplexDisplacements= [1.0,1.0,1.0]
 
 ---
 ::
-  NumberOfRuns = 19
-  NumberOfParameters=2  
-  
-  ControllerType='NelderMead'
-  
-  BoundaryConditions ='Hard'
-  MinimumBoundaries= [-1.0, -1.0]
-  MaximumBoundaries= [1.0,1.0]
-  
-  InitialConditionsSetup=  'Random'
-  SimplexScaleSize= 0.1
+ 
+    NumberOfRuns = 19
+    NumberOfParameters=2  
+    
+    ControllerType='NelderMead'
+    
+    BoundaryConditions ='Hard'
+    MinimumBoundaries= [-1.0, -1.0]
+    MaximumBoundaries= [1.0,1.0]
+    
+    InitialConditionsSetup=  'Random'
+    SimplexScaleSize= 0.1
 
 ---
 ::
-  InitialConditionsSetup=  'Random'
-  MinimumInitialParameters =[-1.0,-2.0]
-  MaximumInitialParameters  =[10,20]
-  SimplexScaleSize= 0.1
-  
-  NumberOfRuns = 200
-  
-  NumberOfParameters=2  
-  
-  BoundaryConditions ='None'
-  
-  ControllerType='NelderMead'
+
+    InitialConditionsSetup=  'Random'
+    MinimumInitialParameters =[-1.0,-2.0]
+    MaximumInitialParameters  =[10,20]
+    SimplexScaleSize= 0.1
+    
+    NumberOfRuns = 200
+    
+    NumberOfParameters=2  
+    
+    BoundaryConditions ='None'
+    
+    ControllerType='NelderMead'
 
 ---
 
@@ -167,22 +181,22 @@ Now we give a few examples of valid Nelder Mead Controller congif files
 
 This optimizer/learner, isn't really either of these options. It's more of a randomized brute force approach. It just randomly tries a parameter given the boundaries. As a file it must be formatted as follows::
 
-  ControllerType='Random'
-  NumberOfRuns=(int)
-  NumberOfParameters=(int)  
-  MinimumBoundaries= (arrays of floats of size NumberOfParameters)
-  MaximumBoundaries= (arrays of floats of size NumberOfParameters)
+    ControllerType='Random'
+    NumberOfRuns=(int)
+    NumberOfParameters=(int)  
+    MinimumBoundaries= (arrays of floats of size NumberOfParameters)
+    MaximumBoundaries= (arrays of floats of size NumberOfParameters)
 
 An example of a correctly formatted random:
 
 ---
-
 ::
-  ControllerType='Random'
-  NumberOfRuns=100
-  NumberOfParameters=10  
-  MinimumBoundaries=[-10.0,-10.0,-10.0,-10.0,-10.0,-10.0,-10.0,-10.0,-10.0,-10.0]
-  MaximumBoundaries=[10.0,10.0,10.0,10.0,10.0,10.0,10.0,10.0,10.0,10.0]
+
+    ControllerType='Random'
+    NumberOfRuns=100
+    NumberOfParameters=10  
+    MinimumBoundaries=[-10.0,-10.0,-10.0,-10.0,-10.0,-10.0,-10.0,-10.0,-10.0,-10.0]
+    MaximumBoundaries=[10.0,10.0,10.0,10.0,10.0,10.0,10.0,10.0,10.0,10.0]
 
 ---
 
@@ -199,16 +213,17 @@ Internally the learners attempts to logically normalize the parameters and costs
 #####Common Learner Settings
 
 All learners require the ControllerType currently it must be::
-  ControllerType='GlobalLearner'
+
+    ControllerType='GlobalLearner'
 
 The learner must then be provided the following set of data::
 
-  NumberOfParameters=(int)
-  NumberOfRuns = (int)
-  MinimumBoundaries= (array of floats of the size NumberOfParameters)
-  MaximumBoundaries= (array of floats of the size NumberOfParameters)
-  MinimumCost = (float)
-  MaximumCost = (float)
+    NumberOfParameters=(int)
+    NumberOfRuns = (int)
+    MinimumBoundaries= (array of floats of the size NumberOfParameters)
+    MaximumBoundaries= (array of floats of the size NumberOfParameters)
+    MinimumCost = (float)
+    MaximumCost = (float)
 
 Which are mostly self explanatory and/or the same as previous controllers. 
 
@@ -217,13 +232,14 @@ There are three options for the initial conditions: Random, Simplex or FromFile.
 Random
 
 The random initial training will simple try uniformly distributed random points inside the boundaries. The required definitions are::
-  InitialTrainingSource='RandomSearch'
-  NumberOfTrainingRuns=(int)
+  
+    InitialTrainingSource='RandomSearch'
+    NumberOfTrainingRuns=(int)
 
 The total number of runs the simulation will go for will now be NumberOfRuns + NumberOfTrainingRuns. You can optionally prescribe custom boundaries just for the random search::
 
-  MinimumInitialParameters= (array of floats of the size NumberOfParameters)
-  MaximumInitialParameters= (array of floats of the size NumberOfParameters)
+    MinimumInitialParameters= (array of floats of the size NumberOfParameters)
+    MaximumInitialParameters= (array of floats of the size NumberOfParameters)
 
 Under the hood the controller will initiate a RandomSearch controller to get the training data.
 
@@ -231,48 +247,56 @@ Simplex
 
 Here the controller will create the initial points from a simplex. Under the hood the controller actually initiates a NelderMead controller, hence if you can also prescribe a certain number of runs and the training data will then be formed from an initial Nelder Mead run. The required definitions are::
 
-  InitialTrainingSource='Simplex'
-  InitialParameters=(array of floats of the size NumberOfParameters)
-  SimplexScaleSize=(float between 0.0 and 1.0)
+    InitialTrainingSource='Simplex'
+    InitialParameters=(array of floats of the size NumberOfParameters)
+    SimplexScaleSize=(float between 0.0 and 1.0)
 
 By default the number of runs with be the NumberOfParameters + 1 + NumberOfRuns. But if you want the training data to be based on a NelderMead run you can add the additional optional definition::
-  NumberOfTrainingRuns=(int)
+  
+    NumberOfTrainingRuns=(int)
 
 You can also provide custom boundaries for the NelderMead and simplex training run::
-  MinimumInitialParameters= (array of floats of the size NumberOfParameters)
-  MaximumInitialParameters= (array of floats of the size NumberOfParameters)
+ 
+    MinimumInitialParameters= (array of floats of the size NumberOfParameters)
+    MaximumInitialParameters= (array of floats of the size NumberOfParameters)
 
 FromFile
 
 Lastly you can train the learner from any previous run. This includes previous learning runs. Simply provide the _python_ archive of the previous run. By default this is output as 'ControllerArchive.pkl'. (In future version we may allow import from matlab files). The following is required to take from a file::
-  InitialTrainingSource='FromFile'
+
+    InitialTrainingSource='FromFile'
 
 Optionally you can also provide the specific file name as::
-  FileName=(string)
+
+    FileName=(string)
 
 Optional Common Parameters
 
 All learners use common code when searching for the next point to sample. The only difference between the algorithms is how much they prioritize new information of minimizing cost. The learner can spend a vast amount of resources when trying to best estimate the next point to sample, if you're finding the solver it taking longer to find a new point then the downtime of the experiment. You can make reduce the learners Accuracy, but vastly increase it's speed with the following settings.
 
 The primary method to speed up your solver is by using the following settings::
-  NumberOfParticles = (int)
+
+    NumberOfParticles = (int)
 
 Controls the maximum number of particles that is stored by the learner::
-  NumberOfThetaSearches = (int)
+
+    NumberOfThetaSearches = (int)
 
 Number of searches looking for the correlations lengths::
-  NumberOfParameterSearches = (int)
+
+    NumberOfParameterSearches = (int)
 
 Number of searches looking for best parameters.
 
 The global learner has a many unique setting related to how the sweep is performed. However, all of them have a default setting. The first quantity you can change is the number of runs in a sweep::
-  RunsInSweep = (int)
+
+    RunsInSweep = (int)
 
 The default value for RunsInSweep = 5. This gives reasonable performance for all dimensions.
 
 The default ramp is a linear sweep from SweepMinBias to SweepMaxBias. If this does not suit your needs you can actually completely customize the ramps function using::
   
-  SweepRampFunction = (lambda function which takes one integer and returns a float between 0.0 and 1.0)
+    SweepRampFunction = (lambda function which takes one integer and returns a float between 0.0 and 1.0)
 
 Under the hood lambda functions are actually what are used to implement default ramp. Here we let the user directly make such a function. This is mostly included just to show off how flexible python is, and how clever the input method we are using is. If you use this trick make sure the lambda function behaves sanely. It will check it with a few random input integers, but that's it.
 
@@ -280,13 +304,13 @@ In high dimensional spaces the learners propensity to search the space, and part
 
 The definition you need to add to turn the leash on is::
 
-  LeashSize = (float between 0.0 and 1.0)
+    LeashSize = (float between 0.0 and 1.0)
 
 The leash size refers to the fraction of the difference between the minimum and maximum boundaries that the controller can maximally move in each direction. By default the Leash is set to LeashSize = 1.0, basically meaning it can go anywhere. But you can set it to be whatever length you want.
 
 Much like the sweep function, if you instead want the leash to grow or shrink as a function of the run number you can instead specify a LeashFunction::
 
-  LeashFunction = (lambda function which takes one integer and returns a float between 0.0 and 1.0)
+    LeashFunction = (lambda function which takes one integer and returns a float between 0.0 and 1.0)
 
 Again under the hood, all leashes are implemented with lambda functions. Here we are just giving the user direct control. Note that if LeashFunction is defined the program will ignore LeashSize if it is also specified.
 
@@ -294,91 +318,95 @@ Examples
 
 ---
 ::
-  ControllerType='SimpleLearner'
-  
-  NumberOfRuns = 10
-  
-  NumberOfParameters=3
-  MinimumBoundaries= [-1.0,-2.0,-3.0]
-  MaximumBoundaries= [0.0,0.0,0.0]
-  MinimumCost = 0
-  MaximumCost = 1
-  
-  InitialTrainingSource='Random'
-  NumberOfTrainingRuns = 100
+
+    ControllerType='SimpleLearner'
+    
+    NumberOfRuns = 10
+    
+    NumberOfParameters=3
+    MinimumBoundaries= [-1.0,-2.0,-3.0]
+    MaximumBoundaries= [0.0,0.0,0.0]
+    MinimumCost = 0
+    MaximumCost = 1
+    
+    InitialTrainingSource='Random'
+    NumberOfTrainingRuns = 100
 
 ---
 ::
-  ControllerType='GlobalLearner'
-  
-  NumberOfRuns = 12
-  
-  NumberOfParameters=2
-  MinimumBoundaries= [0.0,0.0]
-  MaximumBoundaries= [5.1,5.2]
-  MinimumCost = -2
-  MaximumCost = 3
-  
-  InitialTrainingSource='Random'
-  NumberOfTrainingRuns = 100
-  MinimumInitialParameters= [1.9,2.9]
-  MaximumInitialParameters= [2.1,3.1]
+ 
+    ControllerType='GlobalLearner'
+    
+    NumberOfRuns = 12
+    
+    NumberOfParameters=2
+    MinimumBoundaries= [0.0,0.0]
+    MaximumBoundaries= [5.1,5.2]
+    MinimumCost = -2
+    MaximumCost = 3
+    
+    InitialTrainingSource='Random'
+    NumberOfTrainingRuns = 100
+    MinimumInitialParameters= [1.9,2.9]
+    MaximumInitialParameters= [2.1,3.1]
 
 ---
 ::
-  ControllerType='GlobalLearner'
-  
-  NumberOfRuns = 312
-  RunsInSweep = 50
-  
-  NumberOfParameters=1
-  MinimumBoundaries= [-10.0]
-  MaximumBoundaries= [10.0]
-  MinimumCost = -1
-  MaximumCost = 0.5
-  
-  InitialTrainingSource='Simplex'
-  InitialParameters = [-5]
-  SimplexScaleSize= 0.4
-  NumberOfTrainingRuns = 20
-  
-  SweepMinBias = 0.1
-  SweepMaxBias = 0.9
+ 
+    ControllerType='GlobalLearner'
+    
+    NumberOfRuns = 312
+    RunsInSweep = 50
+    
+    NumberOfParameters=1
+    MinimumBoundaries= [-10.0]
+    MaximumBoundaries= [10.0]
+    MinimumCost = -1
+    MaximumCost = 0.5
+    
+    InitialTrainingSource='Simplex'
+    InitialParameters = [-5]
+    SimplexScaleSize= 0.4
+    NumberOfTrainingRuns = 20
+    
+    SweepMinBias = 0.1
+    SweepMaxBias = 0.9
 
 ---
 ::
-  ControllerType='GlobalLearner'
-  
-  NumberOfRuns = 256
-  RunsInSweep = 8
-  
-  NumberOfParameters=9
-  MinimumBoundaries= [-9,-8,-7,-6,-5,-4,-3,-2,-1]
-  MaximumBoundaries= [9,8,7,6,5,4,3,2,1]
-  MinimumCost = -1
-  MaximumCost = 10
-  
-  InitialTrainingSource='FromFile'
-  FileName = 'ControllerArchiveBack.pkl'
-
+ 
+    ControllerType='GlobalLearner'
+    
+    NumberOfRuns = 256
+    RunsInSweep = 8
+    
+    NumberOfParameters=9
+    MinimumBoundaries= [-9,-8,-7,-6,-5,-4,-3,-2,-1]
+    MaximumBoundaries= [9,8,7,6,5,4,3,2,1]
+    MinimumCost = -1
+    MaximumCost = 10
+    
+    InitialTrainingSource='FromFile'
+    FileName = 'ControllerArchiveBack.pkl'
 
 ---
 ::
-  ControllerType='GlobalLearner'
-  
-  NumberOfRuns = 312
-  
-  NumberOfParameters=3
-  MinimumBoundaries= [-9,-8,-7]
-  MaximumBoundaries= [-1,-1,-1]
-  MinimumCost = -9
-  MaximumCost = -8
-  
-  InitialTrainingSource='Simplex'
-  InitialParameters = [0.0,0.0,0.0]
-  SimplexScaleSize= 0.4
-  
-  SweepFunction = lambda x: (x%10+1)/10.0
+
+    ControllerType='GlobalLearner'
+    
+    NumberOfRuns = 312
+    
+    NumberOfParameters=3
+    MinimumBoundaries= [-9,-8,-7]
+    MaximumBoundaries= [-1,-1,-1]
+    MinimumCost = -9
+    MaximumCost = -8
+    
+    InitialTrainingSource='Simplex'
+    InitialParameters = [0.0,0.0,0.0]
+    SimplexScaleSize= 0.4
+    
+    SweepFunction = lambda x: (x%10+1)/10.0
 
 ---