Added Iris datasets + benchmark (TODO: dataset splitting)

Changed error value calculation (RMS, divide by number of datasets)
added benchs
reworked activation function handling (no lambda functions anymore)
merged matrix activationfunctions and normal activationfunctions

(overloaded functions in activationfunction.h)

replaced "createnetwork(layersizes)" with "addInputLayer(...)",
"addHiddenLayer(...)", "addOutputLayer(...)"

added "addLayer(...)" to the networkfactory to add layers with different actfunctions

fixed bug in neuronmatrix: setDesiredOutputValues():
error was squared two times
modified network factory (constructor now takes input and outputlayer args)
fixed bug in backpropagation learning algorithm

--> hidden neuron: error = act'(input) * sum(error * weight)

weight value init: added random device to get "real" random values

RPROP:
added "initial step size" parameter to the rprop network
fixed bug in rprop network: network used backpropagation learning algorithm
revised rprop algorithms --> IRPropPlus works now most of the time
revised rprop algorithms:

algorithms now work most of the time
Merge branch 'master' into rprop

Conflicts:
	quickprop/quickpropnetwork.cpp
	quickprop/quickpropnetwork.h
	quickprop/tests/quickproptest.cpp