def __init__(self, params, population):
# Copy params.
if params is not None:
self.__dict__ = params.copy()
# Set another params
self.area = self.cols * self.rows
self.popsize = population
self.functionCnt = len(
self.functionSet) - 1 # Last idx, because of rand_int(a,b)
self.graphInputCnt = len(self.dataInput)
self.graphOutputCnt = len(self.dataOutput)
self.maxFitness = 0
self.bestFitness = float("inf")
CgpEquation.run = CgpCore.runDescending
self.c = cgp.Cgp()
self.arrayTypecode = CgpEquation.ARRAY_TYPECODE
self.trainingVectors = len(self.dataInput[0])
self.__convertData()
self._initOutputBuff()
self._initUsedNodes(population)
self.__initByteCodes(population)
# Becouse we are using float we need to divide it 2
if sys.maxsize > 2**32:
self.bufferUsedNodes = self.bufferUsedNodes >> 1
self.buffOffsetSize = self.buffOffsetSize >> 1
def __init__(self, params, population):
# Copy params.
if params is not None:
self.__dict__ = params.copy()
# Set another params
self.area = self.cols * self.rows
self.popsize = population
self.functionCnt = len(
self.functionSet) - 1 # Last idx, because of rand_int(a,b)
self.graphInputCnt = len(self.dataInput)
self.graphOutputCnt = len(self.dataOutput)
self.maxFitness = 2**self.graphInputCnt * self.graphOutputCnt
self.bestFitness = 0
CgpCircuit.run = CgpCore.runAscending # Type of selecting parent.
self.c = cgp.Cgp() #$%^& hack
self.arrayTypecode = CgpCircuit.ARRAY_TYPECODE
self.trainingVectors = 1
if sys.maxsize > 2**32:
if self.graphInputCnt > 6:
self.trainingVectors = int((2**self.graphInputCnt) / 64)
else:
if self.graphInputCnt > 5:
self.trainingVectors = int((2**self.graphInputCnt) / 32)
self.__convertData()
self._initOutputBuff()
self._initUsedNodes(population)
self.__initLookUp()
self.__initByteCodes(population)
if sys.maxsize > 2**32:
CgpCircuit.bitMax = 2**64
self.mask = 2**64 - 1
else:
CgpCircuit.bitMax = 2**32
self.mask = 2**32 - 1