pool = genome.Pool(db)
data = numpy.array(data).astype(numpy.float32)
data_gpu = cuda.mem_alloc(data.nbytes)
cuda.memcpy_htod(data_gpu, data)
validData = numpy.array(validData).astype(numpy.float32)
validData_gpu = cuda.mem_alloc(validData.nbytes)
cuda.memcpy_htod(validData_gpu, validData)
#### transfer data array and winner table to GPU
while True:
trees = []
for x in range(poolSize):
trees.append(genome.randomTree(treeLength))
### Main Loop
generations = 0
while True:
winnerTable = numpy.zeros(len(data) * poolSize, dtype=numpy.bool)
winnerTable_gpu = cuda.mem_alloc(winnerTable.nbytes)
cuda.memcpy_htod(winnerTable_gpu, winnerTable)
trees = numpy.array(trees).astype(numpy.float32)
trees_gpu = cuda.mem_alloc(trees.nbytes)
cuda.memcpy_htod(trees_gpu, trees)
winCounter = numpy.zeros(12 * poolSize, dtype=numpy.int32)
data = numpy.array(data).astype(numpy.float32) data_gpu = cuda.mem_alloc(data.nbytes) cuda.memcpy_htod(data_gpu, data) #validData = numpy.array(validData).astype(numpy.float32) #validData_gpu = cuda.mem_alloc(validData.nbytes) #cuda.memcpy_htod(validData_gpu, validData) #### transfer data array and winner table to GPU while True: trees = [] for x in range(poolSize): trees.append( genome.randomTree(treeLength) ) ### Main Loop generations = 0 dataDim = math.floor(len(data)/64.0) evalArray = None lastTrees = None winCount = None lossCount = None winTradeCount = None lossTradeCount = None drawdownCount = None while True: winnerTable = numpy.zeros(len(data) * poolSize, dtype=numpy.bool)
