def f_second(i,pi,attribute_values,df):
#-----------------CUDA
#Cheetah Variables
template = Template(f_source)
template.DF = np.array(df[pi], dtype = np.int32)
template.nrow = df[pi].shape[0]
nfeat = df[pi].shape[1]
template.ncol = nfeat
template.ll = np.array(df[i], dtype=np.int32)
phi_i_ = [attribute_values[item] for item in pi]
combinations = np.array(list(itertools.product(*phi_i_)), dtype=np.int32)
combinations = np.array(combinations.ravel().tolist(), dtype=np.int32)
height = combinations.shape[0]
template.H = height
f_kernel = nvcc_compile(template, "my_f")
##Threads
block_x = np.int(height/nfeat)
blocksize = (block_x,1,1)
gridsize = (1,1)
##Kernel
h_res = np.zeros(height/nfeat,dtype=np.float32)
d_combinations = gpu.to_gpu(combinations)
d_res = gpu.to_gpu(h_res)
f_kernel(d_combinations,d_res,block=blocksize,grid=gridsize)
ress = d_res.get()
ress = list(ress)
return sum(ress)
for i in xrange(n):
OKToProceed = False
pi = []
pred = node_order[0:i]
P_old = f(i,pi,attribute_values,df)
if len(pred) > 0:
OKToProceed = True
while (OKToProceed == True and len(pi) < u):
iters = [item for item in pred if item not in pi]
if len(iters) > 0:
f_to_max = {};
#Cheetah Variables
template = Template(f_source)
template.nrow = df[pi].shape[0]
nfeat = df[pi].shape[1]+1
template.ncol = nfeat
f_kernel = nvcc_compile(template, "my_f")
for z_hat in iters:
f_to_max[z_hat] = f_second(i,pi+[z_hat],attribute_values,df)
#print 'i = %i, val = %f'% (i,f_to_max[z_hat])
z = max(f_to_max.iteritems(), key=operator.itemgetter(1))[0]
P_new = f_to_max[z]
if P_new > P_old:
P_old = P_new
pi = pi+[z]
else:
OKToProceed = False
else:
OKToProceed = False
parents.append(pi)
