def getPredictionRelative(self):
model = load_model("/Users/sean/Documents/Projects/Packing-Algorithm/model/lstm_num_5_layer_128_56_2_epochs_70.h5")
pre_train = pd.read_csv("/Users/sean/Documents/Projects/Data/pre_train.csv") # 读取形状
_input = pd.read_csv("/Users/sean/Documents/Projects/Data/input_seq.csv") # 读取输入
_output = pd.read_csv("/Users/sean/Documents/Projects/Data/output_relative_position.csv") # 读取输入
# index=random.randint(4000,5000)
index=4500
polys=json.loads(pre_train["polys"][index]) # 形状
X = np.array([json.loads(_input["x_256"][index])]) # 输入
predicted_Y = model.predict(X, verbose=0)[0]*1500
print(predicted_Y)
Y=np.array(json.loads(_output["y"][index]))*1500
print(Y)
old_centroid=[0,0]
for i,poly in enumerate(polys):
# 获得初始的中心和预测的位置
centroid_origin=GeoFunc.getPt(Polygon(poly).centroid)
centroid_predicted=[Y[i][0]+old_centroid[0],Y[i][1]+old_centroid[1]]
# 获得新的形状并更新
new_poly=GeoFunc.getSlide(poly,centroid_predicted[0]-centroid_origin[0],centroid_predicted[1]-centroid_origin[1])
old_centroid=GeoFunc.getPt(Polygon(new_poly).centroid)
PltFunc.addPolygon(poly)
PltFunc.addPolygonColor(new_poly)
PltFunc.showPlt()
def __init__(self, polys, **kw):
self.polys = PolyListProcessor.deleteRedundancy(copy.deepcopy(polys))
self.area_list, self.first_vec_list, self.centroid_list = [], [], [
] # 作为参考
for poly in self.polys:
P = Polygon(poly)
self.centroid_list.append(GeoFunc.getPt(P.centroid))
self.area_list.append(int(P.area))
self.first_vec_list.append(
[poly[1][0] - poly[0][0], poly[1][1] - poly[0][1]])
self.nfp_list = [[0] * len(self.polys) for i in range(len(self.polys))]
self.load_history = False
self.history_path = None
self.history = None
if 'history_path' in kw:
self.history_path = kw['history_path']
if 'load_history' in kw:
if kw['load_history'] == True:
# 从内存中加载history 直接传递pandas的df对象 缩短I/O时间
if 'history' in kw:
self.history = kw['history']
self.load_history = True
self.loadHistory()
self.store_nfp = False
if 'store_nfp' in kw:
if kw['store_nfp'] == True:
self.store_nfp = True
self.store_path = None
if 'store_path' in kw:
self.store_path = kw['store_path']
if 'get_all_nfp' in kw:
if kw['get_all_nfp'] == True and self.load_history == False:
self.getAllNFP()
if 'fast' in kw: # 为BLF进行多进程优化
if kw['fast'] == True:
self.res = [[0] * len(self.polys)
for i in range(len(self.polys))]
#pool=Pool()
for i in range(1, len(self.polys)):
for j in range(0, i):
# 计算nfp(j,i)
#self.res[j][i]=pool.apply_async(getNFP,args=(self.polys[j],self.polys[i]))
self.nfp_list[j][i] = GeoFunc.getSlide(
getNFP(self.polys[j],
self.polys[i]), -self.centroid_list[j][0],
-self.centroid_list[j][1])
def getDirectNFP(self, poly1, poly2, **kw):
if 'index' in kw:
i = kw['index'][0]
j = kw['index'][1]
centroid = GeoFunc.getPt(Polygon(self.polys[i]).centroid)
else:
# 首先获得poly1和poly2的ID
i = self.getPolyIndex(poly1)
j = self.getPolyIndex(poly2)
centroid = GeoFunc.getPt(Polygon(poly1).centroid)
# 判断是否计算过并计算nfp
if self.nfp_list[i][j] == 0:
nfp = NFP(poly1, poly2).nfp
#self.nfp_list[i][j]=GeoFunc.getSlide(nfp,-centroid[0],-centroid[1])
if self.store_nfp == True:
with open(
"/Users/sean/Documents/Projects/Packing-Algorithm/record/nfp.csv",
"a+") as csvfile:
writer = csv.writer(csvfile)
writer.writerows([[poly1, poly2, nfp]])
return nfp
else:
return GeoFunc.getSlide(self.nfp_list[i][j], centroid[0],
centroid[1])
def getPredictionAbsolute(self):
model = load_model("/Users/sean/Documents/Projects/Packing-Algorithm/model/absolute_lstm_num_8_layer_128_56_2_epochs_200.h5")
file= pd.read_csv("/Users/sean/Documents/Projects/Data/8_lstm_test.csv") # 读取输入
index=random.randint(3700,4700)
index=3000
polys=json.loads(file["polys"][index]) # 形状
X = np.array([json.loads(file["x_256"][index])]) # 输入
predicted_Y = model.predict(X, verbose=0)[0]*4000
for i,poly in enumerate(polys):
centroid_origin=GeoFunc.getPt(Polygon(poly).centroid)
PltFunc.addPolygon(poly)
new_poly=GeoFunc.getSlide(poly,predicted_Y[i][0]-centroid_origin[0],predicted_Y[i][1]-centroid_origin[1])
PltFunc.addPolygonColor(new_poly)
PltFunc.showPlt()