如何防止 AssertionError 停止 Python 代码？
How to prevents a AssertionError from stop Python code?
我想做一个使用 Autorules 模糊控制器的系统，我在其中根据过程真实数据创建规则。
我的问题是，当我使用新数据和从旧数据中提取的规则来模拟模糊控制器时，我得到一个关于无法计算的清晰输出的错误，因为不存在足够的规则，这是完全正常的，因为我的模糊系统需要更多规则，这就是我的观点！我想实现一个新例程来分析清晰的 input/output 并根据这些数据创建新规则，之后，我想返回我的代码并再次模拟。
是否有阻止 AssertionError 停止代码并重定向到另一个 def 或先前代码行的函数？
我试图找到一些具有允许我稳定地重定向错误以停止代码但没有成功的功能的库。我想我将不得不更改 skfuzzy defuzz def 代码以允许它。
非常感谢。
''' python
step = stp_ini
k = 0
delay = stp_ini
end = stp_fim - stp_ini
Tout_sim = pd.DataFrame(columns=['val'])
Vent_sim = pd.DataFrame(columns=['val'])
start = timeit.default_timer()
for step in range(end-k):
clear_output(wait=True)
simulation.input['PCA1'] = comp1n[step+delay]
simulation.input['PCA2'] = comp2n[step+delay]
simulation.input['PCA3'] = comp3n[step+delay]
simulation.input['PCA4'] = comp4n[step+delay]
simulation.input['Vent'] = dataoutf.NumVentOn[step+delay]
simulation.compute()
Tout_sim = Tout_sim.append({'val':simulation.output['Tout']},ignore_index=True)
stop = timeit.default_timer()
if ((step/(stp_fim-k-1))*100) < 5:
expected_time = "Calculating..."
else:
time_perc = timeit.default_timer()
expected_time = np.round( ( (time_perc-start)/(step/(end-k-1)) )/60,2)
'''
~\AppData\Local\Continuum\anaconda3\lib\site-packages\skfuzzy\control\controlsystem.py in defuzz(self)
第587章【=36=】）
588 除了 AssertionError：
--> 589 引发 ValueError("Crisp output cannot be calculated, likely "
590"because the system is too sparse. Check to "
第591章"make sure this set of input values will "
ValueError：无法计算出清晰的输出，可能是因为系统太稀疏了。检查以确保这组输入值将通过当前的规则集在每个前提中激活至少一个连接的术语。
编辑：
我尝试通过 try 包装行代码 ValueError 但 ValueError 已激活
def defuzz(self):
"""Derive crisp value based on membership of adjective(s)."""
if not self.sim._array_inputs:
ups_universe, output_mf, cut_mfs = self.find_memberships()
if len(cut_mfs) == 0:
raise ValueError("No terms have memberships. Make sure you "
"have at least one rule connected to this "
"variable and have run the rules calculation.")
try:
return defuzz(ups_universe, output_mf,
self.var.defuzzify_method)
except AssertionError:
try:
new_c1 = []
new_c2 = []
new_c3 = []
new_c4 = []
new_vent = []
new_tout = []
newcondition1 = []
newcondition2 = []
newcondition3 = []
newcondition4 = []
newcondition5 = []
newcondition6 = []
#input
n = 0
for n in range(len(namespca)):
new_c1.append(fuzz.interp_membership(PCA1.universe, PCA1[namespcapd.name.loc[n]].mf, comp1n[step]))
new_c2.append(fuzz.interp_membership(PCA2.universe, PCA2[namespcapd.name.loc[n]].mf, comp2n[step]))
new_c3.append(fuzz.interp_membership(PCA3.universe, PCA3[namespcapd.name.loc[n]].mf, comp3n[step]))
new_c4.append(fuzz.interp_membership(PCA4.universe, PCA4[namespcapd.name.loc[n]].mf, comp4n[step]))
n = 0
for n in range(len(namesvent)):
new_vent.append(fuzz.interp_membership(Vent.universe, Vent[namesventpd.name.loc[n]].mf, dataoutf.NumVentOn[step]))
#output
n = 0
for n in range(len(namestemp)):
new_tout.append(fuzz.interp_membership(Tout.universe, Tout[namestemppd.name.loc[n]].mf, dataoutf.TsaidaHT[step]))
#new_c1 = np.transpose(new_c1)
new_c1_conv = pd.DataFrame(new_c1)
#new_c2 = np.transpose(new_c2)
new_c2_conv = pd.DataFrame(new_c2)
#new_c3 = np.transpose(new_c3)
new_c3_conv = pd.DataFrame(new_c3)
#new_c4 = np.transpose(new_c4)
new_c4_conv = pd.DataFrame(new_c4)
#new_vent = np.transpose(new_vent)
new_vent_conv = pd.DataFrame(new_vent)
#new_tout = np.transpose(new_tout)
new_tout_conv = pd.DataFrame(new_tout)
i=0
for i in range(pcamf):
newcondition1.append([new_c1_conv.idxmax(axis=0) == i])
newcondition2.append([new_c2_conv.idxmax(axis=0) == i])
newcondition3.append([new_c3_conv.idxmax(axis=0) == i])
newcondition4.append([new_c4_conv.idxmax(axis=0) == i])
i=0
for i in range(ventmf):
newcondition5.append([new_vent_conv.idxmax(axis=0) == i])
i=0
for i in range(tempmf):
newcondition6.append([new_tout_conv.idxmax(axis=0) == i])
choicelistpca = namespca
choicelistvent = namesvent
choicelisttout = namestemp
new_c1_rules = np.select(newcondition1, choicelistpca)
new_c2_rules = np.select(newcondition2, choicelistpca)
new_c3_rules = np.select(newcondition3, choicelistpca)
new_c4_rules = np.select(newcondition4, choicelistpca)
new_vent_rules = np.select(newcondition5, choicelistvent)
new_tout_rules = np.select(newcondition6, choicelisttout)
new_rules = np.vstack([new_c1_rules,new_c2_rules,new_c3_rules,new_c4_rules,new_vent_rules,new_tout_rules])
new_rules = new_rules.T
new_rulespd = pd.DataFrame(new_rules,columns=['PCA1','PCA2','PCA3','PCA4','Vent','Tout'])
#Checar se a nova regra está dentro do conjunto de regras fuzzy atual
if pd.merge(new_rulespd,AutoRules, on=['PCA1','PCA2','PCA3','PCA4','Vent','Tout'],how='inner').empty:
print('Nova regra não encontrada no conjunto atual de regras fuzzy!')
else:
pd.merge(new_rulespd,AutoRules, on=['PCA1','PCA2','PCA3','PCA4','Vent','Tout'],how='inner')
"""except AssertionError:
raise ValueError("Crisp output cannot be calculated, likely "
"because the system is too sparse. Check to "
"make sure this set of input values will "
"activate at least one connected Term in each "
"Antecedent via the current set of Rules.")"""
else:
# Calculate using array-aware version, one cut at a time.
output = np.zeros(self.sim._array_shape, dtype=np.float64)
it = np.nditer(output, ['multi_index'], [['writeonly', 'allocate']])
for out in it:
universe, mf = self.find_memberships_nd(it.multi_index)
out[...] = defuzz(universe, mf, self.var.defuzzify_method)
return output
将引发 ValueError 的代码行包装在 try 中。并在其 except ValueError: 子句中决定要做什么。也许 continue 继续下一次迭代可能是合理的。
我想做一个使用 Autorules 模糊控制器的系统，我在其中根据过程真实数据创建规则。 我的问题是，当我使用新数据和从旧数据中提取的规则来模拟模糊控制器时，我得到一个关于无法计算的清晰输出的错误，因为不存在足够的规则，这是完全正常的，因为我的模糊系统需要更多规则，这就是我的观点！我想实现一个新例程来分析清晰的 input/output 并根据这些数据创建新规则，之后，我想返回我的代码并再次模拟。
是否有阻止 AssertionError 停止代码并重定向到另一个 def 或先前代码行的函数？
我试图找到一些具有允许我稳定地重定向错误以停止代码但没有成功的功能的库。我想我将不得不更改 skfuzzy defuzz def 代码以允许它。
非常感谢。
''' python
step = stp_ini
k = 0
delay = stp_ini
end = stp_fim - stp_ini
Tout_sim = pd.DataFrame(columns=['val'])
Vent_sim = pd.DataFrame(columns=['val'])
start = timeit.default_timer()
for step in range(end-k):
clear_output(wait=True)
simulation.input['PCA1'] = comp1n[step+delay]
simulation.input['PCA2'] = comp2n[step+delay]
simulation.input['PCA3'] = comp3n[step+delay]
simulation.input['PCA4'] = comp4n[step+delay]
simulation.input['Vent'] = dataoutf.NumVentOn[step+delay]
simulation.compute()
Tout_sim = Tout_sim.append({'val':simulation.output['Tout']},ignore_index=True)
stop = timeit.default_timer()
if ((step/(stp_fim-k-1))*100) < 5:
expected_time = "Calculating..."
else:
time_perc = timeit.default_timer()
expected_time = np.round( ( (time_perc-start)/(step/(end-k-1)) )/60,2)
'''
~\AppData\Local\Continuum\anaconda3\lib\site-packages\skfuzzy\control\controlsystem.py in defuzz(self) 第587章【=36=】） 588 除了 AssertionError： --> 589 引发 ValueError("Crisp output cannot be calculated, likely " 590"because the system is too sparse. Check to " 第591章"make sure this set of input values will "
ValueError：无法计算出清晰的输出，可能是因为系统太稀疏了。检查以确保这组输入值将通过当前的规则集在每个前提中激活至少一个连接的术语。
编辑： 我尝试通过 try 包装行代码 ValueError 但 ValueError 已激活
def defuzz(self):
"""Derive crisp value based on membership of adjective(s)."""
if not self.sim._array_inputs:
ups_universe, output_mf, cut_mfs = self.find_memberships()
if len(cut_mfs) == 0:
raise ValueError("No terms have memberships. Make sure you "
"have at least one rule connected to this "
"variable and have run the rules calculation.")
try:
return defuzz(ups_universe, output_mf,
self.var.defuzzify_method)
except AssertionError:
try:
new_c1 = []
new_c2 = []
new_c3 = []
new_c4 = []
new_vent = []
new_tout = []
newcondition1 = []
newcondition2 = []
newcondition3 = []
newcondition4 = []
newcondition5 = []
newcondition6 = []
#input
n = 0
for n in range(len(namespca)):
new_c1.append(fuzz.interp_membership(PCA1.universe, PCA1[namespcapd.name.loc[n]].mf, comp1n[step]))
new_c2.append(fuzz.interp_membership(PCA2.universe, PCA2[namespcapd.name.loc[n]].mf, comp2n[step]))
new_c3.append(fuzz.interp_membership(PCA3.universe, PCA3[namespcapd.name.loc[n]].mf, comp3n[step]))
new_c4.append(fuzz.interp_membership(PCA4.universe, PCA4[namespcapd.name.loc[n]].mf, comp4n[step]))
n = 0
for n in range(len(namesvent)):
new_vent.append(fuzz.interp_membership(Vent.universe, Vent[namesventpd.name.loc[n]].mf, dataoutf.NumVentOn[step]))
#output
n = 0
for n in range(len(namestemp)):
new_tout.append(fuzz.interp_membership(Tout.universe, Tout[namestemppd.name.loc[n]].mf, dataoutf.TsaidaHT[step]))
#new_c1 = np.transpose(new_c1)
new_c1_conv = pd.DataFrame(new_c1)
#new_c2 = np.transpose(new_c2)
new_c2_conv = pd.DataFrame(new_c2)
#new_c3 = np.transpose(new_c3)
new_c3_conv = pd.DataFrame(new_c3)
#new_c4 = np.transpose(new_c4)
new_c4_conv = pd.DataFrame(new_c4)
#new_vent = np.transpose(new_vent)
new_vent_conv = pd.DataFrame(new_vent)
#new_tout = np.transpose(new_tout)
new_tout_conv = pd.DataFrame(new_tout)
i=0
for i in range(pcamf):
newcondition1.append([new_c1_conv.idxmax(axis=0) == i])
newcondition2.append([new_c2_conv.idxmax(axis=0) == i])
newcondition3.append([new_c3_conv.idxmax(axis=0) == i])
newcondition4.append([new_c4_conv.idxmax(axis=0) == i])
i=0
for i in range(ventmf):
newcondition5.append([new_vent_conv.idxmax(axis=0) == i])
i=0
for i in range(tempmf):
newcondition6.append([new_tout_conv.idxmax(axis=0) == i])
choicelistpca = namespca
choicelistvent = namesvent
choicelisttout = namestemp
new_c1_rules = np.select(newcondition1, choicelistpca)
new_c2_rules = np.select(newcondition2, choicelistpca)
new_c3_rules = np.select(newcondition3, choicelistpca)
new_c4_rules = np.select(newcondition4, choicelistpca)
new_vent_rules = np.select(newcondition5, choicelistvent)
new_tout_rules = np.select(newcondition6, choicelisttout)
new_rules = np.vstack([new_c1_rules,new_c2_rules,new_c3_rules,new_c4_rules,new_vent_rules,new_tout_rules])
new_rules = new_rules.T
new_rulespd = pd.DataFrame(new_rules,columns=['PCA1','PCA2','PCA3','PCA4','Vent','Tout'])
#Checar se a nova regra está dentro do conjunto de regras fuzzy atual
if pd.merge(new_rulespd,AutoRules, on=['PCA1','PCA2','PCA3','PCA4','Vent','Tout'],how='inner').empty:
print('Nova regra não encontrada no conjunto atual de regras fuzzy!')
else:
pd.merge(new_rulespd,AutoRules, on=['PCA1','PCA2','PCA3','PCA4','Vent','Tout'],how='inner')
"""except AssertionError:
raise ValueError("Crisp output cannot be calculated, likely "
"because the system is too sparse. Check to "
"make sure this set of input values will "
"activate at least one connected Term in each "
"Antecedent via the current set of Rules.")"""
else:
# Calculate using array-aware version, one cut at a time.
output = np.zeros(self.sim._array_shape, dtype=np.float64)
it = np.nditer(output, ['multi_index'], [['writeonly', 'allocate']])
for out in it:
universe, mf = self.find_memberships_nd(it.multi_index)
out[...] = defuzz(universe, mf, self.var.defuzzify_method)
return output
将引发 ValueError 的代码行包装在 try 中。并在其 except ValueError: 子句中决定要做什么。也许 continue 继续下一次迭代可能是合理的。