极限学习机实现

Question

我是极端学习机器（ELM）的新手，我正在尝试实现它的代码。我正在使用这个link中的示例代码。在

import elm

# load dataset
data = elm.read("iris.data")

# create a classifier
elmk = elm.ELMKernel()

# search for best parameter for this dataset
# define "kfold" cross-validation method, "accuracy" as a objective function
# to be optimized and perform 10 searching steps.
# best parameters will be saved inside 'elmk' object
elmk.search_param(data, cv="kfold", of="accuracy", eval=10)

# split data in training and testing sets
tr_set, te_set = elm.split_sets(data, training_percent=.8, perm=True)

#train and test
tr_result = elmk.train(tr_set)
te_result = elmk.test(te_set)

print(te_result.get_accuracy)

到目前为止，我只运行了代码的以下部分，但出现了一个错误：

^{pr2}$

错误如下：

Traceback (most recent call last):
File "C:/Users/Mahsa/PycharmProjects/test/ELM.py", line 16, in <module>
elmk.search_param(data, cv="kfold", of="accuracy", eval=10)
File "C:\Users\Mahsa\AppData\Local\Programs\Python\Python37\lib\site-packages\elm\elmk.py", line 489, in search_param
param_kernel=param_ranges[1])
File "C:\Users\Mahsa\AppData\Local\Programs\Python\Python37\lib\site-packages\optunity\api.py", line 212, in minimize
pmap=pmap)
File "C:\Users\Mahsa\AppData\Local\Programs\Python\Python37\lib\site-packages\optunity\api.py", line 245, in optimize
solution, report = solver.optimize(f, maximize, pmap=pmap)
File "C:\Users\Mahsa\AppData\Local\Programs\Python\Python37\lib\site-packages\optunity\solvers\CMAES.py", line 139, in optimize
sigma=self.sigma)
File "C:\Users\Mahsa\AppData\Local\Programs\Python\Python37\lib\site-packages\deap\cma.py", line 90, in __init__
self.dim = len(self.centroid)
TypeError: len() of unsized object

我花了很多时间寻找解决办法，但似乎很多人都有这个问题，没有人给他们提供任何解决办法。谁能帮帮我吗？在

顺便说一句，我知道有人在stackoverflow上发了同样的问题，但由于他没有收到任何答案，我又问了这个问题。在