error while running the example:
elmk = elm.ELMKernel()
elmk.search_param(X, y, cv="kfold", of="accuracy", eval=10)
tr_set, te_set = elm.split_sets(data, training_percent=.8, perm=True)
tr_result = elmk.train(tr_set)
te_result = elmk.test(te_set)
print(te_result.get_accuracy)
error message:
`elmk
Start search
TypeError Traceback (most recent call last)
in
6 # to be optimized and perform 10 searching steps.
7 # best parameters will be saved inside 'elmk' object
----> 8 elmk.search_param(X, y, cv="kfold", of="accuracy", eval=10)
9
10 # split data in training and testing sets
c:\users\e580\appdata\local\programs\python\python36\lib\site-packages\elm\elmk.py in search_param(self, database, dataprocess, path_filename, save, cv, of, kf, eval)
487 num_evals=eval,
488 param_c=param_ranges[0],
--> 489 param_kernel=param_ranges[1])
490
491 elif kernel_function == "poly":
c:\users\e580\appdata\local\programs\python\python36\lib\site-packages\optunity\api.py in minimize(f, num_evals, solver_name, pmap, **kwargs)
210 solver = make_solver(**suggestion)
211 solution, details = optimize(solver, func, maximize=False, max_evals=num_evals,
--> 212 pmap=pmap)
213 return solution, details, suggestion
214
c:\users\e580\appdata\local\programs\python\python36\lib\site-packages\optunity\api.py in optimize(solver, func, maximize, max_evals, pmap)
243 time = timeit.default_timer()
244 try:
--> 245 solution, report = solver.optimize(f, maximize, pmap=pmap)
246 except fun.MaximumEvaluationsException:
247 # early stopping because maximum number of evaluations is reached
c:\users\e580\appdata\local\programs\python\python36\lib\site-packages\optunity\solvers\CMAES.py in optimize(self, f, maximize, pmap)
137 else:
138 strategy = deap.cma.Strategy(centroid=self.start.values(),
--> 139 sigma=self.sigma)
140 toolbox.register("generate", strategy.generate, Individual)
141 toolbox.register("update", strategy.update)
c:\users\e580\appdata\local\programs\python\python36\lib\site-packages\deap\cma.py in init(self, centroid, sigma, **kargs)
88 self.centroid = numpy.array(centroid)
89
---> 90 self.dim = len(self.centroid)
91 self.sigma = sigma
92 self.pc = numpy.zeros(self.dim)
TypeError: len() of unsized object
`