python – sklearn SVM fit()“ValueError：使用序列设置数组元素”

2019年8月3日 267次阅读

我正在使用sklearn在我自己的图像集上应用svm.图像放在数据框中.

我传递给fit函数一个具有2D列表的numpy数组,这些2D列表代表图像,我传递给函数的第二个输入是目标列表(目标是数字).

我总是得到这个错误“ValueError：设置一个带序列的数组元素”.

trainingImages = images.ix[images.partID <=9]
trainingTargets = images.clustNo.ix[images.partID<=9]
trainingImages.reset_index(inplace=True,drop=True)
trainingTargets.reset_index(inplace=True,drop=True)

classifier = svm.SVC(gamma=0.001)
classifier.fit(trainingImages.image.values,trainingTargets.values.tolist())

错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-43-5336fbeca868> in <module>()
      8 classifier = svm.SVC(gamma=0.001)
      9 
---> 10 classifier.fit(trainingImages.image.values,trainingTargets.values.tolist())
     11 
     12 #classifier.fit(t, list(range(0,2899)))

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/sklearn/svm/base.py in fit(self, X, y, sample_weight)
    148         self._sparse = sparse and not callable(self.kernel)
    149 
--> 150         X = check_array(X, accept_sparse='csr', dtype=np.float64, order='C')
    151         y = self._validate_targets(y)
    152 

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    371                                       force_all_finite)
    372     else:
--> 373         array = np.array(array, dtype=dtype, order=order, copy=copy)
    374 
    375         if ensure_2d:

ValueError: setting an array element with a sequence.

最佳答案 我有同样的错误,它是两种可能性之一：

1- Data and labels are not in the same length.

2- For a specific feature vector, the number of elements are not
equal.

点赞