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<p>我正在使用sklearn在我自己的一组图像上应用支持向量机。图像被放入数据帧中。
我给fit函数传递一个numpy数组,它有2D列表,这些2D列表表示图像,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:用序列设置数组元素”。</p>
<pre><code>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())
</code></pre>
<p>错误:</p>
<pre><code>---------------------------------------------------------------------------
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.
</code></pre>