我目前正在尝试训练在sklearn中实施的MLPClassifier ... 当我尝试用给定的值训练它时，我得到这个错误：
ValueError：使用序列设置数组元素。
feature_vector的格式为
[[one_hot_encoded brandname]，[不同的应用程序缩放为0和方差1]]
有人知道我做错了吗？
谢谢！
feature_vectors：
[
数组（[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0，0， 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0， 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0， 0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0， 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0， 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0， 0.，0.0,0.0,0.0,0.0,0.0,0.0,0。]），
数组（[0.82211852，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818,4.45590895，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818,0.3439882，-0.22976818，-0.22976818，-0.22976818， 4.93403927，-0.22976818，-0.22976818，-0.22976818，0.63086639， 1.10899671，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818,1.58712703，-0.22976818， 1.77837916，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818,2.16088342，-0.22976818,2.16088342， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818,9.42846428，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， 0.91774459，-0.22976818，-0.22976818,4.16903076，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818,2.44776161， -0.22976818，-0.22976818，-0.22976818,1.96963129,1.96963129， 1.96963129，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818,7.13343874， 5.98592598，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， 3.02151799,4.26465682，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818,2.25650948，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， 1.30024884，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818,4.74278714，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818,0.3439882， -0.22976818,0.3439882，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818,0.53524033，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818， -0.22976818，-0.22976818，-0.22976818，-0.22976818,3.49964831， -0.22976818，-0.22976818，-0.22976818，-0.22976818，-0.22976818]）
g_a_group：
[0。0. 0. 0 0. 0 0. 0 0. 0 1. 1. 0。]
MLP：
来自sklearn.neural_network导入MLPClassifier
clf = MLPClassifier（求解器=＆＃39; lbfgs＆＃39;，alpha = 1e-5， hidden_layer_sizes =（5,2），random_state = 1）
clf.fit（feature_vectors，g_a_group）
答案 0 :(得分：1)
从.fit调用中对scikit-learn的看法，您的数据没有任何意义。特征向量应该是大小为N x d的矩阵，其中N - 数据点的数量和d 特征的数量 ，并且您的第二个变量应该包含标签，因此它应该是长度为N的向量（或N x k，其中k是每个点的输出/标签数量）。无论变量中代表什么 - 它们的大小与它们应该代表的大小都不匹配。