python - Gaussian NB fit() 函数需要固定长度的向量

Question

我正在尝试实现这样的高斯 NB 训练。然而，如果 X 的维度不相等(即 X 内的所有列表需要具有相同的长度)，则 gnb.fit() 会引发异常。如果我的训练样本是不同长度的向量，调用 fit() 的正确方法是什么？

 def train(X, Y):
   gnb = GaussianNB()
   gnb.fit(X, Y)
   return gnb

 >>> X = [[1,2,3], [4,5,6,7], [8,9]]
 >>> Y = [1,1,1]
 >>> snb.train(X, Y)

 /Library/Python/2.7/site-packages/sklearn/utils/validation.py:395: 
 DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 
 and will raise ValueError in 0.19. Reshape your data either using 
 X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) 
 if it contains a single sample.
 DeprecationWarning)
 Traceback (most recent call last):
 File "<stdin>", line 1, in <module>
 File "snb.py", line 113, in train
 gnb.fit(X, Y)
 File "/Library/Python/2.7/site-packages/sklearn/naive_bayes.py", line 
 182, in fit
 X, y = check_X_y(X, y)
 File "/Library/Python/2.7/site-packages/sklearn/utils/validation.py", 
 line 521, in check_X_y
 ensure_min_features, warn_on_dtype, estimator)
 File "/Library/Python/2.7/site-packages/sklearn/utils/validation.py", 
 line 402, in check_array
 array = array.astype(np.float64)
 ValueError: setting an array element with a sequence.

Answer 1

所有 X 向量必须长度相同。高斯朴素贝叶斯估计器旨在根据一组因素进行预测。如果每个 X 都有一个可变的数字，分类器如何确定哪个元素属于哪个因子？

一种选择是用 0 值填充 X 向量，以确保它们的长度相等。否则，您需要考虑变量预处理。