我一直在使用scikit-learn库。我正在尝试在scikit-learn库下使用高斯朴素贝叶斯模块,但遇到以下错误。 TypeError:无法使用灵活类型执行归约

下面是代码片段。

training = GaussianNB()
training = training.fit(trainData, target)
prediction = training.predict(testData)

这是目标
['ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'ALL', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML', 'AML']

这是trainData
[['-214' '-153' '-58' ..., '36' '191' '-37']
['-139' '-73' '-1' ..., '11' '76' '-14']
['-76' '-49' '-307' ..., '41' '228' '-41']
...,
['-32' '-49' '49' ..., '-26' '133' '-32']
['-124' '-79' '-37' ..., '39' '298' '-3']
['-135' '-186' '-70' ..., '-12' '790' '-10']]

下面是堆栈跟踪
Traceback (most recent call last):
File "prediction.py", line 90, in <module>
  gaussianNaiveBayes()
File "prediction.py", line 76, in gaussianNaiveBayes
  training = training.fit(trainData, target)
File "/Library/Python/2.7/site-packages/sklearn/naive_bayes.py", line 163, in fit
  self.theta_[i, :] = np.mean(Xi, axis=0)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/ core/fromnumeric.py", line 2716, in mean
  out=out, keepdims=keepdims)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/core/_methods.py", line 62, in _mean
  ret = um.add.reduce(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
TypeError: cannot perform reduce with flexible type

最佳答案

看起来您的“trainData”是一个字符串列表:

['-214' '-153' '-58' ..., '36' '191' '-37']

将您的“trainData”更改为数字类型。
 import numpy as np
 np.array(['1','2','3']).astype(np.float)

08-24 23:29