类型错误:无法使用灵活类型执行减少 [英] TypeError: cannot perform reduce with flexible type

问题描述

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

I have been using the scikit-learn library. I'm trying to use the Gaussian Naive Bayes Module under the scikit-learn library but I'm running into the following error. TypeError: cannot perform reduce with flexible type

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

This is 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

推荐答案

看起来你的 'trainData' 是一个字符串列表:

It looks like your 'trainData' is a list of strings:

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

将您的trainData"更改为数字类型.

Change your 'trainData' to a numeric type.

 import numpy as np
 np.array(['1','2','3']).astype(np.float)

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