2015-02-08 30 views
25

我一直在使用scikit-learn庫。我試圖在scikit-learn庫中使用高斯樸素貝葉斯模塊,但我遇到了以下錯誤。 TypeError:無法使用彈性類型執行縮減TypeError:無法使用靈活類型執行縮減

下面是代碼段。

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

這是目標

['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'] 

這是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']] 

下面是堆棧跟蹤

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 

回答

69

它看起來像你的 'trainData' 是名單字符串:

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

將您的'trainData'更改爲數字類型。

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

謝謝,這解決了問題:-) – 2015-02-09 17:55:16

+1

優秀的觀察! :) – Dataman 2016-05-09 13:41:33

+1

@JAB爲什麼我們需要數字型數據? – 2017-10-25 10:12:11

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