Scikit的隱藏錯誤消息學習Svm Fit

我試圖用scikit學習使用these docs來訓練支持向量機，但我收到一條錯誤消息，我不明白。難道我做錯了什麼？Scikit的隱藏錯誤消息學習Svm Fit

這是我的腳本。這個想法是我有一個文件，每行是「標籤數據」的形式。數據是一串零和一串。

svm-learn.py

import os 
import re 
from sklearn import svm 

classifier = svm.SVC() 

data = open("sd19-train-binary.txt", "r") 

labels = [] 
training_data = [] 
i = 0 

for line in data: 
    match = re.search("^(\S+) (\d+)", line) 
    label = match.group(1) 
    vector = list(match.group(2)) 
    vector = [int(x) for x in vector] 
    labels.append(label) 
    training_data.append([vector]) 
    i += 1 
    if i > 100: 
     break 

classifier.fit(training_data, labels)

當我運行它，出現這種情況：

$ python svm-learn.py 
    Traceback (most recent call last): 
    File "svm-learn.py", line 26, in <module> 
     classifier.fit(training_data, labels) 
    File "/Library/Python/2.7/site-packages/scikit_learn-0.14_git-py2.7-macosx-10.8-intel.egg/sklearn/svm/base.py", line 184, in fit 
     fit(X, y, sample_weight, solver_type, kernel) 
    File "/Library/Python/2.7/site-packages/scikit_learn-0.14_git-py2.7-macosx-10.8-intel.egg/sklearn/svm/base.py", line 228, in _dense_fit 
     max_iter=self.max_iter) 
    File "libsvm.pyx", line 53, in sklearn.svm.libsvm.fit (sklearn/svm/libsvm.c:1660) 
    ValueError: Buffer has wrong number of dimensions (expected 2, got 3)

在我輸入文件中的一行是這樣的：

W 1111111111111100001111111100011111111111100011111110011111000111111111110111111111

這用於nist特殊數據庫上的字形識別19

來源

2013-04-07 David Williams

固定，問題在附加。應該

training_data.append(vector)

，而不是

training_data.append([vector])

來源

2013-04-07 18:19:05

Scikit的隱藏錯誤消息學習Svm Fit

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