vowpal_wabbit 训练数据
vowpal_wabbit training data
我正在尝试理解 vowpal_wabbit 训练和测试数据的数据结构，但似乎无法理解它们。
我有一些像这样的训练数据。
功能 1：0
特征 2：1
特征 3：10
特征 4：5
Class 标签：A
功能 1：0
特征 2：2
特征 3：30
特征 4：8
Class 标签：C
特征 1：2
特征 2：10
特征 3：9
特征 4：7
Class 标签：B
我已经根据这个网站探索了一些训练数据示例。
http://hunch.net/~vw/validate.html
我的验证数据
1 | haha:1 hehe:2 hoho:3
1 | haha:2 hehe:2 hoho:3
3 | haha:3 hehe:2 hoho:3
1 | haha:4 hehe:2 hoho:3
2 | haha:5 hehe:2 hoho:3
但是，我不明白为什么它说我分别有4个和5个特征。
验证：
验证反馈
Total of 5 examples pasted.
(example #1) Example “1 | haha:1 hehe:2 hoho:3”.
(example #1) Found “[label] |…” prefix format.
(example #1) Example label / response / class is “1”.
(example #1) Example has default “1.0” importance weight.
(example #1) Example has default “0” base.
(example #1, namespace #1) Using default namespace.
(example #1, namespace #1) Found 3 feature(s).
(example #1, namespace #1, feature #1) Label “haha”.
(example #1, namespace #1, feature #1) Value “1”.
(example #1, namespace #1, feature #2) Label “hehe”.
(example #1, namespace #1, feature #2) Value “2”.
(example #1, namespace #1, feature #3) Label “hoho”.
(example #1, namespace #1, feature #3) Value “3”.
(example #2) Example “1 | haha:2 hehe:2 hoho:3 ”.
(example #2) Found “[label] |…” prefix format.
(example #2) Example label / response / class is “1”.
(example #2) Example has default “1.0” importance weight.
(example #2) Example has default “0” base.
(example #2, namespace #1) Using default namespace.
(example #2, namespace #1) Found 4 feature(s).
(example #2, namespace #1, feature #1) Label “haha”.
(example #2, namespace #1, feature #1) Value “2”.
(example #2, namespace #1, feature #2) Label “hehe”.
(example #2, namespace #1, feature #2) Value “2”.
(example #2, namespace #1, feature #3) Label “hoho”.
(example #2, namespace #1, feature #3) Value “3”.
(example #2, namespace #1, feature #4) Label “”.
(example #2, namespace #1, feature #4) Using default value of “1” for feature.
(example #3) Example “3 | haha:3 hehe:2 hoho:3 ”.
(example #3) Found “[label] |…” prefix format.
(example #3) Example label / response / class is “3”.
(example #3) Example has default “1.0” importance weight.
(example #3) Example has default “0” base.
(example #3, namespace #1) Using default namespace.
(example #3, namespace #1) Found 4 feature(s).
(example #3, namespace #1, feature #1) Label “haha”.
(example #3, namespace #1, feature #1) Value “3”.
(example #3, namespace #1, feature #2) Label “hehe”.
(example #3, namespace #1, feature #2) Value “2”.
(example #3, namespace #1, feature #3) Label “hoho”.
(example #3, namespace #1, feature #3) Value “3”.
(example #3, namespace #1, feature #4) Label “”.
(example #3, namespace #1, feature #4) Using default value of “1” for feature.
(example #4) Example “1 | haha:4 hehe:2 hoho:3 ”.
(example #4) Found “[label] |…” prefix format.
(example #4) Example label / response / class is “1”.
(example #4) Example has default “1.0” importance weight.
(example #4) Example has default “0” base.
(example #4, namespace #1) Using default namespace.
(example #4, namespace #1) Found 4 feature(s).
(example #4, namespace #1, feature #1) Label “haha”.
(example #4, namespace #1, feature #1) Value “4”.
(example #4, namespace #1, feature #2) Label “hehe”.
(example #4, namespace #1, feature #2) Value “2”.
(example #4, namespace #1, feature #3) Label “hoho”.
(example #4, namespace #1, feature #3) Value “3”.
(example #4, namespace #1, feature #4) Label “”.
(example #4, namespace #1, feature #4) Using default value of “1” for feature.
(example #5) Example “2 | haha:5 hehe:2 hoho:3 ”.
(example #5) Found “[label] |…” prefix format.
(example #5) Example label / response / class is “2”.
(example #5) Example has default “1.0” importance weight.
(example #5) Example has default “0” base.
(example #5, namespace #1) Using default namespace.
(example #5, namespace #1) Found 5 feature(s).
(example #5, namespace #1, feature #1) Label “haha”.
(example #5, namespace #1, feature #1) Value “5”.
(example #5, namespace #1, feature #2) Label “hehe”.
(example #5, namespace #1, feature #2) Value “2”.
(example #5, namespace #1, feature #3) Label “hoho”.
(example #5, namespace #1, feature #3) Value “3”.
(example #5, namespace #1, feature #4) Label “”.
(example #5, namespace #1, feature #4) Using default value of “1” for feature.
(example #5, namespace #1, feature #5) Label “”.
(example #5, namespace #1, feature #5) Using default value of “1” for feature.
why it claims that i have 4 and 5 features respectively
行末的额外 space 符号被 http://hunch.net/~vw/validate.html 解释为额外特征。 （是的，示例中的最后一行有两个额外的 space。）请注意 validate.html 报告额外功能的空名称：
(example #4, namespace #1, feature #4) Label “”.
请注意 validate.html 在 JavaScript 中实现并且完全独立于 VW 本身的实现（在 C 中）。 VW 忽略尾随的 spaces。您可以使用以下方法对其进行测试：
$ vw -P 1 < sample.data
...
average since example example current current current
loss last counter weight label predict features
1.000000 1.000000 1 1.0 1.0000 0.0000 4
0.522042 0.044084 2 2.0 1.0000 0.7900 4
1.838150 4.470366 3 3.0 3.0000 0.8857 4
1.488676 0.440255 4 4.0 1.0000 1.6635 4
1.270585 0.398217 5 5.0 2.0000 1.3690 4
所以所有五个示例都被报告为具有 4 个特征（请参阅最后一列）。
为什么是四个？自动添加了一个额外的常量（拦截）功能。如果你不想，你可以使用 vw --noconstant.
我正在尝试理解 vowpal_wabbit 训练和测试数据的数据结构，但似乎无法理解它们。
我有一些像这样的训练数据。
功能 1：0 特征 2：1 特征 3：10 特征 4：5 Class 标签：A
功能 1：0 特征 2：2 特征 3：30 特征 4：8 Class 标签：C
特征 1：2 特征 2：10 特征 3：9 特征 4：7 Class 标签：B
我已经根据这个网站探索了一些训练数据示例。
http://hunch.net/~vw/validate.html
我的验证数据
1 | haha:1 hehe:2 hoho:3
1 | haha:2 hehe:2 hoho:3
3 | haha:3 hehe:2 hoho:3
1 | haha:4 hehe:2 hoho:3
2 | haha:5 hehe:2 hoho:3
但是，我不明白为什么它说我分别有4个和5个特征。
验证：
验证反馈
Total of 5 examples pasted.
(example #1) Example “1 | haha:1 hehe:2 hoho:3”.
(example #1) Found “[label] |…” prefix format.
(example #1) Example label / response / class is “1”.
(example #1) Example has default “1.0” importance weight.
(example #1) Example has default “0” base.
(example #1, namespace #1) Using default namespace.
(example #1, namespace #1) Found 3 feature(s).
(example #1, namespace #1, feature #1) Label “haha”.
(example #1, namespace #1, feature #1) Value “1”.
(example #1, namespace #1, feature #2) Label “hehe”.
(example #1, namespace #1, feature #2) Value “2”.
(example #1, namespace #1, feature #3) Label “hoho”.
(example #1, namespace #1, feature #3) Value “3”.
(example #2) Example “1 | haha:2 hehe:2 hoho:3 ”.
(example #2) Found “[label] |…” prefix format.
(example #2) Example label / response / class is “1”.
(example #2) Example has default “1.0” importance weight.
(example #2) Example has default “0” base.
(example #2, namespace #1) Using default namespace.
(example #2, namespace #1) Found 4 feature(s).
(example #2, namespace #1, feature #1) Label “haha”.
(example #2, namespace #1, feature #1) Value “2”.
(example #2, namespace #1, feature #2) Label “hehe”.
(example #2, namespace #1, feature #2) Value “2”.
(example #2, namespace #1, feature #3) Label “hoho”.
(example #2, namespace #1, feature #3) Value “3”.
(example #2, namespace #1, feature #4) Label “”.
(example #2, namespace #1, feature #4) Using default value of “1” for feature.
(example #3) Example “3 | haha:3 hehe:2 hoho:3 ”.
(example #3) Found “[label] |…” prefix format.
(example #3) Example label / response / class is “3”.
(example #3) Example has default “1.0” importance weight.
(example #3) Example has default “0” base.
(example #3, namespace #1) Using default namespace.
(example #3, namespace #1) Found 4 feature(s).
(example #3, namespace #1, feature #1) Label “haha”.
(example #3, namespace #1, feature #1) Value “3”.
(example #3, namespace #1, feature #2) Label “hehe”.
(example #3, namespace #1, feature #2) Value “2”.
(example #3, namespace #1, feature #3) Label “hoho”.
(example #3, namespace #1, feature #3) Value “3”.
(example #3, namespace #1, feature #4) Label “”.
(example #3, namespace #1, feature #4) Using default value of “1” for feature.
(example #4) Example “1 | haha:4 hehe:2 hoho:3 ”.
(example #4) Found “[label] |…” prefix format.
(example #4) Example label / response / class is “1”.
(example #4) Example has default “1.0” importance weight.
(example #4) Example has default “0” base.
(example #4, namespace #1) Using default namespace.
(example #4, namespace #1) Found 4 feature(s).
(example #4, namespace #1, feature #1) Label “haha”.
(example #4, namespace #1, feature #1) Value “4”.
(example #4, namespace #1, feature #2) Label “hehe”.
(example #4, namespace #1, feature #2) Value “2”.
(example #4, namespace #1, feature #3) Label “hoho”.
(example #4, namespace #1, feature #3) Value “3”.
(example #4, namespace #1, feature #4) Label “”.
(example #4, namespace #1, feature #4) Using default value of “1” for feature.
(example #5) Example “2 | haha:5 hehe:2 hoho:3 ”.
(example #5) Found “[label] |…” prefix format.
(example #5) Example label / response / class is “2”.
(example #5) Example has default “1.0” importance weight.
(example #5) Example has default “0” base.
(example #5, namespace #1) Using default namespace.
(example #5, namespace #1) Found 5 feature(s).
(example #5, namespace #1, feature #1) Label “haha”.
(example #5, namespace #1, feature #1) Value “5”.
(example #5, namespace #1, feature #2) Label “hehe”.
(example #5, namespace #1, feature #2) Value “2”.
(example #5, namespace #1, feature #3) Label “hoho”.
(example #5, namespace #1, feature #3) Value “3”.
(example #5, namespace #1, feature #4) Label “”.
(example #5, namespace #1, feature #4) Using default value of “1” for feature.
(example #5, namespace #1, feature #5) Label “”.
(example #5, namespace #1, feature #5) Using default value of “1” for feature.
why it claims that i have 4 and 5 features respectively
行末的额外 space 符号被 http://hunch.net/~vw/validate.html 解释为额外特征。 （是的，示例中的最后一行有两个额外的 space。）请注意 validate.html 报告额外功能的空名称：
(example #4, namespace #1, feature #4) Label “”.
请注意 validate.html 在 JavaScript 中实现并且完全独立于 VW 本身的实现（在 C 中）。 VW 忽略尾随的 spaces。您可以使用以下方法对其进行测试：
$ vw -P 1 < sample.data
...
average since example example current current current
loss last counter weight label predict features
1.000000 1.000000 1 1.0 1.0000 0.0000 4
0.522042 0.044084 2 2.0 1.0000 0.7900 4
1.838150 4.470366 3 3.0 3.0000 0.8857 4
1.488676 0.440255 4 4.0 1.0000 1.6635 4
1.270585 0.398217 5 5.0 2.0000 1.3690 4
所以所有五个示例都被报告为具有 4 个特征（请参阅最后一列）。
为什么是四个？自动添加了一个额外的常量（拦截）功能。如果你不想，你可以使用 vw --noconstant.