在scikitt中实现K邻居分类器，每个对象有3个特性。

I would like to implement a KNeighborsClassifier with scikit-learn module (http://scikit-learn.org/dev/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)

我想要实现一个与scikitt - learning模块的KNeighborsClassifier (http://scikit-learn.org/dev/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)

I retrieve from my image solidity, elongation and Humoments features. How can i prepare these datas for training and validation? I must create a list with the 3 features [Hm, e, s] for every object i retrieved from my images (from 1 image have more objects)?

我从我的图像的稳定性，延伸和Humoments的特征。我如何为培训和验证准备这些数据?我必须为我从图像中检索到的每个对象(从1个图像中有更多的对象)创建一个包含3个特性的列表(Hm, e, s)。

I read this example(http://scikit-learn.org/dev/modules/generated/sklearn.neighbors.KNeighborsClassifier.html):

我读这个例子(http://scikit-learn.org/dev/modules/generated/sklearn.neighbors.KNeighborsClassifier.html):

X = [[0], [1], [2], [3]]
y = [0, 0, 1, 1]
from sklearn.neighbors import KNeighborsClassifier
neigh = KNeighborsClassifier(n_neighbors=3)
neigh.fit(X, y) 

print(neigh.predict([[1.1]]))
print(neigh.predict_proba([[0.9]]))

X and y are 2 features?

X和y是两个特征?

samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
from sklearn.neighbors import NearestNeighbors
neigh = NearestNeighbors(n_neighbors=1)
neigh.fit(samples) 

print(neigh.kneighbors([1., 1., 1.])) 

Why in first example use X and y and now sample?

为什么在第一个例子中使用X和y，然后是样本?

1 个解决方案

#1

Your first segment of code defines a classifier on 1d data.

X represents the feature vectors.

X表示特征向量。

[0] is the feature vector of the first data example
[1] is the feature vector of the second data example
....
[[0],[1],[2],[3]] is a list of all data examples, 
  each example has only 1 feature.

y represents the labels.

y代表了标签。

Below graph shows the idea:

下图显示了这个想法:

• Green nodes are data with label 0
• 绿色节点是带有标签0的数据。
• Red nodes are data with label 1
• 红色节点是带有标签1的数据。
• Grey nodes are data with unknown labels.
• 灰色节点是带有未知标签的数据。

    print(neigh.predict([[1.1]]))

This is asking the classifier to predict a label for x=1.1.

这要求分类器预测x=1.1的标签。

    print(neigh.predict_proba([[0.9]]))

This is asking the classifier to give membership probability estimate for each label.

这是要求分类器给出每个标签的成员概率估计。

Since both grey nodes located closer to the green, below outputs make sense.

由于两个灰色节点都靠近绿色，下面的输出是有意义的。

    [0] # green label
    [[ 0.66666667  0.33333333]]  # green label has greater probability

The second segment of code actually has good instructions on scikit-learn:

In the following example, we construct a NeighborsClassifier class from an array representing our data set and ask who’s the closest point to [1,1,1]

在下面的示例中，我们从一个表示数据集的数组构造一个邻居分类器类，并询问谁是最近的指向[1,1,1]

>>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
>>> from sklearn.neighbors import NearestNeighbors
>>> neigh = NearestNeighbors(n_neighbors=1)
>>> neigh.fit(samples) 
NearestNeighbors(algorithm='auto', leaf_size=30, ...)
>>> print(neigh.kneighbors([1., 1., 1.])) 
(array([[ 0.5]]), array([[2]]...))

There is no target value here because this is only a NearestNeighbors class, it's not a classifier, hence no labels are needed.

这里没有目标值，因为这只是一个近邻类，它不是一个分类器，因此不需要标签。

For your own problem:

Since you need a classifier, you should resort to KNeighborsClassifier if you want to use KNN approach. You might want to construct your feature vector X and label y as below:

由于您需要一个分类器，所以如果您想使用KNN方法，就应该求助于KNeighborsClassifier。你可能想要构造你的特征向量X和标签y如下:

X = [ [h1, e1, s1], 
      [h2, e2, s2],
      ...
    ]
y = [label1, label2, ..., ]