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我正在尝试探索 Scikit DBSCAN。有件事我想知道。我怎样才能知道每个集群中的点。

此代码是scipy 网站中的示例：

import numpy as np

from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn.datasets.samples_generator import make_blobs
from sklearn.preprocessing import StandardScaler


##############################################################################
# Generate sample data
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,
                            random_state=0)

X = StandardScaler().fit_transform(X)

##############################################################################
# Compute DBSCAN
db = DBSCAN(eps=0.3, min_samples=10).fit(X)
core_samples = db.core_sample_indices_
labels = db.labels_

# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)

print('Estimated number of clusters: %d' % n_clusters_)
print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))
print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))
print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))
print("Adjusted Rand Index: %0.3f"
      % metrics.adjusted_rand_score(labels_true, labels))
print("Adjusted Mutual Information: %0.3f"
      % metrics.adjusted_mutual_info_score(labels_true, labels))
print("Silhouette Coefficient: %0.3f"


         % metrics.silhouette_score(X, labels))
        ##############################################################################
    #Modification I am doing 
    print labels 
    print labels[0]

unique_labels = set(labels)

for k in unique_labels:
    class_members = [index[0] for index in np.argwhere(labels == k)]
    #cluster_core_samples = [index for index in core_samples if labels[index] == k]

    print class_members[0]

    for index in class_members:
        x = X[index]
    print x

看来我需要找到一种算法来逆向工程

StandardScaler().fit_transform(X)

DBSCAN 的 scipy 实现见DBSCAN Code - DBSCAN Test Unit

我想打印属于每个集群的三个集群和点。

更新

当我尝试运行 inverse_transform() 函数时，出现错误

文件“/Users/macbook/anaconda/lib/python2.7/site-packages/sklearn/preprocessing/data.py”，第 384 行，inverse_transform

你可以在这里找到代码： https ://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/data.py

        if self.with_std:
            X *= self.std_
        if self.with_mean:
            X += self.mean_

这是我得到错误的地方。有什么想法可以解决这个问题吗？

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1 回答 1

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看来我需要找到一种算法来逆向工程

StandardScaler().fit_transform(X)

sklearn 中的数据转换是“可逆的”（如果它们不是有损的），您应该存储您的缩放器对象。

s = StandardScaler()
X = s.fit_transform(X)

然后如果你想检索未缩放的版本

X = s.inverse_transform(X)

关于评论

Standard Scaler 可以很好地双向转换数据。

>>> from sklearn.preprocessing import StandardScaler
>>> import numpy as np
>>> x = np.array( [[1.0,2.0],[0.0,-4.0]])
>>> s = StandardScaler()
>>> x
array([[ 1.,  2.],
       [ 0., -4.]])
>>> a=s.fit_transform(x)
>>> a
array([[ 1.,  1.],
       [-1., -1.]])
>>> s.inverse_transform(a)
array([[ 1.,  2.],
       [ 0., -4.]])
>>>
于 2014-04-09T08:48:45.913 回答