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scikit-learn - 如何获得具有所有极端方差的 PCA 所需的组件数量？

转载 作者：行者123 更新时间：2023-12-04 07:36:20 26 4
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我正在尝试获取用于分类的组件数量。我读过类似的问题 Finding the dimension with highest variance using scikit-learn PCA以及关于此的 scikit 文档:

http://scikit-learn.org/dev/tutorial/statistical_inference/unsupervised_learning.html#principal-component-analysis-pca

但是，这仍然没有解决我的问题。我所有的 PCA 组件都非常大，因此我可以选择所有这些组件，但如果我这样做，PCA 将毫无用处。

我还阅读了 scikit learn 中的 PCA 库
http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
这表明L:

if n_components == ‘mle’, Minka’s MLE is used to guess the dimension if 0 < n_components < 1, select the number of components such that the amount of variance that needs to be explained is greater than the percentage specified by n_components



但是，我找不到有关使用此技术分析 PCA 的 n_components 的更多信息

这是我的PCA分析代码: 
from sklearn.decomposition import PCA
    pca = PCA()
    pca.fit(x_array_train)
    print(pca.explained_variance_)

结果: 
   [  6.58902714e+50   6.23266555e+49   2.93568652e+49   2.25418736e+49
       1.10063872e+49   3.25107359e+40   4.72113817e+39   1.40411862e+39
       4.03270198e+38   1.60662882e+38   3.20028861e+28   2.35570241e+27
       1.54944915e+27   8.05181151e+24   1.42231553e+24   5.05155955e+23
       2.90909468e+23   2.60339206e+23   1.95672973e+23   1.22987336e+23
       9.67133111e+22   7.07208772e+22   4.49067983e+22   3.57882593e+22
       3.03546737e+22   2.38077950e+22   2.18424235e+22   1.79048845e+22
       1.50871735e+22   1.35571453e+22   1.26605081e+22   1.04851395e+22
       8.88191944e+21   6.91581346e+21   5.43786989e+21   5.05544020e+21
       4.33110823e+21   3.18309135e+21   3.06169368e+21   2.66513522e+21
       2.57173046e+21   2.36482212e+21   2.32203521e+21   2.06033130e+21
       1.89039408e+21   1.51882514e+21   1.29284842e+21   1.26103770e+21
       1.22012185e+21   1.07857244e+21   8.55143095e+20   4.82321416e+20
       2.98301261e+20   2.31336276e+20   1.31712446e+20   1.05253795e+20
       9.84992112e+19   8.27574150e+19   4.66007620e+19   4.09687463e+19
       2.89855823e+19   2.79035170e+19   1.57015298e+19   1.39218538e+19
       1.00594159e+19   7.31960049e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.29043685e+18   5.29043685e+18   5.29043685e+18
       5.29043685e+18   5.24952686e+18   2.09685699e+18   4.16588190e+17]

我试过 PCA(n_components = 'mle') 但是我得到了这些错误.. 
    Traceback (most recent call last):
  File "xx", line 166, in <module>
    pca.fit(x_array_train)
  File "xx", line 225, in fit
    self._fit(X)
  File "/Users/lib/python2.7/site-packages/sklearn/decomposition/pca.py", line 294, in _fit
    n_samples, n_features)
  File "/Users/lib/python2.7/site-packages/sklearn/decomposition/pca.py", line 98, in _infer_dimension_
    ll[rank] = _assess_dimension_(spectrum, rank, n_samples, n_features)
  File "/Users/lib/python2.7/site-packages/sklearn/decomposition/pca.py", line 83, in _assess_dimension_
    (1. / spectrum_[j] - 1. / spectrum_[i])) + log(n_samples)
ValueError: math domain error

真的很感激任何帮助......

最佳答案

我没有使用 Python ，但我在 C++ 做了一些你需要的事情& opencv .希望您成功将其转换为任何语言。

// choose how many eigenvectors you want:
int nEigensOfInterest = 0;
float sum = 0.0;
for (int i = 0; i < mEiVal.rows; ++i)
{
    sum += mEiVal.at<float>(i, 0);
    if (((sum * 100) / (sumOfEigens)) > 80)
    {
        nEigensOfInterest = i;
        break;
    }
}
logfile << "No of Eigens of interest: " << nEigensOfInterest << std::endl << std::endl;

基本思想是决定您需要继续使用的“任何 %”组件。我选择了那些 80 . mEiVal是按降序排序的特征值的列矩阵。 sumOfEigens是所有特征值的总和。

我没有 scikit-learn 的经验，请告诉我，我会删除答案。

关于scikit-learn - 如何获得具有所有极端方差的 PCA 所需的组件数量？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/30802444/

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