那些遇到过的问题

如何获得所有极端方差的PCA所需的组件数量？

Yan*_*ank 8 pca scikit-learn

我试图获得需要用于分类的组件数量.我已经阅读了类似的问题使用scikit-learn PCA和关于此的scikit文档查找具有最高方差的维度:

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.我的所有PCA组件都非常大,因为我可以选择所有这些组件,但如果我这样做,PCA将毫无用处.

我还在scikit中阅读了PCA库 http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html 它表明了L:

如果n_components =='mle',如果0 <n_components <1,则使用Minka的MLE猜测维度,选择组件数量,使得需要解释的方差量大于n_components指定的百分比

但是,我无法找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_)
Run Code Online (Sandbox Code Playgroud)

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]
Run Code Online (Sandbox Code Playgroud)

我尝试过PCA(n_components ='mle')但是我遇到了这些错误.. 

    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
Run Code Online (Sandbox Code Playgroud)

非常感谢任何帮助......

Ado*_*orn 5

我没有使用Python,但我没有你需要的东西C++: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;
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基本的想法是决定你需要的"任何%"组件.我选择那些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.

我没有经验scikit-learn,请让我知道,我会删除答案.

Bob*_*ith 5

我自己刚刚学习这一点，但在我看来，对 using 的引用0 < n_components < 1表明您可以设置n_components为 0.85，并且将使用解释 85% 方差所需的确切组件数量。您还可以通过打印来验证是否选择了正确数量的组件sum(pca.explained_variance_)。您应该获得数据可能达到的超过 0.85（或您选择的任何值）的最小方差百分比总和。

当然，还有更复杂的方法来选择多个组件，但根据经验，70% - 90% 是一个合理的开始。

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6 年，10 月 前
使用scikit-learn PCA查找具有最高方差的维度 22
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