[英]Get projection matrix of lda in scikit-learn
I need to get the projection matrix from lda, which has been supplied the train data, so that I can use that to project the train data in the lda space. 我需要从提供了火车数据的lda中获得投影矩阵,以便可以使用它在lda空间中投影火车数据。
I have done the following : 我已经完成以下工作:
def get_projection(features,label):
transformer = LDA(store_covariance=True)
transformer.fit_transform(features,label)
cov_mat = transformer.covariance_
return cov_mat
I have then extracted the eigen vectors of the covariance matrix. 然后,我提取了协方差矩阵的特征向量。 But that doesn't seem to give correct solution.
但这似乎并未提供正确的解决方案。 Even the .scalings_ attribute doesn't seem to be helpful.
甚至.scalings_属性似乎也无济于事。 Kindly help me find the projection matrix from this method, so that I can apply it on test data, which don't have labels.
请帮助我从此方法中找到投影矩阵,以便将其应用于没有标签的测试数据。
You can apply the transformer directly on test data by transformer.transform(test_data)
. 您可以通过
transformer.transform(test_data)
将变压器直接应用于测试数据。 See documentation of LDA here. 请参阅此处的LDA 文档 。
Note: LDA has been deprecated and now its recommended to use LinearDiscriminantAnalysis . 注意:LDA已被弃用,现在建议使用LinearDiscriminantAnalysis 。
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