svd

问题 This question already has an answer here : Why Sklearn TruncatedSVD's explained variance ratios are not in descending order? (1 answer) Closed 9 months ago . This question is actually a duplicate of this one, which however remains unanswered at the time of writing. Why is the explained_variance_ratio_ from TruncatedSVD not in descending order like it would be from PCA ? In my experience it seems that the first element of the list is always the lowest, and then at the second element the value

问题 I'm training a model in Pytorch and I want to use truncated SVD decomposition of input. For calculating SVD I transfer input witch is a Pytorch Cuda Tensor to CPU and using TruncatedSVD from scikit-learn perform truncate, after that, I transfer the result back to GPU. The following is code for my model: class ImgEmb(nn.Module): def __init__(self, input_size, hidden_size): super(ImgEmb, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.drop = nn.Dropout(0.2)

问题 I'm training a model in Pytorch and I want to use truncated SVD decomposition of input. For calculating SVD I transfer input witch is a Pytorch Cuda Tensor to CPU and using TruncatedSVD from scikit-learn perform truncate, after that, I transfer the result back to GPU. The following is code for my model: class ImgEmb(nn.Module): def __init__(self, input_size, hidden_size): super(ImgEmb, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.drop = nn.Dropout(0.2)

来源： https://stackoverflow.com/questions/63270458/bad-alloc-exception-thrown-when-using-svd-from-eigen-library

来源： https://stackoverflow.com/questions/63270458/bad-alloc-exception-thrown-when-using-svd-from-eigen-library

来源： https://stackoverflow.com/questions/60133809/speed-up-svd-in-pytorch

来源： https://stackoverflow.com/questions/63351431/opencv-c-segmentation-fault-11-after-mat-multiplication

问题 I'm trying to reproduce some large project that was written in Matlab, using python. I managed to reproduce most of the results, but I have a problem specifically with SVD decomposition. (I'm looking only on the last, V, part.) In Matlab: [~, ~, V] = svd([4.719, -17.257, -11.5392; -17.2575, 63.9545, 40.5581; -11.5392, 40.5581, 31.3256]); This gets me the following V: -0.2216 0.0241 -0.9748 0.8081 -0.5549 -0.1974 0.5457 0.8316 -0.1035 in numpy: np.linalg.svd(np.array([[4.71993, -17.2575, -11

问题 I'm trying to reproduce some large project that was written in Matlab, using python. I managed to reproduce most of the results, but I have a problem specifically with SVD decomposition. (I'm looking only on the last, V, part.) In Matlab: [~, ~, V] = svd([4.719, -17.257, -11.5392; -17.2575, 63.9545, 40.5581; -11.5392, 40.5581, 31.3256]); This gets me the following V: -0.2216 0.0241 -0.9748 0.8081 -0.5549 -0.1974 0.5457 0.8316 -0.1035 in numpy: np.linalg.svd(np.array([[4.71993, -17.2575, -11

问题 Why Sklearn.decomposition.TruncatedSVD 's explained variance ratios are not ordered by singular values? My code is below: X = np.array([[1,1,1,1,0,0,0,0,0,0,0,0,0,0], [0,0,1,1,1,1,1,1,1,0,0,0,0,0], [0,0,0,0,0,0,1,1,1,1,1,1,0,0], [0,0,0,0,0,0,0,0,0,0,1,1,1,1]]) svd = TruncatedSVD(n_components=4) svd.fit(X4) print(svd.explained_variance_ratio_) print(svd.singular_values_) and the results: [0.17693405 0.46600983 0.21738089 0.13967523] [3.1918354 2.39740372 1.83127499 1.30808033] I heard that a