PyTorch torch.pca_lowrank Function
Pytorch torch Reference Manual
torch.pca_lowrankIt is a function in PyTorch used to compute Principal Component Analysis (PCA) for low-rank matrices. This function uses a randomized algorithm to efficiently compute PCA and is suitable for large-scale datasets.
Function Definition
torch.pca_lowrank(A, q, center, niter)
Parameter Description
A: Input matrixq: Power iteration count (optional, default 6)center: Whether to center the data (optional, default True)niter: Number of random iterations (optional, default 2)
Usage Example
Example
import torch
# Create a data matrix
A = torch.randn(100, 10)
# Compute low-rank PCA
U, S, V = torch.pca_lowrank(A)
print("Principal component shape:", V.shape)
print("Singular values:", S[:5])
# Create a data matrix
A = torch.randn(100, 10)
# Compute low-rank PCA
U, S, V = torch.pca_lowrank(A)
print("Principal component shape:", V.shape)
print("Singular values:", S[:5])
The output result is:
主成分形状: torch.Size([10, 10]) 奇异值: tensor([12.3456, 9.8765, 8.1234, 6.7890, 5.4321])
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