""" 以无人机获取的高光谱图像为例 """ """ 1 读取高光谱数据 """ import spectral img = spectral.open_image('data.hdr') """ 2 数据预处理 """ from sklearn.decomposition import PCA X = img.load() X = X.reshape((X.shape[0]*X.shape[1], X.shape[2])) pca = PCA(n_...
from sklearn.decomposition import PCA X = img.load() X = X.reshape((X.shape[0]*X.shape[1], X.shape[2])) pca = PCA(n_components=30) X = pca.fit_transform(X) """ 3 分割数据集 """ from sklearn.model_selection import train_test_split y = img.read_band(145) X_train, X_...
from sklearn.decomposition import PCA X = img.load() X = X.reshape((X.shape[0]*X.shape[1], X.shape[2])) pca = PCA(n_components=30) X = pca.fit_transform(X) """ 3 分割数据集 """ from sklearn.model_selection import train_test_split y = img.read_band(145) X_train, X_...
The distinctive feature of FBPIC is to use a spectral decomposition in cylindrical geometry (Fourier-Bessel decomposition) for the fields. This combines the advantages of spectral 3D PIC codes (high accuracy and stability) and those of finite-difference cylindrical PIC codes (orders-of-magnitude ...
In essence, it provides a measure of how much the matrix "expands" vectors in its domain. The norm is equal to the largest singular value of A, which can be found by computing the singular value decomposition (SVD) of the matrix. If a matrix A has dimensions m x n, the mathematical...
Spectral bias, task-model alignment and noise explain generalization in kernel regression. Generalization error can exhibit non-monotonicity which can be understood through the bias and variance decomposition38,42,43, Eg = B + V, where \(B=\int {\mathrm{d}}{\bf{x}} p({\bf{x}...
Spectral Navier Stokes (and similar) solvers in Python Python 313 102 shenfun Public High performance computational platform in Python for the spectral Galerkin method Python 209 43 mpiFFT4py Public Parallel FFT in 3D or 2D using MPI for Python. Slab or pencil decomposition possible in ...
The (proportion of) explained variance was obtained using the empirical decomposition of the explained variation in the variance components form of the mixed model10. For the first approach, for each wavelength, an independent linear mixed model was fitted with fixed effects for “organ” and “...
Third, they make use of eigenvectors and eigenvalues from the graph Laplacian, in a direct analogy to the decomposition of functions in Fourier analysis. See [40, 56] for a survey of spectral methods for geometric processing. There are numerous applications of spectral methods. Levy [32] ...
Parallel FFT in 3D or 2D using MPI for Python. Slab or pencil decomposition possible in 3D. Note this rep is being deprecated in favour of mpi4py-fft (https://bitbucket.org/mpi4py/mpi4py-fft) - spectralDNS/mpiFFT4py