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The first step is to perform Principal Components Analysis on X, using the pca function, and retaining two principal components.
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This tutorial explains how to run a B-spline fitting algorithm on a point-cloud, to obtain a smooth, parametric surface representation. Fitting trimmed B-splines to unordered point clouds.If you are interested, you can take a look.Code address, The operation result is shown in the figure below. The use of PCA to fit the plane is what I saw when I was reading Feng Chen’s "Fast Plane Extraction in Organized Point Clouds Using Agglomerative Hierarchical Clustering" paper.pca calls pmag.domean(), so that is what we will do. The file format is a simple space delimited file with specimen name, treatment step, intensity, declination and inclination. pca calculates best-fit lines, planes or Fisher means through selected treatment steps along with Kirschvink (1980, doi: 10.1111/j.) MAD values.The n_components parameter denotes the number of Principal Components you want to fit your data with: 1.
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The below code snippet uses the pre-defined PCA () function provided by the sklearn package in order to transform the data.