Months later, here’s a small class PCA, and a picture: #!/usr/bin/env python “”” a small class for Principal Component Analysis Usage: p = PCA( A, fraction=0.90 ) In: A: an array of e.g. 1000 observations x 20 variables, 1000 rows x 20 columns fraction: use principal components that account for e.g. 90 % of the … Read more
Foreword I think you are falling prey to the XY problem, since trying to find 153.600 dimensions in your data is completely non-physical, please ask about the problem (X) and not your proposed solution (Y) in order to get a meaningful answer. I will use this post only to tell you why PCA is not … Read more
This information is included in the pca attribute: components_. As described in the documentation, pca.components_ outputs an array of [n_components, n_features], so to get how components are linearly related with the different features you have to: Note: each coefficient represents the correlation between a particular pair of component and feature import pandas as pd import … Read more
Here’s a quick walkthrough. First we create a matrix of your hidden variables (or “factors”). It has 100 observations and there are two independent factors. >> factors = randn(100, 2); Now create a loadings matrix. This is going to map the hidden variables onto your observed variables. Say your observed variables have four features. Then … Read more
First of all, I assume that you call features the variables and not the samples/observations. In this case, you could do something like the following by creating a biplot function that shows everything in one plot. In this example, I am using the iris data. Before the example, please note that the basic idea when … Read more
I posted my answer even though another answer has already been accepted; the accepted answer relies on a deprecated function; additionally, this deprecated function is based on Singular Value Decomposition (SVD), which (although perfectly valid) is the much more memory- and processor-intensive of the two general techniques for calculating PCA. This is particularly relevant here … Read more
When logical vectors are used for indexing, they are recycled so this gets you odd columns or odd rows calld[ c(TRUE,FALSE), ] # rows calld[ , c(TRUE,FALSE) ] #columns Even rows or columns: calld[ !c(TRUE,FALSE), ] # rows calld[ , !c(TRUE,FALSE) ] #columns Every third column: calld[ , c(TRUE,FALSE, FALSE) ] #columns 1,4,7 , …. … Read more