I ran into a strange issue fitting a line to a small number of data points using numpy.polyfit that I thought was worth documenting.
I ran a command of the form:
p, cov = np.polyfit(x,y,1,w,cov=True),
where x, y and w were arrays of length 3.
The command returned the correct slope and y-intercept values, however the covariance matrix, cov, had strictly negative diagonal terms. This is apparantly because numpy scales the covariance matrix as described in here.
The scaling applied is a factor such that
factor = resids / (len(x) - order - 2.0)
If, like me, you are making a first order polynomial fit to a dataset of 3 values, the denominator has the effect of multiplying the expected matrix by -1. If I was unlucky enough to have 4 points, it would have thrown bigger errors.
In my case, looking at the results here, I could recover the correct values just by multiplying the matrix by minus one. This is a strange weighting to apply to a small dataset - I assume it makes sense if you have many points and the developers wanted to keep numpy.polyfit consistent.
Thursday, 19 November 2015
Alchemy allows users to donate to their chose charities via text message, which it used to do automatically using Android's SmsManager ...
Recently I needed to use python to extract the contents of a password-protected zip archive and I came across a few issues I thought would b...
I need to use python for face recognition as part of the server for a charity app I'm working on. The app is supposed to show charity lo...
I've recently installed AIPS (version 31DEC16) on Ubuntu 16.04. Here are my notes on the installation experience. Due to recent compiler...