Here is an example showing how you can use numpy.linalg.lstsq for this task: import numpy as np x = np.linspace(0, 1, 20) y = np.linspace(0, 1, 20) X, Y = np.meshgrid(x, y, copy=False) Z = X**2 + Y**2 + np.random.rand(*X.shape)*0.01 X = X.flatten() Y = Y.flatten() A = np.array([X*0+1, X, Y, X**2, X**2*Y, X**2*Y**2, Y**2, … Read more
Here are both software and hardware versions of CRC-32C. The software version is optimized to process eight bytes at a time. The hardware version is optimized to run three crc32q instructions effectively in parallel on a single core, since the throughput of that instruction is one cycle, but the latency is three cycles. crc32c.c: /* … Read more
To get a third order polynomial in x (x^3), you can do lm(y ~ x + I(x^2) + I(x^3)) or lm(y ~ poly(x, 3, raw=TRUE)) You could fit a 10th order polynomial and get a near-perfect fit, but should you? EDIT: poly(x, 3) is probably a better choice (see @hadley below).