Understand your Mac and iPhone more deeply by tracing the evolution of Mac OS X from prelease to Swift. John Siracusa delivers the details.
You've got two main options:
import numpy as np | |
import pylab as pl | |
from numpy import fft | |
def fourierExtrapolation(x, n_predict): | |
n = x.size | |
n_harm = 10 # number of harmonics in model | |
t = np.arange(0, n) | |
p = np.polyfit(t, x, 1) # find linear trend in x | |
x_notrend = x - p[0] * t # detrended x |
# prior - likelihood conflict | |
library(rethinking) | |
yobs <- 0 | |
mtt <- ulam( | |
alist( | |
y ~ dstudent(2,mu,1), | |
mu ~ dstudent(2,10,1) |