r = deriv.sin(x)
print "r", deriv.v(r)
print "dr/dx", deriv.v(deriv.d(r, x))
print "dr/dy", deriv.v(deriv.d(r, y))
print "d2r/dx2", deriv.v(deriv.d(r, x, x))
print "d2r/dy2", deriv.v(deriv.d(r, y, y))
print "d2r/dx/dy", deriv.v(deriv.d(r, x, y))
print "d2r/dy/dx", deriv.v(deriv.d(r, y, x))
print
print
print
import noise
x = -1 + noise.noise(3)
y = 2 + noise.noise(7)
r = x, y, x + y, x - y, x / y, x * x, x * y, y * y
print ' '.join("%7.03f" % noise.E(x) for x in r), "E"
print ' '.join("%7.03f" % noise.var(x) for x in r), "variance"
print
print "covariance matrix:"
for row in noise.cov_matrix(r):
for col in row:
print "%7.03f" % col,
print
print
for i in xrange(len(r)):
for j in xrange(len(r)):
print "%6.01f" % cov(r[i], r[j]),
print
from noise import noise, var, cov, cov_matrix, E
r = f()
v = r
go()
print cov_matrix(r)
print
print
import random
import math
def noise(variance):
return random.gauss(0, math.sqrt(variance))
def avg(l):
l = list(l)
return sum(l)/len(l)
def E(x): return x
