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