def test5DPreferences(self):
def foo(x):
return sum(x)
X = [
array([.1, .2, .3, .4]),
array([.7, .2, .3, .4]),
array([.5, .5, .5, .5]),
array([.5, .5, .5, .5]),
array([.5, .8, .5, .5]),
array([.5, .9, .9, .1]),
array([.2, .6, .1, .5])
]
GP = PrefGaussianProcess(GaussianKernel_iso(array([.1])))
for i in xrange(len(X)):
for j in xrange(i):
if foo(X[i]) == foo(X[j]):
continue
elif foo(X[i]) > foo(X[j]):
p = [X[i], X[j], 0]
else:
p = [X[j], X[i], 0]
GP.addPreferences([p])
for i in xrange(len(GP.X)):
for j in xrange(i):
# print 'foo = %.1f, GP = %.3f vs foo = %.1f, GP = %.3f' % (foo(GP.X[i]), GP.Y[i], foo(GP.X[j]), GP.Y[j])
if foo(GP.X[i]) > foo(GP.X[j]):
self.failUnless(GP.mu(GP.X[i]) > GP.mu(GP.X[j]))
else:
self.failUnless(GP.mu(GP.X[j]) > GP.mu(GP.X[i]))
def test5DPreferences(self):
def foo(x):
return sum(x)
X = [array([.1, .2, .3, .4]),
array([.7, .2, .3, .4]),
array([.5, .5, .5, .5]),
array([.5, .5, .5, .5]),
array([.5, .8, .5, .5]),
array([.5, .9, .9, .1]),
array([.2, .6, .1, .5])]
GP = PrefGaussianProcess(GaussianKernel_iso(array([.1])))
for i in xrange(len(X)):
for j in xrange(i):
if foo(X[i]) == foo(X[j]):
continue
elif foo(X[i]) > foo(X[j]):
p = [X[i], X[j], 0]
else:
p = [X[j], X[i], 0]
GP.addPreferences([p])
for i in xrange(len(GP.X)):
for j in xrange(i):
# print 'foo = %.1f, GP = %.3f vs foo = %.1f, GP = %.3f' % (foo(GP.X[i]), GP.Y[i], foo(GP.X[j]), GP.Y[j])
if foo(GP.X[i]) > foo(GP.X[j]):
self.failUnless(GP.mu(GP.X[i]) > GP.mu(GP.X[j]))
else:
self.failUnless(GP.mu(GP.X[j]) > GP.mu(GP.X[i]))
def test1DPreferences(self):
showit = False # show figures for debugging
x1 = array([.2])
x2 = array([.7])
x3 = array([.4])
x4 = array([.35])
x5 = array([.9])
x6 = array([.1])
GP = PrefGaussianProcess(GaussianKernel_ard(array([.1])))
GP.addPreferences([(x1, x2, 0)])
self.failUnless(GP.mu(x1) > GP.mu(x2))
# print GP.X
if showit:
figure(1)
clf()
S = arange(0, 1, .01)
ax = subplot(1, 3, 1)
ax.plot(S, [GP.mu(x) for x in S], 'k-')
ax.plot(GP.X, GP.Y, 'ro')
GP.addPreferences([(x3, x4, 0)])
self.failUnless(GP.mu(x1) > GP.mu(x2))
self.failUnless(GP.mu(x3) > GP.mu(x4))
if showit:
ax = subplot(1, 3, 2)
ax.plot(S, [GP.mu(x) for x in S], 'k-')
ax.plot(GP.X, GP.Y, 'ro')
# x5 is greatly preferred to x6 - we should expect f(x5)-f(x6) to have
# the most pronounced difference
GP.addPreferences([(x5, x6, 1)])
self.failUnless(GP.mu(x1) > GP.mu(x2))
self.failUnless(GP.mu(x3) > GP.mu(x4))
self.failUnless(GP.mu(x5) > GP.mu(x6))
self.failUnless(GP.mu(x5) - GP.mu(x6) > GP.mu(x1) - GP.mu(x2))
self.failUnless(GP.mu(x5) - GP.mu(x6) > GP.mu(x3) - GP.mu(x4))
if showit:
ax = subplot(1, 3, 3)
ax.plot(S, [GP.mu(x) for x in S], 'k-')
ax.plot(GP.X, GP.Y, 'ro')
show()
def test1DPreferences(self):
showit = False # show figures for debugging
x1 = array([.2])
x2 = array([.7])
x3 = array([.4])
x4 = array([.35])
x5 = array([.9])
x6 = array([.1])
GP = PrefGaussianProcess(GaussianKernel_ard(array([.1])))
GP.addPreferences([(x1, x2, 0)])
self.failUnless(GP.mu(x1) > GP.mu(x2))
# print GP.X
if showit:
figure(1)
clf()
S = arange(0, 1, .01)
ax = subplot(1, 3, 1)
ax.plot(S, [GP.mu(x) for x in S], 'k-')
ax.plot(GP.X, GP.Y, 'ro')
GP.addPreferences([(x3, x4, 0)])
self.failUnless(GP.mu(x1) > GP.mu(x2))
self.failUnless(GP.mu(x3) > GP.mu(x4))
if showit:
ax = subplot(1, 3, 2)
ax.plot(S, [GP.mu(x) for x in S], 'k-')
ax.plot(GP.X, GP.Y, 'ro')
# x5 is greatly preferred to x6 - we should expect f(x5)-f(x6) to have
# the most pronounced difference
GP.addPreferences([(x5, x6, 1)])
self.failUnless(GP.mu(x1) > GP.mu(x2))
self.failUnless(GP.mu(x3) > GP.mu(x4))
self.failUnless(GP.mu(x5) > GP.mu(x6))
self.failUnless(GP.mu(x5)-GP.mu(x6) > GP.mu(x1)-GP.mu(x2))
self.failUnless(GP.mu(x5)-GP.mu(x6) > GP.mu(x3)-GP.mu(x4))
if showit:
ax = subplot(1, 3, 3)
ax.plot(S, [GP.mu(x) for x in S], 'k-')
ax.plot(GP.X, GP.Y, 'ro')
show()