def plot1(gp, ngrid=100, lim=None, k=range(-3, 4)):
k = sorted(k)
if lim is None:
lim = (np.amin(gp.x[:, 1]), np.amax(gp.x[:, 1]))
x = np.linspace(lim[0], lim[1], ngrid).T
(m, v) = gp.inf(x)
m = np.asarray(m).squeeze()
v = np.asarray(v).squeeze()
plt.plot(x, m,
color=DARKBLUE,
linewidth=2)
for i in k:
if i == 0: continue
lo = m - i*np.sqrt(v)
hi = m + i*np.sqrt(v)
plt.fill_between(x, lo, hi,
linestyle='solid',
edgecolor=DARKGRAY,
facecolor=LIGHTGRAY,
alpha=0.2)
plt.plot(gp.x, gp.y,
'o',
markersize=8,
markeredgewidth=1,
markeredgecolor=DARKGRAY,
markerfacecolor=LIGHTBLUE)
plt.xlim(lim)
def __init__(self, kernel, fun=None, data=None, h=0, name='untitled'):
if not fun and not data:
raise Exception('Need to provide either fun or data')
self.kernel = kernel
if fun:
self.type = 'fun'
self.fun = fun
self.lim = fun.lim
xtrain = np.zeros((0, 2))
ytrain = np.zeros((0, 1))
else:
self.type = 'data'
self.data = data
self.lim = {}
self.lim['x1'] = (np.amin(self.data['x'][:,0]),
np.amax(self.data['x'][:,0]))
self.lim['x2'] = (np.amin(self.data['x'][:,1]),
np.amax(self.data['x'][:,1]))
xtrain = self.data['x']
ytrain = self.data['y']
self.model = gp.GP(kernel)
self.model.add(xtrain, ytrain)
self.name = name
self.h = h
def __init__(self, kernel, fun=None, data=None, h=0, name='untitled'):
if not fun and not data:
raise Exception('Need to provide either fun or data')
self.kernel = kernel
if fun:
self.type = 'fun'
self.fun = fun
self.lim = fun.lim
xtrain = np.zeros((0, 2))
ytrain = np.zeros((0, 1))
else:
self.type = 'data'
self.data = data
self.lim = {}
self.lim['x1'] = (np.amin(self.data['x'][:, 0]),
np.amax(self.data['x'][:, 0]))
self.lim['x2'] = (np.amin(self.data['x'][:, 1]),
np.amax(self.data['x'][:, 1]))
xtrain = self.data['x']
ytrain = self.data['y']
self.model = gp.GP(kernel)
self.model.add(xtrain, ytrain)
self.name = name
self.h = h
def almostEqual(A, B, eps=0.01):
return np.amax(abs(A - B)) < eps
def softmax(self, y):
maxy = np.amax(y)
probas = np.exp(y-maxy)
probas = probas / probas.sum(axis=0)
return np.array(probas)
def almostEqual(A, B, eps=0.01):
return np.amax(abs(A - B)) < eps
