code stringlengths 3 6.57k |
|---|
range(self.n_class) |
range(i+1,self.n_class) |
self.createBinaryClass(i,j) |
GPC() |
model.setPrior(mean=self.meanfunc, kernel=self.covfunc) |
model.useInference(self.newInf) |
model.useLikelihood(self.newLik) |
model.getPosterior(x,y) |
model.predict(xs) |
np.zeros((xs.shape[0],self.n_class) |
np.zeros((xs.shape[0],self.n_class) |
predictive_vote.sum(axis=1) |
optimizeAndPredict(self, xs) |
points (given by xs) |
np.reshape(xs, (xs.shape[0],1) |
np.zeros((xs.shape[0],self.n_class) |
range(self.n_class) |
range(i+1,self.n_class) |
self.createBinaryClass(i,j) |
GPC() |
model.setPrior(mean=self.meanfunc, kernel=self.covfunc) |
model.useInference(self.newInf) |
model.useLikelihood(self.newLik) |
model.optimize(x,y) |
model.predict(xs) |
np.zeros((xs.shape[0],self.n_class) |
np.zeros((xs.shape[0],self.n_class) |
predictive_vote.sum(axis=1) |
createBinaryClass(self, i,j) |
x(data) |
y(label) |
x(data) |
y(label) |
range(len(self.y_all) |
class_i.append(index) |
class_j.append(index) |
len(class_i) |
len(class_j) |
class_i.extend(class_j) |
np.concatenate((np.ones((1,n1) |
np.ones((1,n2) |
GP_FITC(GP) |
__init__(self) |
super(GP_FITC, self) |
__init__() |
setData(self, x, y, value_per_axis=5) |
shape (n,D) |
shape (n,1) |
np.reshape(x, (x.shape[0],1) |
np.reshape(y, (y.shape[0],1) |
np.mean(y) |
mean.Const(c) |
range(x.shape[1]) |
np.min(column) |
np.max(column) |
np.linspace(mini,maxi,value_per_axis) |
gridAxis.append(axis) |
np.array(list(itertools.product(*gridAxis) |
self.covfunc.fitc(self.u) |
setPrior(self, mean=None, kernel=None, inducing_points=None) |
class. (e.g. mean.Linear() |
class. (e.g. cov.RBF() |
of (nu,D) |
kernel.fitc(inducing_points) |
kernel.fitc(self.u) |
Exception("To use default inducing points, please call setData() |
type(kernel) |
GPR_FITC(GP_FITC) |
__init__(self) |
super(GPR_FITC, self) |
__init__() |
mean.Zero() |
cov.RBF() |
lik.Gauss() |
inf.FITC_Exact() |
opt.Minimize(self) |
setNoise(self,log_sigma) |
lik.Gauss(log_sigma) |
setOptimizer(self, method, num_restarts=None, min_threshold=None, meanRange=None, covRange=None, likRange=None) |
if (num_restarts!=None) |
or (min_threshold!=None) |
pyGPs.Optimization.conf.random_init_conf(self.meanfunc,self.covfunc,self.likfunc) |
opt.Minimize(self,conf) |
opt.SCG(self,conf) |
opt.CG(self,conf) |
opt.BFGS(self,conf) |
plot(self,axisvals=None) |
plt.figure() |
np.reshape(self.xs,(self.xs.shape[0],) |
np.reshape(self.ym,(self.ym.shape[0],) |
np.reshape(self.ys2,(self.ys2.shape[0],) |
plt.plot(self.x, self.y, color=DATACOLOR, ls='None', marker='+',ms=12, mew=2) |
plt.plot(self.xs, self.ym, color=MEANCOLOR, ls='-', lw=3.) |
plt.fill_between(xss,ymm + 2.*np.sqrt(ys22) |
np.sqrt(ys22) |
plt.grid() |
plt.axis(axisvals) |
plt.xlabel('input x') |
plt.ylabel('output y') |
plt.plot(self.u,np.ones_like(self.u) |
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