code stringlengths 3 6.57k |
|---|
__repr__(self) |
str(type(self) |
setData(self, x, y) |
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) |
plotData_1d(self, axisvals=None) |
plt.figure() |
plt.plot(self.x, self.y, ls='None', marker='+', color=DATACOLOR, ms=12, mew=2) |
plt.axis(axisvals) |
plt.grid() |
plt.xlabel('input x') |
plt.ylabel('target y') |
plt.show() |
plotData_2d(self,x1,x2,t1,t2,p1,p2,axisvals=None) |
plt.contour(t1, t2, p2/(p1+p2) |
are (only) |
plt.figure() |
plt.plot(x1[:,0], x1[:,1], 'b+', markersize = 12) |
plt.plot(x2[:,0], x2[:,1], 'r+', markersize = 12) |
plt.contour(t1, t2, np.reshape(old_div(p2,(p1+p2) |
fig.colorbar(pc) |
plt.grid() |
plt.axis(axisvals) |
plt.show() |
setPrior(self, mean=None, kernel=None) |
class. (e.g. mean.Linear() |
class. (e.g. cov.RBF() |
isinstance(mean, pyGPs.mean.Mean) |
isinstance(kernel, pyGPs.cov.Kernel) |
type(kernel) |
setOptimizer(self, method, num_restarts=None, min_threshold=None, meanRange=None, covRange=None, likRange=None) |
Rasmussen (python implementation of "minimize" in GPML) |
Shanno (BFGS) |
gradient (faster than CG) |
range (low, high) |
range.
(-5,5) |
optimize40(self, x=None, y=None, numIterations=40) |
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) |
self.optimizer.findMin(self.x, self.y, numIters = numIterations) |
self.optimizer._apply_in_objects(optimalHyp) |
self.getPosterior() |
optimize(self, x=None, y=None, numIterations=1000) |
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) |
self.optimizer.findMin(self.x, self.y, numIters = numIterations) |
self.optimizer._apply_in_objects(optimalHyp) |
self.getPosterior() |
getPosterior(self, x=None, y=None, der=True) |
likelihood(nlZ) |
hyperparameter(dnlZ) |
the (approximate) |
posterior(post) |
getPosterior(x, y, der=True) |
getPosterior(x, y, der=False ) |
shape (n,D) |
shape (n,1) |
likelihood (nlZ) |
nlZ (dnlZ) |
structure(post) |
np.reshape(x, (x.shape[0],1) |
np.reshape(y, (y.shape[0],1) |
np.mean(y) |
mean.Const(c) |
isinstance(self.likfunc, lik.Erf) |
instance(self.likfunc, lik.Logistic) |
unique(self.y) |
any( uy[ind] != -1) |
Exception('You attempt classification using labels different from {+1,-1}') |
self.inffunc.evaluate(self.meanfunc, self.covfunc, self.likfunc, self.x, self.y, 2) |
deepcopy(post) |
self.inffunc.evaluate(self.meanfunc, self.covfunc, self.likfunc, self.x, self.y, 3) |
deepcopy(dnlZ) |
deepcopy(post) |
predict(self, xs, ys=None) |
points (given by xs) |
means(ym) |
variances(ys2) |
means(fm) |
variances(fs2) |
probabilities(lp) |
property. (e.g. model.ym) |
target(optional) |
np.reshape(xs, (xs.shape[0],1) |
np.reshape(ys, (ys.shape[0],1) |
self.getPosterior() |
list(range(len(alpha[:,0]) |
len(L) |
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