svm_err, svs, t2 - t1)
# Time
# Memory usage
# Number of SV
# Create Prediction
dimTrain = Xc.shape
nPred = 1000
xPred = np.zeros((nPred, dimTrain[1]))
for i in range(dimTrain[1]):
xPred[:, i] = np.linspace(min(Xc[:, i]), max(Xc[:, i]), nPred).reshape(
(nPred, ))
y_RVR_pred, var_RVR_pred = rvr.predict_dist(xPred)
y_SVR_pred = svr.predict(xPred)
## plot test vs .1predicted data
predDim = 0 #5-1 # dim-1
plt.figure(figsize=(16, 10))
plt.plot(X[:, predDim], Y, "k+", markersize=3, label="train data")
plt.plot(x[:, predDim], y, "b+", markersize=3, label="test data")
plt.plot(xPred[:, predDim],
y_RVR_pred,
"b",
markersize=3,
label="RVR prediction")
plt.plot(xPred[:, predDim],
