# only needed for 2-d contour plotting x1 = demoData['x1'] # x for class 1 (with label -1) x2 = demoData['x2'] # x for class 2 (with label +1) t1 = demoData['t1'] # y for class 1 (with label -1) t2 = demoData['t2'] # y for class 2 (with label +1) p1 = demoData['p1'] # prior for class 1 (with label -1) p2 = demoData['p2'] # prior for class 2 (with label +1) #---------------------------------------------------------------------- # First example -> state default values #---------------------------------------------------------------------- print 'Basic Example - Data' model = pyGPs.GPC() # binary classification (default inference method: EP) model.plotData_2d(x1,x2,t1,t2,p1,p2) model.getPosterior(x, y) # fit default model (mean zero & rbf kernel) with data model.optimize(x, y) # optimize hyperparamters (default optimizer: single run minimize) model.predict(z) # predict test cases print 'Basic Example - Prediction' model.plot(x1,x2,t1,t2) #---------------------------------------------------------------------- # GP classification example #---------------------------------------------------------------------- print 'More Advanced Example' # Start from a new model model = pyGPs.GPC()
else:
y[i, 0] = -1
y = np.int8(y)
#----------------------------------------------------------------------
# Cross Validation
#----------------------------------------------------------------------
K = 10 # number of fold
ACC = [] # accuracy
RMSE = [] # root-mean-square error
cv_run = 0
for x_train, x_test, y_train, y_test in valid.k_fold_validation(x, y, K):
print 'Run:', cv_run
# This is a binary classification problem
model = pyGPs.GPC()
# Since no prior knowldege, leave everything default
model.optimize(x_train, y_train)
# Predit
ymu, ys2, fmu, fs2, lp = model.predict(x_test, ys=y_test)
# ymu for classification is a continuous value over -1 to +1
# If you want predicting result to either one of the classes, take a sign of ymu.
ymu_class = np.sign(ymu)
# Evluation
acc = valid.ACC(ymu_class, y_test)
print ' accuracy =', round(acc, 2)
rmse = valid.RMSE(ymu_class, y_test)
print ' rmse =', round(rmse, 2)
ACC.append(acc)