count_cls=np.bincount(y_map).astype(np.int32) start_cls = count_cls.cumsum() start_cls=np.insert(start_cls,0,0).astype(np.int32) i=start_cls[ bin_cls[0] ]+1 j=start_cls[ bin_cls[1] ]+1 print i,j #--------------------- num_el,dim = X.shape gamma = 0.5 threadsPerRow = 1 prefetch=2 rbf = ker.RBF() rbf.gamma=gamma rbf.init(X,Y) vecI = X[i,:].toarray() vecJ = X[j,:].toarray() import time #t0=time.clock() t0=time.time() #ki =Y[i]*Y* rbf.K_vec(vecI).flatten() #kj =Y[j]*Y*rbf.K_vec(vecJ).flatten()
#
# xy = start_pos
# xx = xy[:,0]
# yy = xy[:,1]
c1 = 1.
c2 = 1.
gamma = 1.1
epsilon = 0.01
sv_val_2d = []
sv_test_2d = []
sk_test_2d = []
sk_val_2d = []
kernel_2d_gpr = Kernels.RBF([1., 1.]) * Kernels.ConstantKernel()
sv_test_2d.append(sv.GPR(Kernels.ConstantKernel() * kernel_2d_gpr))
sv_test_2d.append(sv.RLS(Kernels.RBF()))
sk_test_2d.append(
gpr.GaussianProcessRegressor(gpr.kernels.RBF([1., 1.]) *
gpr.kernels.ConstantKernel(),
normalize_y=True))
sk_test_2d.append(svm.SVR(kernel='rbf', gamma=gamma, epsilon=epsilon))
##
for element in sk_test_2d:
element.fit(xy, energy(xx, yy).reshape(-1))
sk_val_2d.append((element.__class__.__name__, element.predict(xy_pred)))
grad_x, grad_y = gradient(xx, yy)
grad = np.concatenate([grad_x.reshape(-1, 1), grad_y.reshape(-1, 1)], axis=1)
max_steps = 50
if molecule == 'Ammonia':
neb_method = 'improved'
max_force = 1e-3
elif molecule == 'Ethane':
neb_method = 'simple_improved'
max_force = 2e-3
calc_idpp = True
# optimizer
delta_t = 3.5
opt_fire = NEB.Fire(delta_t, 2 * delta_t, trust_radius)
opt = NEB.Optimizer()
kernel = Kernels.RBF([0.8])
C1 = 1e6
C2 = 1e7
eps = 1e-5
restarts = 5
opt_steps = 1
optimize_parameters = True
norm_y = True
#ml_method = MLDerivative.IRWLS(kernel, C1=C1, C2=C2, epsilon=1e-5, epsilon_prime=1e-5, max_iter=1e4)
# ml_method = MLDerivative.RLS(kernel, C1=C1, C2=C2)
#ml_method = MLDerivative.GPR(kernel, opt_restarts=restarts, opt_parameter=optimize_parameters, noise_value = 1./C1,
# noise_derivative=1./C2, normalize_y=norm_y)
ml_method = NNModel.NNModel(molecule,
C1=C1,
acc = (0.0 + sum(Y == pred1)) / len(Y) print 'acc=', acc print '--------------\n' #np.random.seed(0) #n=6 #X = np.random.randn(n, 2) #Y = np.random.randint(1,4,n) #X = np.array([ (1,2), (3,4), (5,6), (7,8), (9,0)]) #Y = np.array([4,1,2,1,4]) svm_solver = slv.FOSVM(X, Y, C) #kernel = Linear() kernel = ker.RBF() t0 = time.clock() svm_solver.init(kernel) t1 = time.clock() print '\nInit takes', t1 - t0 t0 = time.clock() svm_solver.train() t1 = time.clock() print '\nTakes: ', t1 - t0 for k in xrange(len(svm_solver.models)):
