def _fit_gp(X, covX, t):
xx, xy, yy = covX[0, 0], covX[0, 1], covX[1, 1]
# Perform hyper-parameter optimization with different
# initial points and choose the GP with best model evidence
theta0 = np.array((t.std(), sqrt(xx), sqrt(yy), xy, .01))
from gp import GaussianProcess, sqexp2D_covariancef
best_gp = GaussianProcess.fit(X, t, sqexp2D_covariancef, theta0)
from sklearn.model_selection import ParameterSampler
from scipy.stats import uniform
grid = {
'sigmaf': uniform(0.1 * t.std(), 10 * t.std()),
'cov_xx': uniform(50, 2000),
'cov_yy': uniform(50, 2000),
'noise_prec': uniform(0.1, 10)
}
for i, sample in enumerate(ParameterSampler(grid, n_iter=500)):
sys.stdout.write('iter: %d/500 evidence: %.4f\r' %
(i, best_gp.model_evidence()))
sys.stdout.flush()
theta0 = np.array((sample['sigmaf'], sample['cov_xx'],
sample['cov_yy'], 0, sample['noise_prec']))
gp = GaussianProcess.fit(X, t, sqexp2D_covariancef, theta0)
if gp.model_evidence() > best_gp.model_evidence():
best_gp = gp
return best_gp
class BayesianOpt(object):
def __init__(self, kernel):
self.kernel_init = kernel
self.gp = None
self.X = []
self.Y = []
def init(self):
self.gp = None
self.X = []
self.Y = []
def train(self, x, y,
doOptmization = False, params_min = None, params_max = None):
self.X.append(x)
self.Y.append(y)
self.gp = GaussianProcess(self.X, self.Y,
self.kernel_init if len(self.X) == 1 else self.gp.kernel)
if doOptmization:
self.gp.optimize(params_min = params_min, params_max = params_max)
def pick_next_input(self):
raise NotImplementedError("overwrite this")
def predict(sa):
idx = kdt.query(sa.T, k=K, return_distance=False)
X_nn = Xtrain[idx, :].reshape(K, state_action_dim)
Y_nn = Ytrain[idx, :].reshape(K, state_dim)
if useDiffusionMaps:
X_nn, Y_nn = reduction(sa, X_nn, Y_nn)
m = np.zeros(state_dim)
s = np.zeros(state_dim)
for i in range(state_dim):
if i == 0:
gp_est = GaussianProcess(X_nn[:, :4],
Y_nn[:, i],
optimize=True,
theta=None)
theta = gp_est.cov.theta
else:
gp_est = GaussianProcess(X_nn[:, :4],
Y_nn[:, i],
optimize=False,
theta=theta)
m[i], s[i] = gp_est.predict(sa[:4].reshape(1, -1)[0])
return m, s
def experiment_kiss(dataset=PUMADYN32NM, m=100, proj_d=2, cov='covSEard', standardize=True, proj=None):
assert proj in {None, 'norm', 'orth'}
train_x, train_y, test_x, test_y = load_dataset(dataset)
if standardize:
scaler = StandardScaler()
train_x = scaler.fit_transform(train_x)
test_x = scaler.transform(test_x)
n, d = train_x.shape
print 'Train KISS with {} data points and {} dimension'.format(n, d)
# get GP functionality
gp = GaussianProcess()
# subtract mean
train_y -= np.mean(train_y)
test_y -= np.mean(train_y)
# projection matrix
P = np.random.normal(size=(proj_d, d))
# initialization
hyp_lik = float(0.5 * np.log(np.var(train_y) / 4))
if cov == 'covSEard':
init_x = np.dot(train_x, P.T)
init_ell = np.log((np.max(init_x, axis=0) - np.min(init_x, axis=0)) / 2)
hyp_cov = np.append(init_ell, [0.5 * np.log(np.var(train_y))])
else:
hyp_cov = np.asarray([np.log(5), 0.5 * np.log(np.var(train_y))])
hyp_old = {'mean': [], 'lik': hyp_lik, 'cov': hyp_cov, 'proj': P}
opt = {'cg_maxit': 500, 'cg_tol': 1e-5}
if proj is not None:
opt['proj'] = proj
hyp = gp.train_kiss(train_x, train_y.reshape(-1, 1), k=m, hyp=hyp_old, opt=opt, n_iter=100)
test_mean, test_var = gp.predict_kiss(train_x, train_y.reshape(-1, 1), k=m, xstar=test_x, opt=opt, hyp=hyp, cov=cov)
print 'KISS error:'
print_error(test_y, test_mean)
def batch_predict(SA):
sa = np.mean(SA, 0)
idx = kdt.query(sa.reshape(1, -1), k=K, return_distance=False)
X_nn = Xtrain[idx, :].reshape(K, state_action_dim)
Y_nn = Ytrain[idx, :].reshape(K, state_dim)
if useDiffusionMaps:
X_nn, Y_nn = reduction(sa, X_nn, Y_nn)
m = np.zeros((SA.shape[0], state_dim))
s = np.zeros((SA.shape[0], state_dim))
for i in range(state_dim):
if i == 0:
gp_est = GaussianProcess(X_nn[:, :4],
Y_nn[:, i],
optimize=False,
theta=None)
theta = gp_est.cov.theta
else:
gp_est = GaussianProcess(X_nn[:, :4],
Y_nn[:, i],
optimize=False,
theta=theta)
mm, ss = gp_est.batch_predict(SA[:, :4])
m[:, i] = mm
s[:, i] = np.diag(ss)
return m, s
def one_predict(self, sa):
Theta, _ = self.get_theta(
sa) # Get hyper-parameters for this query point
K = self.K
idx = self.kdt.query(sa.reshape(1, -1), k=K, return_distance=False)
X_nn = self.Xtrain[idx, :].reshape(K, self.state_action_dim)
Y_nn = self.Ytrain[idx, :].reshape(K, self.state_dim)
if useDiffusionMaps:
X_nn, Y_nn = self.reduction(sa, X_nn, Y_nn)
ds_next = np.zeros((self.state_dim, ))
std_next = np.zeros((self.state_dim, ))
for i in range(self.state_dim):
gp_est = GaussianProcess(X_nn[:, :self.state_action_dim],
Y_nn[:, i],
optimize=False,
theta=Theta[i],
algorithm='Matlab')
mm, vv = gp_est.predict(sa[:self.state_action_dim])
ds_next[i] = mm
std_next[i] = np.sqrt(vv)
s_next = sa[:self.
state_dim] + ds_next #np.random.normal(ds_next, std_next)
if self.state_dim == 5:
s_next[4] += 1.0 if s_next[4] < 0.0 else 0.0
s_next[4] -= 1.0 if s_next[4] > 1.0 else 0.0
return s_next
def batch_predict(self, SA):
sa = np.mean(SA, 0)
# Theta, K = self.get_theta(sa) # Get hyper-parameters for this query point
K = 1
idx = self.kdt.query(sa.reshape(1, -1), k=K, return_distance=False)
X_nn = self.Xtrain[idx, :].reshape(K, self.state_action_dim)
Y_nn = self.Ytrain[idx, :].reshape(K, self.state_dim)
if useDiffusionMaps:
X_nn, Y_nn = self.reduction(sa, X_nn, Y_nn)
dS_next = np.zeros((SA.shape[0], self.state_dim))
std_next = np.zeros((SA.shape[0], self.state_dim))
for i in range(self.state_dim):
gp_est = GaussianProcess(X_nn[:, :self.state_action_dim],
Y_nn[:, i],
optimize=True,
theta=None,
algorithm='Matlab')
mm, vv = gp_est.batch_predict(SA[:, :self.state_action_dim])
dS_next[:, i] = mm
std_next[:, i] = np.sqrt(np.diag(vv))
S_next = SA[:, :self.
state_dim] + dS_next #np.random.normal(dS_next, std_next)
return S_next
def example_1d():
kern = Matern23(1, l=0.2, noise=0.01)
cov = np.zeros((1, 1))
X = [(np.array(e), cov) for e in [[0.0], [0.3], [1.0]]]
Y = [5.0, 7.0, 6.0]
gp = GaussianProcess(X, Y, kern)
gp.show(N_grid=200, bmin=-5, bmax=5)
plt.show()
def train(self, x, y,
doOptmization = False, params_min = None, params_max = None):
self.X.append(x)
self.Y.append(y)
self.gp = GaussianProcess(self.X, self.Y,
self.kernel_init if len(self.X) == 1 else self.gp.kernel)
if doOptmization:
self.gp.optimize(params_min = params_min, params_max = params_max)
def one_predict(sa, X, Y, kdt, K=100):
state_dim = 4
idx = kdt.query(sa.reshape(1, -1), k=K, return_distance=False)
X_nn = X[idx, :].reshape(K, state_dim + 2)
Y_nn = Y[idx, :].reshape(K, state_dim)
ds_next = np.zeros((state_dim, ))
std_next = np.zeros((state_dim, ))
try:
for i in range(state_dim):
if i == 0:
gp_est = GaussianProcess(X_nn[:, :state_dim],
Y_nn[:, i],
optimize=True,
theta=None)
Theta = gp_est.cov.theta
else:
gp_est = GaussianProcess(X_nn[:, :state_dim],
Y_nn[:, i],
optimize=False,
theta=Theta)
mm, vv = gp_est.predict(sa[:state_dim])
ds_next[i] = mm
std_next[i] = np.sqrt(np.diag(vv))
except:
print "pass"
return np.array([-1, -1, -1, -1]), np.array([-1, -1, -1, -1])
# SKlearn
# for i in range(state_dim):
# gp_est = GaussianProcessRegressor(n_restarts_optimizer=9)
# gp_est.fit(X_nn[:,:state_dim], Y_nn[:,i])
# mm, vv = gp_est.predict(sa[:state_dim].reshape(1,-1), return_std=True)
# ds_next[i] = mm
# std_next[i] = vv#np.sqrt(np.diag(vv))
# GPy
# for i in range(state_dim):
# kernel = GPy.kern.RBF(input_dim=state_dim, variance=1., lengthscale=1.)
# gp_est = GPy.models.GPRegression(X_nn[:,:state_dim], Y_nn[:,i].reshape(-1,1), kernel)
# gp_est.optimize(messages=False)
# # m.optimize_restarts(num_restarts = 10)
# mm, vv = gp_est.predict(sa[:state_dim].reshape(1,state_dim))
# ds_next[i] = mm
# std_next[i] = np.sqrt(np.diag(vv))
s_next = sa[:4] + ds_next
return s_next, std_next, X_nn
def test_cpu_gpu(self):
C = torch.tensor([1.0])
l = torch.rand(5)
X = torch.randn(10, 5)
y = torch.randn(10, 1)
Y = torch.randn(10, 5)
X1 = torch.randn(2, 5)
matern = Matern52(C, l)
d2matern = Deriv2Matern52(C, l)
gp1 = GaussianProcess(ZeroScalarMean(), matern, 1e-2)
gp2 = GaussianProcess(ZeroVectorMean(), d2matern, 1e-2)
self.assertTrue(check_device(gp1, X, y, X1))
self.assertTrue(check_device(gp2, X, Y, X1))
def _fit_gp(X, covX, t):
xx, xy, yy = covX[0,0], covX[0,1], covX[1,1]
# Perform hyper-parameter optimization with different
# initial points and choose the GP with best model evidence
theta0 = np.array(( t.std(), sqrt(xx), sqrt(yy), xy, 10. ))
best_gp = GaussianProcess.fit(X, t, sqexp2D_covariancef, theta0)
for tau in xrange(50, 800, 100):
theta0 = np.array(( t.std(), tau, tau, 0, 10. ))
gp = GaussianProcess.fit(X, t, sqexp2D_covariancef, theta0)
if gp.model_evidence() > best_gp.model_evidence():
best_gp = gp
return best_gp
def make_gp():
gp = GaussianProcess(
lambda x, y, params: partial(periodic, params=params)
(x, y) + params["ratio"] * partial(rbf, params=params)(x, y),
sigma=0.03,
)
return gp
class TestGP:
def setUp(self):
self.gp = GaussianProcess()
def test_sparse(self):
N = 1000
x = 1000*np.random.rand(N)
y = np.sin(x) + 0.1*np.random.randn(N)
# do it using dense algebra
Kxx = self.gp.K(x,x).todense()
alpha = np.linalg.solve(Kxx,y)
# sparse algebra
self.gp.fit(x,y)
assert np.linalg.norm(alpha-self.gp._alpha) < 1e-10
def example_2d():
kern = RBF(dim=2, l=0.3, noise=0.01)
cov = np.zeros((2, 2))
X = [np.array(e) for e in [[0, 0.0], [0.3, 0.7], [0.6, 0.2], [1.0, 1.0]]]
Y = [2, 3, -2, 1]
gp = GaussianProcess(X, Y, kern)
return gp
def example_3d():
kern = RBF(dim=3, l=0.3, noise=0.01)
cov = np.zeros((3, 3))
X = [(np.array(e, dtype=float), cov)
for e in [[0, 0, 0], [0.3, 0.7, -0.2], [0.6, 0.2, -0.8],
[0.3, 1.0, 1.0]]]
Y = [2, 3, -2, 1]
gp = GaussianProcess(X, Y, kern)
return gp
def batch_predict_iterative(self, SA):
S_next = []
while SA.shape[0]:
sa = np.copy(SA[np.random.randint(SA.shape[0]), :])
Theta, K = self.get_theta(
sa) # Get hyper-parameters for this query point
D, idx = self.kdt.query(sa.reshape(1, -1),
k=K,
return_distance=True)
r = np.max(D) * 1.1
X_nn = self.Xtrain[idx, :].reshape(K, self.state_action_dim)
Y_nn = self.Ytrain[idx, :].reshape(K, self.state_dim)
neigh = NearestNeighbors(radius=r)
neigh.fit(SA)
idx_local = neigh.radius_neighbors(sa.reshape(1, -1),
return_distance=False)[0]
SA_local = np.copy(SA[idx_local, :])
SA = np.delete(SA, idx_local, axis=0)
if useDiffusionMaps:
X_nn, Y_nn = self.reduction(sa, X_nn, Y_nn)
dS_next = np.zeros((SA_local.shape[0], self.state_dim))
std_next = np.zeros((SA_local.shape[0], self.state_dim))
for i in range(self.state_dim):
gp_est = GaussianProcess(X_nn[:, :self.state_action_dim],
Y_nn[:, i],
optimize=False,
theta=Theta[i],
algorithm='Matlab')
mm, vv = gp_est.batch_predict(
SA_local[:, :self.state_action_dim])
dS_next[:, i] = mm
std_next[:, i] = np.sqrt(np.diag(vv))
S_next_local = SA_local[:, :self.state_dim] + np.random.normal(
dS_next, std_next)
for s in S_next_local:
S_next.append(s)
return np.array(S_next)
def test_basic(self):
"""
1. GP forward output is a mvNormal
2. GP predict output is a set of normal distributions
3. sample shape
"""
C = torch.tensor([3.0])
l = torch.rand(3)
X1 = torch.randn(10, 3)
X2 = torch.randn(2, 3)
y1 = torch.randn(10, 1)
y1prime = torch.randn(10, 3)
scalar_kernels = [Matern52(C, l), RBF(C, l)]
for k in scalar_kernels:
gp = GaussianProcess(ZeroScalarMean(), k, 0.1)
self.assertTrue(basic_test(gp, X1, y1, X2))
vector_kernels = [Deriv2Matern52(C, l), Deriv2RBF(C, l)]
for k in vector_kernels:
gp = GaussianProcess(ZeroVectorMean(), k, 0.1)
self.assertTrue(basic_test(gp, X1, y1prime, X2))
def one_predict(sa, X, Y, kdt, K=100):
state_dim = 4
idx = kdt.query(sa.reshape(1, -1), k=K, return_distance=False)
X_nn = X[idx, :].reshape(K, state_dim + 2)
Y_nn = Y[idx, :].reshape(K, state_dim)
ds_next = np.zeros((state_dim, ))
std_next = np.zeros((state_dim, ))
try:
for i in range(state_dim):
if i == 0:
gp_est = GaussianProcess(X_nn[:, :state_dim],
Y_nn[:, i],
optimize=True,
theta=None)
Theta = gp_est.cov.theta
else:
gp_est = GaussianProcess(X_nn[:, :state_dim],
Y_nn[:, i],
optimize=False,
theta=Theta)
mm, vv = gp_est.predict(sa[:state_dim])
ds_next[i] = mm
std_next[i] = np.sqrt(np.diag(vv))
except:
print "pass"
return np.array([-1, -1, -1, -1]), np.array([-1, -1, -1, -1])
# for i in range(state_dim):
# gp_est = GaussianProcessRegressor(n_restarts_optimizer=9)
# gp_est.fit(X_nn[:,:state_dim], Y_nn[:,i])
# mm, vv = gp_est.predict(sa[:state_dim].reshape(1,-1), return_std=True)
# ds_next[i] = mm
# std_next[i] = vv#np.sqrt(np.diag(vv))
s_next = sa[:4] + ds_next
return s_next, std_next
def __init__(
self,
model,
model_weight,
model_bias,
model_type,
savedir,
use_gp=True,
sigma=1,
r_loc=0.5,
r_year=1.5,
sigma_e=0.32,
sigma_b=0.01,
device=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')):
self.savedir = savedir / model_type
self.savedir.mkdir(parents=True, exist_ok=True)
print(f'Using {device.type}')
if device.type != 'cpu':
model = model.cuda()
self.model = model
self.model_type = model_type
self.model_weight = model_weight
self.model_bias = model_bias
self.device = device
# for reproducability
torch.manual_seed(42)
torch.cuda.manual_seed_all(42)
self.gp = None
if use_gp:
self.gp = GaussianProcess(sigma, r_loc, r_year, sigma_e, sigma_b)
def test_statistics(self):
"""
1. sample from GP and calculate mean and covariance
"""
C = torch.tensor([3.0])
l = torch.rand(3)
X1 = torch.randn(10, 3)
kernels = [Matern52(C, l), RBF(C, l)]
for k in kernels:
gp = GaussianProcess(ZeroScalarMean(), k, 0.0)
margin = gp(X1)
n_sample = 100000
samples = margin.sample([n_sample])
statistic_mean = torch.mean(samples, dim=0)
logging.debug("mean:{}".format(statistic_mean))
self.assertTrue(torch.allclose(statistic_mean, torch.zeros(10), atol=0.1))
statistic_cov = samples.T @ samples / (n_sample-1)
K = k(X1)
logging.debug("Cov:{}".format(statistic_cov))
self.assertTrue(torch.allclose(K, statistic_cov, atol=0.1))
import math import numpy as np from gp import GaussianProcess, DotKernel from gp import SMKernel N_ROWS = 5 N_COLS = 50 X = np.matrix([1+(0.1*i)*j for j in range(N_ROWS) for i in range(1,N_COLS+1)]).reshape((N_ROWS,N_COLS)) y1 = np.matrix([(0.2*i) + np.random.normal(0,0.001,1)[0] for i in range(1,N_COLS+1)]).T my_GP = GaussianProcess(SMKernel(N_ROWS, 1), X) #my_GP = GaussianProcess(DotKernel(), X) my_GP.add_task(y1) x_star = np.matrix([1+1.5*j for j in range(N_ROWS)]).T my_GP.gpr_make_prediction(my_GP.cov_function.INITIAL_GUESS, 0, x_star) print my_GP.gpr_optimize(0, x_star) print my_GP.mean, my_GP.variance print my_GP.mlog_ML
kss = self.cov(Xs[idx], diag=True) # self variance
Ks = self.cov(Xs[idx], X) # cross-covariances
ms = self.mean(Xs[idx])
al, sW, L, C = self.post.alpha, self.post.sW, self.post.L, self.post.C
fmu[idx] = ms + np.dot(Ks, al)
if L == None:
fs2[idx] = kss + np.sum(Ks * np.dot(Ks, L), axis=1)
else:
V = np.linalg.solve(L, sW * Ks.T)
fs2[idx] = kss - np.sum(V * V, axis=0)
if ys == 0: yi = 0
else: yi = ys[idx]
lp[idx], ymu[idx], ys2[idx] = self.lik.pred(yi, fmu[idx], fs2[idx])
na += nb
return fmu, fs2, ymu, ys2, lp
if __name__ == "__main__":
def f(x):
return x * np.sin(x)
X = np.atleast_2d([1., 3., 5., 6., 7., 8.]).T
y = f(X).ravel()
Xs = np.atleast_2d(np.linspace(0, 10, 2007)).T
from gp import GaussianProcess as gp
gp = gp(mean=1.0 * one())
post, nlZ, dnlZ = gp.inference(X, y, deriv=True)
fmu, fs2, ymu, ys2, lp = gp.predict(X, y, Xs)
N = 20
# Generate noisy data
x_data = np.random.uniform(0.4, 4, N).reshape(
-1, 1) # Generate N data points between 0.4 and 4
y_data = np.array([func(i, 0.2)
for i in x_data]) # Add Gaussian noise with std. of 0.2
# Compute real function
x_real = np.linspace(0, 6, 100).reshape(-1, 1)
y_real = np.array([func(i, 0) for i in x_real])
# Initiate GP with training data
gp_est = GaussianProcess(x_data,
y_data.reshape((-1, )),
optimize=True,
theta=None,
algorithm='Girard')
# Compute prediction for new data
x_new = np.linspace(0, 6, 100).reshape(-1, 1)
means = np.empty(100)
variances = np.empty(100)
for i in range(100):
means[i], variances[i] = gp_est.predict(x_new[i])
# Plot
plt.plot(x_data, y_data, '+k', label='data')
plt.plot(x_real, y_real, '--k', label='true function')
msl = (means.reshape(1, -1)[0] - np.sqrt(variances)) #.reshape(-1,1)
msu = (means.reshape(1, -1)[0] + np.sqrt(variances)) #.reshape(-1,1)[0]
from gp import GaussianProcess, sqexp2D_covariancef
if __name__ == "__main__":
#--------------------------------------
# sample training data
x = np.array([
np.array([i, j]) for i in np.arange(-1, 1, 0.2)
for j in np.arange(-1, 1, 0.2)
])
t = np.array(
[r[0]**2 + r[1]**2 + r[0] * r[1] + 0.1 * np.random.randn() for r in x])
t = t - np.mean(t)
# Find the best set of hyper-parameters
theta0 = [np.std(t), 1, 1, 0, 10]
gp = GaussianProcess.fit(x, t, sqexp2D_covariancef, theta0)
print gp.covf.theta
# Plot predictions and samples from the Gaussian Process
q = np.array([
np.array([i, j]) for i in np.arange(-1.1, 1.1, 0.2)
for j in np.arange(-1.1, 1.1, 0.2)
])
mean, cov = gp.predict(q, cov=True)
assert (mean == gp.predict(q)).all()
sig_bnd = np.sqrt(np.diag(cov))
mlab.points3d(x[:, 0], x[:, 1], t, t, scale_mode='none', scale_factor=0.01)
pts = mlab.points3d(q[:, 0],
return x * np.sin(x) + np.random.normal(0, v)
# return 3*x+4+np.random.normal(0, 0.01)
ax1 = plt.subplot2grid((3, 5), (0, 2), colspan=3, rowspan=2)
x_data = np.random.uniform(0, 4, 20).reshape(-1, 1)
y_data = np.array([func(i, 0.2) for i in x_data]) #
plt.plot(x_data, y_data, '+k')
x_real = np.linspace(0, 6, 100).reshape(-1, 1)
y_real = np.array([func(i, 0) for i in x_real])
plt.plot(x_real, y_real, '--k')
gp_est = GaussianProcess(x_data,
y_data.reshape((-1, )),
optimize=True,
theta=None)
gpup_est = UncertaintyPropagation(x_data,
y_data.reshape((-1, )),
optimize=True,
theta=None,
method=3)
x_n = np.array([3.0]) # The mean of a normal distribution
var_n = np.diag([
0.2**2
]) # The covariance matrix (must be diagonal because of lazy programming)
m, s = gpup_est.predict(x_n, var_n)
# m, s = gp_est.predict(x_n)
print m, s
plt.errorbar(x_n, m, yerr=np.sqrt(s), ecolor='y')
import math import numpy as np from gp import GaussianProcess, DotKernel from gp import SMKernel N_ROWS = 5 N_COLS = 100 X = np.matrix([(0.1*(1+i))**(1+j) for j in range(N_ROWS) for i in range(N_COLS)]).reshape((N_ROWS,N_COLS)) y1 = np.matrix([(0.1*(1+i)) for i in range(N_COLS)]).T y2 = np.matrix([np.sin(0.1*(1+i)) for i in range(N_COLS)]).T my_GP = GaussianProcess(SMKernel(N_ROWS, 1), X) my_GP = GaussianProcess(DotKernel(), X) my_GP.add_task(y1) my_GP.add_task(y2) x_star = np.matrix([3**(1+j) for j in range(N_ROWS)]).T my_GP.gpr_make_prediction(my_GP.cov_function.INITIAL_GUESS, 0, x_star) print my_GP.gpr_optimize(0, x_star) print my_GP.mean, my_GP.variance print my_GP.mlog_ML my_GP.gpr_make_prediction(my_GP.cov_function.INITIAL_GUESS, 0, x_star) print my_GP.mean, my_GP.variance
def experiment1D():
gp = GaussianProcess()
# setup data
n = 500
m = 50
f = lambda x: np.sin(x) * np.exp(-x**2 / 50)
X = np.random.uniform(-10, 10, size=n)
X = np.sort(X)
y = f(X) + np.random.normal(0, 1, size=n)
y -= np.mean(y)
x_min, x_max = np.min(X), np.max(X)
U = np.linspace(x_min, x_max, m).reshape(-1, 1)
X = X.reshape(-1, 1)
hyp_cov = np.asarray([np.log(1), np.log(2)])
hyp_lik = float(np.log(1))
hyp_old = {'mean': [], 'lik': hyp_lik, 'cov': hyp_cov}
hyp = gp.train_exact(X, y.reshape(-1, 1), hyp_old)
hyp_cov = hyp['cov'][0]
sigmasq = np.exp(2 * hyp['lik'])
kernel = SEiso()
distill = Distillation(X=X,
y=y,
U=U,
kernel=kernel,
hyp=hyp_cov,
num_iters=10,
eta=5e-4,
sigmasq=sigmasq,
width=3,
use_kmeans=True,
optimizer='sgd')
distill.grad_descent()
distill.precompute(use_true_K=False)
xx = np.linspace(x_min, x_max, 2 * n)
mm_true, vv_true = gp.predict_exact(X,
y.reshape(-1, 1),
xx.reshape(-1, 1),
hyp=hyp)
mm = []
vv = []
opt = {'cg_maxit': 500, 'cg_tol': 1e-5}
k = n / 2
hyp = gp.train_kiss(X, y.reshape(-1, 1), k, hyp=hyp_old, opt=opt)
mm_kiss, vv_kiss = gp.predict_kiss(X,
y.reshape(-1, 1),
xx.reshape(-1, 1),
k,
hyp=hyp,
opt=opt)
for xstar in xx:
xstar = np.asarray([xstar])
mstar, vstar = distill.predict(xstar, width=3)
vv.append(vstar)
mm.append(mstar)
mm = np.asarray(mm).flatten()
vv = np.asarray(vv).flatten()
mm_kiss = np.asarray(mm_kiss).flatten()
vv_kiss = np.asarray(vv_kiss).flatten()
plt.fill_between(xx,
mm - np.sqrt(vv) * 2,
mm + np.sqrt(vv) * 2,
color='gray',
alpha=.5)
plt.plot(xx, mm_true, color='y', lw=3, label='exact mean')
plt.plot(xx, mm, color='r', lw=3, label='distill mean', ls='dotted')
plt.plot(xx, mm_kiss, color='g', lw=3, label='kiss mean', ls=':')
plt.plot(xx, f(xx), lw=3, label='true value', ls='dashed')
plt.scatter(X, y, color='m', label='train data', marker='+')
plt.xlim([x_min, x_max])
plt.legend()
plt.show()
plt.plot(xx, vv_kiss, color='g', lw=3, label='kiss var', ls=':')
plt.plot(xx, vv_true, color='y', lw=3, label='exact var')
plt.plot(xx, vv, color='r', lw=3, label='distill var', ls='dotted')
plt.xlim([x_min, x_max])
plt.legend()
plt.show()
def show_1d(X,y,Xs,ym,ys):
plt.fill(np.concatenate([Xs, Xs[::-1]]),
np.concatenate([ym - 1.96*ys,(ym + 1.96*ys)[::-1]]),
alpha=0.25, fc='k', ec='k', label='95% confidence interval')
plt.plot(X,y,'b+',ms=10)
plt.plot(Xs,ym,'b',lw=2)
plt.grid()
plt.xlabel('input X')
plt.ylabel('output y')
def f(x): return x * np.sin(x)
X = np.atleast_2d([1., 3., 5., 6., 7., 8.]).T
y = f(X).ravel()
Xs = np.atleast_2d(np.linspace(0, 10, 2007)).T
from sklearn import gaussian_process
gp = gaussian_process.GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4, thetaU=1e-1, random_start=100)
gp.fit(X,y)
ymn,ys2 = gp.predict(Xs, eval_MSE=True); ysd = np.sqrt(ys2)
from gp import GaussianProcess as gp
gp = gp(X=X,y=y)
post = gp.inference(X,y)
fmu,fs2,ymu,ys2,lp = gp.predict(X,y,Xs)
f0 = plt.figure(0); plt.clf()
show_1d(X,y,Xs,ymn,ysd)
f1 = plt.figure(1); plt.clf()
show_1d(X,y,Xs,ymu,ys2)
def setUp(self):
self.gp = GaussianProcess()
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
from gp import GaussianProcess
from gp.kernel import RBF
if __name__ == "__main__":
kernel = RBF(1.0)
gp = GaussianProcess(kernel).compile()
x = np.arange(-5, 5, 0.1)
plt.figure()
num_samples = 10
samples = gp.sample(x[:, np.newaxis], num_samples)
for i in xrange(num_samples):
plt.plot(x, samples[i])
plt.show()
gp.observe(np.array([1, 2])[:, np.newaxis], np.array([0, 1]))
plt.figure()
num_samples = 10
samples = gp.sample(x[:, np.newaxis], num_samples)
for i in xrange(num_samples):
plt.plot(x, samples[i])
plt.show()
def precompute_hyperp(self,
K=100,
K_manifold=-1,
sigma=-1,
dim=-1,
simORreal='sim',
discreteORcont='discrete'):
print(
'[data_load] One time pre-computation of GP hyper-parameters data - THIS WILL TAKE A WHILE...!!!!'
)
if K_manifold > 0:
if self.dr == 'diff':
from dr_diffusionmaps import DiffusionMap
DR = DiffusionMap(sigma=sigma, embedding_dim=dim)
elif self.dr == 'spec':
from spectralEmbed import spectralEmbed
DR = spectralEmbed(embedding_dim=dim)
def reduction(sa, X, Y, K_manifold):
inx = DR.ReducedClosestSetIndices(sa, X, k_manifold=K_manifold)
return X[inx, :][0], Y[inx, :][0]
from gp import GaussianProcess
import pickle
if extend_previous_opt and os.path.exists(
self.path + self.prefix + 'opt_data_' + discreteORcont +
self.postfix + '_k' + str(K) + '.obj'):
with open(
self.path + self.prefix + 'opt_data_' + discreteORcont +
self.postfix + '_k' + str(K) + '.obj', 'rb') as f:
SA_opt, theta_opt, K_opt, _ = pickle.load(f)
print(
'[data_load] Loaded precious optimized data with size %d...' %
SA_opt.shape[0])
SA_opt = list(SA_opt)
theta_opt = list(theta_opt)
K_opt = list(K_opt)
else:
SA_opt = []
theta_opt = []
K_opt = []
# [theta_opt.append([]) for _ in range(self.state_dim)] # List for each dimension
N = 4650
for i in range(N):
print(
'[data_load] Computing hyper-parameters for data point %d out of %d.'
% (i, N))
sa = self.Xtrain[np.random.randint(self.Xtrain.shape[0]), :]
best = {'k': 0, 'loglike': 1e9, 'theta': 0}
for jj in range(1):
Kcandidate = [
K
] #range(2, 253, 50) if jj == 0 else range(max(best['k'] - 50, 2), best['k'] + 50, 10)
for k in Kcandidate:
print('[data_load] Running with k = %d (i=%d)...' % (k, i))
try:
idx = self.kdt.query(sa.reshape(1, -1),
k=k,
return_distance=False)
X_nn = self.Xtrain[idx, :].reshape(
k, self.state_action_dim)
Y_nn = self.Ytrain[idx, :].reshape(k, self.state_dim)
except:
continue
if K_manifold > 0:
X_nn, Y_nn = reduction(sa, X_nn, Y_nn, K_manifold)
fail = False
Theta = []
LogLikeAvg = 0
for d in range(self.state_dim):
try:
gp_est = GaussianProcess(
X_nn[:, :self.state_action_dim],
Y_nn[:, d],
optimize=True,
theta=None) # Optimize to get hyper-parameters
except:
print('[data_load] Singular!')
fail = True
break
Theta.append(gp_est.cov.theta)
LogLikeAvg += gp_est.cov.neg_log_marginal_likelihood_value
if fail:
continue
LogLikeAvg /= self.state_dim
if LogLikeAvg < best['loglike']:
best['loglike'] = LogLikeAvg
best['k'] = k
best['theta'] = Theta
print "Best: k = ", best['k'], ", log-likelihood = ", best[
'loglike']
SA_opt.append(sa)
theta_opt.append(best['theta'])
K_opt.append(best['k'])
print('[data_load] %d optimization points exist.' % len(SA_opt))
if not (i % 50):
self.SA_opt = np.array(SA_opt)
self.theta_opt = np.array(theta_opt)
self.K_opt = np.array(K_opt)
self.opt_kdt = KDTree(SA_opt, leaf_size=20, metric='euclidean')
with open(
self.path + self.prefix + 'opt_data_' +
discreteORcont + self.postfix + '_k' + str(K) + '.obj',
'wb') as f:
pickle.dump([
self.SA_opt, self.theta_opt, self.K_opt, self.opt_kdt
], f)
print('[data_load] Saved hyper-parameters data.')
def f(x):
return x * np.sin(x)
X = np.atleast_2d([1., 3., 5., 6., 7., 8.]).T
y = f(X).ravel()
Xs = np.atleast_2d(np.linspace(0, 10, 2007)).T
from sklearn import gaussian_process
gp = gaussian_process.GaussianProcess(corr='cubic',
theta0=1e-2,
thetaL=1e-4,
thetaU=1e-1,
random_start=100)
gp.fit(X, y)
ymn, ys2 = gp.predict(Xs, eval_MSE=True)
ysd = np.sqrt(ys2)
from gp import GaussianProcess as gp
gp = gp(X=X, y=y)
post = gp.inference(X, y)
fmu, fs2, ymu, ys2, lp = gp.predict(X, y, Xs)
f0 = plt.figure(0)
plt.clf()
show_1d(X, y, Xs, ymn, ysd)
f1 = plt.figure(1)
plt.clf()
show_1d(X, y, Xs, ymu, ys2)
import numpy as np
from matplotlib import pyplot
from gp import GaussianProcess, sqexp1D_covariancef
if __name__ == "__main__":
# --------------------------------------
x = (np.random.random(100) - 0.5) * 2
t = np.array(map(lambda a: a ** 2 + 0.05 * np.random.randn(), x))
t = t - np.mean(t)
# Find the best set of hyper-parameters
theta0 = [np.std(t), 1, 10]
gp = GaussianProcess.fit(x, t, sqexp1D_covariancef, theta0)
# Plot predictions and samples from the Gaussian Process
q = np.arange(-1.2, 1.2, 0.051)
mean, cov = gp.predict(q, cov=True)
assert (mean == gp.predict(q)).all()
sig_bnd = np.sqrt(np.diag(cov))
for s in np.random.multivariate_normal(mean, cov, 5):
pyplot.plot(q, s, "y-")
pyplot.plot(x, t, ".")
pyplot.plot(q, mean, "r-")
pyplot.plot(q, mean + 2 * sig_bnd, "k-")
pyplot.plot(q, mean - 2 * sig_bnd, "k-")
pyplot.show(block=True)
import numpy as np
import tf
from controller import Controller
from geometry_msgs.msg import PoseStamped
from dynamixel_workbench_msgs.srv import SetPID
from object_detection.srv import ObjectDetection
from geometry_msgs.msg import Pose, Point, Quaternion
from gp import GaussianProcess
# Initialize controller
ctrl = Controller()
# Initialize GP
gp = GaussianProcess()
gp.fit_GP()
MIN_JOINT_MOTION_FOR_HAND_OVER = 0.1
# define state Idle
class Idle(smach.State):
def __init__(self):
smach.State.__init__(self, outcomes=['gotToolInput'])
def execute(self, userdata):
ctrl.open_gripper()
# ctrl.set_camera_angles(ctrl.HOME_POS_CAMERA_01)
ctrl.set_arm_joint_angles(ctrl.HOME_POS_MANIPULATOR_00)
rospy.loginfo('Executing state IDLE')
# Load training set
train = np.matrix(scipy.io.loadmat("/home/victor/Documents/MTL-GP/_data/sarcos_inv.mat")["sarcos_inv"])
# Inputs (7 joint positions, 7 joint velocities, 7 joint accelerations)
Xtrain = train[:100, :21].T
# Outputs (7 joint torques)
Ytrain = normalize(train[:100, 21:23])[0]
# Load test set
test = np.matrix(scipy.io.loadmat("/home/victor/Documents/MTL-GP/_data/sarcos_inv_test.mat")["sarcos_inv_test"])
Xtest = test[:100, :21].T
Ytest = normalize(test[:100, 21:23])[0]
# my_GP = GaussianProcess(SMKernel(Xtrain.shape[0], 3), Xtrain)
# my_GP = GaussianProcess(DotKernel(), Xtrain)
my_GP = GaussianProcess(SEKernel(), Xtrain)
my_GP.add_task(Ytrain[:,0])
my_GP.add_task(Ytrain[:,1])
# Ytrain = my_GP.gpr_normalize()
x_star = Xtest[:, 0:100]
# my_GP.gpr_make_prediction(my_GP.cov_function.INITIAL_GUESS, [0, 1], x_star)
print my_GP.gpr_optimize([0,1], x_star)
# print my_GP.mean.T
# print Ytest.T
print np.mean(abs((my_GP.mean[:,0] - Ytest[:,0])))
print np.mean(abs((my_GP.mean[:,1] - Ytest[:,1])))
print my_GP.mlog_ML
'''
for i in range(10):
ymu,ys2 = np.zeros(ns),np.zeros(ns)
lp = np.zeros(ns)
while na<ns:
idx = np.arange(na,min(na+nb,ns))
kss = self.cov(Xs[idx],diag=True) # self variance
Ks = self.cov(Xs[idx],X) # cross-covariances
ms = self.mean(Xs[idx])
al,sW,L,C = self.post.alpha,self.post.sW,self.post.L,self.post.C
fmu[idx] = ms + np.dot(Ks,al)
if L==None:
fs2[idx] = kss + np.sum(Ks*np.dot(Ks,L),axis=1)
else:
V = np.linalg.solve(L,sW*Ks.T)
fs2[idx] = kss - np.sum(V*V,axis=0)
if ys==0: yi = 0
else: yi = ys[idx]
lp[idx],ymu[idx],ys2[idx] = self.lik.pred(yi,fmu[idx],fs2[idx])
na += nb
return fmu,fs2,ymu,ys2,lp
if __name__ == "__main__":
def f(x): return x * np.sin(x)
X = np.atleast_2d([1., 3., 5., 6., 7., 8.]).T
y = f(X).ravel()
Xs = np.atleast_2d(np.linspace(0, 10, 2007)).T
from gp import GaussianProcess as gp
gp = gp(mean=1.0*one())
post,nlZ,dnlZ = gp.inference(X,y,deriv=True)
fmu,fs2,ymu,ys2,lp = gp.predict(X,y,Xs)
["sarcos_inv"])
# Inputs (7 joint positions, 7 joint velocities, 7 joint accelerations)
Xtrain = train[:100, :21].T
# Outputs (7 joint torques)
Ytrain = normalize(train[:100, 21:23])[0]
# Load test set
test = np.matrix(
scipy.io.loadmat("/home/victor/Documents/MTL-GP/_data/sarcos_inv_test.mat")
["sarcos_inv_test"])
Xtest = test[:100, :21].T
Ytest = normalize(test[:100, 21:23])[0]
# my_GP = GaussianProcess(SMKernel(Xtrain.shape[0], 3), Xtrain)
# my_GP = GaussianProcess(DotKernel(), Xtrain)
my_GP = GaussianProcess(SEKernel(), Xtrain)
my_GP.add_task(Ytrain[:, 0])
my_GP.add_task(Ytrain[:, 1])
# Ytrain = my_GP.gpr_normalize()
x_star = Xtest[:, 0:100]
# my_GP.gpr_make_prediction(my_GP.cov_function.INITIAL_GUESS, [0, 1], x_star)
print my_GP.gpr_optimize([0, 1], x_star)
# print my_GP.mean.T
# print Ytest.T
print np.mean(abs((my_GP.mean[:, 0] - Ytest[:, 0])))
print np.mean(abs((my_GP.mean[:, 1] - Ytest[:, 1])))
print my_GP.mlog_ML
'''
for i in range(10):
x_star = Xtest[:, i]
import scipy.io
import numpy as np
from gp import GaussianProcess, SMKernel, DotKernel, SEKernel
from gp import LogisticFunction
from numpy.random import normal as dnorm
means, sigma, N = [-6,0,2], 0.8, 30
X = np.matrix(
np.concatenate(
[dnorm(i, sigma, N) for i in means]
)
)
# my_GP = GaussianProcess(DotKernel(), X, LogisticFunction())
my_GP = GaussianProcess(SEKernel(), X, LogisticFunction())
# my_GP = GaussianProcess(SMKernel(X.shape[0], 3), X, LogisticFunction())
y = np.matrix(
[-1]*N + [1]*N + [-1]*N
).T
my_GP.add_task(y)
y2 = []
for i in range(3*N):
if X[0,i] > 0:
y2.append(1)
else:
y2.append(-1)
y2 = np.matrix(y2).T
my_GP.add_task(y2)
