def __init__(self,
X,
Y,
kernel=None,
warping_function=None,
warping_terms=3,
normalize_X=False,
normalize_Y=False):
if kernel is None:
kernel = kern.rbf(X.shape[1])
if warping_function == None:
self.warping_function = TanhWarpingFunction_d(warping_terms)
self.warping_params = (
np.random.randn(self.warping_function.n_terms * 3 + 1, ) * 1)
self.scale_data = False
if self.scale_data:
Y = self._scale_data(Y)
self.has_uncertain_inputs = False
self.Y_untransformed = Y.copy()
self.predict_in_warped_space = False
likelihood = likelihoods.Gaussian(self.transform_data(),
normalize=normalize_Y)
GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
self._set_params(self._get_params())
def __init__(self, X, Y, kernel=None, warping_function=None, warping_terms=3, normalize_X=False, normalize_Y=False):
if kernel is None:
kernel = kern.rbf(X.shape[1])
if warping_function == None:
self.warping_function = TanhWarpingFunction_d(warping_terms)
self.warping_params = (np.random.randn(self.warping_function.n_terms * 3 + 1,) * 1)
Y = self._scale_data(Y)
self.has_uncertain_inputs = False
self.Y_untransformed = Y.copy()
self.predict_in_warped_space = False
likelihood = likelihoods.Gaussian(self.transform_data(), normalize=normalize_Y)
GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
self._set_params(self._get_params())
import numpy as np
import pylab as pb
import sys
from GPy import kern
from colvb import MOHGP
np.random.seed(1)
pb.close('all')
#cool structed GP demo
Nclust = 20
Nx = 12
Nobs = [np.random.randint(20,21) for i in range(Nclust)]
X = np.random.rand(Nx,1)*5
X.sort(0)
Kf = kern.rbf(1) + kern.white(1, 1e-6)
S = Kf.K(X)
means = np.vstack([np.tile(np.random.multivariate_normal(np.zeros(Nx),S,1),(N,1)) for N in Nobs]) # GP draws for mean of each cluster
#add GP draw for noise
Ky = kern.rbf(1,0.3,1) + kern.white(1,0.001)
Y = means + np.random.multivariate_normal(np.zeros(Nx),Ky.K(X),means.shape[0])
#construct model
m = MOHGP(X, Kf.copy(), Ky.copy(), Y, K=Nclust)
m.constrain_positive('')
m.optimize()
m.preferred_optimizer='bfgs'
m.systematic_splits()
m.remove_empty_clusters(1e-3)
import numpy as np
import pylab as pb
import sys
from GPy import kern
from colvb import MOHGP
np.random.seed(1)
pb.close('all')
#cool structed GP demo
Nclust = 20
Nx = 12
Nobs = [np.random.randint(20, 21) for i in range(Nclust)]
X = np.random.rand(Nx, 1) * 5
X.sort(0)
Kf = kern.rbf(1) + kern.white(1, 1e-6)
S = Kf.K(X)
means = np.vstack([
np.tile(np.random.multivariate_normal(np.zeros(Nx), S, 1), (N, 1))
for N in Nobs
]) # GP draws for mean of each cluster
#add GP draw for noise
Ky = kern.rbf(1, 0.3, 1) + kern.white(1, 0.001)
Y = means + np.random.multivariate_normal(np.zeros(Nx), Ky.K(X),
means.shape[0])
#construct model
m = MOHGP(X, Kf.copy(), Ky.copy(), Y, K=Nclust)
m.constrain_positive('')
ground_truth_phi = utilities.blockdiag([np.ones((n,1)) for n in Nobs]) alpha = 2. #(should give approximately 10 clusters) freqs = 2*np.pi + 0.3*(np.random.rand(Nclust)-.5) phases = 2*np.pi*np.random.rand(Nclust) means = np.vstack([np.tile(np.sin(f*X+p).T,(Ni,1)) for f,p,Ni in zip(freqs,phases,Nobs)]) #add a lower freq sin for the noise freqs = .4*np.pi + 0.01*(np.random.rand(means.shape[0])-.5) phases = 2*np.pi*np.random.rand(means.shape[0]) offsets = 0.3*np.vstack([np.sin(f*X+p).T for f,p in zip(freqs,phases)]) Y = means + offsets + np.random.randn(*means.shape)*0.05 #construct full model Kf = kern.rbf(1,0.01,0.001) Ky1 = kern.rbf(1,0.1,0.001) Ky2 = kern.white(1,0.01) Ky = Ky1 + Ky2 m = MOHGP(X,Kf,Ky,Y, K=Nclust, prior_Z = 'DP', alpha=alpha) m.ensure_default_constraints() m.checkgrad(verbose=1) m.randomize() m.optimize() m.systematic_splits() m.systematic_splits() m.plot(1,1,1,0,0,1) #construct model without structure #give it a fighting chance by normalising signals first
freqs = 2 * np.pi + 0.3 * (np.random.rand(Nclust) - .5)
phases = 2 * np.pi * np.random.rand(Nclust)
means = np.vstack([
np.tile(np.sin(f * X + p).T, (Ni, 1))
for f, p, Ni in zip(freqs, phases, Nobs)
])
#add a lower freq sin for the noise
freqs = .4 * np.pi + 0.01 * (np.random.rand(means.shape[0]) - .5)
phases = 2 * np.pi * np.random.rand(means.shape[0])
offsets = 0.3 * np.vstack([np.sin(f * X + p).T for f, p in zip(freqs, phases)])
Y = means + offsets + np.random.randn(*means.shape) * 0.05
#construct full model
Kf = kern.rbf(1, 0.01, 0.001)
Ky1 = kern.rbf(1, 0.1, 0.001)
Ky2 = kern.white(1, 0.01)
Ky = Ky1 + Ky2
m = MOHGP(X, Kf, Ky, Y, K=Nclust, prior_Z='DP', alpha=alpha)
m.ensure_default_constraints()
m.checkgrad(verbose=1)
m.randomize()
m.optimize()
m.systematic_splits()
m.systematic_splits()
m.plot(1, 1, 1, 0, 0, 1)
#construct model without structure
#give it a fighting chance by normalising signals first
def __init__(self, name=None, hypers=None):
super(GPChannel, self).__init__(name=name)
# Specify the number of parameters
self.D = hypers['D']
# Channels are expected to have a n_x variable
self.n_x = self.D
# Set the hyperparameters
self.g = hypers['g_gp']
self.E = hypers['E_gp']
self.a0 = hypers['alpha_0']
self.b0 = hypers['beta_0']
self.sigmas = (self.a0 / self.b0) * np.ones(self.D)
# Create a GP Object for the dynamics model
# This is a function Z x V -> dZ, i.e. a 2D
# Gaussian process regression.
# Lay out a grid of inducing points for a sparse GP
self.grid = 10
self.z_min = sigma_inv(0.001)
self.z_max = sigma_inv(0.999)
self.V_min = -80.
self.V_max = 50.
length = 5.0
self.Z = np.array(
list(
itertools.product(*([
np.linspace(self.z_min, self.z_max, self.grid)
for _ in range(self.D)
] + [np.linspace(self.V_min, self.V_max, self.grid)]))))
# Create independent RBF kernels over Z and V
kernel_z_hyps = {
'input_dim': 1,
'variance': hypers['sigma_kernel'],
'lengthscale': (self.z_max - self.z_min) / length
}
self.kernel_z = rbf(**kernel_z_hyps)
for n in range(1, self.D):
self.kernel_z = self.kernel_z.prod(rbf(**kernel_z_hyps),
tensor=True)
kernel_V_hyps = {
'input_dim': 1,
'variance': hypers['sigma_kernel'],
'lengthscale': (self.V_max - self.V_min) / length
}
self.kernel_V = rbf(**kernel_V_hyps)
# Combine the kernel for z and V
self.kernel = self.kernel_z.prod(self.kernel_V, tensor=True)
# Initialize with a random sample from the prior
self.model_dzdt = True
if self.model_dzdt:
m = np.zeros(self.Z.shape[0])
else:
m = self.Z[:, 0]
C = self.kernel.K(self.Z)
self.hs = []
self.gps = []
for d in range(self.D):
# Sample the function value at the inducing points
self.hs.append(np.random.multivariate_normal(m, C, 1).T)
# self.hs.append(np.zeros((self.Z.shape[0],1)))
# Create a sparse GP model with the sampled function h
# This will be used for prediction
self.gps.append(
SparseGPWithVariance(self.Z,
self.hs[d],
self.kernel,
self.sigmas[d],
num_inducing=25))
def __init__(self, name=None, hypers=None):
super(GPChannel, self).__init__(name=name)
# Specify the number of parameters
self.D = hypers['D']
# Channels are expected to have a n_x variable
self.n_x = self.D
# Set the hyperparameters
self.g = hypers['g_gp']
self.E = hypers['E_gp']
self.a0 = hypers['alpha_0']
self.b0 = hypers['beta_0']
self.sigmas = (self.a0/self.b0) * np.ones(self.D)
# Create a GP Object for the dynamics model
# This is a function Z x V -> dZ, i.e. a 2D
# Gaussian process regression.
# Lay out a grid of inducing points for a sparse GP
self.grid = 10
self.z_min = sigma_inv(0.001)
self.z_max = sigma_inv(0.999)
self.V_min = -80.
self.V_max = 50.
length = 5.0
self.Z = np.array(list(
itertools.product(*([np.linspace(self.z_min, self.z_max, self.grid) for _ in range(self.D)]
+ [np.linspace(self.V_min, self.V_max, self.grid)]))))
# Create independent RBF kernels over Z and V
kernel_z_hyps = { 'input_dim' : 1,
'variance' : hypers['sigma_kernel'],
'lengthscale' : (self.z_max-self.z_min)/length}
self.kernel_z = rbf(**kernel_z_hyps)
for n in range(1, self.D):
self.kernel_z = self.kernel_z.prod(rbf(**kernel_z_hyps), tensor=True)
kernel_V_hyps = { 'input_dim' : 1,
'variance' : hypers['sigma_kernel'],
'lengthscale' : (self.V_max-self.V_min)/length}
self.kernel_V = rbf(**kernel_V_hyps)
# Combine the kernel for z and V
self.kernel = self.kernel_z.prod(self.kernel_V, tensor=True)
# Initialize with a random sample from the prior
self.model_dzdt = True
if self.model_dzdt:
m = np.zeros(self.Z.shape[0])
else:
m = self.Z[:,0]
C = self.kernel.K(self.Z)
self.hs = []
self.gps = []
for d in range(self.D):
# Sample the function value at the inducing points
self.hs.append(np.random.multivariate_normal(m, C, 1).T)
# self.hs.append(np.zeros((self.Z.shape[0],1)))
# Create a sparse GP model with the sampled function h
# This will be used for prediction
self.gps.append(SparseGPWithVariance(self.Z, self.hs[d], self.kernel, self.sigmas[d], num_inducing=25))