def compute(kernel_name, x, y):
if kernel_name == 'hik':
return kernels.histogram_intersection(x, y)
elif kernel_name == 'linear':
return kernels.linear(x, y)
elif kernel_name == 'chi2':
return kernels.chi_square(x, y)
else:
raise ValueError(kernel_name)
def __init__(self, Y, dim, nparticles=100):
self.Xdim = dim
self.T, self.Ydim = Y.shape
"""Use PCA to initalise the problem. Uses EM version in this case..."""
myPCA_EM = PCA_EM(Y, dim)
myPCA_EM.learn(300)
X = np.array(myPCA_EM.m_Z)
self.observation_GP = GP.GP(X, Y)
#create a linear kernel for the dynamics
k = kernels.linear(-1, -1)
self.dynamic_GP = GP.GP(X[:-1], X[1:], k)
#initialise the samples from the state /latent variables
self.particles = np.zeros((self.T, nparticles, self.Xdim))
#sample for x0 TODO: variable priors on X0
self.particles[0, :, :] = np.random.randn(nparticles, self.Xdim)
def __init__(self,Y,dim,nparticles=100): self.Xdim = dim self.T,self.Ydim = Y.shape """Use PCA to initalise the problem. Uses EM version in this case...""" myPCA_EM = PCA_EM(Y,dim) myPCA_EM.learn(300) X = np.array(myPCA_EM.m_Z) self.observation_GP = GP.GP(X,Y) #create a linear kernel for the dynamics k = kernels.linear(-1,-1) self.dynamic_GP = GP.GP(X[:-1],X[1:],k) #initialise the samples from the state /latent variables self.particles = np.zeros((self.T,nparticles,self.Xdim)) #sample for x0 TODO: variable priors on X0 self.particles[0,:,:] = np.random.randn(nparticles,self.Xdim)
args = parser.parse_args()
if args.dataset == "himoon":
x, y, _, _, xtest, ytest = data_gen.himoon(n_samples=args.n_samples,
n_dims=args.n_dims)
elif args.dataset == "mmgauss":
x, y, _, _, xtest, ytest = data_gen.mmgauss(n_samples=args.n_samples,
n_dims=args.n_dims)
else:
raise ValueError("Unknown dataset")
kernels = dict(
zip(
["rbf", "linear", "poly", "sigmoid"],
[k.rbf(), k.linear(), k.poly(),
k.sigmoid()],
))
try:
kernel = kernels.get(args.kernel)
except KeyError as e:
kernel = "linear"
df = pd.DataFrame()
print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}: Running " +
f"{args.dataset} with {args.dimension} dimensions and " +
f"epsilon={args.epsilon} with {args.kernel} kernel for " +
f"{args.repetitions} repetitions.")
for run in range(args.repetitions):