def setUp(self):
SP.random.seed(1)
#1. simulate
self.settings = {'K': 5, 'N': 100, 'D': 80}
self.simulation = self.simulate()
N = self.settings['N']
K = self.settings['K']
D = self.settings['D']
#2. setup GP
covar = dlimix_legacy.CCovLinearISO(K)
ll = dlimix_legacy.CLikNormalIso()
#create hyperparm
covar_params = SP.array([1.0])
lik_params = SP.array([1.0])
hyperparams = dlimix_legacy.CGPHyperParams()
hyperparams['covar'] = covar_params
hyperparams['lik'] = lik_params
hyperparams['X'] = self.simulation['X0']
#cretae GP
self.gp = dlimix_legacy.CGPbase(covar, ll)
#set data
self.gp.setY(self.simulation['Y'])
self.gp.setX(self.simulation['X0'])
self.gp.setParams(hyperparams)
pass
def __init__(self,
Y=None,
X0=None,
k=1,
standardize=False,
interaction=True):
"""
Y: data [NxG]
X0: known latent factors [Nxk0]
k: number of latent factors to infer
"""
assert Y != None, 'gpCLVM:: set Y!'
assert X0 != None, 'gpCLVM:: set X0!'
self.cache = {}
self.interaction = interaction
# read data
self._setY(Y, standardize=standardize)
self._setX0(X0)
self.k = k
# covariance for known latex factor
self.C0 = limix.CFixedCF(self.K0)
# covariance for unknow latent factor
self.C = limix.CProductCF()
self.Ca = limix.CLowRankCF(self.N, self.k)
self.C.addCovariance(self.Ca)
if self.interaction == True:
self.Cb1 = limix.CFixedCF(SP.ones((self.N, self.N)))
self.Cb1.setParamMask(SP.zeros(1))
self.Cb2 = limix.CFixedCF(self.K0)
self.Cb = limix.CSumCF()
self.Cb.addCovariance(self.Cb1)
self.Cb.addCovariance(self.Cb2)
self.C.addCovariance(self.Cb)
# total covariance
covar = limix.CSumCF()
covar.addCovariance(self.C0)
covar.addCovariance(self.C)
# likelihood
self.ll = limix.CLikNormalIso()
# init GP and hyper params
self.gp = limix.CGPbase(covar, self.ll)
self.gp.setY(self.Y)
def setUp(self):
SP.random.seed(1)
#1. simulate
self.settings = {'K': 5, 'N': 100, 'D': 80}
self.simulation = self.simulate()
N = self.settings['N']
K = self.settings['K']
D = self.settings['D']
#2. setup GP
K0 = SP.dot(self.simulation['S'], self.simulation['S'].T)
K0[:] = 0
covar1 = dlimix_legacy.CFixedCF(K0)
covar2 = dlimix_legacy.CCovLinearISO(K)
covar = dlimix_legacy.CSumCF()
covar.addCovariance(covar1)
covar.addCovariance(covar2)
ll = dlimix_legacy.CLikNormalIso()
#create hyperparm
covar_params = SP.array([0.0, 1.0])
lik_params = SP.array([0.1])
hyperparams = dlimix_legacy.CGPHyperParams()
hyperparams['covar'] = covar_params
hyperparams['lik'] = lik_params
hyperparams['X'] = self.simulation['X0']
#cretae GP
self.gp = dlimix_legacy.CGPbase(covar, ll)
#set data
self.gp.setY(self.simulation['Y'])
self.gp.setX(self.simulation['X0'])
self.gp.setParams(hyperparams)
pass
def train(self, rank=20, Kpop=True, LinearARD=False):
"""train panama module"""
if 0:
covar = limix_legacy.CCovLinearISO(rank)
ll = limix_legacy.CLikNormalIso()
X0 = sp.random.randn(self.N, rank)
X0 = PCA(self.Y, rank)[0]
X0 /= sp.sqrt(rank)
covar_params = sp.array([1.0])
lik_params = sp.array([1.0])
hyperparams = limix_legacy.CGPHyperParams()
hyperparams['covar'] = covar_params
hyperparams['lik'] = lik_params
hyperparams['X'] = X0
constrainU = limix_legacy.CGPHyperParams()
constrainL = limix_legacy.CGPHyperParams()
constrainU['covar'] = +5 * sp.ones_like(covar_params)
constrainL['covar'] = 0 * sp.ones_like(covar_params)
constrainU['lik'] = +5 * sp.ones_like(lik_params)
constrainL['lik'] = 0 * sp.ones_like(lik_params)
if 1:
covar = limix_legacy.CSumCF()
if LinearARD:
covar_1 = limix_legacy.CCovLinearARD(rank)
covar_params = []
for d in range(rank):
covar_params.append(1 / sp.sqrt(d + 2))
else:
covar_1 = limix_legacy.CCovLinearISO(rank)
covar_params = [1.0]
covar.addCovariance(covar_1)
for K in self.Ks:
covar.addCovariance(limix_legacy.CFixedCF(K))
covar_params.append(1.0)
ll = limix_legacy.CLikNormalIso()
X0 = PCA(self.Y, rank)[0]
X0 /= sp.sqrt(rank)
covar_params = sp.array(covar_params)
lik_params = sp.array([1.0])
hyperparams = limix_legacy.CGPHyperParams()
hyperparams['covar'] = covar_params
hyperparams['lik'] = lik_params
hyperparams['X'] = X0
constrainU = limix_legacy.CGPHyperParams()
constrainL = limix_legacy.CGPHyperParams()
constrainU['covar'] = +5 * sp.ones_like(covar_params)
constrainL['covar'] = -5 * sp.ones_like(covar_params)
constrainU['lik'] = +5 * sp.ones_like(lik_params)
gp = limix_legacy.CGPbase(covar, ll)
gp.setY(self.Y)
gp.setX(X0)
lml0 = gp.LML(hyperparams)
dlml0 = gp.LMLgrad(hyperparams)
gpopt = limix_legacy.CGPopt(gp)
gpopt.setOptBoundLower(constrainL)
gpopt.setOptBoundUpper(constrainU)
t1 = time.time()
gpopt.opt()
t2 = time.time()
#Kpanama
self.Xpanama = covar_1.getX()
if LinearARD:
self.Xpanama /= self.Xpanama.std(0)
self.Kpanama = covar_1.K()
# self.Kpanama/= self.Kpanama.diagonal().mean()
# Ktot
self.Ktot = covar_1.K()
for c_i in range(len(self.Ks)):
self.Ktot += covar.getCovariance(c_i + 1).K()
# self.Ktot/= self.Ktot.diagonal().mean()
#store variances
V = {}
if LinearARD:
V['LinearARD'] = covar_1.getParams()**2 * covar_1.getX().var(0)
else:
V['Kpanama'] = sp.array([covar_1.K().diagonal().mean()])
# if self.use_Kpop:
# V['Ks'] = sp.array([covar.getCovariance(c_i+1).K().diagonal().mean() for c_i in range(len(self.Ks))])
V['Ks'] = sp.array(
[covar.getCovariance(c_i + 1).K() for c_i in range(len(self.Ks))])
V['noise'] = gp.getParams()['lik']**2
self.varianceComps = V
# predictions
Ki = la.inv(ll.K() + covar.K())
self.Ypanama = sp.dot(covar_1.K(), sp.dot(Ki, self.Y))
self.LL = ll.K()