<s> import numpy as np <EOL> import itertools , logging <EOL> from . . kern import Kern <EOL> from . . core . parameterization . variational import NormalPrior <EOL> from . . core . parameterization import Param <EOL> from paramz import ObsAr <EOL> from . . inference . latent_function_inference . var_dtc import VarDTC <EOL> from . . inference . latent_function_inference import InferenceMethodList <EOL> from . . likelihoods import Gaussian <EOL> from . . util . initialization import initialize_latent <EOL> from . . models . bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch <EOL> class MRD ( BayesianGPLVMMiniBatch ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , Ylist , input_dim , X = None , X_variance = None , <EOL> initx = '<STR_LIT>' , initz = '<STR_LIT>' , <EOL> num_inducing = <NUM_LIT:10> , Z = None , kernel = None , <EOL> inference_method = None , likelihoods = None , name = '<STR_LIT>' , <EOL> Ynames = None , normalizer = False , stochastic = False , batchsize = <NUM_LIT:10> ) : <EOL> self . logger = logging . getLogger ( self . __class__ . __name__ ) <EOL> self . input_dim = input_dim <EOL> self . num_inducing = num_inducing <EOL> if isinstance ( Ylist , dict ) : <EOL> Ynames , Ylist = zip ( * Ylist . items ( ) ) <EOL> self . logger . debug ( "<STR_LIT>" ) <EOL> self . Ylist = [ ObsAr ( Y ) for Y in Ylist ] <EOL> Y = Ylist [ - <NUM_LIT:1> ] <EOL> if Ynames is None : <EOL> self . logger . debug ( "<STR_LIT>" ) <EOL> Ynames = [ '<STR_LIT>' . format ( i ) for i in range ( len ( Ylist ) ) ] <EOL> self . names = Ynames <EOL> assert len ( self . names ) == len ( self . Ylist ) , "<STR_LIT>" <EOL> if inference_method is None : <EOL> self . inference_method = InferenceMethodList ( [ VarDTC ( ) for _ in range ( len ( self . Ylist ) ) ] ) <EOL> else : <EOL> assert isinstance ( inference_method , InferenceMethodList ) , "<STR_LIT>" . format ( inference_method ) <EOL> self . inference_method = inference_method <EOL> if X is None : <EOL> X , fracs = self . _init_X ( initx , Ylist ) <EOL> else : <EOL> fracs = [ X . var ( <NUM_LIT:0> ) ] * len ( Ylist ) <EOL> Z = self . _init_Z ( initz , X ) <EOL> self . Z = Param ( '<STR_LIT>' , Z ) <EOL> self . num_inducing = self . Z . shape [ <NUM_LIT:0> ] <EOL> self . logger . info ( "<STR_LIT>" ) <EOL> if kernel is None : <EOL> from . . kern import RBF <EOL> kernels = [ RBF ( input_dim , ARD = <NUM_LIT:1> , lengthscale = <NUM_LIT:1.> / fracs [ i ] ) for i in range ( len ( Ylist ) ) ] <EOL> elif isinstance ( kernel , Kern ) : <EOL> kernels = [ ] <EOL> for i in range ( len ( Ylist ) ) : <EOL> k = kernel . copy ( ) <EOL> kernels . append ( k ) <EOL> else : <EOL> assert len ( kernel ) == len ( Ylist ) , "<STR_LIT>" <EOL> assert all ( [ isinstance ( k , Kern ) for k in kernel ] ) , "<STR_LIT>" <EOL> kernels = kernel <EOL> self . variational_prior = NormalPrior ( ) <EOL> if likelihoods is None : <EOL> likelihoods = [ Gaussian ( name = '<STR_LIT>' . format ( i ) ) for i in range ( len ( Ylist ) ) ] <EOL> else : likelihoods = likelihoods <EOL> self . logger . info ( "<STR_LIT>" ) <EOL> super ( MRD , self ) . __init__ ( Y , input_dim , X = X , X_variance = X_variance , num_inducing = num_inducing , <EOL> Z = self . Z , kernel = None , inference_method = self . inference_method , likelihood = Gaussian ( ) , <EOL> name = '<STR_LIT>' , normalizer = None , <EOL> missing_data = False , stochastic = False , batchsize = <NUM_LIT:1> ) <EOL> self . _log_marginal_likelihood = <NUM_LIT:0> <EOL> self . unlink_parameter ( self . likelihood ) <EOL> self . unlink_parameter ( self . kern ) <EOL> self . num_data = Ylist [ <NUM_LIT:0> ] . shape [ <NUM_LIT:0> ] <EOL> if isinstance ( batchsize , int ) : <EOL> batchsize = itertools . repeat ( batchsize ) <EOL> self . bgplvms = [ ] <EOL> for i , n , k , l , Y , im , bs in zip ( itertools . count ( ) , Ynames , kernels , likelihoods , Ylist , self . inference_method , batchsize ) : <EOL> assert Y . shape [ <NUM_LIT:0> ] == self . num_data , "<STR_LIT>" <EOL> md = np . isnan ( Y ) . any ( ) <EOL> spgp = BayesianGPLVMMiniBatch ( Y , input_dim , X , X_variance , <EOL> Z = Z , kernel = k , likelihood = l , <EOL> inference_method = im , name = n , <EOL> normalizer = normalizer , <EOL> missing_data = md , <EOL> stochastic = stochastic , <EOL> batchsize = bs ) <EOL> spgp . kl_factr = <NUM_LIT:1.> / len ( Ynames ) <EOL> spgp . unlink_parameter ( spgp . Z ) <EOL> spgp . unlink_parameter ( spgp . X ) <EOL> del spgp . Z <EOL> del spgp . X <EOL> spgp . Z = self . Z <EOL> spgp . X = self . X <EOL> self . link_parameter ( spgp , i + <NUM_LIT:2> ) <EOL> self . bgplvms . append ( spgp ) <EOL> b = self . bgplvms [ <NUM_LIT:0> ] <EOL> self . posterior = b . posterior <EOL> self . kern = b . kern <EOL> self . likelihood = b . likelihood <EOL> self . logger . info ( "<STR_LIT>" ) <EOL> def parameters_changed ( self ) : <EOL> self . _log_marginal_likelihood = <NUM_LIT:0> <EOL> self . Z . gradient [ : ] = <NUM_LIT:0.> <EOL> self . X . gradient [ : ] = <NUM_LIT:0.> <EOL> for b , i in zip ( self . bgplvms , self . inference_method ) : <EOL> self . _log_marginal_likelihood += b . _log_marginal_likelihood <EOL> self . logger . info ( '<STR_LIT>' . format ( hex ( id ( i ) ) ) ) <EOL> self . Z . gradient [ : ] += b . _Zgrad <EOL> if self . has_uncertain_inputs ( ) : <EOL> self . X . gradient += b . _Xgrad <EOL> else : <EOL> self . X . gradient += b . _Xgrad <EOL> def log_likelihood ( self ) : <EOL> return self . _log_marginal_likelihood <EOL> def _init_X ( self , init = '<STR_LIT>' , Ylist = None ) : <EOL> if Ylist is None : <EOL> Ylist = self . Ylist <EOL> if init in "<STR_LIT>" : <EOL> X , fracs = initialize_latent ( '<STR_LIT>' , self . input_dim , np . hstack ( Ylist ) ) <EOL> fracs = [ fracs ] * len ( Ylist ) <EOL> elif init in "<STR_LIT>" : <EOL> X = np . zeros ( ( Ylist [ <NUM_LIT:0> ] . shape [ <NUM_LIT:0> ] , self . input_dim ) ) <EOL> fracs = [ ] <EOL> for qs , Y in zip ( np . array_split ( np . arange ( self . input_dim ) , len ( Ylist ) ) , Ylist ) : <EOL> x , frcs = initialize_latent ( '<STR_LIT>' , len ( qs ) , Y ) <EOL> X [ : , qs ] = x <EOL> fracs . append ( frcs ) <EOL> else : <EOL> X = np . random . randn ( Ylist [ <NUM_LIT:0> ] . shape [ <NUM_LIT:0> ] , self . input_dim ) <EOL> fracs = X . var ( <NUM_LIT:0> ) <EOL> fracs = [ fracs ] * len ( Ylist ) <EOL> X -= X . mean ( ) <EOL> X /= X . std ( ) <EOL> return X , fracs <EOL> def _init_Z ( self , init = "<STR_LIT>" , X = None ) : <EOL> if X is None : <EOL> X = self . X <EOL> if init in "<STR_LIT>" : <EOL> Z = np . random . permutation ( X . copy ( ) ) [ : self . num_inducing ] <EOL> elif init in "<STR_LIT>" : <EOL> Z = np . random . randn ( self . num_inducing , self . input_dim ) * X . var ( ) <EOL> return Z <EOL> def predict ( self , Xnew , full_cov = False , Y_metadata = None , kern = None , Yindex = <NUM_LIT:0> ) : <EOL> """<STR_LIT>""" <EOL> b = self . bgplvms [ Yindex ] <EOL> self . posterior = b . posterior <EOL> self . kern = b . kern <EOL> self . likelihood = b . likelihood <EOL> return super ( MRD , self ) . predict ( Xnew , full_cov , Y_metadata , kern ) <EOL> def plot_scales ( self , titles = None , fig_kwargs = { } , ** kwargs ) : <EOL> """<STR_LIT>""" <EOL> from . . plotting import plotting_library as pl <EOL> if titles is None : <EOL> titles = [ r'<STR_LIT>' . format ( name ) for name in self . names ] <EOL> M = len ( self . bgplvms ) <EOL> fig = pl ( ) . figure ( rows = <NUM_LIT:1> , cols = M , ** fig_kwargs ) <EOL> for c in range ( M ) : <EOL> canvas = self . bgplvms [ c ] . kern . plot_ARD ( title = titles [ c ] , figure = fig , col = c + <NUM_LIT:1> , ** kwargs ) <EOL> return canvas <EOL> def plot_latent ( self , labels = None , which_indices = None , <EOL> resolution = <NUM_LIT> , legend = True , <EOL> plot_limits = None , <EOL> updates = False , <EOL> kern = None , marker = '<STR_LIT>' , <EOL> num_samples = <NUM_LIT:1000> , projection = '<STR_LIT>' , <EOL> predict_kwargs = { } , <EOL> scatter_kwargs = None , ** imshow_kwargs ) : <EOL> """<STR_LIT>""" <EOL> from . . plotting . gpy_plot . latent_plots import plot_latent <EOL> if "<STR_LIT>" not in predict_kwargs : <EOL> predict_kwargs [ '<STR_LIT>' ] = <NUM_LIT:0> <EOL> Yindex = predict_kwargs [ '<STR_LIT>' ] <EOL> self . kern = self . bgplvms [ Yindex ] . kern <EOL> self . likelihood = self . bgplvms [ Yindex ] . likelihood <EOL> return plot_latent ( self , labels , which_indices , resolution , legend , plot_limits , updates , kern , marker , num_samples , projection , scatter_kwargs ) <EOL> def __getstate__ ( self ) : <EOL> state = super ( MRD , self ) . __getstate__ ( ) <EOL> if '<STR_LIT>' in state : <EOL> del state [ '<STR_LIT>' ] <EOL> if '<STR_LIT>' in state : <EOL> del state [ '<STR_LIT>' ] <EOL> return state <EOL> def __setstate__ ( self , state ) : <EOL> super ( MRD , self ) . __setstate__ ( state ) <EOL> self . kern = self . bgplvms [ <NUM_LIT:0> ] . kern <EOL> self . likelihood = self . bgplvms [ <NUM_LIT:0> ] . likelihood <EOL> self . parameters_changed ( ) <EOL> def factorize_space ( self , threshold = <NUM_LIT> , printOut = False , views = None ) : <EOL> """<STR_LIT>""" <EOL> M = len ( self . bgplvms ) <EOL> if views is None : <EOL> if M is not <NUM_LIT:2> : <EOL> raise NotImplementedError ( "<STR_LIT>" ) <EOL> obsMod = [ <NUM_LIT:0> ] <EOL> infMod = <NUM_LIT:1> <EOL> else : <EOL> obsMod = views [ <NUM_LIT:0> ] <EOL> infMod = views [ <NUM_LIT:1> ] <EOL> scObs = [ None ] * len ( obsMod ) <EOL> for i in range ( <NUM_LIT:0> , len ( obsMod ) ) : <EOL> scObs [ i ] = np . atleast_2d ( self . bgplvms [ obsMod [ i ] ] . kern . input_sensitivity ( summarize = False ) ) [ <NUM_LIT:0> ] <EOL> scObs [ i ] /= np . max ( scObs [ i ] ) <EOL> scInf = np . atleast_2d ( self . bgplvms [ infMod ] . kern . input_sensitivity ( summarize = False ) ) [ <NUM_LIT:0> ] <EOL> scInf /= np . max ( scInf ) <EOL> retainedScales = [ None ] * ( len ( obsMod ) + <NUM_LIT:1> ) <EOL> for i in range ( <NUM_LIT:0> , len ( obsMod ) ) : <EOL> retainedScales [ obsMod [ i ] ] = np . where ( scObs [ i ] > threshold ) [ <NUM_LIT:0> ] <EOL> retainedScales [ infMod ] = np . where ( scInf > threshold ) [ <NUM_LIT:0> ] <EOL> for i in range ( len ( retainedScales ) ) : <EOL> retainedScales [ i ] = [ k for k in retainedScales [ i ] ] <EOL> sharedDims = set ( retainedScales [ obsMod [ <NUM_LIT:0> ] ] ) . intersection ( set ( retainedScales [ infMod ] ) ) <EOL> for i in range ( <NUM_LIT:1> , len ( obsMod ) ) : <EOL> sharedDims = sharedDims . intersection ( set ( retainedScales [ obsMod [ i ] ] ) ) <EOL> privateDims = [ None ] * M <EOL> for i in range ( <NUM_LIT:0> , len ( retainedScales ) ) : <EOL> privateDims [ i ] = set ( retainedScales [ i ] ) . difference ( sharedDims ) <EOL> privateDims [ i ] = [ k for k in privateDims [ i ] ] <EOL> sharedDims = [ k for k in sharedDims ] <EOL> sharedDims . sort ( ) <EOL> for i in range ( len ( privateDims ) ) : <EOL> privateDims [ i ] . sort ( ) <EOL> if printOut : <EOL> print ( '<STR_LIT>' + str ( sharedDims ) ) <EOL> for i in range ( len ( retainedScales ) ) : <EOL> print ( '<STR_LIT>' + str ( i ) + '<STR_LIT::>' + str ( privateDims [ i ] ) ) <EOL> return sharedDims , privateDims </s>
