def initialise(self, init_FG='random', init_S='random'): ''' Initialise F, S, G, tau, and lambdaFk, lambdaGl (if ARD). ''' assert init_FG in OPTIONS_INIT_FG, "Unknown initialisation option for F and G: %s. Should be in %s." % ( init_FG, OPTIONS_INIT_FG) assert init_S in OPTIONS_INIT_S, "Unknown initialisation option for S: %s. Should be in %s." % ( init_S, OPTIONS_INIT_S) self.F = numpy.zeros((self.I, self.K)) self.S = numpy.zeros((self.K, self.L)) self.G = numpy.zeros((self.J, self.L)) self.lambdaFk = numpy.zeros(self.K) self.lambdaGl = numpy.zeros(self.L) # Initialise lambdaFk, lambdaGl if self.ARD: for k in range(self.K): self.lambdaFk[k] = self.alpha0 / self.beta0 for l in range(self.L): self.lambdaGl[l] = self.alpha0 / self.beta0 # Initialise F, G if init_FG == 'kmeans': print "Initialising F using KMeans." kmeans_F = KMeans(self.R, self.M, self.K) kmeans_F.initialise() kmeans_F.cluster() self.F = kmeans_F.clustering_results + 0.2 print "Initialising G using KMeans." kmeans_G = KMeans(self.R.T, self.M.T, self.L) kmeans_G.initialise() kmeans_G.cluster() self.G = kmeans_G.clustering_results + 0.2 else: # 'random' or 'exp' for i, k in itertools.product(range(self.I), range(self.K)): hyperparam = self.lambdaFk[k] if self.ARD else self.lambdaF[i, k] self.F[i, k] = exponential_draw( hyperparam) if init_FG == 'random' else 1.0 / hyperparam for j, l in itertools.product(range(self.J), range(self.L)): hyperparam = self.lambdaGl[l] if self.ARD else self.lambdaG[j, l] self.G[j, l] = exponential_draw( hyperparam) if init_FG == 'random' else 1.0 / hyperparam # Initialise S for k, l in itertools.product(range(self.K), range(self.L)): hyperparam = self.lambdaS[k, l] self.S[k, l] = exponential_draw( hyperparam) if init_S == 'random' else 1.0 / hyperparam # Initialise tau self.tau = gamma_draw(self.alpha_s(), self.beta_s())
def initialise(self,init_S='random',init_FG='random',tauFSG={}): self.tauF = tauFSG['tauF'] if 'tauF' in tauFSG else numpy.ones((self.I,self.K)) self.tauS = tauFSG['tauS'] if 'tauS' in tauFSG else numpy.ones((self.K,self.L)) self.tauG = tauFSG['tauG'] if 'tauG' in tauFSG else numpy.ones((self.J,self.L)) assert init_S in ['exp','random'], "Unrecognised init option for S: %s." % init_S self.muS = 1./self.lambdaS if init_S == 'random': for k,l in itertools.product(xrange(0,self.K),xrange(0,self.L)): self.muS[k,l] = exponential_draw(self.lambdaS[k,l]) assert init_FG in ['exp','random','kmeans'], "Unrecognised init option for F,G: %s." % init_FG self.muF, self.muG = 1./self.lambdaF, 1./self.lambdaG if init_FG == 'random': for i,k in itertools.product(xrange(0,self.I),xrange(0,self.K)): self.muF[i,k] = exponential_draw(self.lambdaF[i,k]) for j,l in itertools.product(xrange(0,self.J),xrange(0,self.L)): self.muG[j,l] = exponential_draw(self.lambdaG[j,l]) elif init_FG == 'kmeans': print "Initialising F using KMeans." kmeans_F = KMeans(self.R,self.M,self.K) kmeans_F.initialise() kmeans_F.cluster() self.muF = kmeans_F.clustering_results #+ 0.2 print "Initialising G using KMeans." kmeans_G = KMeans(self.R.T,self.M.T,self.L) kmeans_G.initialise() kmeans_G.cluster() self.muG = kmeans_G.clustering_results #+ 0.2 # Initialise the expectations and variances self.expF, self.varF = numpy.zeros((self.I,self.K)), numpy.zeros((self.I,self.K)) self.expS, self.varS = numpy.zeros((self.K,self.L)), numpy.zeros((self.K,self.L)) self.expG, self.varG = numpy.zeros((self.J,self.L)), numpy.zeros((self.J,self.L)) for k in range(0,self.K): self.update_exp_F(k) for k,l in itertools.product(xrange(0,self.K),xrange(0,self.L)): self.update_exp_S(k,l) for l in range(0,self.L): self.update_exp_G(l) # Initialise tau using the updates self.update_tau() #self.alpha_s, self.beta_s = self.alpha, self.beta self.update_exp_tau()
def initialise(self, init_FG='random', init_S='random', expo_prior=1.): ''' Initialise F, S and G. ''' assert init_FG in OPTIONS_INIT_FG, "Unrecognised init option for F,G: %s. Should be one in %s." % ( init_FG, OPTIONS_INIT_FG) assert init_S in OPTIONS_INIT_S, "Unrecognised init option for S: %s. Should be one in %s." % ( init_S, OPTIONS_INIT_S) if init_S == 'ones': self.S = numpy.ones((self.K, self.L)) elif init_S == 'random': self.S = numpy.random.rand(self.K, self.L) elif init_S == 'exponential': self.S = numpy.empty((self.K, self.L)) for k, l in itertools.product(xrange(0, self.K), xrange(0, self.L)): self.S[k, l] = exponential_draw(expo_prior) if init_FG == 'ones': self.F = numpy.ones((self.I, self.K)) self.G = numpy.ones((self.J, self.L)) elif init_FG == 'random': self.F = numpy.random.rand(self.I, self.K) self.G = numpy.random.rand(self.J, self.L) elif init_FG == 'exponential': self.F = numpy.empty((self.I, self.K)) self.G = numpy.empty((self.J, self.L)) for i, k in itertools.product(xrange(0, self.I), xrange(0, self.K)): self.F[i, k] = exponential_draw(expo_prior) for j, l in itertools.product(xrange(0, self.J), xrange(0, self.L)): self.G[j, l] = exponential_draw(expo_prior) elif init_FG == 'kmeans': print "Initialising F using KMeans." kmeans_F = KMeans(self.R, self.M, self.K) kmeans_F.initialise() kmeans_F.cluster() self.F = kmeans_F.clustering_results + 0.2 print "Initialising G using KMeans." kmeans_G = KMeans(self.R.T, self.M.T, self.L) kmeans_G.initialise() kmeans_G.cluster() self.G = kmeans_G.clustering_results + 0.2
def initialise(self, init_S='random', init_FG='random'): assert init_S in [ 'random', 'exp' ], "Unknown initialisation option for S: %s. Should be 'random' or 'exp'." % init_S assert init_FG in [ 'random', 'exp', 'kmeans' ], "Unknown initialisation option for S: %s. Should be 'random', 'exp', or 'kmeans." % init_FG self.S = 1. / self.lambdaS if init_S == 'random': for k, l in itertools.product(xrange(0, self.K), xrange(0, self.L)): self.S[k, l] = exponential_draw(self.lambdaS[k, l]) self.F, self.G = 1. / self.lambdaF, 1. / self.lambdaG if init_FG == 'random': for i, k in itertools.product(xrange(0, self.I), xrange(0, self.K)): self.F[i, k] = exponential_draw(self.lambdaF[i, k]) for j, l in itertools.product(xrange(0, self.J), xrange(0, self.L)): self.G[j, l] = exponential_draw(self.lambdaG[j, l]) elif init_FG == 'kmeans': print "Initialising F using KMeans." kmeans_F = KMeans(self.R, self.M, self.K) kmeans_F.initialise() kmeans_F.cluster() self.F = kmeans_F.clustering_results + 0.2 print "Initialising G using KMeans." kmeans_G = KMeans(self.R.T, self.M.T, self.L) kmeans_G.initialise() kmeans_G.cluster() self.G = kmeans_G.clustering_results + 0.2 self.tau = self.alpha_s() / self.beta_s()
def initialise(self, init_FG='random', init_S='random'): ''' Initialise F, S, G, tau, and lambdaFk, lambdaGl (if ARD). ''' assert init_FG in OPTIONS_INIT_FG, "Unknown initialisation option for F and G: %s. Should be in %s." % ( init_FG, OPTIONS_INIT_FG) assert init_S in OPTIONS_INIT_S, "Unknown initialisation option for S: %s. Should be in %s." % ( init_S, OPTIONS_INIT_S) # Initialise lambdaFk, lambdaGl, and compute expectations if self.ARD: self.alphaFk_s, self.betaFk_s = numpy.zeros(self.K), numpy.zeros( self.K) self.alphaGl_s, self.betaGl_s = numpy.zeros(self.L), numpy.zeros( self.L) self.exp_lambdaFk, self.exp_loglambdaFk = numpy.zeros( self.K), numpy.zeros(self.K) self.exp_lambdaGl, self.exp_loglambdaGl = numpy.zeros( self.L), numpy.zeros(self.L) for k in range(self.K): self.alphaFk_s[k] = self.alpha0 self.betaFk_s[k] = self.beta0 self.update_exp_lambdaFk(k) for l in range(self.L): self.alphaGl_s[l] = self.alpha0 self.betaGl_s[l] = self.beta0 self.update_exp_lambdaGl(l) # Initialise parameters F, G self.mu_F, self.tau_F = numpy.zeros((self.I, self.K)), numpy.zeros( (self.I, self.K)) self.mu_G, self.tau_G = numpy.zeros((self.J, self.L)), numpy.zeros( (self.J, self.L)) self.mu_S, self.tau_S = numpy.zeros((self.K, self.L)), numpy.zeros( (self.K, self.L)) if init_FG == 'kmeans': print "Initialising F using KMeans." kmeans_F = KMeans(self.R, self.M, self.K) kmeans_F.initialise() kmeans_F.cluster() self.mu_F = kmeans_F.clustering_results for i, k in itertools.product(range(self.I), range(self.K)): self.tau_F[i, k] = 1. print "Initialising G using KMeans." kmeans_G = KMeans(self.R.T, self.M.T, self.L) kmeans_G.initialise() kmeans_G.cluster() self.mu_G = kmeans_G.clustering_results for j, l in itertools.product(range(self.J), range(self.L)): self.tau_G[j, l] = 1. else: # 'random' or 'exp' for i, k in itertools.product(range(self.I), range(self.K)): self.tau_F[i, k] = 1. hyperparam = self.exp_lambdaFk[ k] if self.ARD else self.lambdaF[i, k] self.mu_F[i, k] = exponential_draw( hyperparam) if init_FG == 'random' else 1.0 / hyperparam for j, l in itertools.product(range(self.J), range(self.L)): self.tau_G[j, l] = 1. hyperparam = self.exp_lambdaGl[ l] if self.ARD else self.lambdaG[j, l] self.mu_G[j, l] = exponential_draw( hyperparam) if init_FG == 'random' else 1.0 / hyperparam # Initialise parameters S for k, l in itertools.product(range(self.K), range(self.L)): self.tau_S[k, l] = 1. hyperparam = self.lambdaS[k, l] self.mu_S[k, l] = exponential_draw( hyperparam) if init_S == 'random' else 1.0 / hyperparam # Compute expectations and variances F, G, S self.exp_F, self.var_F = numpy.zeros((self.I, self.K)), numpy.zeros( (self.I, self.K)) self.exp_G, self.var_G = numpy.zeros((self.J, self.L)), numpy.zeros( (self.J, self.L)) self.exp_S, self.var_S = numpy.zeros((self.K, self.L)), numpy.zeros( (self.K, self.L)) for k in range(self.K): self.update_exp_F(k) for l in range(self.L): self.update_exp_G(l) for k, l in itertools.product(range(self.K), range(self.L)): self.update_exp_S(k, l) # Initialise tau and compute expectation self.update_tau() self.update_exp_tau()