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adjusted plotting behaviour in X1d
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Max Zwiessele
parent
9acc6e9723
commit
350497c726
2 changed files with 128 additions and 46 deletions
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@ -118,13 +118,13 @@ def mrd_simulation(plot_sim=False):
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# Y2 -= Y2.mean(0)
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# make_params = lambda ard: np.hstack([[1], ard, [1, .3]])
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D1, D2, D3, N, M, Q = 6, 7, 8, 150, 18, 5
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x = np.linspace(0, 2 * np.pi, N)[:, None]
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D1, D2, D3, N, M, Q = 50, 100, 8, 200, 2, 5
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x = np.linspace(0, 8 * np.pi, N)[:, None]
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s1 = np.vectorize(lambda x: np.sin(x))
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s2 = np.vectorize(lambda x: np.cos(x))
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s3 = np.vectorize(lambda x:-np.exp(-np.cos(2 * x)))
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sS = np.vectorize(lambda x: np.sin(2 * x))
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sS = np.vectorize(lambda x: x * np.sin(2 * x))
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s1 = s1(x)
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s2 = s2(x)
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@ -144,20 +144,30 @@ def mrd_simulation(plot_sim=False):
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S2 = np.hstack([s2, sS])
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S3 = np.hstack([s3, sS])
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from GPy.models import mrd
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from GPy import kern
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reload(mrd); reload(kern)
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# k = kern.rbf(2, ARD=True) + kern.bias(2) + kern.white(2)
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# Y1 = np.random.multivariate_normal(np.zeros(N), k.K(S1), D1).T
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# Y2 = np.random.multivariate_normal(np.zeros(N), k.K(S2), D2).T
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# Y3 = np.random.multivariate_normal(np.zeros(N), k.K(S3), D3).T
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Y1 = S1.dot(np.random.randn(S1.shape[1], D1))
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Y2 = S2.dot(np.random.randn(S2.shape[1], D2))
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Y3 = S3.dot(np.random.randn(S3.shape[1], D3))
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Y1 += .1 * np.random.randn(*Y1.shape)
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Y2 += .1 * np.random.randn(*Y2.shape)
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Y3 += .1 * np.random.randn(*Y3.shape)
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Y1 += .5 * np.random.randn(*Y1.shape)
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Y2 += .5 * np.random.randn(*Y2.shape)
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Y3 += .5 * np.random.randn(*Y3.shape)
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Y1 -= Y1.mean(0)
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Y2 -= Y2.mean(0)
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Y3 -= Y3.mean(0)
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Y1 /= Y1.std(0)
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Y2 /= Y2.std(0)
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Y3 /= Y3.std(0)
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# Y1 -= Y1.mean(0)
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# Y2 -= Y2.mean(0)
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# Y3 -= Y3.mean(0)
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# Y1 /= Y1.std(0)
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# Y2 /= Y2.std(0)
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# Y3 /= Y3.std(0)
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Slist = [s1, s2, sS]
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Ylist = [Y1, Y2]
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@ -173,21 +183,33 @@ def mrd_simulation(plot_sim=False):
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ax.plot(x, S, label=lab)
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ax.legend()
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for i, Y in enumerate(Ylist):
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ax = fig.add_subplot(2, len(Ylist), len(Slist) + i)
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ax = fig.add_subplot(2, len(Ylist), len(Ylist) + 1 + i)
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ax.imshow(Y)
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ax.set_title("Y{}".format(i + 1))
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pylab.draw()
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pylab.tight_layout()
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from GPy.models import mrd
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from GPy import kern
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reload(mrd); reload(kern)
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k = kern.rbf(Q, ARD=True) + kern.bias(Q) + kern.white(Q)
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m = mrd.MRD(*Ylist, Q=Q, M=M, kernel=k, init="single", _debug=False)
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# k = kern.rbf(Q, ARD=True) + kern.bias(Q) + kern.white(Q)
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k = kern.linear(Q, ARD=True) + kern.bias(Q) + kern.white(Q)
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m = mrd.MRD(*Ylist, Q=Q, M=M, kernel=k, initx="concat", _debug=False)
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m.ensure_default_constraints()
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# cstr = "noise|white|variance"
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# m.unconstrain(cstr); m.constrain_bounded(cstr, 1e-10, 1.)
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for i, Y in enumerate(Ylist):
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m.set('{}_noise'.format(i + 1), Y.var() / 100.)
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# import ipdb;ipdb.set_trace()
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cstr = "variance"
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m.unconstrain(cstr); m.constrain_bounded(cstr, 1e-15, 1.)
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# print "initializing beta"
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# cstr = "noise"
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# m.unconstrain(cstr); m.constrain_fixed(cstr)
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# m.optimize('scg', messages=1, max_f_eval=200)
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#
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# print "releasing beta"
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# cstr = "noise"
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# m.unconstrain(cstr); m.constrain_positive(cstr)
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m.auto_scale_factor = True
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@ -5,11 +5,12 @@ Created on 10 Apr 2013
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'''
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from GPy.core import model
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from GPy.models.Bayesian_GPLVM import Bayesian_GPLVM
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import numpy
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from GPy.models.sparse_GP import sparse_GP
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from GPy.util.linalg import PCA
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from scipy import linalg
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import numpy
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import itertools
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import pylab
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from GPy.util.linalg import PCA
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class MRD(model):
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"""
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@ -23,8 +24,10 @@ class MRD(model):
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:type names: [str]
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:param Q: latent dimensionality (will raise
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:type Q: int
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:param init: initialisation method for the latent space
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:type init: 'PCA'|'random'
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:param initx: initialisation method for the latent space
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:type initx: 'PCA'|'random'
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:param initz: initialisation method for inducing inputs
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:type initz: 'permute'|'random'
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:param X:
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Initial latent space
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:param X_variance:
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@ -38,6 +41,7 @@ class MRD(model):
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:param kernel:
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kernel to use
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"""
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def __init__(self, *Ylist, **kwargs):
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if kwargs.has_key("_debug"):
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self._debug = kwargs['_debug']
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@ -55,24 +59,30 @@ class MRD(model):
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del kwargs['kernel']
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else:
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k = lambda: None
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if kwargs.has_key('init'):
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init = kwargs['init']
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del kwargs['init']
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if kwargs.has_key('initx'):
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initx = kwargs['initx']
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del kwargs['initx']
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else:
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init = "PCA"
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initx = "PCA"
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if kwargs.has_key('initz'):
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initz = kwargs['initz']
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del kwargs['initz']
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else:
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initz = "permute"
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try:
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self.Q = kwargs["Q"]
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except KeyError:
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raise ValueError("Need Q for MRD")
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try:
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self.M = kwargs["M"]
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del kwargs["M"]
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except KeyError:
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self.M = 10
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self._init = True
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X = self._init_X(init, Ylist)
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Z = numpy.random.permutation(X.copy())[:self.M]
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self.bgplvms = [Bayesian_GPLVM(Y, kernel=k(), X=X, Z=Z, **kwargs) for Y in Ylist]
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X = self._init_X(initx, Ylist)
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Z = self._init_Z(initz, X)
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self.bgplvms = [Bayesian_GPLVM(Y, kernel=k(), X=X, Z=Z, M=self.M, **kwargs) for Y in Ylist]
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del self._init
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self.gref = self.bgplvms[0]
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@ -96,6 +106,26 @@ class MRD(model):
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if not self._init:
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raise AttributeError("bgplvm list not initialized")
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@property
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def Z(self):
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return self.gref.Z
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@Z.setter
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def Z(self, Z):
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try:
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self.propagate_param(Z=Z)
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except AttributeError:
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if not self._init:
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raise AttributeError("bgplvm list not initialized")
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@property
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def X_variance(self):
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return self.gref.X_variance
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@X_variance.setter
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def X_variance(self, X_var):
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try:
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self.propagate_param(X_variance=X_var)
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except AttributeError:
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if not self._init:
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raise AttributeError("bgplvm list not initialized")
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@property
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def Ylist(self):
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return [g.likelihood.Y for g in self.bgplvms]
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@Ylist.setter
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@ -120,6 +150,11 @@ class MRD(model):
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for g in self.bgplvms:
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g.__setattr__(key, val)
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def randomize(self, initx='concat', initz='permute', *args, **kw):
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super(MRD, self).randomize(*args, **kw)
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self._init_X(initx, self.Ylist)
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self._init_Z(initz, self.X)
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def _get_param_names(self):
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# X_names = sum([['X_%i_%i' % (n, q) for q in range(self.Q)] for n in range(self.N)], [])
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# S_names = sum([['X_variance_%i_%i' % (n, q) for q in range(self.Q)] for n in range(self.N)], [])
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@ -154,14 +189,14 @@ class MRD(model):
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params = numpy.hstack([X, X_var, Z, numpy.hstack(thetas)])
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return params
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def _set_var_params(self, g, X, X_var, Z):
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g.X = X
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g.X_variance = X_var
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g.Z = Z
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def _set_kern_params(self, g, p):
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g.kern._set_params(p[:g.kern.Nparam])
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g.likelihood._set_params(p[g.kern.Nparam:])
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# def _set_var_params(self, g, X, X_var, Z):
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# g.X = X.reshape(self.N, self.Q)
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# g.X_variance = X_var.reshape(self.N, self.Q)
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# g.Z = Z.reshape(self.M, self.Q)
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#
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# def _set_kern_params(self, g, p):
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# g.kern._set_params(p[:g.kern.Nparam])
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# g.likelihood._set_params(p[g.kern.Nparam:])
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def _set_params(self, x):
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start = 0; end = self.NQ
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@ -225,25 +260,50 @@ class MRD(model):
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self.X = X
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return X
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def _init_Z(self, init="permute", X=None):
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if X is None:
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X = self.X
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if init in "permute":
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Z = numpy.random.permutation(X.copy())[:self.M]
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elif init in "random":
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Z = numpy.random.randn(self.M, self.Q) * X.var()
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self.Z = Z
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return Z
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def plot_X_1d(self, colors=None):
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if colors is None:
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colors = pylab.gca()._get_lines.color_cycle
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fig = pylab.figure(num="MRD X 1d", figsize=(4 * len(self.bgplvms), (2 * self.X.shape[1])))
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fig = pylab.figure(num="MRD X 1d", figsize=(min(8, (3 * len(self.bgplvms))), min(12, (2 * self.X.shape[1]))))
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fig.clf()
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ax1 = fig.add_subplot(self.X.shape[1], 1, 1)
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if colors is None:
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colors = ax1._get_lines.color_cycle
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ax1.plot(self.X, c='k', alpha=.3)
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plots = ax1.plot(self.X.T[0], c=colors.next())
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ax1.fill_between(numpy.arange(self.X.shape[0]),
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self.X.T[0] - 2 * numpy.sqrt(self.gref.X_variance.T[0]),
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self.X.T[0] + 2 * numpy.sqrt(self.gref.X_variance.T[0]),
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facecolor=plots[-1].get_color(),
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alpha=.3)
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ax1.text(1, 1, r"$\mathbf{{X_{}}}".format(1),
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horizontalalignment='right',
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verticalalignment='top',
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transform=ax1.transAxes)
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for i in range(self.X.shape[1] - 1):
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ax = fig.add_subplot(self.X.shape[1], 1, i + 2)
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ax.plot(self.X, c='k', alpha=.3)
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plots.extend(ax.plot(self.X.T[i + 1], c=colors.next()))
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ax.fill_between(numpy.arange(self.X.shape[0]),
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self.X.T[i + 1] - 2 * numpy.sqrt(self.gref.X_variance.T[i + 1]),
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self.X.T[i + 1] + 2 * numpy.sqrt(self.gref.X_variance.T[i + 1]),
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facecolor=plots[-1].get_color(),
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alpha=.3)
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if i < self.X.shape[1] - 2:
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ax.set_xticklabels('')
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ax1.set_xticklabels('')
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ax1.legend(plots, [r"$\mathbf{{X_{}}}$".format(i + 1) for i in range(self.X.shape[1])],
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bbox_to_anchor=(0., 1 + .01 * self.X.shape[1],
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1., 1. + .01 * self.X.shape[1]), loc=3,
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ncol=self.X.shape[1], mode="expand", borderaxespad=0.)
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# ax1.legend(plots, [r"$\mathbf{{X_{}}}$".format(i + 1) for i in range(self.X.shape[1])],
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# bbox_to_anchor=(0., 1 + .01 * self.X.shape[1],
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# 1., 1. + .01 * self.X.shape[1]), loc=3,
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# ncol=self.X.shape[1], mode="expand", borderaxespad=0.)
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pylab.draw()
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fig.tight_layout(h_pad=.01, rect=(0, 0, 1, .95))
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return fig
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