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new functions mrd init_X update
This commit is contained in:
Max Zwiessele
parent
cc32825a4a
commit
c63beddcf0
4 changed files with 115 additions and 47 deletions
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@ -6,7 +6,6 @@ import pylab as pb
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from matplotlib import pyplot as plt, pyplot
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import GPy
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from GPy.models.mrd import MRD
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default_seed = np.random.seed(123344)
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@ -102,10 +101,10 @@ def oil_100():
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def mrd_simulation(plot_sim=False):
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# num = 2
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ard1 = np.array([1., 1, 0, 0], dtype=float)
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ard2 = np.array([0., 1, 1, 0], dtype=float)
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ard1[ard1 == 0] = 1E-10
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ard2[ard2 == 0] = 1E-10
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# ard1 = np.array([1., 1, 0, 0], dtype=float)
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# ard2 = np.array([0., 1, 1, 0], dtype=float)
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# ard1[ard1 == 0] = 1E-10
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# ard2[ard2 == 0] = 1E-10
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# ard1i = 1. / ard1
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# ard2i = 1. / ard2
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@ -119,46 +118,74 @@ 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, N, M, Q = 5, 10, 150, 15, 3
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x = np.linspace(0, 4 * np.pi, N)[:, None]
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D1, D2, D3, N, M, Q = 5, 5, 5, 150, 18, 5
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x = np.linspace(0, 2 * 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.cos(4 * x))
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sS = np.vectorize(lambda x: np.sin(2 * x))
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S1 = np.hstack([s1(x), sS(x)]) + .1 * np.random.randn(N, 2)
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S2 = np.hstack([s2(x), sS(x)]) + .1 * np.random.randn(N, 2)
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s1 = s1(x)
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s2 = s2(x)
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s3 = s3(x)
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sS = sS(x)
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s1 /= np.abs(s1).max()
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s2 /= np.abs(s2).max()
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s3 /= np.abs(s3).max()
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sS /= np.abs(sS).max()
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S1 = np.hstack([s1, sS])
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S2 = np.hstack([s2, sS])
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S3 = np.hstack([s3, sS])
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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 += .041 * np.random.randn(*Y1.shape)
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Y2 += .041 * np.random.randn(*Y2.shape)
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Y3 += .041 * 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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Slist = [s1, s2, s3, sS]
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Ylist = [Y1, Y2, Y3]
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if plot_sim:
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import pylab
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fig = pylab.figure("MRD Simulation")
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ax = fig.add_subplot(2, 2, 1)
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ax.imshow(S1)
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ax.set_title("S1")
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ax = fig.add_subplot(2, 2, 2)
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ax.imshow(S2)
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ax.set_title("S2")
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ax = fig.add_subplot(2, 2, 3)
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ax.imshow(Y1)
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ax.set_title("Y1")
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ax = fig.add_subplot(2, 2, 4)
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ax.imshow(Y2)
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ax.set_title("Y2")
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import itertools
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fig = pylab.figure("MRD Simulation", figsize=(12, 12))
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fig.clf()
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ax = fig.add_subplot(2, 1, 1)
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labls = sorted(filter(lambda x: x.startswith("s"), locals()))
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for S, lab in itertools.izip(Slist, labls):
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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.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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k = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q)
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m = MRD(Y1, Y2, Q=Q, M=M, kernel=k, init="concat", _debug=False)
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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="concat", _debug=False)
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m.ensure_default_constraints()
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cstr = "noise|white"
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m.unconstrain(cstr); m.constrain_bounded(cstr, 1e-3, 1.)
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# cstr = "noise|white|variance"
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# m.unconstrain(cstr); m.constrain_bounded(cstr, 1e-6, 1.)
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m.auto_scale_factor = True
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# fig = pyplot.figure("expected", figsize=(8, 3))
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# ax = fig.add_subplot(121)
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@ -105,7 +105,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
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iteration += 1
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if display:
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print '\r',
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print 'Iteration: {0:<5g} Objective:{1:< 12g} Scale:{2:< 12g}'.format(iteration, fnow, beta),
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print 'Iteration: {0:>5g} Objective:{1:> 12e} Scale:{2:> 12e}'.format(iteration, fnow, beta),
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# print 'Iteration:', iteration, ' Objective:', fnow, ' Scale:', beta, '\r',
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sys.stdout.flush()
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@ -11,7 +11,5 @@ from warped_GP import warpedGP
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from sparse_GPLVM import sparse_GPLVM
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from uncollapsed_sparse_GP import uncollapsed_sparse_GP
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from Bayesian_GPLVM import Bayesian_GPLVM
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import mrd
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MRD = mrd.MRD
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del mrd
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from mrd import MRD
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from generalized_FITC import generalized_FITC
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@ -8,7 +8,6 @@ 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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import itertools
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from matplotlib import pyplot
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import pylab
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from GPy.util.linalg import PCA
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@ -59,6 +58,8 @@ class MRD(model):
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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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else:
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init = "PCA"
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try:
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self.Q = kwargs["Q"]
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except KeyError:
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@ -68,11 +69,11 @@ class MRD(model):
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except KeyError:
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self.M = 10
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X = self._init_X(Ylist, init)
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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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del self._init
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self.gref = self.bgplvms[0]
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nparams = numpy.array([0] + [sparse_GP._get_params(g).size - g.Z.size for g in self.bgplvms])
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@ -84,6 +85,41 @@ class MRD(model):
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model.__init__(self) # @UndefinedVariable
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@property
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def X(self):
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return self.gref.X
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@X.setter
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def X(self, X):
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try:
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self.propagate_param(X=X)
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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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def Ylist(self, Ylist):
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for g, Y in itertools.izip(self.bgplvms, Ylist):
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g.likelihood.Y = Y
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@property
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def auto_scale_factor(self):
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"""
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set auto_scale_factor for all gplvms
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:param b: auto_scale_factor
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:type b:
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"""
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return self.gref.auto_scale_factor
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@auto_scale_factor.setter
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def auto_scale_factor(self, b):
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self.propagate_param(auto_scale_factor=b)
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def propagate_param(self, **kwargs):
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for key, val in kwargs.iteritems():
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for g in self.bgplvms:
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g.__setattr__(key, val)
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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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@ -129,11 +165,11 @@ class MRD(model):
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def _set_params(self, x):
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start = 0; end = self.NQ
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X = x[start:end].reshape(self.N, self.Q)
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X = x[start:end]
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start = end; end += start
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X_var = x[start:end].reshape(self.N, self.Q)
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X_var = x[start:end]
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start = end; end += self.MQ
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Z = x[start:end].reshape(self.M, self.Q)
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Z = x[start:end]
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thetas = x[end:]
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if self._debug:
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@ -144,10 +180,14 @@ class MRD(model):
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# set params for all:
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for g, s, e in itertools.izip(self.bgplvms, self.nparams, self.nparams[1:]):
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self._set_var_params(g, X, X_var, Z)
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self._set_kern_params(g, thetas[s:e].copy())
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g._compute_kernel_matrices()
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g._computations()
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g._set_params(numpy.hstack([X, X_var, Z, thetas[s:e]]))
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# self._set_var_params(g, X, X_var, Z)
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# self._set_kern_params(g, thetas[s:e].copy())
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# g._compute_kernel_matrices()
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# if self.auto_scale_factor:
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# g.scale_factor = numpy.sqrt(g.psi2.sum(0).mean() * g.likelihood.precision)
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# # self.scale_factor = numpy.sqrt(self.psi2.sum(0).mean() * self.likelihood.precision)
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# g._computations()
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def log_likelihood(self):
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@ -171,15 +211,18 @@ class MRD(model):
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partial=g.partial_for_likelihood)]) \
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for g in self.bgplvms])))
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def _init_X(self, Ylist, init='PCA_concat'):
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if init in "PCA_concat":
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X = PCA(numpy.hstack(Ylist), self.Q)[0]
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elif init in "PCA_single":
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def _init_X(self, init='PCA', Ylist=None):
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if Ylist is None:
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Ylist = self.Ylist
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if init in "PCA_single":
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X = numpy.zeros((Ylist[0].shape[0], self.Q))
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for qs, Y in itertools.izip(numpy.array_split(numpy.arange(self.Q), len(Ylist)), Ylist):
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X[:, qs] = PCA(Y, len(qs))[0]
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elif init in "PCA_concat":
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X = PCA(numpy.hstack(Ylist), self.Q)[0]
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else: # init == 'random':
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X = numpy.random.randn(Ylist[0].shape[0], self.Q)
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self.X = X
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return X
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def plot_X(self):
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@ -229,7 +272,7 @@ class MRD(model):
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def _debug_optimize(self, opt='scg', maxiters=500, itersteps=10):
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iters = 0
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optstep = lambda: self.optimize(opt, messages=1, max_iters=itersteps)
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optstep = lambda: self.optimize(opt, messages=1, max_f_eval=itersteps)
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self._debug_plot()
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raw_input("enter to start debug")
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while iters < maxiters:
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