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added bgplvm_simulation on same simulation
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Max Zwiessele
parent
3baeeb1e35
commit
009b7314bf
2 changed files with 114 additions and 76 deletions
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@ -6,6 +6,7 @@ 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.Bayesian_GPLVM import Bayesian_GPLVM
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default_seed = np.random.seed(123344)
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@ -99,6 +100,92 @@ def oil_100():
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# m.plot_latent(labels=data['Y'].argmax(axis=1))
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return m
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def _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim=False):
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x = np.linspace(0, 4 * 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: x * np.cos(x))
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s3 = np.vectorize(lambda x: np.sin(2 * x))
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sS = np.vectorize(lambda x:-np.exp(-np.cos(2 * x)))
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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 -= s1.mean()
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s2 -= s2.mean()
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s3 -= s3.mean()
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sS -= sS.mean()
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s1 /= .5 * (np.abs(s1).max() - np.abs(s1).min())
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s2 /= .5 * (np.abs(s2).max() - np.abs(s2).min())
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s3 /= .5 * (np.abs(s3).max() - np.abs(s3).min())
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sS /= .5 * (np.abs(sS).max() - np.abs(sS).min())
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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 += .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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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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import itertools
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fig = pylab.figure("MRD Simulation", figsize=(8, 6))
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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(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(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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return slist, [S1, S2, S3], Ylist
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def bgplvm_simulation(plot_sim=False):
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D1, D2, D3, N, M, Q = 50, 34, 8, 100, 2, 6
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slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim)
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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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Y = Ylist[0]
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k = kern.linear(Q, ARD=True) + kern.bias(Q, .01) + kern.white(Q, .1)
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m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k)
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m.ensure_default_constraints()
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m.set('noise', Y.var() / 100.)
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m.auto_scale_factor = True
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cstr = 'variance'
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m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-20, 1.)
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cstr = 'linear_variance'
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m.unconstrain(cstr), m.constrain_positive(cstr)
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return m
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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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@ -117,32 +204,8 @@ def mrd_simulation(plot_sim=False):
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# Y2 = np.random.multivariate_normal(np.zeros(N), k.K(X), D2).T
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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 = 50, 100, 8, 300, 2, 6
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x = np.linspace(0, 4 * 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: x * np.cos(x))
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sS = np.vectorize(lambda x:-np.exp(-np.cos(2 * x)))
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s3 = np.vectorize(lambda x: np.sin(2 * x))
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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 -= s1.mean()
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s2 -= s2.mean()
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s3 -= s3.mean()
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sS -= sS.mean()
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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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D1, D2, D3, N, M, Q = 50, 34, 8, 100, 2, 6
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slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim)
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from GPy.models import mrd
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from GPy import kern
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@ -153,41 +216,7 @@ def mrd_simulation(plot_sim=False):
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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 += .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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Slist = [s1, s2, sS]
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Ylist = [Y1]
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if plot_sim:
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import pylab
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import itertools
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fig = pylab.figure("MRD Simulation", figsize=(8, 6))
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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(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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Ylist = [Ylist[0]]
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# k = kern.rbf(Q, ARD=True) + kern.bias(Q) + kern.white(Q)
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@ -199,29 +228,28 @@ def mrd_simulation(plot_sim=False):
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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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cstr = "variance"
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m.unconstrain(cstr); m.constrain_bounded(cstr, 1e-12, 1.)
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cstr = 'variance'
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m.unconstrain(cstr), m.constrain_bounded(cstr, 1e-12, 1.)
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cstr = 'linear_variance'
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m.unconstrain(cstr), m.constrain_positive(cstr)
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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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# import ipdb;ipdb.set_trace()
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# m.optimize('scg', messages=1, max_f_eval=200)
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#
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# m.optimize('scg', messages=1, max_f_eval=100)
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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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np.seterr(all='call')
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def ipdbonerr(errtype, flags):
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import ipdb; ipdb.set_trace()
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np.seterrcall(ipdbonerr)
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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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# ax.bar(np.arange(ard1.size) + .1, ard1)
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# ax = fig.add_subplot(122)
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# ax.bar(np.arange(ard2.size) + .1, ard2)
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return m
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return m # , mtest
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def mrd_silhouette():
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@ -345,6 +345,16 @@ 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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import multiprocessing
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class M(multiprocessing.Process):
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def __init__(self, q, *args, **kw):
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self.q = q
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super(M, self).__init__(*args, **kw)
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pass
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def run(self):
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pass
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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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