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example change mrd

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Max Zwiessele 2013-04-15 15:59:54 +01:00
parent 37f94310d5
commit 8af4ddabf1
1 changed files with 14 additions and 10 deletions
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24
GPy/examples/dimensionality_reduction.py
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@ -118,12 +118,12 @@ def mrd_simulation(plot_sim=False):
# Y2 -= Y2.mean(0) # Y2 -= Y2.mean(0)
# make_params = lambda ard: np.hstack([[1], ard, [1, .3]]) # make_params = lambda ard: np.hstack([[1], ard, [1, .3]])
D1, D2, D3, N, M, Q = 5, 5, 5, 150, 18, 5 D1, D2, D3, N, M, Q = 6, 7, 8, 150, 18, 5
x = np.linspace(0, 2 * np.pi, N)[:, None] x = np.linspace(0, 2 * np.pi, N)[:, None]
s1 = np.vectorize(lambda x: np.sin(x)) s1 = np.vectorize(lambda x: np.sin(x))
s2 = np.vectorize(lambda x: np.cos(x)) s2 = np.vectorize(lambda x: np.cos(x))
s3 = np.vectorize(lambda x: np.cos(4 * x)) s3 = np.vectorize(lambda x:-np.exp(-np.cos(2 * x)))
sS = np.vectorize(lambda x: np.sin(2 * x)) sS = np.vectorize(lambda x: np.sin(2 * x))
s1 = s1(x) s1 = s1(x)
@ -131,6 +131,10 @@ def mrd_simulation(plot_sim=False):
s3 = s3(x) s3 = s3(x)
sS = sS(x) sS = sS(x)
s1 -= s1.mean()
s2 -= s2.mean()
s3 -= s3.mean()
sS -= sS.mean()
s1 /= np.abs(s1).max() s1 /= np.abs(s1).max()
s2 /= np.abs(s2).max() s2 /= np.abs(s2).max()
s3 /= np.abs(s3).max() s3 /= np.abs(s3).max()
@ -144,9 +148,9 @@ def mrd_simulation(plot_sim=False):
Y2 = S2.dot(np.random.randn(S2.shape[1], D2)) Y2 = S2.dot(np.random.randn(S2.shape[1], D2))
Y3 = S3.dot(np.random.randn(S3.shape[1], D3)) Y3 = S3.dot(np.random.randn(S3.shape[1], D3))
Y1 += .041 * np.random.randn(*Y1.shape) Y1 += .1 * np.random.randn(*Y1.shape)
Y2 += .041 * np.random.randn(*Y2.shape) Y2 += .1 * np.random.randn(*Y2.shape)
Y3 += .041 * np.random.randn(*Y3.shape) Y3 += .1 * np.random.randn(*Y3.shape)
Y1 -= Y1.mean(0) Y1 -= Y1.mean(0)
Y2 -= Y2.mean(0) Y2 -= Y2.mean(0)
@ -155,13 +159,13 @@ def mrd_simulation(plot_sim=False):
Y2 /= Y2.std(0) Y2 /= Y2.std(0)
Y3 /= Y3.std(0) Y3 /= Y3.std(0)
Slist = [s1, s2, s3, sS] Slist = [s1, s2, sS]
Ylist = [Y1, Y2, Y3] Ylist = [Y1, Y2]
if plot_sim: if plot_sim:
import pylab import pylab
import itertools import itertools
fig = pylab.figure("MRD Simulation", figsize=(12, 12)) fig = pylab.figure("MRD Simulation", figsize=(8, 6))
fig.clf() fig.clf()
ax = fig.add_subplot(2, 1, 1) ax = fig.add_subplot(2, 1, 1)
labls = sorted(filter(lambda x: x.startswith("s"), locals())) labls = sorted(filter(lambda x: x.startswith("s"), locals()))
@ -179,11 +183,11 @@ def mrd_simulation(plot_sim=False):
from GPy import kern from GPy import kern
reload(mrd); reload(kern) reload(mrd); reload(kern)
k = kern.rbf(Q, ARD=True) + kern.bias(Q) + kern.white(Q) k = kern.rbf(Q, ARD=True) + kern.bias(Q) + kern.white(Q)
m = mrd.MRD(*Ylist, Q=Q, M=M, kernel=k, init="concat", _debug=False) m = mrd.MRD(*Ylist, Q=Q, M=M, kernel=k, init="single", _debug=False)
m.ensure_default_constraints() m.ensure_default_constraints()
# cstr = "noise|white|variance" # cstr = "noise|white|variance"
# m.unconstrain(cstr); m.constrain_bounded(cstr, 1e-6, 1.) # m.unconstrain(cstr); m.constrain_bounded(cstr, 1e-10, 1.)
m.auto_scale_factor = True m.auto_scale_factor = True