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dim reduc plotting

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Max Zwiessele 2013-06-05 11:36:20 +01:00
parent 4b4af86e5b
commit 2a39440619
2 changed files with 42 additions and 42 deletions
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76
GPy/examples/dimensionality_reduction.py
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@ -14,20 +14,20 @@ default_seed = np.random.seed(123344)
def BGPLVM(seed=default_seed): def BGPLVM(seed=default_seed):
N = 10 N = 10
M = 3 M = 3
input_dim = 2 Q = 2
D = 4 D = 4
# generate GPLVM-like data # generate GPLVM-like data
X = np.random.rand(N, input_dim) X = np.random.rand(N, Q)
k = GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim, 0.00001) k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
K = k.K(X) K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N), K, D).T Y = np.random.multivariate_normal(np.zeros(N), K, D).T
k = GPy.kern.rbf(input_dim, ARD=True) + GPy.kern.linear(input_dim, ARD=True) + GPy.kern.rbf(input_dim, ARD=True) + GPy.kern.white(input_dim) k = GPy.kern.rbf(Q, ARD=True) + GPy.kern.linear(Q, ARD=True) + GPy.kern.rbf(Q, ARD=True) + GPy.kern.white(Q)
# k = GPy.kern.rbf(input_dim) + GPy.kern.rbf(input_dim) + GPy.kern.white(input_dim) # k = GPy.kern.rbf(Q) + GPy.kern.rbf(Q) + GPy.kern.white(Q)
# k = GPy.kern.rbf(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001) # k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
# k = GPy.kern.rbf(input_dim, ARD = False) + GPy.kern.white(input_dim, 0.00001) # k = GPy.kern.rbf(Q, ARD = False) + GPy.kern.white(Q, 0.00001)
m = GPy.models.Bayesian_GPLVM(Y, input_dim, kernel=k, M=M) m = GPy.models.Bayesian_GPLVM(Y, Q, kernel=k, M=M)
m.constrain_positive('(rbf|bias|noise|white|S)') m.constrain_positive('(rbf|bias|noise|white|S)')
# m.constrain_fixed('S', 1) # m.constrain_fixed('S', 1)
@ -63,7 +63,7 @@ def GPLVM_oil_100(optimize=True):
m.plot_latent(labels=m.data_labels) m.plot_latent(labels=m.data_labels)
return m return m
def swiss_roll(optimize=True, N=1000, M=15, input_dim=4, sigma=.2, plot=False): def swiss_roll(optimize=True, N=1000, M=15, Q=4, sigma=.2, plot=False):
from GPy.util.datasets import swiss_roll from GPy.util.datasets import swiss_roll
from GPy.core.transformations import logexp_clipped from GPy.core.transformations import logexp_clipped
@ -79,10 +79,10 @@ def swiss_roll(optimize=True, N=1000, M=15, input_dim=4, sigma=.2, plot=False):
from sklearn.manifold.isomap import Isomap from sklearn.manifold.isomap import Isomap
iso = Isomap().fit(Y) iso = Isomap().fit(Y)
X = iso.embedding_ X = iso.embedding_
if input_dim > 2: if Q > 2:
X = np.hstack((X, np.random.randn(N, input_dim - 2))) X = np.hstack((X, np.random.randn(N, Q - 2)))
except ImportError: except ImportError:
X = np.random.randn(N, input_dim) X = np.random.randn(N, Q)
if plot: if plot:
from mpl_toolkits import mplot3d from mpl_toolkits import mplot3d
@ -98,14 +98,14 @@ def swiss_roll(optimize=True, N=1000, M=15, input_dim=4, sigma=.2, plot=False):
var = .5 var = .5
S = (var * np.ones_like(X) + np.clip(np.random.randn(N, input_dim) * var ** 2, S = (var * np.ones_like(X) + np.clip(np.random.randn(N, Q) * var ** 2,
- (1 - var), - (1 - var),
(1 - var))) + .001 (1 - var))) + .001
Z = np.random.permutation(X)[:M] Z = np.random.permutation(X)[:M]
kernel = GPy.kern.rbf(input_dim, ARD=True) + GPy.kern.bias(input_dim, np.exp(-2)) + GPy.kern.white(input_dim, np.exp(-2)) kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
m = Bayesian_GPLVM(Y, input_dim, X=X, X_variance=S, M=M, Z=Z, kernel=kernel) m = Bayesian_GPLVM(Y, Q, X=X, X_variance=S, M=M, Z=Z, kernel=kernel)
m.data_colors = c m.data_colors = c
m.data_t = t m.data_t = t
@ -118,18 +118,18 @@ def swiss_roll(optimize=True, N=1000, M=15, input_dim=4, sigma=.2, plot=False):
m.optimize('scg', messages=1) m.optimize('scg', messages=1)
return m return m
def BGPLVM_oil(optimize=True, N=100, input_dim=5, M=25, max_f_eval=4e3, plot=False, **k): def BGPLVM_oil(optimize=True, N=100, Q=5, M=25, max_f_eval=4e3, plot=False, **k):
np.random.seed(0) np.random.seed(0)
data = GPy.util.datasets.oil() data = GPy.util.datasets.oil()
from GPy.core.transformations import logexp_clipped from GPy.core.transformations import logexp_clipped
# create simple GP model # create simple GP model
kernel = GPy.kern.rbf(input_dim, ARD=True) + GPy.kern.bias(input_dim, np.exp(-2)) + GPy.kern.white(input_dim, np.exp(-2)) kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q, np.exp(-2)) + GPy.kern.white(Q, np.exp(-2))
Y = data['X'][:N] Y = data['X'][:N]
Yn = Y - Y.mean(0) Yn = Y - Y.mean(0)
Yn /= Yn.std(0) Yn /= Yn.std(0)
m = GPy.models.Bayesian_GPLVM(Yn, input_dim, kernel=kernel, M=M, **k) m = GPy.models.Bayesian_GPLVM(Yn, Q, kernel=kernel, M=M, **k)
m.data_labels = data['Y'][:N].argmax(axis=1) m.data_labels = data['Y'][:N].argmax(axis=1)
# m.constrain('variance|leng', logexp_clipped()) # m.constrain('variance|leng', logexp_clipped())
@ -168,7 +168,7 @@ def oil_100():
def _simulate_sincos(D1, D2, D3, N, M, input_dim, plot_sim=False): def _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim=False):
x = np.linspace(0, 4 * np.pi, N)[:, None] x = np.linspace(0, 4 * 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))
@ -228,13 +228,13 @@ def bgplvm_simulation_matlab_compare():
Y = sim_data['Y'] Y = sim_data['Y']
S = sim_data['S'] S = sim_data['S']
mu = sim_data['mu'] mu = sim_data['mu']
M, [_, input_dim] = 3, mu.shape M, [_, Q] = 3, mu.shape
from GPy.models import mrd from GPy.models import mrd
from GPy import kern from GPy import kern
reload(mrd); reload(kern) reload(mrd); reload(kern)
k = kern.linear(input_dim, ARD=True) + kern.bias(input_dim, np.exp(-2)) + kern.white(input_dim, np.exp(-2)) k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
m = Bayesian_GPLVM(Y, input_dim, init="PCA", M=M, kernel=k, m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k,
# X=mu, # X=mu,
# X_variance=S, # X_variance=S,
_debug=False) _debug=False)
@ -248,8 +248,8 @@ def bgplvm_simulation(optimize='scg',
plot=True, plot=True,
max_f_eval=2e4): max_f_eval=2e4):
# from GPy.core.transformations import logexp_clipped # from GPy.core.transformations import logexp_clipped
D1, D2, D3, N, M, input_dim = 15, 8, 8, 100, 3, 5 D1, D2, D3, N, M, Q = 15, 8, 8, 100, 3, 5
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, input_dim, plot) slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot)
from GPy.models import mrd from GPy.models import mrd
from GPy import kern from GPy import kern
@ -258,8 +258,8 @@ def bgplvm_simulation(optimize='scg',
Y = Ylist[0] Y = Ylist[0]
k = kern.linear(input_dim, ARD=True) + kern.bias(input_dim, np.exp(-2)) + kern.white(input_dim, np.exp(-2)) # + kern.bias(input_dim) k = kern.linear(Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2)) # + kern.bias(Q)
m = Bayesian_GPLVM(Y, input_dim, init="PCA", M=M, kernel=k, _debug=True) m = Bayesian_GPLVM(Y, Q, init="PCA", M=M, kernel=k, _debug=True)
# m.constrain('variance|noise', logexp_clipped()) # m.constrain('variance|noise', logexp_clipped())
m.ensure_default_constraints() m.ensure_default_constraints()
m['noise'] = Y.var() / 100. m['noise'] = Y.var() / 100.
@ -276,16 +276,16 @@ def bgplvm_simulation(optimize='scg',
return m return m
def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw): def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
D1, D2, D3, N, M, input_dim = 150, 200, 400, 500, 3, 7 D1, D2, D3, N, M, Q = 150, 200, 400, 500, 3, 7
slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, input_dim, plot_sim) slist, Slist, Ylist = _simulate_sincos(D1, D2, D3, N, M, Q, plot_sim)
from GPy.models import mrd from GPy.models import mrd
from GPy import kern from GPy import kern
reload(mrd); reload(kern) reload(mrd); reload(kern)
k = kern.linear(input_dim, [.05] * input_dim, ARD=True) + kern.bias(input_dim, np.exp(-2)) + kern.white(input_dim, np.exp(-2)) k = kern.linear(Q, [.05] * Q, ARD=True) + kern.bias(Q, np.exp(-2)) + kern.white(Q, np.exp(-2))
m = mrd.MRD(Ylist, input_dim=input_dim, M=M, kernels=k, initx="", initz='permute', **kw) m = mrd.MRD(Ylist, Q=Q, M=M, kernels=k, initx="", initz='permute', **kw)
for i, Y in enumerate(Ylist): for i, Y in enumerate(Ylist):
m['{}_noise'.format(i + 1)] = Y.var() / 100. m['{}_noise'.format(i + 1)] = Y.var() / 100.
@ -299,21 +299,21 @@ def mrd_simulation(optimize=True, plot=True, plot_sim=True, **kw):
print "Optimizing Model:" print "Optimizing Model:"
m.optimize('scg', messages=1, max_iters=5e4, max_f_eval=5e4) m.optimize('scg', messages=1, max_iters=5e4, max_f_eval=5e4)
if plot: if plot:
m.plot_X_1d() m.plot_X_1d("MRD Latent Space 1D")
m.plot_scales() m.plot_scales("MRD Scales")
return m return m
def brendan_faces(): def brendan_faces():
from GPy import kern from GPy import kern
data = GPy.util.datasets.brendan_faces() data = GPy.util.datasets.brendan_faces()
input_dim = 2 Q = 2
Y = data['Y'][0:-1:10, :] Y = data['Y'][0:-1:10, :]
# Y = data['Y'] # Y = data['Y']
Yn = Y - Y.mean() Yn = Y - Y.mean()
Yn /= Yn.std() Yn /= Yn.std()
m = GPy.models.GPLVM(Yn, input_dim) m = GPy.models.GPLVM(Yn, Q)
# m = GPy.models.Bayesian_GPLVM(Yn, input_dim, M=100) # m = GPy.models.Bayesian_GPLVM(Yn, Q, M=100)
# optimize # optimize
m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped()) m.constrain('rbf|noise|white', GPy.core.transformations.logexp_clipped())
@ -376,11 +376,11 @@ def cmu_mocap(subject='35', motion=['01'], in_place=True):
# X -= X.mean(axis=0) # X -= X.mean(axis=0)
# X /= X.std(axis=0) # X /= X.std(axis=0)
# #
# input_dim = 10 # Q = 10
# M = 30 # M = 30
# #
# kernel = GPy.kern.rbf(input_dim, ARD=True) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim) # kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q)
# m = GPy.models.Bayesian_GPLVM(X, input_dim, kernel=kernel, M=M) # m = GPy.models.Bayesian_GPLVM(X, Q, kernel=kernel, M=M)
# # m.scale_factor = 100.0 # # m.scale_factor = 100.0
# m.constrain_positive('(white|noise|bias|X_variance|rbf_variance|rbf_length)') # m.constrain_positive('(white|noise|bias|X_variance|rbf_variance|rbf_length)')
# from sklearn import cluster # from sklearn import cluster

8
GPy/models/mrd.py
View file

@ -277,19 +277,19 @@ class MRD(model):
def plot_X_1d(self): def plot_X_1d(self):
return self.gref.plot_X_1d() return self.gref.plot_X_1d()
def plot_X(self, fignum="MRD Predictions", ax=None): def plot_X(self, fignum=None, ax=None):
fig = self._handle_plotting(fignum, ax, lambda i, g, ax: ax.imshow(g.X)) fig = self._handle_plotting(fignum, ax, lambda i, g, ax: ax.imshow(g.X))
return fig return fig
def plot_predict(self, fignum="MRD Predictions", ax=None, **kwargs): def plot_predict(self, fignum=None, ax=None, **kwargs):
fig = self._handle_plotting(fignum, ax, lambda i, g, ax: ax.imshow(g. predict(g.X)[0], **kwargs)) fig = self._handle_plotting(fignum, ax, lambda i, g, ax: ax.imshow(g. predict(g.X)[0], **kwargs))
return fig return fig
def plot_scales(self, fignum="MRD Scales", ax=None, *args, **kwargs): def plot_scales(self, fignum=None, ax=None, *args, **kwargs):
fig = self._handle_plotting(fignum, ax, lambda i, g, ax: g.kern.plot_ARD(ax=ax, *args, **kwargs)) fig = self._handle_plotting(fignum, ax, lambda i, g, ax: g.kern.plot_ARD(ax=ax, *args, **kwargs))
return fig return fig
def plot_latent(self, fignum="MRD Latent Spaces", ax=None, *args, **kwargs): def plot_latent(self, fignum=None, ax=None, *args, **kwargs):
fig = self._handle_plotting(fignum, ax, lambda i, g, ax: g.plot_latent(ax=ax, *args, **kwargs)) fig = self._handle_plotting(fignum, ax, lambda i, g, ax: g.plot_latent(ax=ax, *args, **kwargs))
return fig return fig