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first trivial model touches
This commit is contained in:
Max Zwiessele
parent
eb1d8f211f
commit
4953d71b7a
3 changed files with 56 additions and 22 deletions
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@ -6,26 +6,27 @@ import pylab as pb
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from matplotlib import pyplot as plt
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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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def BGPLVM(seed = default_seed):
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def BGPLVM(seed=default_seed):
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N = 10
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M = 3
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Q = 2
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D = 4
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#generate GPLVM-like data
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# generate GPLVM-like data
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X = np.random.rand(N, Q)
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k = GPy.kern.rbf(Q) + GPy.kern.white(Q, 0.00001)
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K = k.K(X)
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Y = np.random.multivariate_normal(np.zeros(N),K,D).T
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Y = np.random.multivariate_normal(np.zeros(N), K, D).T
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k = GPy.kern.linear(Q, ARD = True) + GPy.kern.white(Q)
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k = GPy.kern.linear(Q, ARD=True) + GPy.kern.white(Q)
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# k = GPy.kern.rbf(Q) + GPy.kern.rbf(Q) + GPy.kern.white(Q)
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# k = GPy.kern.rbf(Q) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.00001)
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# k = GPy.kern.rbf(Q, ARD = False) + GPy.kern.white(Q, 0.00001)
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m = GPy.models.Bayesian_GPLVM(Y, Q, kernel = k, M=M)
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m = GPy.models.Bayesian_GPLVM(Y, Q, kernel=k, M=M)
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m.constrain_positive('(rbf|bias|noise|white|S)')
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# m.constrain_fixed('S', 1)
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@ -38,44 +39,44 @@ def BGPLVM(seed = default_seed):
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# pb.title('After optimisation')
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m.ensure_default_constraints()
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m.randomize()
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m.checkgrad(verbose = 1)
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m.checkgrad(verbose=1)
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return m
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def GPLVM_oil_100(optimize=True,M=15):
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def GPLVM_oil_100(optimize=True, M=15):
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data = GPy.util.datasets.oil_100()
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# create simple GP model
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kernel = GPy.kern.rbf(6, ARD = True) + GPy.kern.bias(6)
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kernel = GPy.kern.rbf(6, ARD=True) + GPy.kern.bias(6)
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m = GPy.models.GPLVM(data['X'], 6, kernel=kernel, M=M)
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m.data_labels = data['Y'].argmax(axis=1)
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# optimize
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m.ensure_default_constraints()
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if optimize:
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m.optimize('scg',messages=1)
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m.optimize('scg', messages=1)
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# plot
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print(m)
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m.plot_latent(labels=m.data_labels)
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return m
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def BGPLVM_oil(optimize=True,N=100,Q=10,M=15):
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def BGPLVM_oil(optimize=True, N=100, Q=10, M=15):
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data = GPy.util.datasets.oil()
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# create simple GP model
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kernel = GPy.kern.rbf(Q, ARD = True) + GPy.kern.bias(Q) + GPy.kern.white(Q,0.001)
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m = GPy.models.Bayesian_GPLVM(data['X'][:N], Q, kernel = kernel,M=M)
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kernel = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q, 0.001)
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m = GPy.models.Bayesian_GPLVM(data['X'][:N], Q, kernel=kernel, M=M)
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m.data_labels = data['Y'][:N].argmax(axis=1)
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# optimize
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if optimize:
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m.constrain_fixed('noise',0.05)
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m.constrain_fixed('noise', 0.05)
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m.ensure_default_constraints()
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m.optimize('scg',messages=1)
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m.optimize('scg', messages=1)
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m.unconstrain('noise')
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m.constrain_positive('noise')
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m.optimize('scg',messages=1)
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m.optimize('scg', messages=1)
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else:
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m.ensure_default_constraints()
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@ -83,7 +84,7 @@ def BGPLVM_oil(optimize=True,N=100,Q=10,M=15):
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print(m)
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m.plot_latent(labels=m.data_labels)
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pb.figure()
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pb.bar(np.arange(m.kern.D),1./m.input_sensitivity())
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pb.bar(np.arange(m.kern.D), 1. / m.input_sensitivity())
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return m
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def oil_100():
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@ -96,7 +97,37 @@ def oil_100():
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# plot
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print(m)
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#m.plot_latent(labels=data['Y'].argmax(axis=1))
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# m.plot_latent(labels=data['Y'].argmax(axis=1))
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return m
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def mrd_simulation():
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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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make_params = lambda ard: np.hstack([[1], ard, [1, .3]])
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D1, D2, N, M, Q = 50, 100, 150, 15, 4
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X = np.random.randn(N, Q)
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k = GPy.kern.rbf(Q, ARD=True, lengthscale=ard1) + GPy.kern.bias(Q, 1) + GPy.kern.white(Q, 0.0001)
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Y1 = np.random.multivariate_normal(np.zeros(N), k.K(X), D1).T
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Y1 -= Y1.mean(0)
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k = GPy.kern.rbf(Q, ARD=True, lengthscale=ard2) + GPy.kern.bias(Q, 1) + GPy.kern.white(Q, 0.0001)
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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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k = GPy.kern.rbf(Q, ARD=True) + GPy.kern.bias(Q) + GPy.kern.white(Q, 1.0)
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m = MRD(Y1, Y2, Q=Q, M=M, kernel=k, _debug=False)
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m.ensure_default_constraints()
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m.optimize(messages=1, max_f_eval=5000)
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import ipdb;ipdb.set_trace()
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return m
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def brendan_faces():
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@ -109,7 +140,7 @@ def brendan_faces():
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m.optimize(messages=1, max_f_eval=10000)
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ax = m.plot_latent()
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y = m.likelihood.Y[0,:]
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, invert=False, scale=False)
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lvm_visualizer = GPy.util.visualize.lvm(m, data_show, ax)
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raw_input('Press enter to finish')
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@ -120,13 +151,13 @@ def brendan_faces():
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def stick():
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data = GPy.util.datasets.stick()
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m = GPy.models.GPLVM(data['Y'], 2)
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# optimize
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m.ensure_default_constraints()
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m.optimize(messages=1, max_f_eval=10000)
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ax = m.plot_latent()
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y = m.likelihood.Y[0,:]
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y = m.likelihood.Y[0, :]
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data_show = GPy.util.visualize.stick_show(y[None, :], connect=data['connect'])
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lvm_visualizer = GPy.util.visualize.lvm(m, data_show, ax)
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raw_input('Press enter to finish')
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@ -55,7 +55,7 @@ class GPLVM(GP):
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def plot(self):
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assert self.likelihood.Y.shape[1]==2
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pb.scatter(self.likelihood.Y[:,0],self.likelihood.Y[:,1],40,self.X[:,0].copy(),linewidth=0,cmap=pb.cm.jet)
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pb.scatter(self.likelihood.Y[:,0],self.likelihood.Y[:,1],40,self.X[:,0].copy(),linewidth=0,cmap=pb.cm.jet) # @UndefinedVariable
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Xnew = np.linspace(self.X.min(),self.X.max(),200)[:,None]
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mu, var, upper, lower = self.predict(Xnew)
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pb.plot(mu[:,0], mu[:,1],'k',linewidth=1.5)
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@ -90,7 +90,7 @@ class GPLVM(GP):
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Xtest_full[:, :2] = Xtest
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mu, var, low, up = self.predict(Xtest_full)
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var = var.mean(axis=1) # this was var[:, :2] edit by Neil
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pb.imshow(var.reshape(resolution,resolution).T[::-1,:],extent=[xmin[0],xmax[0],xmin[1],xmax[1]],cmap=pb.cm.binary,interpolation='bilinear')
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pb.imshow(var.reshape(resolution,resolution).T[::-1,:],extent=[xmin[0],xmax[0],xmin[1],xmax[1]],cmap=pb.cm.binary,interpolation='bilinear') # @UndefinedVariable
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for i,ul in enumerate(np.unique(labels)):
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@ -11,4 +11,7 @@ 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 generalized_FITC import generalized_FITC
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