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plot_latent added for mrd
This commit is contained in:
Max Zwiessele
parent
89a50e260a
commit
de6b00ebfd
1 changed files with 41 additions and 44 deletions
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@ -24,7 +24,7 @@ class GPLVM(GP):
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:type init: 'PCA'|'random'
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"""
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def __init__(self, Y, Q, init='PCA', X=None, kernel=None, **kwargs):
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def __init__(self, Y, Q, init='PCA', X = None, kernel=None, **kwargs):
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if X is None:
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X = self.initialise_latent(init, Q, Y)
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if kernel is None:
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@ -39,28 +39,28 @@ class GPLVM(GP):
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return np.random.randn(Y.shape[0], Q)
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def _get_param_names(self):
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return sum([['X_%i_%i' % (n, q) for q in range(self.Q)] for n in range(self.N)], []) + GP._get_param_names(self)
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return sum([['X_%i_%i'%(n,q) for q in range(self.Q)] for n in range(self.N)],[]) + GP._get_param_names(self)
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def _get_params(self):
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return np.hstack((self.X.flatten(), GP._get_params(self)))
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def _set_params(self, x):
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self.X = x[:self.X.size].reshape(self.N, self.Q).copy()
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def _set_params(self,x):
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self.X = x[:self.X.size].reshape(self.N,self.Q).copy()
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GP._set_params(self, x[self.X.size:])
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def _log_likelihood_gradients(self):
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dL_dX = 2.*self.kern.dK_dX(self.dL_dK, self.X)
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dL_dX = 2.*self.kern.dK_dX(self.dL_dK,self.X)
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return np.hstack((dL_dX.flatten(), GP._log_likelihood_gradients(self)))
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return np.hstack((dL_dX.flatten(),GP._log_likelihood_gradients(self)))
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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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Xnew = np.linspace(self.X.min(), self.X.max(), 200)[:, None]
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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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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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pb.plot(mu[:,0], mu[:,1],'k',linewidth=1.5)
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def plot_latent(self, labels=None, which_indices=None, resolution=50):
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def plot_latent(self,labels=None, which_indices=None, resolution=50):
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"""
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:param labels: a np.array of size self.N containing labels for the points (can be number, strings, etc)
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:param resolution: the resolution of the grid on which to evaluate the predictive variance
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@ -71,57 +71,54 @@ class GPLVM(GP):
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if labels is None:
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labels = np.ones(self.N)
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if which_indices is None:
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if self.Q == 1:
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if self.Q==1:
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input_1 = 0
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input_2 = None
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if self.Q == 2:
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input_1, input_2 = 0, 1
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if self.Q==2:
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input_1, input_2 = 0,1
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else:
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# try to find a linear of RBF kern in the kernel
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k = [p for p in self.kern.parts if p.name in ['rbf', 'linear']]
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if (not len(k) == 1) or (not k[0].ARD):
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try:
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input_1, input_2 = np.argsort(self.input_sensitivity())[:2]
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except:
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raise ValueError, "cannot Atomatically determine which dimensions to plot, please pass 'which_indices'"
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k = k[0]
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if k.name == 'rbf':
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input_1, input_2 = np.argsort(k.lengthscale)[:2]
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elif k.name == 'linear':
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input_1, input_2 = np.argsort(k.variances)[::-1][:2]
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else:
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input_1, input_2 = which_indices
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# first, plot the output variance as a function of the latent space
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Xtest, xx, yy, xmin, xmax = util.plot.x_frame2D(self.X[:, [input_1, input_2]], resolution=resolution)
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#first, plot the output variance as a function of the latent space
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Xtest, xx,yy,xmin,xmax = util.plot.x_frame2D(self.X[:,[input_1, input_2]],resolution=resolution)
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Xtest_full = np.zeros((Xtest.shape[0], self.X.shape[1]))
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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[:, :1]
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pb.imshow(var.reshape(resolution, resolution).T[::-1, :],
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extent=[xmin[0], xmax[0], xmin[1], xmax[1]], cmap=pb.cm.binary, interpolation='bilinear')
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var = var[:, :1]
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pb.imshow(var.reshape(resolution,resolution).T[::-1,:],
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extent=[xmin[0], xmax[0], xmin[1], xmax[1]], cmap=pb.cm.binary,interpolation='bilinear')
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for i, ul in enumerate(np.unique(labels)):
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for i,ul in enumerate(np.unique(labels)):
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if type(ul) is np.string_:
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this_label = ul
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elif type(ul) is np.int64:
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this_label = 'class %i' % ul
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this_label = 'class %i'%ul
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else:
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this_label = 'class %i' % i
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this_label = 'class %i'%i
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index = np.nonzero(labels == ul)[0]
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if self.Q == 1:
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x = self.X[index, input_1]
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index = np.nonzero(labels==ul)[0]
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if self.Q==1:
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x = self.X[index,input_1]
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y = np.zeros(index.size)
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else:
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x = self.X[index, input_1]
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y = self.X[index, input_2]
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pb.plot(x, y, marker='o', color=util.plot.Tango.nextMedium(), mew=0, label=this_label, linewidth=0)
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x = self.X[index,input_1]
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y = self.X[index,input_2]
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pb.plot(x,y,marker='o',color=util.plot.Tango.nextMedium(),mew=0,label=this_label,linewidth=0)
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pb.xlabel('latent dimension %i' % input_1)
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pb.ylabel('latent dimension %i' % input_2)
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pb.xlabel('latent dimension %i'%input_1)
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pb.ylabel('latent dimension %i'%input_2)
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if not np.all(labels == 1.):
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pb.legend(loc=0, numpoints=1)
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if not np.all(labels==1.):
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pb.legend(loc=0,numpoints=1)
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pb.xlim(xmin[0], xmax[0])
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pb.ylim(xmin[1], xmax[1])
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pb.grid(b=False) # remove the grid if present, it doesn't look good
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pb.xlim(xmin[0],xmax[0])
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pb.ylim(xmin[1],xmax[1])
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pb.grid(b=False) # remove the grid if present, it doesn't look good
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ax = pb.gca()
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ax.set_aspect('auto') # set a nice aspect ratio
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ax.set_aspect('auto') # set a nice aspect ratio
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return ax
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