mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-09 12:02:38 +02:00
Merge remote-tracking branch 'origin/devel' into integral
This commit is contained in:
Michael T Smith
commit
d96e71c958
12 changed files with 57 additions and 49 deletions
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@ -773,7 +773,7 @@ class DGPLVM_Lamda(Prior, Parameterized):
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def compute_cls(self, x):
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cls = {}
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# Appending each data point to its proper class
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for j in xrange(self.datanum):
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for j in range(self.datanum):
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class_label = self.get_class_label(self.lbl[j])
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if class_label not in cls:
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cls[class_label] = []
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@ -792,7 +792,7 @@ class DGPLVM_Lamda(Prior, Parameterized):
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# Adding data points as tuple to the dictionary so that we can access indices
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def compute_indices(self, x):
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data_idx = {}
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for j in xrange(self.datanum):
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for j in range(self.datanum):
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class_label = self.get_class_label(self.lbl[j])
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if class_label not in data_idx:
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data_idx[class_label] = []
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@ -811,7 +811,7 @@ class DGPLVM_Lamda(Prior, Parameterized):
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else:
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lst_idx = []
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# Here we put indices of each class in to the list called lst_idx_all
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for m in xrange(len(data_idx[i])):
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for m in range(len(data_idx[i])):
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lst_idx.append(data_idx[i][m][0])
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lst_idx_all.append(lst_idx)
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return lst_idx_all
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@ -847,7 +847,7 @@ class DGPLVM_Lamda(Prior, Parameterized):
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# pdb.set_trace()
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# Calculating Bi
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B_i[i] = (M_i[i] - M_0).reshape(1, self.dim)
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for k in xrange(self.datanum):
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for k in range(self.datanum):
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for i in data_idx:
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N_i = float(len(data_idx[i]))
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if k in lst_idx_all[i]:
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@ -111,8 +111,8 @@ class Symbolic_core():
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# rows = func['function'].shape[0]
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# cols = func['function'].shape[1]
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# self.expressions[key]['derivative'] = sym.zeros(rows, cols)
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# for i in xrange(rows):
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# for j in xrange(cols):
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# for i in range(rows):
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# for j in range(cols):
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# self.expressions[key]['derivative'][i, j] = extract_derivative(func['function'][i, j], derivative_arguments)
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# else:
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self.expressions[key]['derivative'] = extract_derivative(func['function'], derivative_arguments)
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@ -123,7 +123,7 @@ class Symbolic_core():
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val = 1.0
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# TODO: improve approach for initializing parameters.
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if parameters is not None:
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if parameters.has_key(theta.name):
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if theta.name in parameters:
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val = parameters[theta.name]
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# Add parameter.
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@ -176,7 +176,7 @@ class Symbolic_core():
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return gradient
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def eval_gradients_X(self, function, partial, **kwargs):
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if kwargs.has_key('X'):
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if 'X' in kwargs:
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gradients_X = np.zeros_like(kwargs['X'])
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self.eval_update_cache(**kwargs)
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for i, theta in enumerate(self.variables['X']):
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@ -405,7 +405,7 @@ class Symbolic_core():
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if var_name == var.name:
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expr = expr.subs(var, sub)
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break
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for m, r in function_substitutes.iteritems():
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for m, r in function_substitutes.items():
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expr = expr.replace(m, r)#normcdfln, lambda arg : sym.log(normcdf(arg)))
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return expr.simplify()
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@ -417,4 +417,4 @@ class Symbolic_core():
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else:
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return x[0]
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return sorted(var_dict.iteritems(), key=sort_key, reverse=reverse)
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return sorted(var_dict.items(), key=sort_key, reverse=reverse)
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@ -184,7 +184,7 @@ def bgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40,
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data_show = GPy.plotting.matplot_dep.visualize.vector_show((m.Y[0, :]))
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lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X.mean.values[0:1, :], # @UnusedVariable
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m, data_show, latent_axes=latent_axes, sense_axes=sense_axes, labels=m.data_labels)
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raw_input('Press enter to finish')
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input('Press enter to finish')
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plt.close(fig)
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return m
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@ -210,7 +210,7 @@ def ssgplvm_oil(optimize=True, verbose=1, plot=True, N=200, Q=7, num_inducing=40
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data_show = GPy.plotting.matplot_dep.visualize.vector_show((m.Y[0, :]))
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lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm_dimselect(m.X.mean.values[0:1, :], # @UnusedVariable
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m, data_show, latent_axes=latent_axes, sense_axes=sense_axes, labels=m.data_labels)
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raw_input('Press enter to finish')
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input('Press enter to finish')
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plt.close(fig)
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return m
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@ -242,7 +242,7 @@ def _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim=False):
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fig.clf()
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ax = fig.add_subplot(2, 1, 1)
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labls = slist_names
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for S, lab in itertools.izip(slist, labls):
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for S, lab in zip(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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@ -288,7 +288,7 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, plot_sim=False):
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fig.clf()
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ax = fig.add_subplot(2, 1, 1)
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labls = slist_names
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for S, lab in itertools.izip(slist, labls):
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for S, lab in zip(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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@ -520,7 +520,7 @@ def brendan_faces(optimize=True, verbose=True, plot=True):
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y = m.Y[0, :]
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data_show = GPy.plotting.matplot_dep.visualize.image_show(y[None, :], dimensions=(20, 28), transpose=True, order='F', invert=False, scale=False)
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lvm = GPy.plotting.matplot_dep.visualize.lvm(m.X.mean[0, :].copy(), m, data_show, ax)
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raw_input('Press enter to finish')
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input('Press enter to finish')
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return m
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@ -542,7 +542,7 @@ def olivetti_faces(optimize=True, verbose=True, plot=True):
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y = m.Y[0, :]
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data_show = GPy.plotting.matplot_dep.visualize.image_show(y[None, :], dimensions=(112, 92), transpose=False, invert=False, scale=False)
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lvm = GPy.plotting.matplot_dep.visualize.lvm(m.X.mean[0, :].copy(), m, data_show, ax)
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raw_input('Press enter to finish')
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input('Press enter to finish')
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return m
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@ -577,7 +577,7 @@ def stick(kernel=None, optimize=True, verbose=True, plot=True):
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y = m.Y[0, :]
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data_show = GPy.plotting.matplot_dep.visualize.stick_show(y[None, :], connect=data['connect'])
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lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm(m.X[:1, :].copy(), m, data_show, latent_axes=ax)
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raw_input('Press enter to finish')
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input('Press enter to finish')
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lvm_visualizer.close()
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data_show.close()
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return m
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@ -598,7 +598,7 @@ def bcgplvm_linear_stick(kernel=None, optimize=True, verbose=True, plot=True):
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y = m.likelihood.Y[0, :]
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data_show = GPy.plotting.matplot_dep.visualize.stick_show(y[None, :], connect=data['connect'])
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GPy.plotting.matplot_dep.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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raw_input('Press enter to finish')
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input('Press enter to finish')
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return m
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@ -619,7 +619,7 @@ def bcgplvm_stick(kernel=None, optimize=True, verbose=True, plot=True):
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y = m.likelihood.Y[0, :]
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data_show = GPy.plotting.matplot_dep.visualize.stick_show(y[None, :], connect=data['connect'])
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GPy.plotting.matplot_dep.visualize.lvm(m.X[0, :].copy(), m, data_show, ax)
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# raw_input('Press enter to finish')
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# input('Press enter to finish')
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return m
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@ -669,7 +669,7 @@ def stick_bgplvm(model=None, optimize=True, verbose=True, plot=True):
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fig.canvas.draw()
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# Canvas.show doesn't work on OSX.
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#fig.canvas.show()
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raw_input('Press enter to finish')
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input('Press enter to finish')
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return m
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@ -693,7 +693,7 @@ def cmu_mocap(subject='35', motion=['01'], in_place=True, optimize=True, verbose
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y = m.Y[0, :]
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data_show = GPy.plotting.matplot_dep.visualize.skeleton_show(y[None, :], data['skel'])
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lvm_visualizer = GPy.plotting.matplot_dep.visualize.lvm(m.X[0].copy(), m, data_show, latent_axes=ax)
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raw_input('Press enter to finish')
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input('Press enter to finish')
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lvm_visualizer.close()
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data_show.close()
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@ -10,17 +10,17 @@ Y = np.sin(X) + np.random.randn(*X.shape)*0.1
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kernel1 = GPy.kern.Matern32(X.shape[1])
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m1 = GPy.models.GPRegression(X,Y, kernel1)
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print m1
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print(m1)
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m1.optimize(optimizer='bfgs',messages=True)
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print m1
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print(m1)
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kernel2 = GPy.kern.sde_Matern32(X.shape[1])
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#m2 = SS_model.StateSpace(X,Y, kernel2)
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m2 = GPy.models.StateSpace(X,Y, kernel2)
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print m2
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print(m2)
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m2.optimize(optimizer='bfgs',messages=True)
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print m2
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print(m2)
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@ -24,9 +24,6 @@ from .src.ODE_st import ODE_st
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from .src.ODE_t import ODE_t
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from .src.poly import Poly
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from .src.eq_ode2 import EQ_ODE2
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from .src.integral import Integral
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from .src.integral_limits import Integral_Limits
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from .src.multidimensional_integral_limits import Multidimensional_Integral_Limits
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from .src.eq_ode1 import EQ_ODE1
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from .src.trunclinear import TruncLinear,TruncLinear_inf
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from .src.splitKern import SplitKern,DEtime
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@ -678,7 +678,7 @@ class Likelihood(Parameterized):
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burnin_cache = np.zeros(par_chains)
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burnin_cache[:] = starting_loc.flatten()
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burning_in = True
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for i in xrange(burn_in+num_samples):
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for i in range(burn_in+num_samples):
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next_ind = i-burn_in
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if burning_in:
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old_y = burnin_cache
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@ -291,12 +291,12 @@ class SSGPLVM(SparseGP_MPI):
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Xs[b>self.X.gamma.values] = 0
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invcov = (Xs[:,:,:,None]*Xs[:,:,None,:]).sum(1)/noise_var+np.eye(Q)
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cov = np.array([pdinv(invcov[s_idx])[0] for s_idx in xrange(invcov.shape[0])])
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cov = np.array([pdinv(invcov[s_idx])[0] for s_idx in range(invcov.shape[0])])
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Ws = np.empty((nSamples, Q, D))
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tmp = (np.transpose(Xs, (0,2,1)).reshape(nSamples*Q,N).dot(self.Y)).reshape(nSamples,Q,D)
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mean = (cov[:,:,:,None]*tmp[:,None,:,:]).sum(2)/noise_var
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zeros = np.zeros((Q,))
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for s_idx in xrange(Xs.shape[0]):
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for s_idx in range(Xs.shape[0]):
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Ws[s_idx] = (np.random.multivariate_normal(mean=zeros,cov=cov[s_idx],size=(D,))).T+mean[s_idx]
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if raw_samples:
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@ -25,7 +25,7 @@ class SSMRD(Model):
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self.X = NormalPosterior(means=X, variances=X_variance)
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if kernels is None:
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kernels = [RBF(input_dim, lengthscale=1./fracs, ARD=True) for i in xrange(len(Ylist))]
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kernels = [RBF(input_dim, lengthscale=1./fracs, ARD=True) for i in range(len(Ylist))]
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if Zs is None:
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Zs = [None]* len(Ylist)
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if likelihoods is None:
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@ -34,9 +34,9 @@ class SSMRD(Model):
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inference_methods = [None]* len(Ylist)
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if IBP:
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self.var_priors = [IBPPrior_SSMRD(len(Ylist),input_dim,alpha=alpha) for i in xrange(len(Ylist))]
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self.var_priors = [IBPPrior_SSMRD(len(Ylist),input_dim,alpha=alpha) for i in range(len(Ylist))]
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else:
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self.var_priors = [SpikeAndSlabPrior_SSMRD(nModels=len(Ylist),pi=pi,learnPi=False, group_spike=group_spike) for i in xrange(len(Ylist))]
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self.var_priors = [SpikeAndSlabPrior_SSMRD(nModels=len(Ylist),pi=pi,learnPi=False, group_spike=group_spike) for i in range(len(Ylist))]
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self.models = [SSGPLVM(y, input_dim, X=X.copy(), X_variance=X_variance.copy(), Gamma=Gammas[i], num_inducing=num_inducing,Z=Zs[i], learnPi=False, group_spike=group_spike,
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kernel=kernels[i],inference_method=inference_methods[i],likelihood=likelihoods[i], variational_prior=self.var_priors[i], IBP=IBP, tau=None if taus is None else taus[i],
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name='model_'+str(i), mpi_comm=mpi_comm, sharedX=True) for i,y in enumerate(Ylist)]
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@ -73,7 +73,7 @@ class SSMRD(Model):
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# Divide latent dimensions
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idx = np.empty((input_dim,),dtype=np.int)
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residue = (input_dim)%(len(Ylist))
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for i in xrange(len(Ylist)):
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for i in range(len(Ylist)):
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if i < residue:
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size = input_dim/len(Ylist)+1
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idx[i*size:(i+1)*size] = i
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@ -86,7 +86,7 @@ class SSMRD(Model):
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X = np.empty((Ylist[0].shape[0],input_dim))
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fracs = np.empty((input_dim,))
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from ..util.initialization import initialize_latent
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for i in xrange(len(Ylist)):
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for i in range(len(Ylist)):
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Y = Ylist[i]
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dim = (idx==i).sum()
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if dim>0:
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|
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@ -13,7 +13,7 @@ import scipy as sp
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import scipy.linalg as linalg
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try:
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import state_space_setup
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from . import state_space_setup
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setup_available = True
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except ImportError as e:
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setup_available = False
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|
|
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@ -96,3 +96,14 @@ class TestDebug(unittest.TestCase):
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self.assertTrue((2, np.median(X.mean.values[:,2])) in fixed)
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self.assertTrue(len([t for t in fixed if t[0] == 1]) == 0) # Unfixed input should not be in fixed
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def test_subarray(self):
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import GPy
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X = np.zeros((3,6), dtype=bool)
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X[[1,1,1],[0,4,5]] = 1
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X[1:,[2,3]] = 1
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d = GPy.util.subarray_and_sorting.common_subarrays(X,axis=1)
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self.assertTrue(len(d) == 3)
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X[:, d[tuple(X[:,0])]]
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self.assertTrue(d[tuple(X[:,4])] == d[tuple(X[:,0])] == [0, 4, 5])
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self.assertTrue(d[tuple(X[:,1])] == [1])
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|
|
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@ -73,7 +73,7 @@ def prompt_user(prompt):
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try:
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print(prompt)
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choice = raw_input().lower()
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choice = input().lower()
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# would like to test for exception here, but not sure if we can do that without importing IPython
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except:
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print('Stdin is not implemented.')
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@ -96,16 +96,16 @@ def prompt_user(prompt):
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def data_available(dataset_name=None):
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"""Check if the data set is available on the local machine already."""
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try:
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from itertools import izip_longest
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from itertools import zip_longest
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except ImportError:
|
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from itertools import zip_longest as izip_longest
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from itertools import zip_longest as zip_longest
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dr = data_resources[dataset_name]
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zip_urls = (dr['files'], )
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if 'save_names' in dr: zip_urls += (dr['save_names'], )
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else: zip_urls += ([],)
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for file_list, save_list in izip_longest(*zip_urls, fillvalue=[]):
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for f, s in izip_longest(file_list, save_list, fillvalue=None):
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for file_list, save_list in zip_longest(*zip_urls, fillvalue=[]):
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for f, s in zip_longest(file_list, save_list, fillvalue=None):
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if s is not None: f=s # If there is a save_name given, use that one
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if not os.path.exists(os.path.join(data_path, dataset_name, f)):
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return False
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@ -138,7 +138,7 @@ def download_url(url, store_directory, save_name=None, messages=True, suffix='')
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raise ValueError('Tried url ' + url + suffix + ' and received server error ' + str(response.code))
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with open(save_name, 'wb') as f:
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meta = response.info()
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content_length_str = meta.getheaders("Content-Length")
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content_length_str = meta.get("Content-Length")
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if content_length_str:
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file_size = int(content_length_str[0])
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else:
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@ -214,14 +214,14 @@ def download_data(dataset_name=None):
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|||
|
||||
zip_urls = (dr['urls'], dr['files'])
|
||||
|
||||
if dr.has_key('save_names'): zip_urls += (dr['save_names'], )
|
||||
if 'save_names' in dr: zip_urls += (dr['save_names'], )
|
||||
else: zip_urls += ([],)
|
||||
|
||||
if dr.has_key('suffices'): zip_urls += (dr['suffices'], )
|
||||
if 'suffices' in dr: zip_urls += (dr['suffices'], )
|
||||
else: zip_urls += ([],)
|
||||
|
||||
for url, files, save_names, suffices in itertools.izip_longest(*zip_urls, fillvalue=[]):
|
||||
for f, save_name, suffix in itertools.izip_longest(files, save_names, suffices, fillvalue=None):
|
||||
for url, files, save_names, suffices in itertools.zip_longest(*zip_urls, fillvalue=[]):
|
||||
for f, save_name, suffix in itertools.zip_longest(files, save_names, suffices, fillvalue=None):
|
||||
download_url(os.path.join(url,f), dataset_name, save_name, suffix=suffix)
|
||||
|
||||
return True
|
||||
|
|
@ -361,7 +361,7 @@ def football_data(season='1314', data_set='football_data'):
|
|||
return league_dict[string]
|
||||
|
||||
def football2num(string):
|
||||
if football_dict.has_key(string):
|
||||
if string in football_dict:
|
||||
return football_dict[string]
|
||||
else:
|
||||
football_dict[string] = len(football_dict)+1
|
||||
|
|
|
|||
|
|
@ -50,7 +50,7 @@ def common_subarrays(X, axis=0):
|
|||
cnt = count()
|
||||
def accumulate(x, s, c):
|
||||
t = tuple(x)
|
||||
col = c.next()
|
||||
col = next(c)
|
||||
iadd(s[t], [col])
|
||||
return None
|
||||
if axis == 0: [accumulate(x, subarrays, cnt) for x in X]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue