diff --git a/GPy/testing/examples_tests.py b/GPy/testing/examples_tests.py index ec030055..989251a7 100644 --- a/GPy/testing/examples_tests.py +++ b/GPy/testing/examples_tests.py @@ -19,14 +19,14 @@ class ExamplesTests(unittest.TestCase): self.assertTrue(isinstance(Model, GPy.models)) """ -def model_instance_generator(Model): +def model_instance_generator(model): def check_model_returned(self): - self._model_instance(Model) + self._model_instance(model) return check_model_returned -def checkgrads_generator(Model): +def checkgrads_generator(model): def model_checkgrads(self): - self._checkgrad(Model) + self._checkgrad(model) return model_checkgrads """ @@ -37,7 +37,7 @@ def model_checkgrads(model): def model_instance(model): #assert isinstance(model, GPy.core.model) - return isinstance(model, GPy.core.Model) + return isinstance(model, GPy.core.model) @nottest def test_models(): diff --git a/GPy/util/plot_latent.py b/GPy/util/plot_latent.py index c36c5e34..9c832769 100644 --- a/GPy/util/plot_latent.py +++ b/GPy/util/plot_latent.py @@ -2,13 +2,14 @@ import pylab as pb import numpy as np from .. import util -def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None, marker='o', s=40): +def plot_latent(model, labels=None, which_indices=None, resolution=50, ax=None, marker='o', s=40, fignum=None): """ :param labels: a np.array of size model.num_data containing labels for the points (can be number, strings, etc) :param resolution: the resolution of the grid on which to evaluate the predictive variance """ if ax is None: - ax = pb.gca() + fig = pb.figure(num=fignum) + ax = fig.add_subplot(111) util.plot.Tango.reset() if labels is None: diff --git a/GPy/util/visualize.py b/GPy/util/visualize.py index 529f0eff..886e8486 100644 --- a/GPy/util/visualize.py +++ b/GPy/util/visualize.py @@ -103,7 +103,7 @@ class lvm(matplotlib_show): self.cid = latent_axes[0].figure.canvas.mpl_connect('axes_enter_event', self.on_enter) self.data_visualize = data_visualize - self.Model = model + self.model = model self.latent_axes = latent_axes self.sense_axes = sense_axes self.called = False @@ -120,7 +120,7 @@ class lvm(matplotlib_show): def modify(self, vals): """When latent values are modified update the latent representation and ulso update the output visualization.""" self.vals = vals.copy() - y = self.Model.predict(self.vals)[0] + y = self.model.predict(self.vals)[0] self.data_visualize.modify(y) self.latent_handle.set_data(self.vals[self.latent_index[0]], self.vals[self.latent_index[1]]) self.axes.figure.canvas.draw() @@ -148,15 +148,15 @@ class lvm(matplotlib_show): # A click in the bar chart axis for selection a dimension. if self.sense_axes != None: self.sense_axes.cla() - self.sense_axes.bar(np.arange(self.Model.input_dim),1./self.Model.input_sensitivity(),color='b') + self.sense_axes.bar(np.arange(self.model.input_dim),1./self.model.input_sensitivity(),color='b') if self.latent_index[1] == self.latent_index[0]: - self.sense_axes.bar(np.array(self.latent_index[0]),1./self.Model.input_sensitivity()[self.latent_index[0]],color='y') - self.sense_axes.bar(np.array(self.latent_index[1]),1./self.Model.input_sensitivity()[self.latent_index[1]],color='y') + self.sense_axes.bar(np.array(self.latent_index[0]),1./self.model.input_sensitivity()[self.latent_index[0]],color='y') + self.sense_axes.bar(np.array(self.latent_index[1]),1./self.model.input_sensitivity()[self.latent_index[1]],color='y') else: - self.sense_axes.bar(np.array(self.latent_index[0]),1./self.Model.input_sensitivity()[self.latent_index[0]],color='g') - self.sense_axes.bar(np.array(self.latent_index[1]),1./self.Model.input_sensitivity()[self.latent_index[1]],color='r') + self.sense_axes.bar(np.array(self.latent_index[0]),1./self.model.input_sensitivity()[self.latent_index[0]],color='g') + self.sense_axes.bar(np.array(self.latent_index[1]),1./self.model.input_sensitivity()[self.latent_index[1]],color='r') self.sense_axes.figure.canvas.draw() @@ -193,7 +193,7 @@ class lvm_dimselect(lvm): GPy.examples.dimensionality_reduction.BGPVLM_oil() """ - def __init__(self, vals, Model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0, 1], labels=None): + def __init__(self, vals, model, data_visualize, latent_axes=None, sense_axes=None, latent_index=[0, 1], labels=None): if latent_axes==None and sense_axes==None: self.fig,(latent_axes,self.sense_axes) = plt.subplots(1,2) elif sense_axes==None: @@ -202,7 +202,7 @@ class lvm_dimselect(lvm): else: self.sense_axes = sense_axes self.labels = labels - lvm.__init__(self,vals,Model,data_visualize,latent_axes,sense_axes,latent_index) + lvm.__init__(self,vals,model,data_visualize,latent_axes,sense_axes,latent_index) self.show_sensitivities() print "use left and right mouse butons to select dimensions" @@ -210,7 +210,7 @@ class lvm_dimselect(lvm): def on_click(self, event): if event.inaxes==self.sense_axes: - new_index = max(0,min(int(np.round(event.xdata-0.5)),self.Model.input_dim-1)) + new_index = max(0,min(int(np.round(event.xdata-0.5)),self.model.input_dim-1)) if event.button == 1: # Make it red if and y-axis (red=port=left) if it is a left button click self.latent_index[1] = new_index @@ -221,7 +221,7 @@ class lvm_dimselect(lvm): self.show_sensitivities() self.latent_axes.cla() - self.Model.plot_latent(which_indices=self.latent_index, + self.model.plot_latent(which_indices=self.latent_index, ax=self.latent_axes, labels=self.labels) self.latent_handle = self.latent_axes.plot([0],[0],'rx',mew=2)[0] self.modify(self.latent_values) @@ -235,7 +235,7 @@ class lvm_dimselect(lvm): def on_leave(self,event): latent_values = self.latent_values.copy() - y = self.Model.predict(latent_values[None,:])[0] + y = self.model.predict(latent_values[None,:])[0] self.data_visualize.modify(y)