Here is the Syntax of numpy.stack( ) method numpy.stack In Python, the stack() method is used to combine a sequence of numpy arrays along with a given axis. To perform this particular task we are going to use the stack() method.In this section, we will discuss how to use stack() function in NumPy Python.Stack array horizontally by using NumPy Python.Combine row-wise elements in NumPy Python.Mentions = self.dl.get_all_mentions_from_doc(doc_id)Ī_f = ī_f = [self. """builds batch of all mention pairs in one documentįeature representation of mentions and labels Words_keys = words_keys | set(e.keys())Įvents = list(e) +. Xnew = np.vstack((xx.flatten(), yy.flatten())).T (xx, yy) = np.mgrid:xmax:1j * resolution, xmin: Raise ValueError, 'Bad limits for plotting' 'x_frame2D is defined for two-dimensional inputs' Internal helper function for making plots, returns a set of input values to plot as well as lower and upper limits If d_for_all is None and d_same_value is None:ĭ_for_all = numpy.vstack((d_for_all, tmp))ĭ_same_value = numpy.vstack((d_same_value, tmp2))īob.io.base.save(d_for_all, fs.d_matrix_file(group))īob.io.base.save(d_same_value, fs.d_same_value_matrix_file(group))ĭef x_frame2D(X, plot_limits=None, resolution=None): Tmp2 = bob.io.base.load(fs.d_same_value_file(t_model_id, group)) Tmp = bob.io.base.load(fs.d_file(t_model_id, group)) """Compute normalized D scores for the given T-model ids""" (, self.subspace_dim)į = bob.io.base.HDF5File(projector_file, "w")ĭef _scores_d_normalize(t_model_ids, group): Keeping %d PCA dimensions", self.subspace_dim) # compute variance percentage, if desiredĬummulated = numpy.cumsum(self.variances) / numpy.sum(self.variances) Self.machine, self.variances = t.train(data) Training_features : Ī list of 1D training arrays (vectors) to train the PCA projection matrix with.Ī writable file, into which the PCA projection matrix (as a :py:class:``) and the eigenvalues will be written. """Generates the PCA covariance matrix and writes it into the given projector_file. Return numpy.vstack(f.flatten() for f in enroll_features)ĭef train_projector(self, training_features, projector_file): The list of projected features to enroll the model from. If self.k_models!=None and len(self.k_models) modelĮnrolls the model by storing all given input vectors.Įnroll_features : Waveform = f.valueĭef _extract_signals(self, data, metadata, lazy):Īrr = numpy.vstack(self._extract_array(data, channel_index)įor channel_index in range(metadata, metadata + 1)) Gridheight = (height - 2 * crop) // grid # should be 6 for our dataĬell = source Height = source.shape # should be 224 for our data Model = Nystrom(k, n_components=embedding_dim) Print("Average size: %.2f" % np.mean(lens)) Print("Number of communities: ", len(communities)) Graphs, labels = load_data(ds_name, use_node_labels)Ĭommunities, subgraphs = compute_communities(graphs, use_node_labels, community_detection_method) # OpenCVKLT.draw_tracks(self, vis, colored=colored, max_track_length=10)ĭef compute_nystrom(ds_name, use_node_labels, embedding_dim, community_detection_method, kernels): Vis = super(BoundingBoxKLT, self).viz(vis, colored=colored) M = np.bitwise_and(np.isfinite(fx), np.isfinite(fy)) You can also save this page to your account. You can vote up the examples you like or vote down the exmaples you don’t like. They are extracted from open source Python projects. The following are code examples for showing how to use.
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