visualization

问题 Here is the code for LDA topic modelling with R text2vec package: library(text2vec) tokens = docs$text %>% # docs$text: a colection of text documents word_tokenizer it = itoken(tokens, ids = docs$id, progressbar = FALSE) v = create_vocabulary(it) %>% prune_vocabulary(term_count_min = 10, doc_proportion_max = 0.2) vectorizer = vocab_vectorizer(v) dtm = create_dtm(it, vectorizer, type = "dgTMatrix") lda_model = text2vec::LDA$new(n_topics = 10, doc_topic_prior = 0.1, topic_word_prior = 0.01) doc

问题 Here is my code and I need put markers on the orange curve halflife = 0.25 resolution_per_second = 1000 values = np.concatenate([np.zeros(resolution_per_second), np.ones(resolution_per_second * 2), np.zeros(resolution_per_second), np.ones(resolution_per_second * 1), np.zeros(resolution_per_second * 2), ]) t_grid = np.arange(0, len(values)) / resolution_per_second step = 1.0 / resolution_per_second k = np.power(0.5, 1 / (halflife * resolution_per_second)) def ema_fixed_step(y, k): #ema method

问题 I've come across research publications and Q&A's discussing a need for inspecting RNN weights; some related answers are in the right direction, suggesting get_weights() - but how do I actually visualize the weights meaningfully ? Namely, LSTMs and GRUs have gates , and all RNNs have channels that serve as independent feature extractors - so how do I (1) fetch per-gate weights, and (2) plot them in an informative manner? 回答1: Keras/TF build RNN weights in a well-defined order, which can be

问题 I just need to draw scatter plot in 3D. from mpl_toolkits import mplot3d from matplotlib import pyplot as plt ax = plt.axes(projection="3d") ax.scatter(dots_x, dots_y, dots_z, c='red') plt.show() There dots_x, dots_y, dots_z are coordinates (python lists). Every time I running program I get ValueError: Invalid RGBA argument: (1.0, 0.0, 0.0, 0.300088123161615) I don't know anything about this tuple. BUT if i use plot3D instead of scatter3D i get perfect curve. Full error message: Traceback

问题 I just need to draw scatter plot in 3D. from mpl_toolkits import mplot3d from matplotlib import pyplot as plt ax = plt.axes(projection="3d") ax.scatter(dots_x, dots_y, dots_z, c='red') plt.show() There dots_x, dots_y, dots_z are coordinates (python lists). Every time I running program I get ValueError: Invalid RGBA argument: (1.0, 0.0, 0.0, 0.300088123161615) I don't know anything about this tuple. BUT if i use plot3D instead of scatter3D i get perfect curve. Full error message: Traceback

问题 Is it possible to draw a column bar chart with Highcharts.js, where one bar is displayed as an arrow? I have following chart $(function () { $('#container').highcharts({ chart: { type: 'column' }, title: { text: 'Title' }, xAxis: { categories: [ '2016', '2017', '2018' ] }, yAxis: [{ min: 0, title: { text: 'Header' } }, { title: { text: '' }, opposite: true }], legend: { shadow: false }, tooltip: { shared: true }, plotOptions: { column: { grouping: false, shadow: false, borderWidth: 0, } },

问题 I have two samples of 3D point cloud of human face. The blue point cloud denote target face and the red point cloud indicates the template. The image below shows that the target and template face are aligned in different directions (target face roughly along x-axis, template face roughly along y-axis). Figure 1: The region around the nose is displayed in Figure 1. I want to rotate my target face (blue face) with nasal tip as the center of rotation (I translated the target to the template

问题 mayavi I have some data which is on a structured grid in the X and Y directions and is unstructured in the Z direction. This is in the form of a list of data points, e.g [[x,y,z], [x2,y2,z2], ...] . There are 2 points corresponding to most x,y coordinates, and the data is double valued in the z dimension. I would like to plot this shape as an enclosed surface, and if possible remove one of the walls. I have tried the advice here: http://docs.enthought.com/mayavi/mayavi/auto/example_surface

问题 I have a huge sparse matrix (1,000 x 1,000,000) that I cannot load on matlab (not enough RAM). I want to visualize this matrix to have an idea of its sparsity and of the differences of the values. Because of the memory constraints, I want to proceed as follows: 1- Divide the matrix into 4 matrices 2- Load each matrix on matlab and visualize it so that the colors give an idea of the values (and of the zeros particularly) 3- "Stick" the 4 images I will get in order to have a global idea for the

问题 I have a huge sparse matrix (1,000 x 1,000,000) that I cannot load on matlab (not enough RAM). I want to visualize this matrix to have an idea of its sparsity and of the differences of the values. Because of the memory constraints, I want to proceed as follows: 1- Divide the matrix into 4 matrices 2- Load each matrix on matlab and visualize it so that the colors give an idea of the values (and of the zeros particularly) 3- "Stick" the 4 images I will get in order to have a global idea for the