y_true): C = confusion_matrix(y_true, y_pred, labels=['0', '
Code Issues Pull requests Deep Learning: Image classification, feature visualization and transfer learning with Keras search-engine deep-learning feature-extraction image-classification transfer-learning tsne pretrained-network Updated May 23, 2020 Jupyter Notebook 0011001011...
它旨在与任何库分离。 而且,它使用静态文件系统,因此您可以在不需要服务器的情况下发布结果。 例如 。 项目结构 |-- data <-- where to put raw data |-- Feature-extractor.ipynb <-- Demo of Embedding generation in a step by step fashion |-- index.html <-- The GU...
Visualization of disease relationships by multiple maps t-SNE regularization based on Nesterov accelerated gradient From a biological standpoint, due to the special combination of complex symptoms, some type of complex diseases is difficult to be accurately diagnosed. Kn... X Shen,X Zhu,X Jiang,....
Python & Command-line tool to gather text and metadata on the Web: Crawling, scraping, extraction, output as CSV, JSON, HTML, MD, TXT, XML - trafilatura/tests/cache/en.wikipedia.org.tsne.html at 2639b2417c6db8e4df1d4f3b42f454076f7fa140 · purin-blog/traf
feature array. If metric is a string, it must be one of the options allowed by scipy.spatial.distance.pdist for its metric parameter, or a metric listed in pairwise.PAIRWISE_DISTANCE_FUNCTIONS. If metric is "precomputed", X is assumed to be a distance matrix. ...
High-dimensional datasets can be very difficult to visualize. While data in two or three dimensions can be plotted to show the inherent structure of the data, equivalent high-dimensional plots are much less intuitive. To aid visualization of the structure of a dataset, the dimension must be red...
A second feature of t-SNE is a tuneable parameter, “perplexity,” which says (loosely) how to balance attention between local and global aspects of your data. The parameter is, in a sense, a guess about the number of close neighbors each point has. The perplexity value has a complex ef...
单细胞转录组 数据分析||Seurat新版教程:New data visualization methods in v3.0 单细胞转录组数据分析||Seurat并行策略 Seurat Weekly NO.0 || 开刊词 Seurat Weekly NO.1 || 到底分多少个群是合适的?! Seurat Weekly NO.2 || 我该如何取子集
随着生物学背景知识的增加,单细胞图谱的可视化直接用10X的Loup或者seurat的Dimplot函数直接绘制的umap/tsne图往往很难达到要求了,这就要求我们提高绘图技能。我们都知道ggplot2是一款很好的绘图R包,甚至可以说在语法上扩展了R语言本身。那么,当我们需要绘图的时候,自然我们会想到它及其周边。今天我们就主要地看一下ggforce...