| (function (global, factory) { | |
| typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports) : | |
| typeof define === 'function' && define.amd ? define('d3-venn', ['exports'], factory) : | |
| factory((global.d3_venn = {})); | |
| }(this, function (exports) { 'use strict'; | |
| /** | |
| * getSet creates a getter/setter function for a re-usable D3.js component. | |
| * | |
| * @method getSet |
| #include <iostream> | |
| using namespace std; | |
| struct node{ | |
| int value; | |
| node *left; | |
| node *right; | |
| }; |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
| 1: 日 | |
| 2: 一 | |
| 3: 国 | |
| 4: 会 | |
| 5: 人 | |
| 6: 年 | |
| 7: 大 | |
| 8: 十 | |
| 9: 二 | |
| 10: 本 |
http://railstutorial.org/, http://railstutorial.org/book
Based on Rails 4.0, missing:
| /****************************************************** | |
| * @author Nootan Ghimire <[email protected]> | |
| * @file postfix-eval.cpp | |
| * @desc Evaluation of Multi-Digit Postfix Expression | |
| *****************************************************/ | |
| // C++ Includes | |
| #include <iostream> | |
| #include <cctype> | |
| #include <cstdlib> |
The range sliders at the top change the values for the force-directed algorithm and the buttons load new graphs and apply various techniques. This will hopefully serve as a tool for teaching network analysis and visualization principles during my Gephi courses and general Networks in the Humanities presentations.
Notice this includes a pretty straightforward way to load CSV node and edge lists as exported from Gephi.
It also includes a pathfinding algorithm built for the standard data structure of force-directed networks in D3. This requires the addition of .id attributes for the nodes, however.
Now with Clustering Coefficients!
Also, it loads images for nodes but the images are not in the gist. The code also refers to different network types but the data files on Gist only refer to the transportation network.
