| var intrinsic:Vector.<Number> = new Vector.<Number>(9, true); | |
| var intrinsicInverse:Vector.<Number> = new Vector.<Number>(9, true); | |
| var R:Vector.<Number> = new Vector.<Number>( 9, true ); | |
| var t:Vector.<Number> = new Vector.<Number>( 3, true ); | |
| // SVD routine | |
| var svd:SVD = new SVD(); | |
| // input homography[9] - 3x3 Matrix |
| #!/bin/bash | |
| # Don't require you to constantly enter password for sudo: | |
| sudo visudo | |
| # In the bottom of the file, paste the following (without the `#`): | |
| # paperspace ALL=(ALL) NOPASSWD: ALL | |
| # Then press `ctl+o` then `enter` to save your changes, and `ctr+x` to exit nano | |
| # Allow connection from your IP to any port- default seems to be just 22 (ssh) |
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
| """ | |
| 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)) | |
| print '%d unique characters in data.' % (len(chars), ) |
| Latency Comparison Numbers | |
| -------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns | |
| Send 1K bytes over 1 Gbps network 10,000 ns 0.01 ms | |
| Read 4K randomly from SSD* 150,000 ns 0.15 ms |
| brew update | |
| brew versions FORMULA | |
| cd `brew --prefix` | |
| git checkout HASH Library/Formula/FORMULA.rb # use output of "brew versions" | |
| brew install FORMULA | |
| brew switch FORMULA VERSION | |
| git checkout -- Library/Formula/FORMULA.rb # reset formula | |
| ## Example: Using Subversion 1.6.17 | |
| # |
Installing opencv fails, even though $ brew doctor doesn't have any complaints:
$ brew install opencv
==> Downloading https://github.com/Itseez/opencv/archive/2.4.7.1.tar.gz
Already downloaded: /Library/Caches/Homebrew/opencv-2.4.7.1.tar.gz
==> Patching
patching file cmake/OpenCVFindOpenNI.cmake
Warning: Formula#python is deprecated and will go away shortly.
Error: undefined method `incdir' for #<PythonDependency: "python" []>