Format/typo check and other fixes in 2011/dlowe
The README.md file has been typo/format checked/fixed. The try.sh script has been updated with some improvements and additional commands. The bug status was changed to INABIAF as these limitations were documented by the author. I believe this should complete the review of 2011/dlowe.
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@@ -11,7 +11,7 @@ The current status of this entry is:
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```
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STATUS: missing or dead link - please provide them
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STATUS: known bug - please help us fix
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STATUS: INABIAF - please **DO NOT** fix
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```
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For more detailed information see [2011 dlowe in bugs.md](/bugs.md#2011-dlowe).
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@@ -37,22 +37,11 @@ Then to use `trained.net`:
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Try these pre-trained networks:
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```sh
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./dlowe dlowe.c < dlowe-aux-data/ioccc-winlose-trained.net
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./dlowe dlowe < dlowe-aux-data/ioccc-winlose-trained.net
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./dlowe xor-0/00 < dlowe-aux-data/xor-trained.net
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./dlowe xor-0/11 < dlowe-aux-data/xor-trained.net
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./dlowe xor-1/01 < dlowe-aux-data/xor-trained.net
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./dlowe xor-1/10 < dlowe-aux-data/xor-trained.net
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./dlowe README.md < dlowe-aux-data/english-trained.net
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./dlowe dlowe-aux-data.tar.bz2 < dlowe-aux-data/png-trained.net
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./try.sh
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```
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You can run `./try.sh` to do the same thing.
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NOTE: The `dlowe-aux-data/` directory was created by:
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NOTE: The `dlowe-aux-data/` directory, which [try.sh](try.sh) refers to, was
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created by:
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```sh
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@@ -65,10 +54,6 @@ tar -jxf dlowe-aux-data.tar.bz2
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This entry is a ghoulish example of a brain (dead?) neural network classifier.
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It comes with a set of corpora for you to try.
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NOTE: the author's website, http://www.pootpoot.net, no longer exists (as the
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same) and the Internet Wayback Machine did not load scripts. Do you know if the
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domain changed? Please provide us with the proper URL or if you know if it was
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shut down for ever, please let us know that. Thank you!
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### `ioccc-winlose-trained.net`
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@@ -120,6 +105,9 @@ test is interesting:
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./dlowe dlowe.c < dlowe-aux-data/ioccc-winlose-trained.net
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```
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NOTE: this is done in [try.sh](try.sh).
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The match of [dlowe.c](dlowe.c) to the lose/win trailed network was:
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```
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@@ -131,6 +119,7 @@ quirks and their non-linear judgment calls not found in the ioccc trained networ
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Just because C code is or isn't scored highly by this network does not
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mean it will win or lose a future IOCCC. On the other hand ... it just might!
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#### other data sets to try
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While we cannot supply you with the losing IOCCC source code, the author did supply a number
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@@ -183,6 +172,7 @@ This is an [artificially
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intelligent](https://en.wikipedia.org/wiki/Artificial_intelligence) judging tool
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to help the IOCCC judges. Here's to shorter, more frequent contests!
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### Description
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This is a multilayer [perceptron](https://en.wikipedia.org/wiki/Perceptron) (a
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@@ -196,21 +186,23 @@ neuron.
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The neurons' activation function is the logistic function `1 / (1 + e ^ -x)`.
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### Classifying
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```sh
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./dlowe file [file ...] < trained.net
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```
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To classify files, one specifies a trained network (on stdin) and one or more
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To classify files, one specifies a trained network (on `stdin`) and one or more
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files to classify. The program will output one line per successfully-classified
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file to stderr, with the filename and the classification: a number between 0
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file to `stderr`, with the filename and the classification: a number between 0
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and 1.
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The interpretation of the classification number depends on how the network was
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trained, but it's geared toward interpretation as a *probability* or a
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*confidence*.
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### Training
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@@ -220,20 +212,21 @@ trained, but it's geared toward interpretation as a *probability* or a
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**NOTE**: The directory args **must end in a** `/` for them to work.
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To train a network, one specifies a starting network (on stdin), two or more
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To train a network, one specifies a starting network (on `stdin`), two or more
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corpora (directories containing training data), and the number of training
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iterations to run. The program will write some progress data to stderr and,
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when it's done, will serialize the updated network to stdout.
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iterations to run. The program will write some progress data to `stderr` and,
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when it's done, will serialize the updated network to `stdout`.
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If no input network is given, a random new network will be generated as a
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starting point (i.e. provide an empty stdin).
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starting point (i.e. provide an empty `stdin`).
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The first corpus will be assigned a target value of 1. The last will be
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assigned a target value of 0. Intervening directories (if any) will be assigned
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The first corpus will be assigned a target value of `1`. The last will be
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assigned a target value of `0`. Intervening directories (if any) will be assigned
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intermediate target values.
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The learning rate is hard-coded as `0.3`. No momentum factor is used.
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#### png corpora
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The [png-1](dlowe-aux-data/png-1) corpus was obtained by manually scraping the first results from an
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@@ -244,6 +237,7 @@ The [png-0](dlowe-aux-data/png-0) corpus was obtained by manually scraping the f
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<https://images.google.com> search for "[obfuscate
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filetype:gif](https://www.google.com/search?tbm=isch&ved=2ahUKEwiowo7fltf-AhXSOUQIHbJBDp8Q2-cCegQIABAA&oq=obfuscate+filetype:gif&gs_lcp=CgNpbWcQAzoECCMQJzoHCAAQigUQQzoFCAAQgARQ6AxY6AxgpBJoAHAAeACAAUeIAcsBkgEBM5gBAKABAaoBC2d3cy13aXotaW1nwAEB&sclient=img&ei=0UpRZOiVINLzkPIPsoO5-Ak&bih=952&biw=1727&q=obfuscate&tbs=ift:gif)".
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##### Results:
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* Using these corpora as a training set;
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@@ -255,6 +249,7 @@ filetype:gif](https://www.google.com/search?tbm=isch&ved=2ahUKEwiowo7fltf-AhXSOU
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produced a network with about 87% accuracy
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#### English corpora
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The [english-1](dlowe-aux-data/english-1) corpus was obtained by manually scraping the first results from
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@@ -265,6 +260,7 @@ The [english-0](dlowe-aux-data/english-0) corpus was obtained by manually scrapi
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a <https://www.google.com/webhp?lr=lang_fr> search for "[paris
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filetype:txt](https://www.google.com/search?q=paris+filetype%3Atxt&lr=lang_fr&tbs=lr%3Alang_1fr&sxsrf=APwXEddcsQ-gMhrqSUJo6omWFUl7jQMASg%3A1683049870736&source=hp&ei=jk1RZLevK_fKkPIPjbWN-AU&iflsig=AOEireoAAAAAZFFbnvExHGkcavB5H8F0xsLAWN2KfYFK&ved=0ahUKEwi3rrutmdf-AhV3JUQIHY1aA18Q4dUDCAo&uact=5&oq=paris+filetype%3Atxt&gs_lcp=Cgdnd3Mtd2l6EANQAFgAYLMDaABwAHgAgAE-iAE-kgEBMZgBAKABAqABAQ&sclient=gws-wiz)".
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##### Results:
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* Using these corpora as a training set;
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@@ -277,7 +273,8 @@ French"
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produced a network with 100% accuracy.
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#### xor corpora
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### xor corpora
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The [xor-1](dlowe-aux-data/xor-1) corpus consists of two files containing `01`
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and `10` respectively.
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@@ -285,11 +282,13 @@ and `10` respectively.
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The [xor-0](dlowe-aux-data/xor-0) corpus consists of two files containing `00`
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and `11` respectively.
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##### Results:
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Using these corpora as a training set takes about 8000 iterations to learn xor
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to within a tolerance of `<0.01`.
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### Limitations
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The program can't tell you anything meaningful about files with less than two
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@@ -324,6 +323,7 @@ library (untested, but they do exist).
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Backpropagation doesn't always converge: if you play with this long enough,
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you'll eventually have a training session that completely fails to converge.
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### Obfuscation
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- [Zombies](https://en.wikipedia.org/wiki/Zombie)! (Since neural networks are
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@@ -17,16 +17,36 @@ make CC="$CC" all >/dev/null
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# clear screen after compilation so that only the entry is shown
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clear
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read -r -n 1 -p "Press any key to run: ./dlowe dlowe.c < dlowe-aux-data/ioccc-winlose-trained.net: "
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./dlowe dlowe.c < dlowe-aux-data/ioccc-winlose-trained.net
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echo 1>&2
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read -r -n 1 -p "Press any key to run: ./dlowe dlowe < dlowe-aux-data/ioccc-winlose-trained.net: "
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./dlowe dlowe < dlowe-aux-data/ioccc-winlose-trained.net
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echo 1>&2
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read -r -n 1 -p "Press any key to run: ./dlowe xor-0/00 < dlowe-aux-data/xor-trained.net: "
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./dlowe xor-0/00 < dlowe-aux-data/xor-trained.net
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echo 1>&2
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read -r -n 1 -p "Press any key to run: ./dlowe xor-0/11 < dlowe-aux-data/xor-trained.net: "
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./dlowe xor-0/11 < dlowe-aux-data/xor-trained.net
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echo 1>&2
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read -r -n 1 -p "Press any key to run: ./dlowe xor-1/01 < dlowe-aux-data/xor-trained.net: "
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./dlowe xor-1/01 < dlowe-aux-data/xor-trained.net
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echo 1>&2
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read -r -n 1 -p "Press any key to run: ./dlowe xor-1/10 < dlowe-aux-data/xor-trained.net: "
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./dlowe xor-1/10 < dlowe-aux-data/xor-trained.net
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echo 1>&2
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read -r -n 1 -p "Press any key to run: ./dlowe README.html < dlowe-aux-data/english-trained.net: "
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./dlowe README.html < dlowe-aux-data/english-trained.net
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echo 1>&2
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read -r -n 1 -p "Press any key to run: ./dlowe dlowe-aux-data.tar.bz2 < dlowe-aux-data/png-trained.net: "
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./dlowe dlowe-aux-data.tar.bz2 < dlowe-aux-data/png-trained.net
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read -r -n 1 -p "Press any key to run: ./dlowe dlowe.c < dlowe-aux-data/ioccc-winlose-trained.net: "
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./dlowe dlowe.c < dlowe-aux-data/ioccc-winlose-trained.net
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+8
-3
@@ -3224,6 +3224,11 @@ the number of args as well.
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Cody also added the [try.sh](2011/borsanyi/try.sh) script.
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## [2011/dlowe](2011/dlowe/dlowe.c) ([README.md](2011/dlowe/README.md]))
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Cody added the [try.sh](2011/dlowe/try.sh) script.
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## [2011/goren](2011/goren/goren.c) ([README.md](2011/goren/README.md]))
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Cody fixed this for macOS. Before the fix it segfaulted. It worked fine under
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@@ -3263,9 +3268,9 @@ not be worth.
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## [2011/vik](2011/vik/vik.c) ([README.md](2011/vik/README.md]))
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Cody, though he feels slightly sick :-), added a version for Windows based on
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the author's comments (along with looking up the function for the right header
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file). To build try the alt rule of the Makefile.
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Cody added an [alternate version](2011/vik/README.md#alternate-code) for Windows
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based on the author's comments (along with looking up the function for the right
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header files). To build try the alt rule of the Makefile.
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## [2011/zucker](2011/zucker/zucker.c) ([README.md](2011/zucker/README.md]))
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