vatsan-mac$ ls /Library/PostgreSQL/9.2/
@@ -162,7 +162,7 @@
Usage Tutorial
-
+
Also visit PyMADlib IPython NB to download the IPython NB tutorial
@@ -202,9 +202,9 @@
PyMADlib packages publicly available datasets from the UCI machine learning repository and other sources.
-- Wine quality dataset from UCI Machine Learning repository
-- Auto MPG dataset from UCI ML repository from UCI Machine Learning repository
-- Wine quality dataset from UCI Machine Learning repository
+- Wine quality dataset from UCI Machine Learning repository
+- Auto MPG dataset from UCI ML repository from UCI Machine Learning repository
+- Wine quality dataset from UCI Machine Learning repository
- Obama-Romney second presidential debate (2012) transcripts
Questions
diff --git a/params.json b/params.json
index 04e61c5..930d206 100644
--- a/params.json
+++ b/params.json
@@ -1 +1 @@
-{"name":"PyMADlib","tagline":"A Python wrapper for MADlib - an open source library for scalable in-database machine learning algorithms","body":"A Python wrapper for MADlib - an open source library for scalable in-database machine learning algorithms\r\n\r\n## Algorithms\r\n\r\nPyMADlib currently has wrappers for the following algorithms in MADlib (version 0.5).\r\n\r\n1. Linear regression\r\n1. Logistic Regression\r\n1. SVM (regression & classification)\r\n1. K-Means \r\n1. LDA \r\n\r\nRefer [MADlib User Docs](http://doc.madlib.net/v0.5/ ) for MADlib's user documentation. Please note that PyMADlib as of now is only compatible with MADlib v0.5. You can obtain MADlib v0.5 from [MADlib v0.5](https://github.com/madlib/madlib/archive/v0.5.tar.gz). We might add support to more recent versions of MADlib depending on adoption rate. Please email me if you have a strong case for an upgrade.\r\n\r\n\r\n## Dependencies\r\n\r\n1. You'll need the python extension _**psycopg2**_ to use PyMADlib.\r\n1. If you have matplotlib installed, you'll see Matplotlib visualizations for Linear Regression demo.\r\n1. If you have installed [networkx](http://networkx.github.com/download.html), you'll see a visualization of the k-means demo\r\n1. [PyROC](https://github.com/marcelcaraciolo/PyROC) is included in the source of this distribution with permission from its developer. You'll see a visualization of the ROC curves for Logistic Regression.\r\n\r\n\r\n\r\n \r\n\r\n## Configurations\r\n\r\nTo configure your DB Connection parameters\r\nYou should create a file in your home directory\r\n\r\n> ~/.pymadlib.config \r\n\r\nthat should look like so :\r\n\r\n\r\n> [db_connection] \r\n> user = gpadmin \r\n> password = XXXXX \r\n> hostname = 127.0.0.1 (or the IP of your DB server) \r\n> port = 5432 (the port# of your DB) \r\n> database = vatsandb (the database you wish to connect to) \r\n\r\n\r\n\r\n\r\n\r\n\r\n## Installation Instructions\r\n\r\nPyMADlib depends on `MADlib`, `psycopg2` and `Pandas`. It is easiest to work with PyMADlib if you have `Anaconda Python`.\r\n\r\n## Build Environment Setup on Mac OS X 10.8\r\n\r\n* Download & install [Anaconda-1.9.0-MacOSX-x86_64.pkg] (http://repo.continuum.io/archive/Anaconda-1.9.0-MacOSX-x86_64.pkg)\r\n\r\n* Open a terminal and check if you have Anaconda Python & the package manager conda\r\n\r\n> vatsan-mac$ which python\r\n> /Users/vatsan/anaconda/bin/python\r\n> vatsan-mac$ which conda\r\n> /Users/vatsan/anaconda/bin/conda \r\n\r\n* If you haven't installed PostgreSQL on your Mac already, you'll have to download & install `PostGreSQL` for Mac. This is so that we get some required libraries to compile the SQL Engine: psycopg2. The easiest way to install `PostGreSQL` on Mac is via `http://postgresapp.com/`. Once you've downloaded and installed PostGreSQL on Mac, it should typically be found under `/Library/PostgreSQL`\r\n\r\n> vatsan-mac$ ls /Library/PostgreSQL/9.2/\r\n> Library include pg_env.sh uninstall-postgresql.app\r\n> bin installer scripts\r\n> data lib share\r\n> doc pgAdmin3.app stackbuilder.app\r\nI don't think the version of the `PostGreSQL` matters (9.1 or above is fine). \r\n\r\n* You may need to create some symlinks to `libpq` & `libssl` so that `psycopg2` is able to find it:\r\n\r\n> vatsan-mac$ sudo ln -s /Users/vatsan/anaconda/lib/libssl.1.0.0.dylib /usr/lib\r\n> vatsan-mac$ sudo ln -s /Users/vatsan/anaconda/lib/libcrypto.1.0.0.dylib /usr/lib\r\n\r\n* Install `Psycopg2` \r\n\r\n> vatsan-mac$ conda install distribute\r\n> vatsan-mac$ pip install psycopg2\r\n\r\n* Now we're ready to test if the installations of the required libraries were successful.\r\n\r\n> vatsan-mac$ python -c 'import psycopg2'\r\nIf the above command did not error out, then installation was successful.\r\n\r\n* You may install `PyMADlib` by downloading the source (from PyPI) and then run the following\r\n\r\n> sudo python setup.py build\r\n> sudo python setup.py install\r\n\r\n* If you use easy_install or pip, simply run :\r\n\r\n> sudo easy_install pymadlib\r\n\r\n\r\n## Usage Tutorial\r\n\r\nVisit [PyMADlib Tutorial](http://nbviewer.ipython.org/gist/vatsan/dd88abb47c2fbd9e16bd) for a tutorial on using PyMADlib\r\nAlso visit [PyMADlib IPython NB](https://gist.github.com/vatsan/dd88abb47c2fbd9e16bd) to download the IPython NB tutorial\r\n\r\n\r\n## Running the Demos\r\n\r\nYou may run the demo from the extracted directory of pymadlib like so :\r\n\r\n> python example.py\r\n\r\n \r\nIf you installed PyMADlib using instructions in the previous section, then simply run\r\n\r\n> python -c 'from pymadlib.example import runDemos; runDemos()'\r\n\r\nRemember to close the Matplotlib windows that pop-up to continue with the rest of the demo.\r\n\r\n\r\n\r\n\r\n## Gallery\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \r\n\r\n\r\n## Datasets packaged with this installation\r\n\r\nPyMADlib packages publicly available datasets from the UCI machine learning repository and other sources.\r\n\r\n1. [Wine quality dataset from UCI Machine Learning repository](http://archive.ics.uci.edu/ml/datasets/Wine+Quality)\r\n1. [Auto MPG dataset from UCI ML repository from UCI Machine Learning repository](http://archive.ics.uci.edu/ml/datasets/Auto+MPG)\r\n1. [Wine quality dataset from UCI Machine Learning repository](http://archive.ics.uci.edu/ml/datasets/Wine+Quality)\r\n1. Obama-Romney second presidential debate (2012) transcripts\r\n\r\n\r\n\r\n\r\n## Questions\r\n\r\n\r\n","google":"UA-39168204-1","note":"Don't delete this file! It's used internally to help with page regeneration."}
\ No newline at end of file
+{"name":"PyMADlib","tagline":"A Python wrapper for MADlib - an open source library for scalable in-database machine learning algorithms","body":"A Python wrapper for MADlib - an open source library for scalable in-database machine learning algorithms\r\n\r\n## Algorithms\r\n\r\nPyMADlib currently has wrappers for the following algorithms in MADlib (version 0.5).\r\n\r\n1. Linear regression\r\n1. Logistic Regression\r\n1. SVM (regression & classification)\r\n1. K-Means \r\n1. LDA \r\n\r\nRefer [MADlib User Docs](https://madlib.apache.org/docs/v0.5/ ) for MADlib's user documentation. Please note that PyMADlib as of now is only compatible with MADlib v0.5. You can obtain MADlib v0.5 from [MADlib v0.5](https://github.com/madlib/madlib/archive/v0.5.tar.gz). We might add support to more recent versions of MADlib depending on adoption rate. Please email me if you have a strong case for an upgrade.\r\n\r\n\r\n## Dependencies\r\n\r\n1. You'll need the python extension _**psycopg2**_ to use PyMADlib.\r\n1. If you have matplotlib installed, you'll see Matplotlib visualizations for Linear Regression demo.\r\n1. If you have installed [networkx](https://networkx.github.com/download.html), you'll see a visualization of the k-means demo\r\n1. [PyROC](https://github.com/marcelcaraciolo/PyROC) is included in the source of this distribution with permission from its developer. You'll see a visualization of the ROC curves for Logistic Regression.\r\n\r\n\r\n\r\n \r\n\r\n## Configurations\r\n\r\nTo configure your DB Connection parameters\r\nYou should create a file in your home directory\r\n\r\n> ~/.pymadlib.config \r\n\r\nthat should look like so :\r\n\r\n\r\n> [db_connection] \r\n> user = gpadmin \r\n> password = XXXXX \r\n> hostname = 127.0.0.1 (or the IP of your DB server) \r\n> port = 5432 (the port# of your DB) \r\n> database = vatsandb (the database you wish to connect to) \r\n\r\n\r\n\r\n\r\n\r\n\r\n## Installation Instructions\r\n\r\nPyMADlib depends on `MADlib`, `psycopg2` and `Pandas`. It is easiest to work with PyMADlib if you have `Anaconda Python`.\r\n\r\n## Build Environment Setup on Mac OS X 10.8\r\n\r\n* Download & install [Anaconda-1.9.0-MacOSX-x86_64.pkg] (https://repo.continuum.io/archive/Anaconda-1.9.0-MacOSX-x86_64.pkg)\r\n\r\n* Open a terminal and check if you have Anaconda Python & the package manager conda\r\n\r\n> vatsan-mac$ which python\r\n> /Users/vatsan/anaconda/bin/python\r\n> vatsan-mac$ which conda\r\n> /Users/vatsan/anaconda/bin/conda \r\n\r\n* If you haven't installed PostgreSQL on your Mac already, you'll have to download & install `PostGreSQL` for Mac. This is so that we get some required libraries to compile the SQL Engine: psycopg2. The easiest way to install `PostGreSQL` on Mac is via `https://postgresapp.com/`. Once you've downloaded and installed PostGreSQL on Mac, it should typically be found under `/Library/PostgreSQL`\r\n\r\n> vatsan-mac$ ls /Library/PostgreSQL/9.2/\r\n> Library include pg_env.sh uninstall-postgresql.app\r\n> bin installer scripts\r\n> data lib share\r\n> doc pgAdmin3.app stackbuilder.app\r\nI don't think the version of the `PostGreSQL` matters (9.1 or above is fine). \r\n\r\n* You may need to create some symlinks to `libpq` & `libssl` so that `psycopg2` is able to find it:\r\n\r\n> vatsan-mac$ sudo ln -s /Users/vatsan/anaconda/lib/libssl.1.0.0.dylib /usr/lib\r\n> vatsan-mac$ sudo ln -s /Users/vatsan/anaconda/lib/libcrypto.1.0.0.dylib /usr/lib\r\n\r\n* Install `Psycopg2` \r\n\r\n> vatsan-mac$ conda install distribute\r\n> vatsan-mac$ pip install psycopg2\r\n\r\n* Now we're ready to test if the installations of the required libraries were successful.\r\n\r\n> vatsan-mac$ python -c 'import psycopg2'\r\nIf the above command did not error out, then installation was successful.\r\n\r\n* You may install `PyMADlib` by downloading the source (from PyPI) and then run the following\r\n\r\n> sudo python setup.py build\r\n> sudo python setup.py install\r\n\r\n* If you use easy_install or pip, simply run :\r\n\r\n> sudo easy_install pymadlib\r\n\r\n\r\n## Usage Tutorial\r\n\r\nVisit [PyMADlib Tutorial](https://nbviewer.ipython.org/gist/vatsan/dd88abb47c2fbd9e16bd) for a tutorial on using PyMADlib\r\nAlso visit [PyMADlib IPython NB](https://gist.github.com/vatsan/dd88abb47c2fbd9e16bd) to download the IPython NB tutorial\r\n\r\n\r\n## Running the Demos\r\n\r\nYou may run the demo from the extracted directory of pymadlib like so :\r\n\r\n> python example.py\r\n\r\n \r\nIf you installed PyMADlib using instructions in the previous section, then simply run\r\n\r\n> python -c 'from pymadlib.example import runDemos; runDemos()'\r\n\r\nRemember to close the Matplotlib windows that pop-up to continue with the rest of the demo.\r\n\r\n\r\n\r\n\r\n## Gallery\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n \r\n\r\n\r\n## Datasets packaged with this installation\r\n\r\nPyMADlib packages publicly available datasets from the UCI machine learning repository and other sources.\r\n\r\n1. [Wine quality dataset from UCI Machine Learning repository](https://archive.ics.uci.edu/ml/datasets/Wine+Quality)\r\n1. [Auto MPG dataset from UCI ML repository from UCI Machine Learning repository](https://archive.ics.uci.edu/ml/datasets/Auto+MPG)\r\n1. [Wine quality dataset from UCI Machine Learning repository](https://archive.ics.uci.edu/ml/datasets/Wine+Quality)\r\n1. Obama-Romney second presidential debate (2012) transcripts\r\n\r\n\r\n\r\n\r\n## Questions\r\n\r\n\r\n","google":"UA-39168204-1","note":"Don't delete this file! It's used internally to help with page regeneration."}
\ No newline at end of file
diff --git a/stylesheets/normalize.css b/stylesheets/normalize.css
index bc2ba93..16a1351 100644
--- a/stylesheets/normalize.css
+++ b/stylesheets/normalize.css
@@ -1,4 +1,4 @@
-/* normalize.css 2012-02-07T12:37 UTC - http://github.com/necolas/normalize.css */
+/* normalize.css 2012-02-07T12:37 UTC - https://github.com/necolas/normalize.css */
/* =============================================================================
HTML5 display definitions
========================================================================== */
diff --git a/stylesheets/styles.css b/stylesheets/styles.css
index 980ee2b..86eb093 100644
--- a/stylesheets/styles.css
+++ b/stylesheets/styles.css
@@ -42,7 +42,7 @@ by Matt Graham
font-style: normal;
}
-/* normalize.css 2012-02-07T12:37 UTC - http://github.com/necolas/normalize.css */
+/* normalize.css 2012-02-07T12:37 UTC - https://github.com/necolas/normalize.css */
/* =============================================================================
HTML5 display definitions
========================================================================== */