Merge branch 'master' of https://github.com/timocb/py-earth into v0.2dev

Author: jcrudy

README.md

@@ -1,8 +1,8 @@
 py-earth [![Build Status](https://travis-ci.org/scikit-learn-contrib/py-earth.png?branch=master)](https://travis-ci.org/scikit-learn-contrib/py-earth?branch=master)
 ========
 
-A Python implementation of Jerome Friedman's Multivariate Adaptive Regression Splines algorithm, 
-in the style of scikit-learn. The py-earth package implements Multivariate Adaptive Regression Splines using Cython and provides an interface that is compatible with scikit-learn's Estimator, Predictor, Transformer, and Model interfaces.  For more information about 
+A Python implementation of Jerome Friedman's Multivariate Adaptive Regression Splines algorithm,
+in the style of scikit-learn. The py-earth package implements Multivariate Adaptive Regression Splines using Cython and provides an interface that is compatible with scikit-learn's Estimator, Predictor, Transformer, and Model interfaces.  For more information about
 Multivariate Adaptive Regression Splines, see the references below.
 
 ## Now With Missing Data Support!
@@ -22,9 +22,13 @@ If there are other features or improvements you'd like to see in py-earth, pleas
 7. Iterative reweighting during fitting
 
 ## Installation
+#### Install from [PyPI](https://pypi.org/):
+```
+pip install sklearn-contrib-py-earth
+```
 
-Make sure you have numpy and scikit-learn installed.  Then do the following:
-
+#### Manual install
+Make sure you have numpy and scikit-learn installed. Then do the following:
 ```
 git clone git://github.com/scikit-learn-contrib/py-earth.git
 cd py-earth
@@ -36,22 +40,22 @@ sudo python setup.py install
 import numpy
 from pyearth import Earth
 from matplotlib import pyplot
-    
+
 #Create some fake data
 numpy.random.seed(0)
 m = 1000
 n = 10
 X = 80*numpy.random.uniform(size=(m,n)) - 40
 y = numpy.abs(X[:,6] - 4.0) + 1*numpy.random.normal(size=m)
-    
+
 #Fit an Earth model
 model = Earth()
 model.fit(X,y)
-    
+
 #Print the model
 print(model.trace())
 print(model.summary())
-    
+
 #Plot the model
 y_hat = model.predict(X)
 pyplot.figure()
@@ -62,7 +66,7 @@ pyplot.ylabel('y')
 pyplot.title('Simple Earth Example')
 pyplot.show()
  ```
- 
+
 ## Other Implementations
 
 I am aware of the following implementations of Multivariate Adaptive Regression Splines:
@@ -75,32 +79,28 @@ I am aware of the following implementations of Multivariate Adaptive Regression
 6. MARS by Salford Systems (also uses Friedman's code): http://www.salford-systems.com/products/mars
 7. ARESLab (written in Matlab by Gints Jekabsons): http://www.cs.rtu.lv/jekabsons/regression.html
 
-The R package earth was most useful to me in understanding the algorithm, particularly because of Stephen Milborrow's 
+The R package earth was most useful to me in understanding the algorithm, particularly because of Stephen Milborrow's
 thorough and easy to read vignette (http://www.milbo.org/doc/earth-notes.pdf).
- 
+
 ## References
 
-1. Friedman, J. (1991). Multivariate adaptive regression splines. The annals of statistics, 
+1. Friedman, J. (1991). Multivariate adaptive regression splines. The annals of statistics,
    19(1), 1–67. http://www.jstor.org/stable/10.2307/2241837
 2. Stephen Milborrow. Derived from mda:mars by Trevor Hastie and Rob Tibshirani.
    (2012). earth: Multivariate Adaptive Regression Spline Models. R package
    version 3.2-3. http://CRAN.R-project.org/package=earth
-3. Friedman, J. (1993). Fast MARS. Stanford University Department of Statistics, Technical Report No 110. 
+3. Friedman, J. (1993). Fast MARS. Stanford University Department of Statistics, Technical Report No 110.
    https://statistics.stanford.edu/sites/default/files/LCS%20110.pdf
 4. Friedman, J. (1991). Estimating functions of mixed ordinal and categorical variables using adaptive splines.
-   Stanford University Department of Statistics, Technical Report No 108. 
+   Stanford University Department of Statistics, Technical Report No 108.
    http://media.salford-systems.com/library/MARS_V2_JHF_LCS-108.pdf
-5. Stewart, G.W. Matrix Algorithms, Volume 1: Basic Decompositions. (1998). Society for Industrial and Applied 
+5. Stewart, G.W. Matrix Algorithms, Volume 1: Basic Decompositions. (1998). Society for Industrial and Applied
    Mathematics.
-6. Bjorck, A. Numerical Methods for Least Squares Problems. (1996). Society for Industrial and Applied 
+6. Bjorck, A. Numerical Methods for Least Squares Problems. (1996). Society for Industrial and Applied
    Mathematics.
 7. Hastie, T., Tibshirani, R., & Friedman, J. The Elements of Statistical Learning (2nd Edition). (2009).  
    Springer Series in Statistics
 8. Golub, G., & Van Loan, C. Matrix Computations (3rd Edition). (1996). Johns Hopkins University Press.
-   
-References 7, 2, 1, 3, and 4 contain discussions likely to be useful to users of py-earth.  References 1, 2, 6, 5, 
-8, 3, and 4 were useful during the implementation process.
 
-
-
-   
+References 7, 2, 1, 3, and 4 contain discussions likely to be useful to users of py-earth.  References 1, 2, 6, 5,
+8, 3, and 4 were useful during the implementation process.