coverage fix

.travis.yml

@@ -31,8 +31,11 @@ install:
     - pip${PYVERSION} install scikit-learn
     - pip${PYVERSION} install git+https://github.com/natekupp/ffx
 
+before_script:
+    - export PYTHONPATH=$(pwd):$PYTHONPATH;
+
 script:
-  - coverage${PYVERSION} erase
+#  - coverage${PYVERSION} erase
   - coverage${PYVERSION} run --source ffx tests/x_square_test.py
 
 after_success:

Readme.md

@@ -1,5 +1,8 @@
 #FFX: Fast Function Extraction
 
+[![Build Status at Travis CI](https://travis-ci.org/dvolgyes/ffx.svg?branch=master)](https://travis-ci.org/dvolgyes/ffx)
+[![Coverage Status](https://coveralls.io/repos/github/dvolgyes/ffx/badge.svg?branch=master)](https://coveralls.io/github/dvolgyes/ffx?branch=master)
+
 FFX is a technique for symbolic regression. It is:
 
 - __Fast__ - runtime 5-60 seconds, depending on problem size (1GHz cpu)
@@ -9,34 +12,34 @@ FFX is a technique for symbolic regression. It is:
 ## Installation
 To install from PyPI, simply run:
 
-	pip install ffx
+    pip install ffx
 
 ## Usage
 FFX can either be run in stand-alone mode, or within your existing Python code. It installs both a command-line utility `runffx` and the Python module `ffx`.
 
 __Standalone__
 
-	runffx test train_X.csv train_y.csv test_X.csv test_y.csv
+    runffx test train_X.csv train_y.csv test_X.csv test_y.csv
 
 Use `runffx help` for more information on using the command-line utility.
 
 __Python Module__
 
 The following snippet is a simple example of how to use FFX. Note that all arguments are expected to be of type `numpy.ndarray` or `pandas.DataFrame`.
 
-	import numpy as np
-	import ffx
+    import numpy as np
+    import ffx
 
-	train_X = np.array( [ (1.5,2,3), (4,5,6) ] ).T
-	train_y = np.array( [1,2,3])
+    train_X = np.array( [ (1.5,2,3), (4,5,6) ] ).T
+    train_y = np.array( [1,2,3])
 
-	test_X = np.array( [ (5.241,1.23, 3.125), (1.1,0.124,0.391) ] ).T
-	test_y = np.array( [3.03,0.9113,1.823])
+    test_X = np.array( [ (5.241,1.23, 3.125), (1.1,0.124,0.391) ] ).T
+    test_y = np.array( [3.03,0.9113,1.823])
 
-	models = ffx.run(train_X, train_y, test_X, test_y, ["predictor_a", "predictor_b"])
-	for model in models:
-	    yhat = model.simulate(test_X)
-	    print model
+    models = ffx.run(train_X, train_y, test_X, test_y, ["predictor_a", "predictor_b"])
+    for model in models:
+        yhat = model.simulate(test_X)
+        print model
 
 Presently, the FFX Python module only exposes a single API method, `ffx.run()`.