DOC: Updated readme file.

Author: tomlof

OnPLS/estimators.py

@@ -748,7 +748,7 @@ class OnPLS(BaseEstimator):
     ...     numReps=1, verbose=1)
     >>> _ = onpls.fit([X1, X2, X3])
     """
-    def __init__(self, predComp, orthComp, model,
+    def __init__(self, predComp, orthComp, model=None,
                  precomputedW=None, numReps=1, verbose=2,
                  eps=consts.TOLERANCE, max_iter=consts.MAX_ITER):
 

README.md

@@ -1,2 +1,87 @@
-# OnPLS
+OnPLS
+=====
+
 OnPLS: Orthogonal Projections to Latent Structures in Multiblock and Path Model Data Analysis
+
+OnPLS is a Python package for multiblock data analysis with prefiltering of unique and locally joint variation.
+
+
+Installation
+------------
+
+The reference environment for OnPLS is Ubuntu 14.04 LTS with Python 2.7.6 or Python 3.4.3 and Numpy 1.8.2.
+
+Unless you already have Numpy installed, you need to install it:
+```
+$ sudo apt-get install python-numpy
+```
+or
+```
+$ sudo apt-get install python3-numpy
+```
+
+In order to run the tests, you may also need to install Nose:
+```
+$ sudo apt-get install python-nose
+```
+or
+```
+$ sudo apt-get install python3-nose
+```
+
+**Downloading the latest development version**
+
+Clone the Github repository
+
+```
+$ git clone https://github.com/tomlof/OnPLS.git
+```
+Preferably, you would fork it first and clone your own repository.
+
+Add OnPLS to your Python path:
+```
+$ export $PYTHONPATH=$PYTHONPATH:/directory/to/OnPLS
+```
+
+Stable reseases with setup scripts will be included in future versions.
+
+You are now ready to use your fresh installation of OnPLS!
+
+
+Quick start
+-----------
+
+A simple example of the usage:
+
+```python
+import numpy as np
+import OnPLS
+
+np.random.seed(42)
+
+n, p_1, p_2, p_3 = 4, 3, 4, 5
+t = np.sort(np.random.randn(n, 1), axis=0)
+p1 = np.sort(np.random.randn(p_1, 1), axis=0)
+p2 = np.sort(np.random.randn(p_2, 1), axis=0)
+p3 = np.sort(np.random.randn(p_3, 1), axis=0)
+X1 = np.dot(t, p1.T) + 0.1 * np.random.randn(n, p_1)
+X2 = np.dot(t, p2.T) + 0.1 * np.random.randn(n, p_2)
+X3 = np.dot(t, p3.T) + 0.1 * np.random.randn(n, p_3)
+
+# Define the connections between blocks
+predComp = [[0, 1, 1], [1, 0, 1], [1, 1, 0]]
+# Define the numbers of non-global components
+orthComp = [1, 1, 1]
+
+# Create the estimator
+onpls = OnPLS.estimators.OnPLS(predComp, orthComp)
+
+# Fit a model
+onpls.fit([X1, X2, X3])
+
+# Perform prediction of all matrices from all connected matrices
+Xhat = onpls.predict([X1, X2, X3])
+
+# Compute prediction score
+score = onpls.score([X1, X2, X3])
+```