Add example sections in docs

rampy/ml_exploration.py

@@ -4,6 +4,8 @@
 class mlexplorer:
     """use machine learning algorithms from scikit learn to explore spectroscopic datasets
 
+    Performs automatic scaling and train/test split before NMF or PCA fit.
+
     Attributes
     ----------
     x : {array-like, sparse matrix}, shape = (n_samples, n_features)
@@ -32,6 +34,21 @@ class mlexplorer:
 
     Results for machine learning algorithms can vary from run to run. A way to solve that is to fix the random_state.
 
+    Example
+    -------
+
+    Given an array X of n samples by m frequencies, and Y an array of n x 1 concentrations
+
+    >>> explo = rampy.mlexplorer(X) # X is an array of signals built by mixing two partial components
+    >>> explo.algorithm = 'NMF' # using Non-Negative Matrix factorization
+    >>> explo.nb_compo = 2 # number of components to use
+    >>> explo.test_size = 0.3 # size of test set
+    >>> explo.scaler = "MinMax" # scaler
+    >>> explo.fit() # fitting!
+    >>> W = explo.model.transform(explo.X_train_sc) # getting the mixture array
+    >>> H = explo.X_scaler.inverse_transform(explo.model.components_) # components in the original space
+    >>> plt.plot(X,H.T) # plot the two components
+
     """
 
     def __init__(self,x,**kwargs):

rampy/ml_regressor.py

@@ -121,7 +121,10 @@ class mlregressor:
     Given an array X of n samples by m frequencies, and Y an array of n x 1 concentrations
 
     >>> model = rampy.mlregressor(X,y)
-    >>>
+    >>> model.algorithm("SVM")
+    >>> model.user_kernel = 'poly'
+    >>> model.fit()
+    >>> y_new = model.predict(X_new)
 
     Remarks
     -------