Docstrings corrected so that sphinx generates doctest in documentatio…

…n correctly

fda/FDataGrid.py

@@ -32,6 +32,7 @@ class FDataGrid:
     Examples:
         The number of columns of data_matrix have to be the length of 
         sample_points.
+
         >>> FDataGrid(numpy.array([1,2,4,5,8]), range(6))
         Traceback (most recent call last):
             ....
@@ -210,6 +211,7 @@ def derivative(self, order=1):
 
         Examples:
             First order derivative
+
             >>> fdata = FDataGrid([1,2,4,5,8], range(5))
             >>> fdata.derivative()
             FDataGrid(
@@ -219,6 +221,7 @@ def derivative(self, order=1):
                 ,names=['Data set - 1 derivative', 'xlabel', 'ylabel'])
 
             Second order derivative
+
             >>> fdata = FDataGrid([1,2,4,5,8], range(5))
             >>> fdata.derivative(2)
             FDataGrid(

fda/kernel_smoothers.py

@@ -167,6 +167,7 @@ def knn(argvals, k=None, kernel=kernels.uniform, w=None, cv=False):
                [ 0. ,  0. ,  0. ,  0.5,  0.5]])
 
         In case there are two points at the same distance it will take both.
+
         >>> knn(numpy.array([1,2,3,5,7]), 2).round(3)
         array([[ 0.5  ,  0.5  ,  0.   ,  0.   ,  0.   ],
                [ 0.333,  0.333,  0.333,  0.   ,  0.   ],

fda/validation.py

@@ -52,7 +52,7 @@ def gcv(fdatagrid, s_matrix, penalisation_function=None):
 
     .. math::
         GCV(\\nu)=\\Xi(\\nu,n)\\frac{1}{n} \\sum_i \\left(y_i - \\hat{
-        y}_i^\\nu}\\right)^2
+        y}_i^\\nu\\right)^2
 
     Where :math:`\\hat{y}_i^{\\nu}` is the adjusted :math:`y_i` and
     :math:`\\Xi` is a penalisation function. By default the penalisation
@@ -116,6 +116,7 @@ def minimise(fdatagrid, parameters,
     Examples:
         Creates a FDataGrid object of the function :math:`y=x^2` and peforms
         smoothing by means of the k-nearest neighbours method.
+
         >>> x = numpy.linspace(-2, 2, 5)
         >>> fd = fda.FDataGrid(x ** 2, x)
         >>> res = minimise(fd, [2,3], smoothing_method=kernel_smoothers.knn)
@@ -139,7 +140,8 @@ def minimise(fdatagrid, parameters,
             ,names=['Data set', 'xlabel', 'ylabel'])
 
         Other validation methods can be used such as cross-validation or
-        general corss validation using other penalisation functions.
+        general cross validation using other penalisation functions.
+
         >>> res = minimise(fd, [2,3], smoothing_method=kernel_smoothers.knn,
         ...                cv_method=cv)
         >>> numpy.array(res['scores']).round(2)