Update documentation

docs/GettingStartedInC.rst

@@ -87,7 +87,7 @@ Matrix is a user-defined data type that contains:
 
 The data array is selected explicitly as a double type to work with an extensive range of numbers.
 
-.. literalinclude:: ../../src/matrix.h
+.. literalinclude:: ../src/matrix.h
    :language: c
    :lines: 30-33
 
@@ -178,7 +178,7 @@ Tensor is a user-defined data type that contains:
 
 The data array is selected explicitly as a double type to work with an extensive range of numbers.
 
-.. literalinclude:: ../../src/tensor.h
+.. literalinclude:: ../src/tensor.h
    :language: c
    :lines: 33-36
 

docs/Install.rst

@@ -50,7 +50,8 @@ please export the LIBSCIENTIFIC_LIB_DIR variable pointing to the libscientific.s
 For example, if the library is installed in /home/<user>/mylocalenv, then you need to export the 
 following variable
 
-.. coide-block::
+.. code-block::
+
     export LISCIENTIFIC_LIB_DIR=/home/<user>/mylocalenv
 
 

src/python_bindings/libscientific/clustering.py

@@ -33,8 +33,26 @@
 lsci.MDC.restype = None
 
 def most_descriptive_compound(x_input, nobjects):
-    """
-    Most Descriptive Compound algorithm
+    """Most Descriptive Compound selection algorithm.
+
+    Parameters
+    ----------
+    x_input: List[List]
+        Input matrix
+    nobjects: int
+        Number of object select
+
+    Returns
+    -------
+    A list of selected objects/row id.
+
+    Examples
+    --------
+    >>> np.random.seed(12345)
+    >>> x = np.random.rand(100,2)
+    >>> mdc_ids = libscientific.clustering.most_descriptive_compound(x, 10)
+    >>> mdc_ids
+    [74, 97, 95, 7, 35, 25, 50, 10, 32, 59]
     """
     if "Matrix" not in str(type(x_input)):
         x_input_ = mx.new_matrix(x_input)
@@ -65,8 +83,26 @@ def most_descriptive_compound(x_input, nobjects):
 
 
 def max_dissimilarity_selection(x_input, nobjects):
-    """
-    Max dissimilarity compound selection algorithm
+    """Max dissimilarity compound selection algorithm
+
+    Parameters
+    ----------
+    x_input: List[List]
+        Input matrix
+    nobjects: int
+        Number of object select
+
+    Returns
+    -------
+    A list of selected objects/row id.
+
+    Examples
+    --------
+    >>> np.random.seed(12345)
+    >>> x = np.random.rand(100,2)
+    >>> mdis_ids = libscientific.clustering.max_dissimilarity_selection(x, 10)
+    >>> mdis_ids
+    [57, 89, 88, 6, 23, 94, 56, 61, 39, 24]
     """
     if "Matrix" not in str(type(x_input)):
         x_input_ = mx.new_matrix(x_input)
@@ -98,8 +134,27 @@ def max_dissimilarity_selection(x_input, nobjects):
 
 
 def k_means_plus_plus(x_input, n_clusters):
-    """
-    K-Means++ clustering using david arthur initialization
+    """K-Means++ clustering (kmeans + David Arthur initialization)
+
+    Parameters
+    ----------
+    x_input: List[List]
+        Input matrix
+    nobjects: int
+        Number of object select
+
+    Returns
+    -------
+    A list of selected objects/row id.
+
+    Examples
+    --------
+    >>> np.random.seed(12345)
+    >>> x = np.random.rand(100,2)
+    >>> clusters = libscientific.clustering.k_means_plus_plus(x, 3)
+    >>> clusters = libscientific.clustering.k_means_plus_plus(x, 3)
+    >>> clusters
+    [1, 2, 0, 0, 0, 0, 2, 2, 0, 0, 0, 1, ..., 0, 1, 1, 2, 2]
     """
     if "Matrix" not in str(type(x_input)):
         x_input_ = mx.new_matrix(x_input)

src/python_bindings/libscientific/cpca.py

@@ -120,9 +120,36 @@ def print_cpca(mpca):
 
 class CPCA():
     """
-    CPCA model class
+    Consesus Principal Component Analysis
+
+    Examples
+    --------
+    >>> np.random.seed(12345)
+    >>> x = np.random.rand(100,2)
+    >>> clusters = libscientific.clustering.k_means_plus_plus(x, 3)
+    >>> clusters = libscientific.clustering.k_means_plus_plus(x, 3)
+    >>> clusters
+    [1, 2, 0, 0, 0, 0, 2, 2, 0, 0, 0, 1, ..., 0, 1, 1, 2, 2]
+
     """
     def __init__(self, scaling, npc):
+        """Init method
+
+        Parameters
+        ----------
+        scaling: int
+            scaling type to apply. Available scalings:
+                0: No scaling. Only mean centering
+                1: Mean centering and STDEV scaling
+                2: Mean centering and Root-Mean-Square column scaling
+                3: Mean centering and Pareto scaling
+                4: Mean centering and min-max range scaling
+                5: Mean centering and level scaling
+
+        npc: int
+            Number of principal components
+
+        """
         self.model = new_cpca_model()
         self.scaling = scaling
         self.npc = npc
@@ -134,8 +161,16 @@ def __del__(self):
         self.model = None
 
     def fit(self, t_input):
-        """
-        fit a cpca model using an input tensor
+        """Fit the consensus pca
+
+        Parameters
+        ----------
+        t_input: tensor
+            libscientific tensor
+
+        Returns
+        -------
+        The fitted CPCA model class
         """
         if "Tensor" in str(type(t_input)):
             cpca_algorithm(t_input.tns, self.scaling, self.npc, self.model)
@@ -146,44 +181,80 @@ def fit(self, t_input):
             del t_input_
 
     def get_super_scores(self):
-        """
-        get the cpca super scores
+        """Get the CPCA super scores
+
+        Returns
+        -------
+        super_scores: List[List]
+            CPCA super scores
         """
         return mx.matrix_to_list(self.model.contents.super_scores)
 
     def get_super_weights(self):
-        """
-        get the cpca super weights
+        """Get the CPCA super weights
+
+        Returns
+        -------
+        super_weights: List[List]
+            CPCA super weights
         """
         return mx.matrix_to_list(self.model.contents.super_weights)
 
     def get_block_scores(self):
-        """
-        get the cpca block scores
+        """Get the CPCA block scores
+
+        Returns
+        -------
+        block_scores: List[List[List]]
+            CPCA block scores
         """
         return tns.tensor_tolist(self.model.contents.block_scores)
 
     def get_block_loadings(self):
-        """
-        get the cpca block loadings
+        """Get the CPCA block loadings
+
+        Returns
+        -------
+        block_loadings: List[List[List]]
+            CPCA block loadings
         """
         return tns.tensor_tolist(self.model.contents.block_loadings)
 
     def get_block_expvar(self):
-        """
-        get the cpca block variance explained
+        """Get the CPCA block explained variance
+
+        Returns
+        -------
+        block_exp_var: List[List[List]]
+            CPCA block explained variance
         """
         return vlst.dvector_list_tolist(self.model.contents.block_expvar)
 
     def get_total_exp_variance(self):
-        """
-        get the cpca total variance explained
+        """Get the CPCA total explained variance
+
+        Returns
+        -------
+        exp_var: List[List[List]]
+            CPCA total explained variance
         """
         return vect.dvector_tolist(self.model.contents.total_expvar)
 
     def predict(self, t_input):
-        """
-        get the cpca block variance explained
+        """Predict super scores and block scores using the CPCA model
+
+         Parameters
+        ----------
+        t_input: tensor
+            libscientific tensor
+    
+        Returns
+        -------
+        p_super_scores: List[List]
+            CPCA predicted super scores
+
+        p_block_scores: List[List[List]]
+            CPCA predicted block scores
         """
         t_input_ = tns.new_tensor(t_input)
         p_super_scores_ = mx.init_matrix()