Update README.md

README.md

@@ -3,6 +3,23 @@
 
 Data scientists find it really difficult to choose the right features to get maximum accuracy especially if you are dealing with a lot of features. There are currenlty lots of ways to select the right features. But we will have to struggle if the feature space is really big. Genetic algorithm is one solution which searches for one of the best feature set from other features in order to attain a high accuracy.
 
+#### Usage:
+```
+from sklearn.datasets import make_classification
+from sklearn import linear_model
+from feature_selection_ga import FeatureSelectionGA
+import fitness_function as ff
+X, y = make_classification(n_samples=100, n_features=15, n_classes=3,
+                           n_informative=4, n_redundant=1, n_repeated=2,
+                           random_state=1)
+
+model = linear_model.LogisticRegression(solver='lbfgs', multi_class='auto')
+fsga = FeatureSelectionGA(model,X,y, ff_obj = ff.FitnessFunction())
+pop = fsga.generate(100)
+
+#print(pop)
+```
+
 #### Usage (Advanced):
 
 By default, the FeatureSelectionGA has its own fitness function class. We can also define our own
@@ -26,10 +43,10 @@ class FitnessFunction:
 ```
 
 With this, we can design our own fitness function by defining our calculate fitness!
-Consider the following example from Vieira, Mendoca, Sousa, et al. (2013)
+Consider the following example from [Vieira, Mendoca, Sousa, et al. (2013)](http://www.sciencedirect.com/science/article/pii/S1568494613001361)
+```f(X) = \alpha(1-P) + (1-\alpha) \left(1 - \dfrac{N_f}{N_t}\right)```
 
-
-Define the constructor __init__ with needed parameters:
+Define the constructor __init__ with needed parameters: alpha and N_t.
 ```
 class FitnessFunction:
     def __init__(self,n_total_features,n_splits = 5, alpha=0.01, *args,**kwargs):
@@ -52,6 +69,8 @@ class FitnessFunction:
         self.n_total_features = n_total_features
 ```
 
+Next, we define the fitness function, the name has to be
+calculate_fitness:
 ```
     def calculate_fitness(self,model,x,y):
         alpha = self.alpha
@@ -73,12 +92,19 @@ class FitnessFunction:
         return fitness
 ```
 
+Example: 
+You may also see ```example2.py```
 ```
-model = LogisticRegression()
-fsga = FeatureSelectionGA(model,x_train,y_train)
+X, y = make_classification(n_samples=100, n_features=15, n_classes=3,
+                           n_informative=4, n_redundant=1, n_repeated=2,
+                           random_state=1)
+# Define the model
+model = linear_model.LogisticRegression(solver='lbfgs', multi_class='auto')
+# Define the fitness function object
+ff = FitnessFunction(n_total_features= X.shape[1], n_splits=3, alpha=0.05)
+fsga = FeatureSelectionGA(model,X,y, ff_obj = ff)
 pop = fsga.generate(100)
-#Select the best individual from the final population and fit the initialized model
 ```
 
-
+Example adopted from [pyswarms](https://pyswarms.readthedocs.io/en/latest/examples/usecases/feature_subset_selection.html)