minor typo fixes

Author: fmfn

README.md

@@ -12,16 +12,18 @@ suited for optimization of high cost functions, situations where the balance
 between exploration and exploitation is important.
 
 ## Quick Start
-To get a grip of how this method and package works in the [examples](https://github.com/fmfn/BayesianOptimization/tree/master/examples)
-folder you can:
-- Checkout this [notebook](https://github.com/fmfn/BayesianOptimization/blob/master/examples/visualization.ipynb)
+In the [examples](https://github.com/fmfn/BayesianOptimization/tree/master/examples)
+folder you can get a grip of how the method and this package work by:
+- Checking out this
+[notebook](https://github.com/fmfn/BayesianOptimization/blob/master/examples/visualization.ipynb)
 with a step by step visualization of how this method works.
-- Go over this [script](https://github.com/fmfn/BayesianOptimization/blob/master/examples/usage.py)
+- Going over this
+[script](https://github.com/fmfn/BayesianOptimization/blob/master/examples/usage.py)
 to become familiar with this packages basic functionalities.
-- Explore this [notebook](https://github.com/fmfn/BayesianOptimization/blob/master/examples/exploitation%20vs%20exploration.ipynb)
+- Exploring this [notebook](https://github.com/fmfn/BayesianOptimization/blob/master/examples/exploitation%20vs%20exploration.ipynb)
 exemplifying the balance between exploration and exploitation and how to
 control it.
-- Checkout these scripts ([sklearn](https://github.com/fmfn/BayesianOptimization/blob/master/examples/sklearn_example.py),
+- Checking out these scripts ([sklearn](https://github.com/fmfn/BayesianOptimization/blob/master/examples/sklearn_example.py),
 [xgboost](https://github.com/fmfn/BayesianOptimization/blob/master/examples/xgboost_example.py))
 for examples of how to use this package to tune parameters of ML estimators
 using cross validation and bayesian optimization.

bayes_opt/bayesian_optimization.py

@@ -121,11 +121,9 @@ def init(self, init_points):
         self.initialized = True
 
     def explore(self, points_dict):
-        """
-        Method to explore user defined points
+        """Method to explore user defined points
 
         :param points_dict:
-        :return:
         """
 
         # Consistency check
@@ -150,11 +148,9 @@ def explore(self, points_dict):
 
     def initialize(self, points_dict):
         """
-        Method to introduce point for which the target function
-        value is known
+        Method to introduce points for which the target function value is known
 
         :param points_dict:
-        :return:
         """
 
         for points in zip(*(points_dict[k] for k in sorted(points_dict))):

examples/usage.py

@@ -1,12 +1,13 @@
+"""Example of how to use this bayesian optimization package."""
+
 import sys
 sys.path.append("./")
 from bayes_opt import BayesianOptimization
-# Example of how to use this bayesian optimization package.
 
 # Lets find the maximum of a simple quadratic function of two variables
 # We create the bayes_opt object and pass the function to be maximized
 # together with the parameters names and their bounds.
-bo = BayesianOptimization(lambda x, y: -x**2 - (y - 1)**2 + 1,
+bo = BayesianOptimization(lambda x, y: -x ** 2 - (y - 1) ** 2 + 1,
                           {'x': (-4, 4), 'y': (-3, 3)})
 
 # One of the things we can do with this object is pass points
@@ -18,8 +19,8 @@
 # Additionally, if we have any prior knowledge of the behaviour of
 # the target function (even if not totally accurate) we can also
 # tell that to the optimizer.
-# Here we pass a dictionary with target values as keys of another
-# dictionary with parameters names and their corresponding value.
+# Here we pass a dictionary with 'target' and parameter names as keys and a
+# list of corresponding values
 bo.initialize(
     {
         'target': [-1, -1],
@@ -36,7 +37,6 @@
 # The output values can be accessed with self.res
 print(bo.res['max'])
 
-
 # If we are not satisfied with the current results we can pickup from
 # where we left, maybe pass some more exploration points to the algorithm
 # change any parameters we may choose, and the let it run again.