Update README.md

Author: sichkar-valentyn

README.md

@@ -116,8 +116,7 @@ Initial **smooth** function is as following (the same as it is in approximation
 <br/>**f(x) = sin(x / 5) * exp(x / 10) + 5 * exp(-x / 2)**
 
 Finding minimum of **smooth** function with **'BFGS'** method with start point **2**
-<br/><br/>Part of the code is shown below:
-
+<br/>Part of the code is shown below:
 ```py
 # Setting initial point in form of 'ndarray' as 'minimize' function requires it in form of 'ndarray'
 x0_start = np.array([2])
@@ -129,9 +128,8 @@ y0_min = optimize.minimize(f_array, x0_start, method='BFGS')
 print(y0_min)  # fun = 1.7452682903449388, iterations = 6
 ```
 
-Finding minimum of **smooth** function with **'BFGS'** method with start point **30**
-<br/><br/>Part of the code is shown below:
-
+<br/>Finding minimum of **smooth** function with **'BFGS'** method with start point **30**
+<br/>Part of the code is shown below:
 ```py
 # Setting initial point in form of 'ndarray' as 'minimize' function requires it in form of 'ndarray'
 x1_start = np.array([30])
@@ -143,9 +141,8 @@ y1_min = optimize.minimize(f_array, x1_start, method='BFGS')
 print(y1_min)  # fun = -11.898894665981285, iterations = 6 
 ```
 
-Finding minimum of **smooth** function with **'differential evolution'** method in range [1, 30]
-<br/><br/>Part of the code is shown below:
-
+<br/>Finding minimum of **smooth** function with **'differential evolution'** method in range [1, 30]
+<br/>Part of the code is shown below:
 ```py
 # Setting the range for searching in form of tuple inside list as function requires it
 x2_range = [(1, 30)]  # tuple inside list
@@ -154,18 +151,16 @@ y2_min = optimize.differential_evolution(f_array, [(1, 30)])
 print(y2_min)  # fun = -11.89889467, iterations = 5
 ```
 
-Initial **non-smooth** function is takeen as an integer results of smooth function defined above:
-<br/>**h(x) = int(f(x))**
+<br/>Initial **non-smooth** function is takeen as an integer results of smooth function defined above: **h(x) = int(f(x))**
 <br/>Part of the code is shown below:
 ```py
 # By using 'np.int_' we return 'numpy.ndarray' of integer numbers
 def h_array(k):
     return np.int_(f_array(k))
 ```
 
-Finding minimum of **non-smooth** function with **'BFGS'** method with start point **30**
-<br/><br/>Part of the code is shown below:
-
+<br/>Finding minimum of **non-smooth** function with **'BFGS'** method with start point **30**
+<br/>Part of the code is shown below:
 ```py
 # Setting initial point in form of 'ndarray' as 'minimize' function requires it in form of 'ndarray'
 x3_start = np.array([30])
@@ -177,9 +172,8 @@ y3_min = optimize.minimize(h_array, x3_start, method='BFGS')
 print(y3_min)  # fun = -5, iterations = 0
 ```
 
-Finding minimum of **non-smooth** function with **'differential evolution'** method in range [1, 30]
-<br/><br/>Part of the code is shown below:
-
+<br/>Finding minimum of **non-smooth** function with **'differential evolution'** method in range [1, 30]
+<br/>Part of the code is shown below:
 ```py
 # Setting the range for searching in form of tuple inside list as function requires it
 x4_range = [(1, 30)]  # tuple inside list
@@ -188,7 +182,7 @@ y4_min = optimize.differential_evolution(h_array, [(1, 30)])
 print(y4_min)  # fun = -11.0, iterations = 3
 ```
 
-Full code is available here: [Function_Optimization.py](https://github.com/sichkar-valentyn/Machine_Learning_in_Python/tree/master/Codes/Function_Optimization.py)
+<br/>Full code is available here: [Function_Optimization.py](https://github.com/sichkar-valentyn/Machine_Learning_in_Python/tree/master/Codes/Function_Optimization.py)
 
 Results are plot in order to understand the difference in finding minimums in smooth and non-smooth functions. Figure is shown below: