C / C++ / MFC

#define ARRAY_INDEX(tab, i) ((void*)((char*)(tab) + (i) * type_size))

void merge(void *tab, void *aux, int a, int b)
{
	int		i, j, k, mid;

	ft_copy_tab(tab, aux, a, b);
	mid = (a + b) / 2;
	i = a;
	j = mid + 1;
	k = a;
	while (k <= b)
	{
		if (i > mid)
			ft_copy_index(tab, aux, k, j++);
		else if (j > b)
			ft_copy_index(tab, aux, k, i++);
		else if (f_cmp(ARRAY_INDEX(aux, i), ARRAY_INDEX(aux, j)) > 0)
			ft_copy_index(tab, aux, k, j++);
		else
			ft_copy_index(tab, aux, k, i++);
		k++;
	}
	return ;
}

void ft_quicksort(void *tab, int a, int b)
{
	int i, lower, upper;

	if (a >= b)
		return ;
	lower = a;
	upper = b;
	i = a;
	while (i <= upper)
	{
		if (f_cmp(ARRAY_INDEX(tab, lower), ARRAY_INDEX(tab, i)) > 0)
		{
			ft_swap(tab, lower, i, type_size);
			lower++;
			i++;
		}
		else if (f_cmp(ARRAY_INDEX(tab, lower), ARRAY_INDEX(tab, i)) < 0)
		{
			ft_swap(tab, i, upper, type_size);
			upper--;
		}
		else
			++i;
	}
	ft_sort(tab, a, lower - 1);
	ft_sort(tab, upper + 1, b);
}

Problem: This is our first programming assignment. This assignment has two parts:
•   Part 1: I ask you to write a program to build a decision tree using Gini impurity measurement to guide tree generation. The data set is  the poker hand data set archived at UCI Machine Learning Repository: Poker Hand Data Set
Data Set 
Characteristics:   	Multivariate 	Number of Instances: 	1025010 	Area: 	Game 
		
Attribute 
Characteristics: 	Categorical, Integer 	Number of Attributes: 	11 	Date Donated 	2007-01-
01 
		
Associated Tasks: 	Classification 	Missing Values? 	No 	Number of Web 
Hits: 	212827 

You shall use the training data set to build your decision tree and then use the testing data set to evaluate your decision tree. You need to report classification accuracy using a bar chart and compare it with the distance based classification which is given in Part II.
•   Part II: For this part, I ask you to use the same training data set in Part I to build a distance-based classification model. Here, you need to find a good distance metric and a parameter k that serves as the threshold to bound the nearest neighbors for any given data item (or point). Then, you need to apply your model to the testing data set to evaluate your classification model. You shall record the classification accuracy and compare it in a
bar chart with that of the decision tree model built in Part I.
Programming Language:
C++ or Java, but C++ is preferred.