benapie

//
// Created by Ben Napier on 08/03/2019.
//
#include <iostream>
#include "Graph.h"

int main() {
    // Lets implement a bit of Prim's...
    // First set up our graph.
    Graph graph;

benapie

% Typical maths
    \usepackage{amssymb} % Cool symbols like \mathbb{R}
    \usepackage{bm} % Bold symbols, \bm{v}
    \usepackage{mathtools} % Main maths package, imports amsmath
    \usepackage{enumitem} % allows next line
    \setlist[enumerate,1]{label={(\roman*)}} % changing default numbering scheme
    \DeclarePairedDelimiter{\abs}{\lvert}{\rvert} % absolute operator
    
% Probability
    \usepackage{mathtools}

benapie

void bucket_sort(int data_array[], int array_size) {
    // First we find the maximum value in the list so we know how much space to allocate.
    int maximum_value = 0;
    for (int i = 0; i < array_size; ++i) {
        if (data_array[i] > maximum_value) {
            maximum_value = data_array[i];
        }
    }
    auto bucket_vector = std::vector<unsigned >(static_cast<unsigned int>(maximum_value + 1));
    // And now we fill our buckets.

benapie

#define MAX_SIZE 32

/**
 * Implementation of a queue data type using an array.
 */
class QueueWithArray {
private:
    int data_array[MAX_SIZE] = {};
    int size = 0;
    int front_index = 0;

benapie

#define MAX_SIZE 32

/**
 * Implementation of a stack data type using an array.
 */
class Stack {
private:
    int data_array[MAX_SIZE] = {};
    int size = 0;
public:

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/**
 * Simple node data structure for a circularly linked list.
 */
class Node {
public:
    Node *next = nullptr;
    int data;
};

/**

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/**
 * Simple node data structure for a doubly linked list.
 */
class Node {
public:
    Node *next = nullptr;
    Node *prev = nullptr;
    int data;
};

benapie

/**
 * Simple node data structure for a singly linked list.
 */
class Node {
public:
    Node *next = nullptr;
    int data;
};

/**

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table <- tibble(Measure = numeric(), Percent = numeric(), Count = numeric())
for (i in seq(main$Measure %>% quantile(0.01) %>% round(1), max(main$Measure), 0.1)) {
    main.sub <- main %>% filter(Measure > i)
    prop.count <- main.sub$IsCondition %>% which() %>% length()
    count <- main.sub %>% nrow()
    if (prop.count < 30) {
        break
    }
    table <- table %>% add_row(Measure = i,
                               Percent = 100 * prop.count / count,

benapie

ggplot(data)+
  geom_point(aes(Value.X, Value.Y), shape = 0)