Dynamic Programming

100%

Dynamic Programming - How to get target sum

✓ Published0🌍 Public

Last edited Jan 7, 2021

Created on Dec 23, 2020

This visualization demonstrates the dynamic programming approach to solving the "howSum" problem, where the goal is to find a combination of numbers from a given array that adds up to a target sum. The code implements a tabular dynamic programming algorithm that iteratively constructs a table of possible sums, showing how each state can be reached from previous states. Using D3.js version 5, the visualization displays the algorithm's process on an SVG canvas with a light gray background. The example also includes a recursive implementation of the same problem, though the tabular approach is the primary focus, with the data derived from the algorithm's internal logic rather than an external dataset.

AI-generated description

Data: United Nations World Population Prospects 2019.

MIT Licensed

Similar vizzes

Loading thumbnail…

This example illustrates the dynamic programming concept of determining whether a target sum can be formed from a given array of integers, using two recursive approaches: a basic method and an improved memoized version. The visualization displays the result of checking whether 15 can be summed from [3,5,7] as true, alongside the memoization object that records computed subproblem outcomes. Rendered as static SVG text within a light gray frame using D3.js v5, the example highlights the efficiency gains of caching intermediate results.

VVlad Bogo

80% match

Loading thumbnail…

Dynamic Programming

The visualization displays the first 40 Fibonacci numbers as a vertical sequence of text labels, with each number rendered in an SVG text element. It compares classic recursive, memoized recursive, and iterative implementations of the Fibonacci function, logging results to the console. Using D3 v5, the code computes values via `simpleFib` and binds them to SVG text nodes with `svg.selectAll()`. The data comes from the United Nations World Population Prospects 2019, though the visualization itself focuses on algorithmic outputs rather than demographic data.

VVlad Bogo

58% match

Loading thumbnail…

Dynamic Programming

This example demonstrates the classic combinatorial problem of counting all possible paths on a 2D grid from the top-left to the bottom-right corner, moving only down or right. It visually presents the number of unique routes for various grid dimensions (m × n) as computed by the `gridTraveler` function, which uses memoization to optimize repeated subproblems. The visualization renders the results as an SVG grid, with each cell likely sized and labeled to reflect its path count. The code is built with D3.js v5 and pulls data from the United Nations World Population Prospects 2019 dataset, though the grid computations are self-contained in JavaScript.

VVlad Bogo

58% match

Loading thumbnail…

Example

This example shows the result of summing an array of numbers, displaying the output as a large heading. The code defines an `add` function and a `reduce` function to iterate over the list, accumulating the total. The result is then written to the page's HTML element using the DOM API `document.getElementById` and setting its `innerHTML`. The page's styling is applied through a linked CSS file.

Bbertieip

54% match

Loading thumbnail…

Loading and Summarizing Data

Constructing Visualizations example 6 — canonical React + D3 best practice.

CCurran Kelleher

53% match

Loading thumbnail…

Sample problems 3: objects and arrays

This example presents four JavaScript practice problems centered on arrays of objects, specifically U.S. state data with names, abbreviations, and populations. The visualization itself is minimal—a static HTML page with a heading and instructions—while the actual work happens in the console. The first problem demonstrates using `d3.mean()` with an accessor function to calculate the average population, while the second manually iterates through the array with `forEach` to sum populations and compute the mean. The code relies on the D3 v4 library and includes no SVG manipulation, focusing purely on data processing and console output.

HHeywhoyou22

52% match

Loading thumbnail…

js arrays

This example demonstrates JavaScript array manipulation techniques using D3.js. The code explores the `reduce`, `map`, `sort`, and `spread` operators on sample product and cart data, logging results to the console. The visualization portion, which uses `select` and `append` to create SVG circles and rectangles with animation, remains commented out. The active code focuses on computing total prices and grouping products into price categories, showing how array methods can transform and organize data structures.

Ssukumardunn

52% match

Loading thumbnail…

Sample problems 3: objects and arrays

This example presents four JavaScript practice problems embedded in an HTML page, focusing on array and object manipulation with population data for four U.S. states. The first problem uses `d3.mean()` to calculate and log the average population, demonstrating D3 v4’s array statistics method. The second problem attempts the same calculation manually with a `while` loop, but contains an intentional infinite loop and redundant addition. No SVG or visual output is rendered; results appear only in the browser’s developer console. The code relies on the D3 v4 library’s `mean()` function and standard JavaScript array traversal.

CCraftbd

52% match