
What
Brief
This is a standalone Binary Tree data structure from the data-structure-typed collection. If you wish to access more
data structures or advanced features, you can transition to directly installing the
complete data-structure-typed package
How
install
npm
npm i binary-tree-typed --save
yarn
yarn add binary-tree-typed
snippet
determine loan approval using a decision tree
const loanDecisionTree = new BinaryTree<string>(
['stableIncome', 'goodCredit', 'Rejected', 'Approved', 'Rejected'],
{ isDuplicate: true }
);
function determineLoanApproval(
node?: BinaryTreeNode<string> | null,
conditions?: { [key: string]: boolean }
): string {
if (!node) throw new Error('Invalid node');
if (!node.left && !node.right) return node.key;
return conditions?.[node.key]
? determineLoanApproval(node.left, conditions)
: determineLoanApproval(node.right, conditions);
}
console.log(determineLoanApproval(loanDecisionTree.root, { stableIncome: true, goodCredit: true }));
console.log(determineLoanApproval(loanDecisionTree.root, { stableIncome: true, goodCredit: false }));
console.log(determineLoanApproval(loanDecisionTree.root, { stableIncome: false, goodCredit: true }));
console.log(determineLoanApproval(loanDecisionTree.root, { stableIncome: false, goodCredit: false }));
evaluate the arithmetic expression represented by the binary tree
const expressionTree = new BinaryTree<number | string>(['+', 3, '*', null, null, 5, '-', null, null, 2, 8]);
function evaluate(node?: BinaryTreeNode<number | string> | null): number {
if (!node) return 0;
if (typeof node.key === 'number') return node.key;
const leftValue = evaluate(node.left);
const rightValue = evaluate(node.right);
switch (node.key) {
case '+':
return leftValue + rightValue;
case '-':
return leftValue - rightValue;
case '*':
return leftValue * rightValue;
case '/':
return rightValue !== 0 ? leftValue / rightValue : 0;
default:
throw new Error(`Unsupported operator: ${node.key}`);
}
}
console.log(evaluate(expressionTree.root));
API docs & Examples
API Docs
Live Examples
Examples Repository
Data Structures
Data Structure |
Unit Test |
Performance Test |
API Docs |
Binary Tree |
 |
 |
Binary Tree |
Standard library data structure comparison
Data Structure Typed |
C++ STL |
java.util |
Python collections |
BinaryTree<K, V> |
- |
- |
- |
Benchmark
binary-tree
test name | time taken (ms) | executions per sec | sample deviation |
---|
1,000 add randomly | 12.35 | 80.99 | 7.17e-5 |
1,000 add & delete randomly | 15.98 | 62.58 | 7.98e-4 |
1,000 addMany | 10.96 | 91.27 | 0.00 |
1,000 get | 18.61 | 53.73 | 0.00 |
1,000 dfs | 164.20 | 6.09 | 0.04 |
1,000 bfs | 58.84 | 17.00 | 0.01 |
1,000 morris | 256.66 | 3.90 | 7.70e-4 |
Built-in classic algorithms
Algorithm |
Function Description |
Iteration Type |
Binary Tree DFS |
Traverse a binary tree in a depth-first manner, starting from the root node, first visiting the left subtree,
and then the right subtree, using recursion.
|
Recursion + Iteration |
Binary Tree BFS |
Traverse a binary tree in a breadth-first manner, starting from the root node, visiting nodes level by level
from left to right.
|
Iteration |
Binary Tree Morris |
Morris traversal is an in-order traversal algorithm for binary trees with O(1) space complexity. It allows tree
traversal without additional stack or recursion.
|
Iteration |
Software Engineering Design Standards
Principle |
Description |
Practicality |
Follows ES6 and ESNext standards, offering unified and considerate optional parameters, and simplifies method names. |
Extensibility |
Adheres to OOP (Object-Oriented Programming) principles, allowing inheritance for all data structures. |
Modularization |
Includes data structure modularization and independent NPM packages. |
Efficiency |
All methods provide time and space complexity, comparable to native JS performance. |
Maintainability |
Follows open-source community development standards, complete documentation, continuous integration, and adheres to TDD (Test-Driven Development) patterns. |
Testability |
Automated and customized unit testing, performance testing, and integration testing. |
Portability |
Plans for porting to Java, Python, and C++, currently achieved to 80%. |
Reusability |
Fully decoupled, minimized side effects, and adheres to OOP. |
Security |
Carefully designed security for member variables and methods. Read-write separation. Data structure software does not need to consider other security aspects. |
Scalability |
Data structure software does not involve load issues. |