!NPM
!GitHub top language
!npm
!eslint
!npm bundle size
!npm bundle size
!npm

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

$3

``bash npm i binary-tree-typed --save`

`$3`

`bash yarn add binary-tree-typed`

`$3`

[//]: # (No deletion!!! Start of Example Replace Section)

`$3`

typescript
 // Create a BinaryTree with entries
    const entries: [number, string][] = [
      [6, 'six'],
      [1, 'one'],
      [2, 'two'],
      [7, 'seven'],
      [5, 'five'],
      [3, 'three'],
      [4, 'four'],
      [9, 'nine'],
      [8, 'eight']
    ];
    const tree = new BinaryTree(entries);
    // Verify size
    console.log(tree.size); // 9;

// Add new element tree.set(10, 'ten'); console.log(tree.size); // 10;`

`$3`

typescript
 const tree = new BinaryTree(
      [
        [5, 'five'],
        [3, 'three'],
        [7, 'seven'],
        [1, 'one'],
        [4, 'four'],
        [6, 'six'],
        [8, 'eight']
      ],
      { isMapMode: false }
    );
    // Check if key exists
    console.log(tree.has(5)); // true;
    console.log(tree.has(10)); // false;
    // Get value by key
    console.log(tree.get(3)); // 'three';
    console.log(tree.get(7)); // 'seven';
    console.log(tree.get(100)); // undefined;

// Get node structure const node = tree.getNode(5); console.log(node?.key); // 5; console.log(node?.value); // 'five';`

`$3`

typescript
 const tree = new BinaryTree([
      [1, 'one'],
      [2, 'two'],
      [3, 'three'],
      [4, 'four'],
      [5, 'five'],
      [6, 'six'],
      [7, 'seven']
    ]);
    // Binary tree maintains level-order insertion
    // Complete binary tree structure
    console.log(tree.size); // 7;
    // Verify all keys are present
    console.log(tree.has(1)); // true;
    console.log(tree.has(4)); // true;
    console.log(tree.has(7)); // true;

// Iterate through tree const keys: number[] = []; for (const [key] of tree) { keys.push(key); } console.log(keys.length); // 7;`

`$3`

typescript
 // Decision tree structure
    const loanDecisionTree = new BinaryTree(
      ['stableIncome', 'goodCredit', 'Rejected', 'Approved', 'Rejected'],
      { isDuplicate: true }
    );
    function determineLoanApproval(
      node?: BinaryTreeNode | null,
      conditions?: { [key: string]: boolean }
    ): string {
      if (!node) throw new Error('Invalid node');
      // If it's a leaf node, return the decision result
      if (!node.left && !node.right) return node.key;
      // Check if a valid condition exists for the current node's key
      return conditions?.[node.key]
        ? determineLoanApproval(node.left, conditions)
        : determineLoanApproval(node.right, conditions);
    }
    // Test case 1: Stable income and good credit score
    console.log(determineLoanApproval(loanDecisionTree.root, { stableIncome: true, goodCredit: true })); // 'Approved';
    // Test case 2: Stable income but poor credit score
    console.log(determineLoanApproval(loanDecisionTree.root, { stableIncome: true, goodCredit: false })); // 'Rejected';
    // Test case 3: No stable income
    console.log(determineLoanApproval(loanDecisionTree.root, { stableIncome: false, goodCredit: true })); // 'Rejected';

// Test case 4: No stable income and poor credit score console.log(determineLoanApproval(loanDecisionTree.root, { stableIncome: false, goodCredit: false })); // 'Rejected';`

`$3`

typescript
 const expressionTree = new BinaryTree(['+', 3, '*', null, null, 5, '-', null, null, 2, 8]);
    function evaluate(node?: BinaryTreeNode | null): number {
      if (!node) return 0;
      if (typeof node.key === 'number') return node.key;
      const leftValue = evaluate(node.left); // Evaluate the left subtree
      const rightValue = evaluate(node.right); // Evaluate the right subtree

// Perform the operation based on the current node's operator switch (node.key) { case '+': return leftValue + rightValue; case '-': return leftValue - rightValue; case '*': return leftValue * rightValue; case '/': return rightValue !== 0 ? leftValue / rightValue : 0; // Handle division by zero default: throw new Error(Unsupported operator: ${node.key}); } }

console.log(evaluate(expressionTree.root)); // -27;``

[//]: # (No deletion!!! End of Example Replace Section)

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

[//]: # (No deletion!!! Start of Replace Section)

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

[//]: # (No deletion!!! End of Replace Section)

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.

!NPM
!GitHub top language
!npm
!eslint
!npm bundle size
!npm bundle size
!npm

What

Brief

How

install

$3

``bash npm i binary-tree-typed --save`

`$3`

`bash yarn add binary-tree-typed`

`$3`

[//]: # (No deletion!!! Start of Example Replace Section)

`$3`

typescript
 // Create a BinaryTree with entries
    const entries: [number, string][] = [
      [6, 'six'],
      [1, 'one'],
      [2, 'two'],
      [7, 'seven'],
      [5, 'five'],
      [3, 'three'],
      [4, 'four'],
      [9, 'nine'],
      [8, 'eight']
    ];
    const tree = new BinaryTree(entries);
    // Verify size
    console.log(tree.size); // 9;

// Add new element tree.set(10, 'ten'); console.log(tree.size); // 10;`

`$3`

typescript
 const tree = new BinaryTree(
      [
        [5, 'five'],
        [3, 'three'],
        [7, 'seven'],
        [1, 'one'],
        [4, 'four'],
        [6, 'six'],
        [8, 'eight']
      ],
      { isMapMode: false }
    );
    // Check if key exists
    console.log(tree.has(5)); // true;
    console.log(tree.has(10)); // false;
    // Get value by key
    console.log(tree.get(3)); // 'three';
    console.log(tree.get(7)); // 'seven';
    console.log(tree.get(100)); // undefined;

// Get node structure const node = tree.getNode(5); console.log(node?.key); // 5; console.log(node?.value); // 'five';`

`$3`

typescript
 const tree = new BinaryTree([
      [1, 'one'],
      [2, 'two'],
      [3, 'three'],
      [4, 'four'],
      [5, 'five'],
      [6, 'six'],
      [7, 'seven']
    ]);
    // Binary tree maintains level-order insertion
    // Complete binary tree structure
    console.log(tree.size); // 7;
    // Verify all keys are present
    console.log(tree.has(1)); // true;
    console.log(tree.has(4)); // true;
    console.log(tree.has(7)); // true;

// Iterate through tree const keys: number[] = []; for (const [key] of tree) { keys.push(key); } console.log(keys.length); // 7;`

`$3`

typescript
 // Decision tree structure
    const loanDecisionTree = new BinaryTree(
      ['stableIncome', 'goodCredit', 'Rejected', 'Approved', 'Rejected'],
      { isDuplicate: true }
    );
    function determineLoanApproval(
      node?: BinaryTreeNode | null,
      conditions?: { [key: string]: boolean }
    ): string {
      if (!node) throw new Error('Invalid node');
      // If it's a leaf node, return the decision result
      if (!node.left && !node.right) return node.key;
      // Check if a valid condition exists for the current node's key
      return conditions?.[node.key]
        ? determineLoanApproval(node.left, conditions)
        : determineLoanApproval(node.right, conditions);
    }
    // Test case 1: Stable income and good credit score
    console.log(determineLoanApproval(loanDecisionTree.root, { stableIncome: true, goodCredit: true })); // 'Approved';
    // Test case 2: Stable income but poor credit score
    console.log(determineLoanApproval(loanDecisionTree.root, { stableIncome: true, goodCredit: false })); // 'Rejected';
    // Test case 3: No stable income
    console.log(determineLoanApproval(loanDecisionTree.root, { stableIncome: false, goodCredit: true })); // 'Rejected';

// Test case 4: No stable income and poor credit score console.log(determineLoanApproval(loanDecisionTree.root, { stableIncome: false, goodCredit: false })); // 'Rejected';`

`$3`

typescript
 const expressionTree = new BinaryTree(['+', 3, '*', null, null, 5, '-', null, null, 2, 8]);
    function evaluate(node?: BinaryTreeNode | null): number {
      if (!node) return 0;
      if (typeof node.key === 'number') return node.key;
      const leftValue = evaluate(node.left); // Evaluate the left subtree
      const rightValue = evaluate(node.right); // Evaluate the right subtree

console.log(evaluate(expressionTree.root)); // -27;``

[//]: # (No deletion!!! End of Example Replace Section)

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

[//]: # (No deletion!!! Start of Replace Section)

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

[//]: # (No deletion!!! End of Replace Section)

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.