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fix(instructions): fix spacing and correct example code for max heap and binary search tree problems

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Peter Weinberg
2017-10-17 22:49:35 -04:00
parent bdeaf99092
commit a44eaf0796
1 changed files with 18 additions and 24 deletions
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challenges/08-coding-interview-questions-and-take-home-assignments/coding-interview-data-structure-questions.json
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@ -989,7 +989,7 @@
"Let's create a <code>linked list</code> class. Every linked list should start out with a few basic properties: a <code>head</code> (the first item in your list) and a <code>length</code> (number of items in your list). Sometimes you'll see implementations of linked lists that incorporate a <code>tail</code> for the last element of the list, but for now we'll just stick with these two. Whenever we add an element to the linked list, our <code>length</code> property should be incremented by one.", "Let's create a <code>linked list</code> class. Every linked list should start out with a few basic properties: a <code>head</code> (the first item in your list) and a <code>length</code> (number of items in your list). Sometimes you'll see implementations of linked lists that incorporate a <code>tail</code> for the last element of the list, but for now we'll just stick with these two. Whenever we add an element to the linked list, our <code>length</code> property should be incremented by one.",
"We'll want to have a way to add items to our linked list, so the first method we'll want to create is the <code>add</code> method.", "We'll want to have a way to add items to our linked list, so the first method we'll want to create is the <code>add</code> method.",
"If our list is empty, adding an element to our linked list is straightforward enough: we just wrap that element in a <code>Node</code> class, and we assign that node to the <code>head</code> of our linked list." , "If our list is empty, adding an element to our linked list is straightforward enough: we just wrap that element in a <code>Node</code> class, and we assign that node to the <code>head</code> of our linked list." ,
"But what if our list already has one or more members? How do we add an element to the list? Recall that each node in a linked list has a <code>next</code> property. To add a node to the list, find the last node in the list, and point that last node's <code>next</code> property at our new node. (Hint: you know you've reached the end of a linked list when a node's <code>next</code> property is <code>null</node>.)", "But what if our list already has one or more members? How do we add an element to the list? Recall that each node in a linked list has a <code>next</code> property. To add a node to the list, find the last node in the list, and point that last node's <code>next</code> property at our new node. (Hint: you know you've reached the end of a linked list when a node's <code>next</code> property is <code>null</code>.)",
"<hr>", "<hr>",
"Write an add method that assigns the first node you push to the linked list to the <code>head</code>; after that, whenever adding a node, every node should be referenced by the previous node's <code>next</code> property.", "Write an add method that assigns the first node you push to the linked list to the <code>head</code>; after that, whenever adding a node, every node should be referenced by the previous node's <code>next</code> property.",
"Note", "Note",
@ -1835,8 +1835,7 @@
"description": [ "description": [
"Here we will introduce another tree traversal method: breadth-first search. In contrast to the depth-first search methods from the last challenge, breadth-first search explores all the nodes in a given level within a tree before continuing on to the next level. Typically, queues are utilized as helper data structures in the design of breadth-first search algorithms.", "Here we will introduce another tree traversal method: breadth-first search. In contrast to the depth-first search methods from the last challenge, breadth-first search explores all the nodes in a given level within a tree before continuing on to the next level. Typically, queues are utilized as helper data structures in the design of breadth-first search algorithms.",
"In this method, we start by adding the root node to a queue. Then we begin a loop where we dequeue the first item in the queue, add it to a new array, and then inspect both its child subtrees. If its children are not null, they are each enqueued. This process continues until the queue is empty.", "In this method, we start by adding the root node to a queue. Then we begin a loop where we dequeue the first item in the queue, add it to a new array, and then inspect both its child subtrees. If its children are not null, they are each enqueued. This process continues until the queue is empty.",
"Instructions: Let's create a breadth-first search method in our tree called levelOrder. This method should return an array containing the values of all the tree nodes, explored in a breadth-first manner. Be sure to return the values in the array, not the nodes themselves. A level should be", "Instructions: Let's create a breadth-first search method in our tree called levelOrder. This method should return an array containing the values of all the tree nodes, explored in a breadth-first manner. Be sure to return the values in the array, not the nodes themselves. A level should be traversed from left to right. Next, let's write a similar method called reverseLevelOrder which performs the same search but in the reverse direction (right to left) at each level."
"traversed from left to right. Next, let's write a similar method called reverseLevelOrder which performs the same search but in the reverse direction (right to left) at each level."
], ],
"challengeSeed": [ "challengeSeed": [
"var displayTree = (tree) => console.log(JSON.stringify(tree, null, 2));", "var displayTree = (tree) => console.log(JSON.stringify(tree, null, 2));",
@ -1907,8 +1906,7 @@
"One Child: The target to delete only has one child.", "One Child: The target to delete only has one child.",
"Two Children: The target to delete has two child nodes.", "Two Children: The target to delete has two child nodes.",
"Removing a leaf node is easy, we simply remove it. Deleting a node with one child is also relatively easy, we simply remove it and link its parent to child of the node we deleted. Removing a node with two children is more difficult, however, because this creates two child nodes that need to be reconnected to the parent tree. We'll see how to deal with this case in the third challenge. Additionally, you need to be mindful of some edge cases when handling deletion. What if the tree is empty? What if the node to delete is the root node? What if there are only two elements in the tree? For now, let's handle the first case where we delete a leaf node.", "Removing a leaf node is easy, we simply remove it. Deleting a node with one child is also relatively easy, we simply remove it and link its parent to child of the node we deleted. Removing a node with two children is more difficult, however, because this creates two child nodes that need to be reconnected to the parent tree. We'll see how to deal with this case in the third challenge. Additionally, you need to be mindful of some edge cases when handling deletion. What if the tree is empty? What if the node to delete is the root node? What if there are only two elements in the tree? For now, let's handle the first case where we delete a leaf node.",
"Instructions: Create a method on our binary tree called remove. We'll build the logic for our deletion operation in here. First, you'll want to create a function within remove that finds the node we are trying to delete in the current tree. If the node is not present in the tree, remove should return null. Now, if the target node is a leaf node with no children, then the parent reference to it should be set to null. This effectively deletes the node from the tree. To do this, you will have to keep track of", "Instructions: Create a method on our binary tree called remove. We'll build the logic for our deletion operation in here. First, you'll want to create a function within remove that finds the node we are trying to delete in the current tree. If the node is not present in the tree, remove should return null. Now, if the target node is a leaf node with no children, then the parent reference to it should be set to null. This effectively deletes the node from the tree. To do this, you will have to keep track of the parent of the node we are trying to delete as well. It will also be useful to create a way to track the number of children the target node has, as this will determine which case our deletion falls under.",
"the parent of the node we are trying to delete as well. It will also be useful to create a way to track the number of children the target node has, as this will determine which case our deletion falls under.",
"We will handle the second and third cases in the next challenges. Good luck!" "We will handle the second and third cases in the next challenges. Good luck!"
], ],
"challengeSeed": [ "challengeSeed": [
@ -2018,10 +2016,8 @@
"id": "587d8258367417b2b2512c81", "id": "587d8258367417b2b2512c81",
"title": "Delete a Node with One Child in a Binary Search Tree", "title": "Delete a Node with One Child in a Binary Search Tree",
"description": [ "description": [
"Now that we can delete leaf nodes let's move on to the second case: deleting a node with one child. For this case, say we have a tree with the following nodes 1 — 2 — 3 where 1 is the root. To delete 2, we simply need to make the right reference in 1 point to 3. More generally,", "Now that we can delete leaf nodes let's move on to the second case: deleting a node with one child. For this case, say we have a tree with the following nodes 1 — 2 — 3 where 1 is the root. To delete 2, we simply need to make the right reference in 1 point to 3. More generally to delete a node with only one child, we make that node's parent reference the next node in the tree.",
"to delete a node with only one child, we make that node's parent reference the next node in the tree.", "Instructions: We've provided some code in our remove method that accomplishes the tasks from the last challenge. We find the target to delete and its parent and define the number of children the target node has. Let's add the next case here for target nodes with only one child. Here, we'll have to determine if the single child is a left or right branch in the tree and then set the correct reference in the parent to point to this node. In addition, let's account for the case where the target is the root node (this means the parent node will be null). Feel free to replace all the starter code with your own as long as it passes the tests."
"Instructions: We've provided some code in our remove method that accomplishes the tasks from the last challenge. We find the target to delete and its parent and define the number of children",
"the target node has. Let's add the next case here for target nodes with only one child. Here, we'll have to determine if the single child is a left or right branch in the tree and then set the correct reference in the parent to point to this node. In addition, let's account for the case where the target is the root node (this means the parent node will be null). Feel free to replace all the starter code with your own as long as it passes the tests."
], ],
"challengeSeed": [ "challengeSeed": [
"var displayTree = (tree) => console.log(JSON.stringify(tree, null, 2));", "var displayTree = (tree) => console.log(JSON.stringify(tree, null, 2));",
@ -2424,14 +2420,11 @@
"id": "587d8259367417b2b2512c84", "id": "587d8259367417b2b2512c84",
"title": "Create a Trie Search Tree", "title": "Create a Trie Search Tree",
"description": [ "description": [
"Here we will move on from binary search trees and take a look at another type of tree structure called a trie. A trie is an ordered search tree commonly used to hold strings, or more generically associative arrays or dynamic datasets in which the keys are strings. They are very good at storing sets of data when many keys will have overlapping prefixes, for example, all", "Here we will move on from binary search trees and take a look at another type of tree structure called a trie. A trie is an ordered search tree commonly used to hold strings, or more generically associative arrays or dynamic datasets in which the keys are strings. They are very good at storing sets of data when many keys will have overlapping prefixes, for example, all the words in a dictionary.",
"the words in a dictionary.",
"Unlike a binary tree, nodes are not associated with actual values. Instead, the path to a node represents a specific key. For instance, if we wanted to store the string code in a trie, we would have four nodes, one for each letter: c — o — d — e. Following that path through all these nodes will then create code as a string — that path is the key we stored. Then, if we wanted to add the string coding, it would share the first three nodes of code before branching away after the d. In this way, large datasets can be stored very compactly. In addition, search can be very quick because it is effectively limited to the length of the string you are storing. Furthermore, unlike binary trees a node can store any number of child nodes.", "Unlike a binary tree, nodes are not associated with actual values. Instead, the path to a node represents a specific key. For instance, if we wanted to store the string code in a trie, we would have four nodes, one for each letter: c — o — d — e. Following that path through all these nodes will then create code as a string — that path is the key we stored. Then, if we wanted to add the string coding, it would share the first three nodes of code before branching away after the d. In this way, large datasets can be stored very compactly. In addition, search can be very quick because it is effectively limited to the length of the string you are storing. Furthermore, unlike binary trees a node can store any number of child nodes.",
"As you might have guessed from the above example, some metadata is commonly stored at nodes that hold the end of a key so that on later traversals that key can still be retrieved. For instance, if we added codes in our example above we would need some way to know that the e in code represents the end of a key that was previously entered. Otherwise, this information would effectively be lost when we add codes.", "As you might have guessed from the above example, some metadata is commonly stored at nodes that hold the end of a key so that on later traversals that key can still be retrieved. For instance, if we added codes in our example above we would need some way to know that the e in code represents the end of a key that was previously entered. Otherwise, this information would effectively be lost when we add codes.",
"Instructions: Let's create a trie to store words. It will accept words through an add method and store these in a trie data structure. It will also allow us to query if a given string is a word with an isWord method, and retrieve all the words entered into the trie with a print method. isWord should return a", "Instructions: Let's create a trie to store words. It will accept words through an add method and store these in a trie data structure. It will also allow us to query if a given string is a word with an isWord method, and retrieve all the words entered into the trie with a print method. isWord should return a boolean value and print should return an array of all these words as string values.",
"boolean value and print should return an array of all these words as string values.", "In order for us to verify that this data structure is implemented correctly, we've provided a Node structure for each node in the tree. Each node will be an object with a keys property which is a JavaScript Map object. This will hold the individual letters that are valid keys of each node. We've also created an end property on the nodes that can be set to true if the node represents the termination of a word."
"In order for us to verify that this data structure is implemented correctly, we've provided a Node structure for each node in the tree. Each node will be an object with a keys property which is a JavaScript Map object. This will hold the individual letters that are valid keys of each node. We've also",
"created an end property on the nodes that can be set to true if the node represents the termination of a word."
], ],
"challengeSeed": [ "challengeSeed": [
"var displayTree = (tree) => console.log(JSON.stringify(tree, null, 2));", "var displayTree = (tree) => console.log(JSON.stringify(tree, null, 2));",
@ -2469,10 +2462,11 @@
"description": [ "description": [
"Now we will move on to another tree data structure, the binary heap. A binary heap is a partially ordered binary tree which satisfies the heap property. The heap property specifies a relationship between parent and child nodes. You may have a max heap, in which all parent nodes are greater than or equal to their child nodes, or a min heap, in which the reverse is true. Binary heaps are also complete binary trees. This means that all levels of the tree are fully filled and if the last level is partially filled it is filled from left to right.", "Now we will move on to another tree data structure, the binary heap. A binary heap is a partially ordered binary tree which satisfies the heap property. The heap property specifies a relationship between parent and child nodes. You may have a max heap, in which all parent nodes are greater than or equal to their child nodes, or a min heap, in which the reverse is true. Binary heaps are also complete binary trees. This means that all levels of the tree are fully filled and if the last level is partially filled it is filled from left to right.",
"While binary heaps may be implemented as tree structures with nodes that contain left and right references, the partial ordering according to the heap property allows us to represent the heap with an array. The parent-children relationship is what we're interested in and with simple arithmetic we can compute the children of any parent and the parent of any child node.", "While binary heaps may be implemented as tree structures with nodes that contain left and right references, the partial ordering according to the heap property allows us to represent the heap with an array. The parent-children relationship is what we're interested in and with simple arithmetic we can compute the children of any parent and the parent of any child node.",
"For instance, consider this array representation of a binary max heap:", "For instance, consider this array representation of a binary min heap:",
"<code>[ 76, 63, 48, 42, 37, 6, 42, 22 ]</code>", "<code>[ 6, 22, 30, 37, 63, 48, 42, 76 ]</code>",
"The root node is the first element, 6. Its children are 22 and 30. If we look at the relationship between the array indices of these values, for index i the children are 2 * i + 1 and 2 * i + 2. Similarly, the element at index 0 is the parent of these two children at indices 1 and 2. More generally, we can find the parent of a node at any index with the following: i - 1 / 2. These patterns will hold true as the binary tree grows to any size. Finally, we can make a slight adjustment to make this arithmetic even easier by skipping the first element in the array. Doing this creates the following relationship for any element at a given index i:", "The root node is the first element, 6. Its children are 22 and 30. If we look at the relationship between the array indices of these values, for index i the children are 2 * i + 1 and 2 * i + 2. Similarly, the element at index 0 is the parent of these two children at indices 1 and 2. More generally, we can find the parent of a node at any index with the following: (i - 1) / 2. These patterns will hold true as the binary tree grows to any size. Finally, we can make a slight adjustment to make this arithmetic even easier by skipping the first element in the array. Doing this creates the following relationship for any element at a given index i:",
"Example Array representation: <code>[ null, 76, 63, 48, 42, 37, 6, 42, 22 ]</code>", "Example Array representation:",
"<code>[ null, 6, 22, 30, 37, 63, 48, 42, 76 ]</code>",
"An element's left child: i * 2", "An element's left child: i * 2",
"An element's right child: i * 2 + 1", "An element's right child: i * 2 + 1",
"An element's parent: i / 2", "An element's parent: i / 2",
@ -2518,11 +2512,11 @@
"};" "};"
], ],
"tests": [ "tests": [
"assert((function() { var test = false; if (typeof MaxHeap !== 'undefined') { test = new MaxHeap() }; return (typeof test == 'object')})(), 'The MaxHeap data structure exists.');", "assert((function() { var test = false; if (typeof MaxHeap !== 'undefined') { test = new MaxHeap() }; return (typeof test == 'object')})(), 'message: The MaxHeap data structure exists.');",
"assert((function() { var test = false; if (typeof MaxHeap !== 'undefined') { test = new MaxHeap() } else { return false; }; return (typeof test.print == 'function')})(), 'MaxHeap has a method called print.');", "assert((function() { var test = false; if (typeof MaxHeap !== 'undefined') { test = new MaxHeap() } else { return false; }; return (typeof test.print == 'function')})(), 'message: MaxHeap has a method called print.');",
"assert((function() { var test = false; if (typeof MaxHeap !== 'undefined') { test = new MaxHeap() } else { return false; }; return (typeof test.insert == 'function')})(), 'MaxHeap has a method called insert.');", "assert((function() { var test = false; if (typeof MaxHeap !== 'undefined') { test = new MaxHeap() } else { return false; }; return (typeof test.insert == 'function')})(), 'message: MaxHeap has a method called insert.');",
"assert((function() { var test = false; if (typeof MaxHeap !== 'undefined') { test = new MaxHeap() } else { return false; }; return (typeof test.remove == 'function')})(), 'MaxHeap has a method called remove.');", "assert((function() { var test = false; if (typeof MaxHeap !== 'undefined') { test = new MaxHeap() } else { return false; }; return (typeof test.remove == 'function')})(), 'message: MaxHeap has a method called remove.');",
"assert((function() { var test = false; if (typeof MaxHeap !== 'undefined') { test = new MaxHeap() } else { return false; }; test.insert(30); test.insert(300); test.insert(500); test.insert(10); let result = []; result.push(test.remove()); result.push(test.remove()); result.push(test.remove()); result.push(test.remove()); return (result.join('') == '5003003010') })(), 'The remove method removes the greatest element from the max heap while maintaining the max heap property.');" "assert((function() { var test = false; if (typeof MaxHeap !== 'undefined') { test = new MaxHeap() } else { return false; }; test.insert(30); test.insert(300); test.insert(500); test.insert(10); let result = []; result.push(test.remove()); result.push(test.remove()); result.push(test.remove()); result.push(test.remove()); return (result.join('') == '5003003010') })(), 'message: The remove method removes the greatest element from the max heap while maintaining the max heap property.');"
], ],
"type": "waypoint", "type": "waypoint",
"releasedOn": "Feb 17, 2017", "releasedOn": "Feb 17, 2017",