Top K Frequent Elements Using Heap in DSA Go - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to declare a map to count frequencies of elements.

DSA Go

freqMap := make(map[int][1])

Drag options to blanks, or click blank then click option'

Aint

Bstring

Cfloat64

Dbool

Attempts:

3 left

💡 Hint

Common Mistakes

Using string or bool as map value type instead of int.

Forgetting to initialize the map.

The frequency map stores counts as integers, so the value type must be int.

2fill in blank

medium

Complete the code to push an element with its frequency into the heap.

DSA Go

heap.Push(&h, [1]{num: num, freq: freqMap[num]})

Drag options to blanks, or click blank then click option'

Aelement

Bpair

Cnode

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect struct names like element or node.

Not passing the address of the heap.

The heap stores pair structs containing the number and its frequency.

3fill in blank

hard

Fix the error in the heap comparison function to create a max-heap by frequency.

DSA Go

func (h pairHeap) Less(i, j int) bool {
    return h[i].freq [1] h[j].freq
}

Drag options to blanks, or click blank then click option'

A<

B!=

C==

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' creates a min-heap, which is incorrect here.

Using '==' or '!=' does not order elements properly.

To create a max-heap by frequency, the element with higher frequency should be 'less' so it comes first, so use >.

4fill in blank

hard

Fill both blanks to pop the top element from the heap and append its number to the result slice.

DSA Go

top := heap.[1](&h).([2])
result = append(result, top.num)

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APop

BPush

Cpair

DRemove

Attempts:

3 left

💡 Hint

Common Mistakes

Using Push instead of Pop.

Not type asserting the popped value.

Use heap.Pop to remove the top element and type assert it to pair.

5fill in blank

hard

Fill all three blanks to build the frequency map, push all pairs into the heap, and initialize the heap.

DSA Go

freqMap := make(map[int]int)
for _, num := range nums {
    freqMap[num] = freqMap[num] [1] 1
}
h := pairHeap{}
for num := range freqMap {
    heap.[2](&h, pair{num: num, freq: freqMap[num]})
}
heap.[3](&h)

Drag options to blanks, or click blank then click option'

A+=

BPush

CInit

D-=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '-=' instead of '+=' for counting.

Forgetting to call heap.Init before popping.

Using wrong heap functions.

Increment frequency with +=, push pairs with heap.Push, and initialize heap with heap.Init.

Practice

1. What is the main purpose of using a heap in the Top K Frequent Elements Using Heap algorithm?

easy

A. To efficiently find and keep track of the top K frequent elements

B. To sort all elements in ascending order

C. To store elements without any order

D. To remove duplicates from the list

Solution

1. Step 1: Understand the role of heap in frequency tracking

   A heap helps keep track of elements based on their frequency efficiently, especially when we want only the top K frequent elements.

2. Step 2: Compare with other options

   Sorting all elements or removing duplicates is not the main goal here; the heap is used for efficient frequency comparison and retrieval.

3. Final Answer:

   To efficiently find and keep track of the top K frequent elements -> Option A

4. Quick Check:

   Heap usage = Efficient top K tracking [OK]

Hint: Heap keeps top K frequent elements efficiently [OK]

Common Mistakes:

• Thinking heap sorts all elements fully
• Confusing heap with simple list storage
• Assuming heap removes duplicates

2. Which of the following Go code snippets correctly initializes a min-heap to store pairs of (frequency, element) for the top K frequent elements problem?

easy

A. heap := new([]int); heap.Init(heap)

B. heap := make([]int, 0); heap.Push(heap, pair)

C. heap := &IntHeap{}; heap.Init(heap)

D. var h IntHeap; heap.Init(&h)

Solution

1. Step 1: Understand Go heap initialization

   In Go, a heap is usually implemented by defining a type that implements heap.Interface, then initializing it with heap.Init(&heap).

2. Step 2: Analyze options

   var h IntHeap; heap.Init(&h) correctly declares a variable of type IntHeap and initializes it by passing its address to heap.Init. Other options misuse pointers or heap methods.

3. Final Answer:

   var h IntHeap; heap.Init(&h) -> Option D

4. Quick Check:

   Correct heap init = var h IntHeap; heap.Init(&h) [OK]

Hint: Use heap.Init with pointer to heap variable [OK]

Common Mistakes:

• Calling heap.Init on non-pointer
• Using make for heap without type
• Incorrect pointer usage with new([]int)

3. What will be the output of the following Go code snippet that finds top 2 frequent elements?

nums := []int{1,1,1,2,2,3}
result := topKFrequent(nums, 2)
fmt.Println(result)

Assuming topKFrequent is correctly implemented using a min-heap.

medium

A. [1, 3]

B. [2, 3]

C. [1, 2]

D. [3, 2]

Solution

1. Step 1: Count frequencies of elements

   1 appears 3 times, 2 appears 2 times, 3 appears 1 time.

2. Step 2: Extract top 2 frequent elements

   The top 2 frequent elements are 1 (3 times) and 2 (2 times). So the result is [1, 2].

3. Final Answer:

   [1, 2] -> Option C

4. Quick Check:

   Top 2 frequent = [1, 2] [OK]

Hint: Count frequencies, pick top K by frequency [OK]

Common Mistakes:

• Confusing element order in output
• Including less frequent elements
• Ignoring frequency counts

4. The following Go code snippet is intended to find the top 3 frequent elements but has a bug. What is the bug?

func topKFrequent(nums []int, k int) []int {
    freqMap := make(map[int]int)
    for _, num := range nums {
        freqMap[num]++
    }
    
    h := &IntHeap{}
    heap.Init(h)
    
    for num, freq := range freqMap {
        heap.Push(h, Pair{freq, num})
        if h.Len() > k {
            heap.Pop(h)
        }
    }
    
    res := make([]int, 0, k)
    for h.Len() > 0 {
        p := heap.Pop(h).(Pair)
        res = append(res, p.num)
    }
    return res
}

medium

A. Result list is in ascending frequency order, needs reversing

B. Heap is not initialized before use

C. Frequency map is not counting correctly

D. Heap size check should be < k, not > k

Solution

1. Step 1: Understand heap usage and result order

   The min-heap keeps smallest frequency on top, so popping elements gives ascending frequency order.

2. Step 2: Identify output order issue

   Appending popped elements directly results in ascending order, but top K frequent elements should be in descending order of frequency.

3. Final Answer:

   Result list is in ascending frequency order, needs reversing -> Option A

4. Quick Check:

   Output order issue = Result list is in ascending frequency order, needs reversing [OK]

Hint: Pop from min-heap gives ascending order; reverse result [OK]

Common Mistakes:

• Ignoring output order of popped elements
• Not initializing heap properly
• Incorrect frequency counting

5. Given a large list of integers where many elements have the same frequency, how should the Top K Frequent Elements Using Heap algorithm be adapted to return the top K elements sorted by frequency and then by element value ascending when frequencies tie?

hard

A. Use a map only without heap to track frequencies

B. Use a heap with custom comparison: first by frequency, then by element value ascending

C. Use a max-heap with frequency only, ignoring element value

D. Sort the entire list first, then pick first K elements

Solution

1. Step 1: Understand tie-breaking requirement

   When frequencies tie, elements should be ordered by their value ascending to maintain consistent order.

2. Step 2: Adapt heap comparison logic

   Implement a custom comparison in the heap that compares frequency first, and if equal, compares element values ascending.

3. Final Answer:

   Use a heap with custom comparison: first by frequency, then by element value ascending -> Option B

4. Quick Check:

   Custom heap comparison handles ties correctly [OK]

Hint: Add tie-breaker in heap comparison for stable ordering [OK]

Common Mistakes:

• Ignoring tie-breaker in heap comparison
• Sorting entire list unnecessarily
• Using max-heap without tie-break logic