Unique Number of Occurrences
Python solution:


The code first initializes a dictionary count_map
to store the number of occurrences of each value in the array. The code then iterates over the array, and for each number, it increments the count of the number in the dictionary if the number is already in the dictionary, or it initializes the count to 1 if the number is not in the dictionary.
After the loop, the code iterates over the dictionary and checks if any of the counts is greater than 1. If any of the counts is greater than 1, then the code returns False. Otherwise, the code returns True.
To run the code, you can save it as a Python file and then run it from the command line. For example, if you save the code as unique_occurrences.py
, you can run it by typing the following command into the command line:
python unique_occurrences.py
This will print True for the input array [1,2,2,1,1,3]
.
The code is simple and efficient. The solution is easy to understand and implement, and it is also relatively fast. The solution is also not likely to cause stack overflow errors, as the dictionary is only of size 1000.
One of the basic building block of the solution is frequency counter. Is that correct?
Yes, that is correct. The basic building block of the solution is a frequency counter. A frequency counter is a data structure that stores the number of occurrences of each value in a set. In this case, the set is the set of all the numbers in the array.
The frequency counter is used to count the number of occurrences of each number in the array. The code then checks if any of the counts is greater than 1. If any of the counts is greater than 1, then the code returns False. Otherwise, the code returns True.
The frequency counter is a simple and efficient data structure. It is easy to understand and implement, and it is also relatively fast. The frequency counter is also not likely to cause stack overflow errors, as the dictionary is only of size 1000.
Here are some of the benefits of using a frequency counter to solve this problem:
 It is a simple and efficient data structure.
 It is easy to understand and implement.
 It is relatively fast.
 It is not likely to cause stack overflow errors.
Here are some of the drawbacks of using a frequency counter to solve this problem:
 It requires additional space to store the counts of the numbers.
 It may not be as efficient as other data structures for some problems.
The frequency counter is a good choice for solving this problem because it is simple, efficient, and easy to understand.
Why is set used in this solution?
A set is used in this solution to store the unique counts of the numbers in the array. A set is a data structure that only stores unique elements. This means that if a number is already in the set, the set will not add it again.
The set is used to check if any of the counts is greater than 1. If any of the counts is greater than 1, then the code returns False. Otherwise, the code returns True.
Here are some of the benefits of using a set to store the unique counts of the numbers in the array:
 It is a simple and efficient data structure.
 It is easy to understand and implement.
 It is relatively fast.
Here are some of the drawbacks of using a set to store the unique counts of the numbers in the array:
 It requires additional space to store the counts of the numbers.
 It may not be as efficient as other data structures for some problems.
Overall, the set is a good choice for storing the unique counts of the numbers in the array because it is simple, efficient, and easy to understand.
In addition to the benefits mentioned above, the set also has the following properties that make it a good choice for this solution:
 Sets are unordered, which means that the order of the elements in the set does not matter. This is important because the order of the elements in the array does not matter for this problem.
 Sets are hashable, which means that they can be used as keys in dictionaries. This is important because the set is used as a key in the dictionary
unique_counts
.
Overall, the set is a good choice for storing the unique counts of the numbers in the array because it is simple, efficient, and easy to understand. It also has the properties of being unordered and hashable, which are important for this problem.
I don’t understand why counts greater than 1 should return false.
Sure, I can help you understand that. The problem asks if the number of occurrences of each value in the array is unique. This means that no two values in the array should have the same number of occurrences. If any of the counts is greater than 1, then this means that there are two values in the array with the same number of occurrences, which means that the number of occurrences of each value in the array is not unique.
For example, if the array is [1,2,2,1,1,3]
, then the number of occurrences of 1 is 3, and the number of occurrences of 2 is 2. This means that the number of occurrences of each value in the array is not unique, and the code would return False.
On the other hand, if the array is [1,2,3,1,1,3]
, then the number of occurrences of 1 is 3, the number of occurrences of 2 is 1, and the number of occurrences of 3 is 1. This means that the number of occurrences of each value in the array is unique, and the code would return True.
I hope this explanation helps you understand why counts greater than 1 should return false. Let me know if you have any other questions.
This problem is similar to Two Sum
You are correct. The problem is similar to the Two Sum problem. The Two Sum problem asks if there are two numbers in the array that add up to a given target. The problem that we are solving asks if the number of occurrences of each value in the array is unique.
Both problems can be solved using a frequency counter. The frequency counter is used to count the number of occurrences of each number in the array. The Two Sum problem then checks if there are two numbers in the array with a sum equal to the target. The problem that we are solving checks if any of the counts in the frequency counter is greater than 1.
Here is a table that summarizes the similarities and differences between the two problems:
Similarities  Differences 

Both problems use a frequency counter.  The Two Sum problem asks if there are two numbers in the array that add up to a given target. The problem that we are solving asks if the number of occurrences of each value in the array is unique. 
Both problems can be solved in O(n) time.  The Two Sum problem can be solved in O(n) time using a hash table. The problem that we are solving can be solved in O(n) time using a set. 
Identifying Problem Isomorphism
“Unique Occurrences” can be mapped to “No Duplicates”. The two problems are isomorphic because they both ask if the number of occurrences of each value in the array is unique. The only difference between the two problems is that the “Unique Occurrences” problem requires that the number of occurrences of each value be unique, while the “No Duplicates” problem only requires that there be no duplicate values in the array.
Here is a table that summarizes the similarities and differences between the two problems:
Similarities  Differences 

Both problems use a frequency counter.  The “Unique Occurrences” problem requires that the number of occurrences of each value be unique. The “No Duplicates” problem only requires that there be no duplicate values in the array. 
Both problems can be solved in O(n) time.  The “Unique Occurrences” problem can be solved in O(n) time using a set. The “No Duplicates” problem can be solved in O(n) time using a hash table. 
“No Duplicates” is simpler than the “Unique Occurrences” because it does not require that the number of occurrences of each value be unique. This means that the “No Duplicates” problem can be solved using a hash table, while the “Unique Occurrences” problem requires a set.
Here is the order of the problems from simplest to most complex:
 No Duplicates
 Unique Occurrences