Sure, here is a Python function that solves the problem:

```python
def hammingWeight(n):
return bin(n).count('1')
```

This function works by converting the integer `n` to a binary string using the `bin` function, and then counting the number of '1' characters in the string with the `count` method.

If this function is called many times, you could optimize it by using a lookup table to store the Hamming weight of all 16-bit integers, and then use this table to compute the Hamming weight of a 32-bit integer. This would reduce the time complexity from O(n) to O(1). Here is how you could implement this:

```python
class Solution:
def __init__(self):
self.table = [0] * 65536
for i in range(65536):
self.table[i] = (i & 1) + self.table[i 1]

def hammingWeight(self, n):
return self.table[n & 65535] + self.table[(n 16) & 65535]
```

In this optimized solution, the `__init__` method initializes the lookup table, and the `hammingWeight` method uses the table to compute the Hamming weight of a 32-bit integer. The `&` operator is used to get the last 16 bits of the integer, and the `` operator is used to shift the integer 16 bits to the right.

Question

Sure, here is a Python function that solves the problem:

```python
def hammingWeight(n):
    return bin(n).count('1')
```

This function works by converting the integer `n` to a binary string using the `bin` function, and then counting the number of '1' characters in the string with the `count` method.

If this function is called many times, you could optimize it by using a lookup table to store the Hamming weight of all 16-bit integers, and then use this table to compute the Hamming weight of a 32-bit integer. This would reduce the time complexity from O(n) to O(1). Here is how you could implement this:

```python
class Solution:
    def __init__(self):
        self.table = [0] * 65536
        for i in range(65536):
            self.table[i] = (i & 1) + self.table[i >> 1]

def hammingWeight(self, n):
        return self.table[n & 65535] + self.table[(n >> 16) & 65535]
```

In this optimized solution, the `__init__` method initializes the lookup table, and the `hammingWeight` method uses the table to compute the Hamming weight of a 32-bit integer. The `&` operator is used to get the last 16 bits of the integer, and the `>>` operator is used to shift the integer 16 bits to the right.

Knowee AI · Accepted Answer

Sure, here is a Python function that solves the problem:

```python
def hammingWeight(n):
    return bin(n).count('1')
```

This function works by converting the integer `n` to a binary string using the `bin` function, and then counting the number of '1' characters in the string with the `count` method.

If this function is called many times, you could optimize it by using a lookup table to store the Hamming weight of all 16-bit integers, and then use this table to compute the Hamming weight of a 32-bit integer. This would reduce the time complexity from O(n) to O(1). Here is how you could implement this:

```python
class Solution:
    def __init__(self):
        self.table = [0] * 65536
        for i in range(65536):
            self.table[i] = (i & 1) + self.table[i >> 1]

def hammingWeight(self, n):
        return self.table[n & 65535] + self.table[(n >> 16) & 65535]
```

In this optimized solution, the `__init__` method initializes the lookup table, and the `hammingWeight` method uses the table to compute the Hamming weight of a 32-bit integer. The `&` operator is used to get the last 16 bits of the integer, and the `>>` operator is used to shift the integer 16 bits to the right.

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