Maximum Product of Word Lengths

本文介绍了一种算法,用于计算给定单词列表中不共享任何字母的两个单词的最大乘积长度。通过位运算技巧,实现高效计算。
class Solution {
public:
    int maxProduct(vector<string>& words) {
        int n=words.size();
        vector<int> dict(n,0);
        for(int i=0;i<n;++i){
            for(char c:words[i]){
                dict[i]|=(1<<(c-'a'));
            }
        }
        
        int maxP=0;
        for(int i=0;i<n;++i){
            for(int j=i+1;j<n;++j){
                if(!(dict[i]&dict[j])){
                    int p=words[i].length()*words[j].length();
                    maxP=max(maxP,p);
                }
            }
        }
        return maxP;
    }
};

Sure, I can help you with that. A non-weighted histogram of read lengths is a graphical representation of the distribution of read lengths in a set of sequencing data. In this type of histogram, each read length is represented by a single count, regardless of the number of times that length occurs in the dataset. To create a non-weighted histogram of read lengths, you can follow these steps: 1. Collect the read length data from your sequencing dataset. 2. Determine the range of read lengths in your dataset. 3. Divide the range of read lengths into a series of bins, each representing a range of read lengths. 4. Count the number of reads in each bin. 5. Plot the bin counts on the y-axis and the bin ranges on the x-axis to create a histogram. Here's an example code snippet in Python to create a non-weighted histogram of read lengths using the Matplotlib library: ``` import matplotlib.pyplot as plt # Example read length data read_lengths = [100, 200, 300, 400, 500, 200, 300, 300, 100, 100, 200, 500, 500] # Define the bin ranges bins = range(0, 600, 100) # Count the number of reads in each bin bin_counts, _, _ = plt.hist(read_lengths, bins=bins, color='blue') # Plot the histogram plt.xlabel('Read length') plt.ylabel('Count') plt.title('Non-weighted histogram of read lengths') plt.show() ``` This code will create a histogram with five bins representing read lengths from 0-100, 100-200, 200-300, 300-400, and 400-500. The bin counts will be [4, 4, 3, 1, 2], respectively, and the resulting histogram will show the distribution of read lengths in the dataset.
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