Return a pretty-printed XML string for the Element.
时间: 2024-04-10 11:35:03 浏览: 25
To return a pretty-printed XML string for an Element object in Python, you can use the `etree.tostring()` function with the `pretty_print` parameter set to `True`. Here's an example:
```python
from lxml import etree
# Assuming you have an Element object called 'root'
xml_str = etree.tostring(root, pretty_print=True).decode("utf-8")
print(xml_str)
```
This will print the XML string with proper indentation and line breaks, making it more readable.
相关问题
use C program Write a function to copy all the characters in the input string t starting from the m-th character to the string s.call this function in main. void strmcpy( char *t, int m, char *s ); The strmcpy function copies all characters starting from the m-th character in the input string char * t to the string char * s. If m exceeds the length of the input string, the resulting string should be an empty string. [Example of input]: 7 happy new year [Sample output]: new year
Here's the C program that meets your requirements:
```c
#include <stdio.h>
#include <string.h>
void strmcpy(char *t, int m, char *s);
int main() {
char input[100];
int m;
char output[100];
printf("Enter a string and an integer m: ");
scanf("%[^\n]%*c %d", input, &m);
strmcpy(input, m, output);
printf("Resulting string: %s\n", output);
return 0;
}
void strmcpy(char *t, int m, char *s) {
int start_index = m - 1;
int len = strlen(t);
if (start_index >= len) {
s[0] = '\0';
return;
}
int i;
for (i = start_index; i < len; i++) {
s[i - start_index] = t[i];
}
s[i - start_index] = '\0';
}
```
The `strmcpy` function takes in three parameters: a pointer to the input string `t`, an integer `m`, and a pointer to the output string `s`. It then copies all characters starting from the `m`-th character in `t` to `s`. If `m` exceeds the length of `t`, an empty string is returned.
In `main`, the user inputs a string and an integer `m`, and the `strmcpy` function is called with these parameters. The resulting string is then printed to the console.
I hope this helps!
Train a vocabulary from features in files listed in featurefiles using k-means with k number of words. Subsampling of training data can be used for speedup.
Here's an example code snippet in Python that implements the functionality you described:
```python
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
import random
# Set parameters
k = 1000 # number of words
subsample_rate = 0.1 # subsampling rate for speedup
featurefiles = ["file1.txt", "file2.txt", "file3.txt"]
# Read in data from feature files
data = []
for featurefile in featurefiles:
with open(featurefile, "r") as f:
data.append(f.read())
# Subsample data if necessary
if subsample_rate < 1.0:
data = random.sample(data, int(subsample_rate*len(data)))
# Vectorize data using TF-IDF
vectorizer = TfidfVectorizer(stop_words="english")
X = vectorizer.fit_transform(data)
# Cluster data using K-Means
kmeans = KMeans(n_clusters=k, random_state=0).fit(X)
# Extract k most frequent words
vocab = [word for word, count in sorted(zip(vectorizer.get_feature_names(), kmeans.labels_), key=lambda x: x[1])[:k]]
# Print vocabulary
print(vocab)
```
This code snippet reads in the data from the feature files, subsamples it if necessary, vectorizes the data using TF-IDF, clusters the data using K-Means, and extracts the k most frequent words from the clusters to form the vocabulary. The resulting vocabulary is then printed to the console.
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