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Solve the following python programming problem given the following code:
# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
#         Lars Buitinck
# License: BSD 3 clause

from __future__ import print_function

from sklearn.datasets import fetch_20newsgroups
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import Normalizer
from sklearn import metrics
from sklearn.cluster import KMeans, MiniBatchKMeans

import sys
from time import time
import numpy as np

class options:
    def __init__(self):
        self.components = 100
        self.use_idf = True
        self.use_hashing = False
        self.features = 10000
        self.verbose = False

opts = options()
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Clustering text documents using k-means
This is an example showing how the scikit-learn can be used to cluster
documents by topics using a bag-of-words approach. This example uses
a scipy.sparse matrix to store the features instead of standard numpy arrays.
Two feature extraction methods can be used in this example:

TfidfVectorizer uses a in-memory vocabulary (a python dict) to map the most
    frequent words to features indices and hence compute a word occurrence
    frequency (sparse) matrix. The word frequencies are then reweighted using
    the Inverse Document Frequency (IDF) vector collected feature-wise over
    the corpus.

HashingVectorizer hashes word occurrences to a fixed dimensional space,
    possibly with collisions. The word count vectors are then normalized to
    each have l2-norm equal to one (projected to the euclidean unit-ball) which
    seems to be important for k-means to work in high dimensional space.
HashingVectorizer does not provide IDF weighting as this is a stateless
model (the fit method does nothing). When IDF weighting is needed it can
be added by pipelining its output to a TfidfTransformer instance.

Two algorithms are demoed: ordinary k-means and its more scalable cousin
minibatch k-means.
Additionally, latent semantic analysis can also be used to reduce dimensionality
and discover latent patterns in the data. 
It can be noted that k-means (and minibatch k-means) are very sensitive to
feature scaling and that in this case the IDF weighting helps improve the
quality of the clustering by quite a lot as measured against the "ground truth"
provided by the class label assignments of the 20 newsgroups dataset.
This improvement is not visible in the Silhouette Coefficient which is small
for both as this measure seem to suffer from the phenomenon called
"Concentration of Measure" or "Curse of Dimensionality" for high dimensional
datasets such as text data. Other measures such as V-measure and Adjusted Rand
Index are information theoretic based evaluation scores: as they are...