View difference between Paste ID: uyFKjQLG and R9s9XPdy

 import click
 
 from sklearn.cluster import KMeans
 from sklearn import metrics
 
 from scipy.linalg import svd
 
 import numpy as np
 import pandas as pd
 
 
 @click.command()
 @click.argument('input-file', help='Name of the input file. Should be stored as a Tab Separated File')
 @click.option('--output-file', default=None, help='Output results to a file instead of printing results')
 @click.option('--n-clusters', default=3, type=int, help='The number of KMeans clusters')
 @click.option('--pca', is_flag=True, help='Should we use PCA precprocessing of the data?')
 @click.option('--pca-min-variance', default=1.0, type=float,
               help='The amount of variance to perserve if using PCA. Ignored if not used with --pca flag')
 @click.option('--evaluate-clusters', is_flag=True, help='Should we evaluate cluster quality after clustering?')
 @click.option('--verbose', is_flag=True, help='Print progress')
 def run_clustering(input_file, output_file, n_clusters, pca, pca_min_variance, evaluate_clusters, verbose):
     """Run KMeans Clustering on an input TSV File with Optional PCA"""
     input_data = pd.read_table(input_file)
 
     X = np.array(input_data)
     if pca:
         if verbose:
             print 'Fitting PCA'
 
         X = pca(X, pca_min_variance, verbose)
 
     if verbose:
         print 'Fitting K-Means with %s clusters' % n_clusters
 
     kmeans = KMeans(n_clusters=n_clusters).fit(X)
 
     clusters = kmeans.labels_
 
     if evaluate_clusters:
         if verbose:
             print 'Calculating Calinski-Harabaz Index'
 
         print 'Calinski-Harabaz Index: %s' % metrics.calinski_harabaz_score(X, clusters)
 
     if output_file is not None:
         input_data['clusters'] = clusters
         input_data.to_csv(output_file, index=False, columns='clusters', sep='\t')
 
     else:
         print 'clusters'
         for i in clusters:
             print i
 
 
 def pca(X, variance_explained=1., verbose=True):
     """Compute PCA on data matrix X"""
     X = np.array(X)
     m, n = X.shape
     mu = X.mean(0)
 
     Xn = X - mu
 
     if verbose:
         print 'Fitting covariance matrix'
 
     XtX = Xn.T.dot(Xn) / m
 
     if verbose:
         print 'Computing SVD'
 
     U, s, V = svd(XtX)
 
     variance_explained_ratio = (s / s.sum()).cumsum()
     n_comp = (variance_explained_ratio < variance_explained).sum()
 
     if verbose:
         print 'Selecting %s components with an explained variance of %s' % (n_comp, variance_explained_ratio[n_comp])
 
     return Xn.dot(U[:, :n_comp])
 
 
 if __name__ == '__main__':
     run_clustering()