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-rwxr-xr-xsource/clusters.py137
-rwxr-xr-xsource/generatefeedvector.py65
-rw-r--r--source/testclusters.py23
3 files changed, 225 insertions, 0 deletions
diff --git a/source/clusters.py b/source/clusters.py
new file mode 100755
index 00000000..7122c55d
--- /dev/null
+++ b/source/clusters.py
@@ -0,0 +1,137 @@
+# -*- coding: utf-8 -*-
+import random
+
+from math import sqrt
+from PIL import Image, ImageDraw
+
+def readfile(filename):
+ lines=[line for line in file(filename)]
+
+ # First line is the column titles
+ colnames=lines[0].strip().split('\t')[1:]
+ rownames=[]
+ data=[]
+ for line in lines[1:]:
+ p=line.strip().split('\t')
+ # First column in each row is the rowname
+ rownames.append(p[0])
+ # The data for this row is the remainder of the row
+ data.append([float(x) for x in p[1:]])
+ return rownames,colnames,data
+
+def pearson(v1,v2):
+ # Simple sums
+ sum1=sum(v1)
+ sum2=sum(v2)
+
+ # Sums of the squares
+ sum1Sq=sum([pow(v,2) for v in v1])
+ sum2Sq=sum([pow(v,2) for v in v2])
+
+ # Sum of the products
+ pSum=sum([v1[i]*v2[i] for i in range(len(v1))])
+
+ # Calculate r (Pearson score)
+ num=pSum-(sum1*sum2/len(v1))
+ den=sqrt((sum1Sq-pow(sum1,2)/len(v1))*(sum2Sq-pow(sum2,2)/len(v1)))
+ if den==0: return 0
+
+ return 1.0-num/den
+
+def kcluster(rows,distance=pearson,k=4):
+ # Determine the minimum and maximum values for each point
+ ranges=[(min([row[i] for row in rows]),max([row[i] for row in rows]))
+ for i in range(len(rows[0]))]
+
+ # Create k randomly placed centroids
+ clusters=[[random.random()*(ranges[i][1]-ranges[i][0])+ranges[i][0]
+ for i in range(len(rows[0]))] for j in range(k)]
+
+ lastmatches=None
+ for t in range(100):
+ print 'Iteration %d' % t
+ bestmatches=[[] for i in range(k)]
+
+ # Find which centroid is the closest for each row
+ for j in range(len(rows)):
+ row=rows[j]
+ bestmatch=0
+ for i in range(k):
+ d=distance(clusters[i],row)
+ if d<distance(clusters[bestmatch],row): bestmatch=i
+ bestmatches[bestmatch].append(j)
+
+ # If the results are the same as last time, this is complete
+ if bestmatches==lastmatches: break
+ lastmatches=bestmatches
+
+ # Move the centroids to the average of their members
+ for i in range(k):
+ avgs=[0.0]*len(rows[0])
+ if len(bestmatches[i])>0:
+ for rowid in bestmatches[i]:
+ for m in range(len(rows[rowid])):
+ avgs[m]+=rows[rowid][m]
+ for j in range(len(avgs)):
+ avgs[j]/=len(bestmatches[i])
+ clusters[i]=avgs
+
+ return bestmatches
+
+def scaledown(data,distance=pearson,rate=0.01):
+ n=len(data)
+
+ # The real distances between every pair of items
+ realdist=[[distance(data[i],data[j]) for j in range(n)]
+ for i in range(0,n)]
+
+ # Randomly initialize the starting points of the locations in 2D
+ loc=[[random.random(),random.random()] for i in range(n)]
+ fakedist=[[0.0 for j in range(n)] for i in range(n)]
+
+ lasterror=None
+ for m in range(0,1000):
+ # Find projected distances
+ for i in range(n):
+ for j in range(n):
+ fakedist[i][j]=sqrt(sum([pow(loc[i][x]-loc[j][x],2)
+ for x in range(len(loc[i]))]))
+
+ # Move points
+ grad=[[0.0,0.0] for i in range(n)]
+
+ totalerror=0
+ for k in range(n):
+ for j in range(n):
+ if j==k: continue
+ # The error is percent difference between the distances
+ errorterm=(fakedist[j][k]-realdist[j][k])/realdist[j][k]
+
+ # Each point needs to be moved away from or towards the other
+ # point in proportion to how much error it has
+ grad[k][0]+=((loc[k][0]-loc[j][0])/fakedist[j][k])*errorterm
+ grad[k][1]+=((loc[k][1]-loc[j][1])/fakedist[j][k])*errorterm
+
+ # Keep track of the total error
+ totalerror+=abs(errorterm)
+
+
+ # If the answer got worse by moving the points, we are done
+ if lasterror and lasterror<totalerror: break
+ lasterror=totalerror
+
+ # Move each of the points by the learning rate times the gradient
+ for k in range(n):
+ loc[k][0]-=rate*grad[k][0]
+ loc[k][1]-=rate*grad[k][1]
+
+ return loc
+
+def draw2d(data,labels,jpeg='mds2d.jpg'):
+ img=Image.new('RGB',(2000,2000),(255,255,255))
+ draw=ImageDraw.Draw(img)
+ for i in range(len(data)):
+ x=(data[i][0]+0.5)*1000
+ y=(data[i][1]+0.5)*1000
+ draw.text((x,y),labels[i],(0,0,0))
+ img.save(jpeg,'JPEG')
diff --git a/source/generatefeedvector.py b/source/generatefeedvector.py
new file mode 100755
index 00000000..3c33efa5
--- /dev/null
+++ b/source/generatefeedvector.py
@@ -0,0 +1,65 @@
+# -*- coding: utf-8 -*-
+import feedparser
+import re
+
+import conf
+import mongodb
+
+# Returns title and dictionary of word counts for an RSS feed
+def getwordcounts(feed_id):
+ wc={}
+ # Loop over all the entries
+ for article in mongo.get_articles_from_collection(feed_id):
+ summary = article["article_content"]
+
+ # Extract a list of words
+ words = getwords(feed["feed_title"] + ' ' + summary)
+ for word in words:
+ wc.setdefault(word,0)
+ wc[word] += 1
+ return feed["feed_title"], wc
+
+def getwords(html):
+ # Remove all the HTML tags
+ txt=re.compile(r'<[^>]+>').sub('',html)
+
+ # Split words by all non-alpha characters
+ words=re.compile(r'[^A-Z^a-z]+').split(txt)
+
+ # Convert to lowercase
+ return [word.lower() for word in words if word!='']
+
+
+apcount={}
+wordcounts={}
+mongo = mongodb.Articles(conf.MONGODB_ADDRESS, conf.MONGODB_PORT, \
+ conf.MONGODB_DBNAME, conf.MONGODB_USER, conf.MONGODB_PASSWORD)
+feeds = mongo.get_all_feeds()
+for feed in feeds:
+ try:
+ title,wc=getwordcounts(feed["feed_id"])
+ wordcounts[title]=wc
+ for word,count in list(wc.items()):
+ apcount.setdefault(word,0)
+ if count>1:
+ apcount[word]+=1
+ except:
+ print('Failed to parse feed %s' % feed["feed_title"])
+
+wordlist=[]
+for w,bc in list(apcount.items()):
+ frac=float(bc)/len(feeds)
+ if frac>0.1 and frac<0.5:
+ wordlist.append(w)
+
+out=open('blogdata1.txt','w')
+out.write('Blog')
+for word in wordlist: out.write('\t%s' % word)
+out.write('\n')
+for blog,wc in list(wordcounts.items()):
+ print(blog)
+ out.write(blog)
+ for word in wordlist:
+ if word in wc: out.write('\t%d' % wc[word])
+ else: out.write('\t0')
+ out.write('\n')
diff --git a/source/testclusters.py b/source/testclusters.py
new file mode 100644
index 00000000..0e1e2a32
--- /dev/null
+++ b/source/testclusters.py
@@ -0,0 +1,23 @@
+# -*- coding: utf-8 -*-
+
+import clusters
+
+K = 10
+
+blognames,labels,data = clusters.readfile("blogdata1.txt")
+
+#coords = clusters.scaledown(data)
+
+print "Generating clusters..."
+kclust = clusters.kcluster(data, k=K)
+print
+print "Clusters:"
+for i in range(K):
+ print "Cluster" + str(i)
+ print ", ".join([blognames[r] for r in kclust[i]])
+ print
+
+
+
+
+#clusters.draw2d(coords,blognames,jpeg='mds2d.jpg') \ No newline at end of file
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