## BUILDING UP DENSITY print "computing density" maxi = 0 for key in brands.keys(): if brands[key] > maxi: maxi = brands[key] n_key = key print "key : %s, number : %s" % (n_key,brands[n_key]) density = compute_density(brands) ################################################################################## ## PRINTING STUFF for i in xrange(1,15): try: print "number of items : %s, number of brands : %s" % (i, density[i]) except: pass print "number of brands : %s" % (len(brands.keys()),) print "number of items : %s" % (count) print "ratio items/brands : %s" % ((count-none_number)/float(len(brands.keys()))) print "number of un_brand items : %s" % (none_number,) smart_plot(map(lambda x : density[x],density.keys()),x_list=sorted(density.keys()))
break spam_reader = parser(file_name) leng = int(prix_max / pas_interval) + int( bool(prix_max / pas_interval - int(prix_max / pas_interval))) + 1 l = [0] * leng #print prix_max ##remplir la liste l avec le nombre d'objet de categorie cat pour chaque intervalle count = 0 for row in spam_reader: if row[3] == cat: #print int(float(row[8])/pas_interval) l[int(float(row[8]) / pas_interval)] += 1 count += 1 if count == limit: break ##tracer la densité empirique pour la categorie cat g = range(leng) def foo(x): return x * pas_interval g = map(foo, g) smart_plot(l, g)
for row in spam_reader: if row[3] == cat: #print int(float(row[8])/pas_interval) l[int(float(row[8])/pas_interval)]+=1 count += 1 if count == limit: break ##tracer la densité empirique pour la categorie cat g=range(leng) def foo(x): return x*pas_interval g=map(foo,g) smart_plot(l,g)
################################################################################## ## BUILDING UP DENSITY print "computing density" maxi = 0 for key in brands.keys(): if brands[key] > maxi: maxi = brands[key] n_key = key print "key : %s, number : %s" % (n_key, brands[n_key]) density = compute_density(brands) ################################################################################## ## PRINTING STUFF for i in xrange(1, 15): try: print "number of items : %s, number of brands : %s" % (i, density[i]) except: pass print "number of brands : %s" % (len(brands.keys()), ) print "number of items : %s" % (count) print "ratio items/brands : %s" % ( (count - none_number) / float(len(brands.keys()))) print "number of un_brand items : %s" % (none_number, ) smart_plot(map(lambda x: density[x], density.keys()), x_list=sorted(density.keys()))