# function to find the similarity between one track
# and all other tracks, returns top 5 tracks
# uses scipy correlation() function to calculate similarity
# author: Taylor Keppler
import trim2
import math
import scipy.spatial.distance as dis
allSongsDict = trim2.makeGenomes()
def getSimilarity(baseDict, tidDict):
# generates vectors for two tracks to be used in similarity function
#returns similarity between base vector and track vector
baseVector = []
tidVector = []
if len(baseDict) >= len(tidDict) :
for (k,v) in baseDict.iteritems():
baseVector.append(v)
if k in tidDict:
tidVector.append(tidDict[k])
else:
tidVector.append(0)
else:
for (k,v) in tidDict.iteritems():
tidVector.append(v)
if k in baseDict:
# Author: Taylor Keppler
# Critique Recommender
# file will be used to, given a track and a tag, return tracks similar to the track,
# similar to that track but more *tag*, and similar to that track but less *tag*
# currently these functions work less than ideally. Asking for more or less *tag*
# often returns similar songs. To fix this we would create a better metric of adding
# or subtracting tag values resulting in a more effective critique.
import trim2
import calcSimilarity
import ChosenTags as CT
import get_metadata as getMB
songDict = trim2.makeGenomes()
def initialSims(tid):
# returns original top 5 similar songs for a given track
baseGenome = songDict[tid]
return calcSimilarity.allCorrelations(baseGenome)
def critiqueTagMore(baseDict, tag):
#given a tag, return songs that are similar to inputed tag vector, but more *tag*
newBase = baseDict
if not baseDict.has_key(tag):
newBase[tag] = 15
else:
newBase[tag] += 15
return calcSimilarity.allCorrelations(newBase)
def critiqueTagLess(baseDict, tag):
