def read_user_data_from_ratings(data_file): data = Data() format = {'col': 0, 'row': 1, 'value': 2, 'ids': 'int'} data.load(dat_file, sep='::', format=format) userdict = {} for d in data.get(): if d[2] in userdict: user = userdict[d[2]] else: user = User(d[2]) user.add_item(d[1], d[0]) userdict[d[2]] = user return userdict
def read_user_data_from_ratings(data_file): data = Data() format = {'col':0, 'row':1, 'value':2, 'ids': 'int'} data.load(dat_file, sep='::', format=format) userdict = {} for d in data.get(): if d[2] in userdict: user = userdict[d[2]] else: user = User(d[2]) user.add_item(d[1],d[0]) userdict[d[2]] = user return userdict
def test_user_add_item(): u = User(USERID) item = Item(ARTISTID) item.add_data({'name': ARTISTID}) u.add_item(item, PLAYS) assert_equal(str(u.get_items()), '[(u2, 25)]')
'tags': [0, 0, 1] }) item2 = Item(1) item2.add_data({'name': 'project1', 'popularity': 0.5, 'tags': [0, 0, 1] }) # create a user userId = 0 user = User(userId) # link an item with a user rating = 1 user.add_item(itemId, rating) data = Data() data.add_tuple((rating, itemId, userId)) data.add_tuple((10, 1, 2)) svd = SVD() svd.set_data(data) svd.compute(k=100, min_values=0, pre_normalize=None, mean_center=True, post_normalize=True) svd.similarity(0, 0) l1 = ['a', 0, 1, 1] l2 = ['b', 0, 1, 1] print 1- spatial.distance.cosine(l1, l2)