def update_cluster_user_rec_item(self, cluster_user_id_arr):
data_smat = get_data_smat()
all_user_rec_item_dict = self.all_user_rec_item_dict
cluster_user_data_smat = data_smat[cluster_user_id_arr]
user_id_arr, item_id_arr, _ = find_nonzero_indices(cluster_user_data_smat)
cluster_user_dict = {}
for user_id in cluster_user_id_arr:
cluster_user_dict[user_id] = set()
item_set = set(item_id_arr)
item_count_dict = {}
for i,item_id in enumerate(item_id_arr):
try:
item_count_dict[item_id] += 1
except KeyError:
item_count_dict[item_id] = 1
user_item_set = cluster_user_dict[user_id_arr[i]]
user_item_set.add(item_id)
for user_id in cluster_user_id_arr:
user_item_set = cluster_user_dict[user_id]
user_new_item_set = item_set - user_item_set
user_rec_item_dict = all_user_rec_item_dict[user_id]
for item_id in user_new_item_set:
weight = item_count_dict[item_id]
try:
user_rec_item_dict[item_id] += weight
except KeyError:
user_rec_item_dict[item_id] = weight
def gen_toy_dataset(n_user, n_item):
mat = random.choice(2, n_user*n_item, p=[0.7,0.3]).reshape((n_user,n_item))
# print mat
smat = csr_matrix(mat)
# print smat
row_index_arr,column_index_arr,_ = find_nonzero_indices(smat)
relationships = zip(row_index_arr, column_index_arr)
return relationships
def gen_debug_dataset():
data = [
[1,1,1,1,0,0,0,0,0,0],
[1,1,1,0,0,1,0,0,0,0],
[1,0,0,1,0,1,1,0,0,0],
[1,1,0,1,1,0,0,1,0,0],
[0,0,1,1,1,1,0,0,0,0],
[0,0,0,1,1,0,0,1,1,0],
[0,0,0,1,0,1,1,1,0,1],
[0,0,0,0,1,0,1,1,1,1],
[0,0,1,0,0,0,1,0,1,1],
[0,0,0,1,0,1,1,0,1,1],
]
n_user = len(data)
n_item = len(data[0])
print n_user, n_item
smat = csr_matrix(data, shape=(n_user, n_item), dtype=UINT)
row_index_arr,column_index_arr,_ = find_nonzero_indices(smat)
relationships = zip(row_index_arr, column_index_arr)
return n_user, n_item, relationships
def op_sparse_matrix():
mat = random.randint(0,2,size=(10,10))
print mat
smat = csr_matrix(mat)
indices = find_nonzero_indices(smat)[1]