def clusterise_data(data_obj):
""" Assigns a cluster label to each days present in the data received
using three different algorithms: MeanShift, Affinity Propagation,
or KMeans.
@param data_obj: List of dictionaries
"""
L = len(data_obj)
#Simply converts data_obj to a 2D list for computation
List2D = [[None for _ in range(4)] for _ in range(L-1)]
for i in range(L-1): #don't include current day
#wake_up and sleep_duration are the most important factors
List2D[i][0] = 5 * data_obj[i]["wake_up"]
List2D[i][1] = 1 * data_obj[i]["sleep"]
List2D[i][2] = 5 * data_obj[i]["sleep_duration"]
List2D[i][3] = 0.5 * data_obj[i]["activity"]
points = NumpyArray(List2D) #converts 2D list to numpyarray
if ALGO == "Affinity Propagation":
labels = AffinityPropagation().fit_predict(points)
elif ALGO == "KMeans":
labels= KMeans(init='k-means++', n_clusters=5, n_init=10) .fit_predict(points)
elif ALGO == "MeanShift":
bandwidth = estimate_bandwidth(points, quantile=0.2, n_samples=20)
labels = MeanShift(bandwidth=bandwidth, bin_seeding=True).fit_predict(points)
else:
raise Exception("Algorithm not defined: "+str(ALGO))
for i in range(L-1):
data_obj[i]["cluster"] = labels[i]
for unique_label in remove_duplicates(labels):
debug_print(ALGO+": Cluster "+str(unique_label)+" contains "+str(labels.tolist().count(unique_label))+" data points")
debug_print(ALGO+": Silhouette coefficient"+ str(metrics.silhouette_score(points, labels, metric='euclidean')*100)+"%")
string_summary = []
for s in token_summary:
string_summary.append(" ".join(token for token in s))
print("Getting TFIDF...")
vectorizer = TfidfVectorizer(norm='l2')
sim_matrix = cosine_similarity(vectorizer.fit_transform(string_summary))
#print("the shape of similarity matrix: " % np.shape(sim_matrix))
print("Start clustering...")
start = time.time()
labels = AffinityPropagation().fit_predict(sim_matrix)
print("{:.2f}s".format(time.time() - start))
labels = labels.tolist()
# print(labels)
dict = {}
for i in labels:
if i not in dict:
dict[i]=1
else:
dict[i]+=1
def dict2list(dic:dict):
''' 将字典转化为列表 '''
keys = dic.keys()
vals = dic.values()
lst = [(key, val) for key, val in zip(keys, vals)]
return lst
