def step_clustering(ps, minstep=10):
clustering = Clustering()
centers = clustering.deal_with_ps_b(ps=ps)
centers.sort()
diff = np.diff(centers)
if min(diff) < minstep:
return centers
else:
data = ps.values.reshape(-1, 1)
clustering = KMeans(n_clusters=len(centers) + 1, init='k-means++', n_init=20)
clustering.fit(data)
centers = np.copy(clustering.cluster_centers_).reshape(-1)
centers.sort()
maxiter = 20
iternum = 1
while min(np.diff(centers))> minstep or iternum < maxiter:
clustering = KMeans(n_clusters=len(centers) + 1, init='k-means++', n_init=20)
clustering.fit(data)
centers = np.copy(clustering.cluster_centers_).reshape(-1)
centers.sort()
iternum += 1
return list(centers.reshape(-1))
def getCenters(self, ps):
clustering = Clustering()
# centers_list = clustering.deal_with_ps(ps=ps, not_deal_off=False)
centers_list = clustering.deal_with_ps_b(ps=ps, not_deal_off=False)
centers_list.append(0)
return centers_list
def staticGo(datastore,datarange, home='xusuqian', unknown=True):
def r3_get_r2(r3list, label, app_statedict: dict, psdict: dict):
print('Using history data to extract r2 belonging to r3')
df = pd.DataFrame(columns=list(psdict.keys()))
r3combi = {}
# generating dataframe
for key, ps in psdict.items():
app_state = app_statedict[key]
state_num = len(app_state)
clf = NearestCentroid()
clf.fit(np.append([0], np.array([i.center_value for i in app_state])).reshape(-1, 1),
np.array(range(state_num + 1)))
df[key] = clf.predict(ps.values.reshape(-1, 1))
for idn, r3 in enumerate(r3list):
idx = label == idn
tempt = df.iloc[idx]
combination = set([tuple(i) for i in list(tempt.values)])
r3combi[idn] = combination
r2list = []
state_count = []
for r2row in combination:
app_state_tuple = []
for kk, key in enumerate(df.columns):
if r2row[kk] > 0:
app_state_tuple.append(app_statedict[key][r2row[kk] - 1])
if app_state_tuple != []:
r2list.append(State_r2(tuple(app_state_tuple)))
state_count.append(np.count_nonzero((tempt == np.array(r2row)).all(1)))
else:
r2list.append(State_r2(None))
state_count.append(np.count_nonzero((tempt == np.array(r2row)).all(1)))
r3.set_state_r2_list(r2list, False)
r3.statecount = state_count
if (len(r2list) != len(state_count)):
print()
print('r2 extracting finished')
appliance_truth = {}
appliance_consumtion = {}
if unknown:
ps = None
for app in datastore.appliance_names:
if (app in ['TVbox', 'TV']) and home == 'xusuqian': continue
if (app in ['lamp', 'TV']) and home == 'xiayurong': continue
if (app in ['sterilizer', 'iron', 'kitchen', 'TV']) and home == 'zhouqi': continue
if (app == 'meter'):
for key in datastore.keys_dict[app]:
meterdata = datastore.get_instance_ps(appliance_name=app,
instance=key).loc[datarange[0]: datarange[-1]]
if ps == None: ps = meterdata
else: ps += meterdata
continue
for key in datastore.keys_dict[app]:
theps = datastore.get_instance_ps(appliance_name=app,
instance=key).loc[datarange[0]: datarange[-1]]
appliance_truth[app + '_' + key] = theps
appliance_consumtion[app + '_' + key] = Tools.ps_consumption(theps=theps)
else:
pss = []
for app in datastore.appliance_names:
if (app in ['TVbox', 'TV']) and home == 'xusuqian': continue
if (app in ['lamp', 'TV']) and home == 'xiayurong': continue
if (app in ['sterilizer', 'iron', 'kitchen', 'TV']) and home == 'zhouqi': continue
if (app in ['meter', 'unknown']): continue
for key in datastore.keys_dict[app]:
theps = datastore.get_instance_ps(appliance_name=app,
instance=key).loc[datarange[0]:datarange[-1]]
appliance_truth[app + '_' + key] = theps
appliance_consumtion[app + '_' + key] = Tools.ps_consumption(theps=theps)
pss.append(theps)
ps = aggregate_with_resample(pss)
del pss
ps_dict = appliance_truth
print('搞定了ps')
# ps = median_filter(ps=ps)
_, states_list = getDistribitions(ps=ps, redd_hdf5_path='/home/uftp/hubei/4test/30%s.h5' % home,
center_path='/home/uftp/hubei/ori/%s' % home, load=False)
clustering = Clustering()
centers = clustering.deal_with_ps_b(ps=ps)
centers = step_clustering(ps)
print('类中心是：')
print(centers)
# centers=Tools.deserialize_object('centers')
Tools.serialize_object(centers, 'centers')
state_r3_list = [State_r3(value=center) for center in centers]
centers_array = np.array([[center] for center in centers]).reshape(-1, 1)
kmeans = KMeans(n_clusters=len(centers), random_state=0)
kmeans.cluster_centers_ = centers_array
toLabel = ps.values.reshape(-1, 1)
label_idxs = kmeans.predict(toLabel)
states_dict = {}
for state in states_list:
appliance_name = state.appliance_type
instance = state.instance
try:
states_dict[appliance_name + '_' + instance].append(state)
except KeyError:
states_dict[appliance_name + '_' + instance] = [state]
# states_dict, ps_dict, label_idxs, state_r3_list
r3_get_r2(state_r3_list, label_idxs, states_dict, ps_dict)
print('rer3')
return appliance_truth, ps, states_list, centers, state_r3_list,appliance_consumtion
# staticGo()