def exct_mtf_autoencoder_hierarchy(n_clicks, mtf_data, ae_data, hrc_data):
print("MTF Autoencoder & Hierarchy")
# init
result, result_norm = initialize_data()
result_resample = exec_ts_resampler(result_norm, mtf_data[0]['image_size'])
#(242,28,1)
result_ = result_resample.reshape(result_resample.shape[0], 1,
result_resample.shape[1])
#(242,28,28)
X = toMTFdata(tsdatas=result_,
image_size=mtf_data[0]['image_size'],
n_bins=mtf_data[0]['n_bins'],
strategy=mtf_data[0]['mtf_strategy'])
X_expand = np.expand_dims(X, axis=3)
all_feature = fit_autoencoder(
X_expand, mtf_data[0]['image_size'], ae_data[0]['dimension_feature'],
ae_data[0]['optimizer'], (3e-7) * (10**ae_data[0]['learning_rate']),
ae_data[0]['activation_function'], ae_data[0]['loss_function'],
ae_data[0]['batch_size'], ae_data[0]['epoch'])
print(f'feature shape{all_feature.shape}')
cluster = hierarchicalClustering(all_feature,
hrc_data[0]['number_of_cluster'],
hrc_data[0]['linkage'])
send_result_data(result, hrc_data[0]['number_of_cluster'],
"MTF Autoencoder & Hierarchy", cluster.labels_,
all_feature)
return []
def exct_gaf_autoencoder_kmeans(n_clicks, gaf_data, ae_data, km_data):
print("GAF Autoencoder & Kmeans 실행중입니다...")
# init
result, result_norm = initialize_data()
result_resample = exec_ts_resampler(result_norm, gaf_data[0]['image_size'])
#(242,28,1)
result_ = result_resample.reshape(result_resample.shape[0], 1,
result_resample.shape[1])
#(242,28,28)
X = toGAFdata(tsdatas=result_,
image_size=gaf_data[0]['image_size'],
method=gaf_data[0]['gaf_method'])
X_expand = np.expand_dims(X, axis=3)
all_feature = fit_autoencoder(
X_expand, gaf_data[0]['image_size'], ae_data[0]['dimension_feature'],
ae_data[0]['optimizer'], (3e-7) * (10**ae_data[0]['learning_rate']),
ae_data[0]['activation_function'], ae_data[0]['loss_function'],
ae_data[0]['batch_size'], ae_data[0]['epoch'])
print(f'feature shape{all_feature.shape}')
cluster = kmeans(all_feature, km_data[0]['number_of_cluster'],
km_data[0]['tolerance'], km_data[0]['try_n_init'],
km_data[0]['try_n_kmeans'])
send_result_data(result, km_data[0]['number_of_cluster'],
"GAF Autoencoder & Kmeans", cluster.labels_, all_feature)
return []
def exct_gaf_autoencoder_dbscan(n_clicks, gaf_data, ae_data, dbs_data):
print("GAF Autoencoder & DBSCAN 실행중입니다...")
# init
result, result_norm = initialize_data()
result_resample = exec_ts_resampler(result_norm, gaf_data[0]['image_size'])
#(242,28,1)
result_ = result_resample.reshape(result_resample.shape[0], 1,
result_resample.shape[1])
#(242,28,28)
X = toGAFdata(tsdatas=result_,
image_size=gaf_data[0]['image_size'],
method=gaf_data[0]['gaf_method'])
X_expand = np.expand_dims(X, axis=3)
all_feature = fit_autoencoder(
X_expand, gaf_data[0]['image_size'], ae_data[0]['dimension_feature'],
ae_data[0]['optimizer'], (3e-7) * (10**ae_data[0]['learning_rate']),
ae_data[0]['activation_function'], ae_data[0]['loss_function'],
ae_data[0]['batch_size'], ae_data[0]['epoch'])
cluster = dbscan(all_feature,
eps=dbs_data[0]['epsilon'],
min_samples=dbs_data[0]['min_sample'])
cluster_num = max(cluster.labels_) + 1
send_result_data(result, cluster_num, "GAF Autoencoder & DBSCAN",
cluster.labels_, all_feature)
# init
result, result_norm = initialize_data()
return []
def exct_rp_autoencoder_hierarchy(n_clicks, rp_data, ae_data, hrc_data):
print("RP Autoencoder & hierarchy 실행중입니다...")
threshold = rp_data[0]['threshold']
if threshold == 'None':
threshold = None
# init
result, result_norm = initialize_data()
result_resample = exec_ts_resampler(result_norm, rp_data[0]['image_size'])
#(242,28,1)
result_ = result_resample.reshape(result_resample.shape[0], 1,
result_resample.shape[1])
#(242,28,28)
X = toRPdata(result_, rp_data[0]['dimension'], rp_data[0]['time_delay'],
threshold, rp_data[0]['percentage'] / 100)
X_expand = np.expand_dims(X, axis=3)
all_feature = fit_autoencoder(
X_expand, rp_data[0]['image_size'], ae_data[0]['dimension_feature'],
ae_data[0]['optimizer'], (3e-7) * (10**ae_data[0]['learning_rate']),
ae_data[0]['activation_function'], ae_data[0]['loss_function'],
ae_data[0]['batch_size'], ae_data[0]['epoch'])
cluster = hierarchicalClustering(all_feature,
hrc_data[0]['number_of_cluster'],
hrc_data[0]['linkage'])
send_result_data(result, hrc_data[0]['number_of_cluster'],
"RP Autoencoder & Hierarchy", cluster.labels_,
all_feature)
return 0
def exct_rp_autoencoder_dbscan(n_clicks, rp_data, ae_data, dbs_data):
print("RP Autoencoder & DBSCAN 실행중 입니다...")
threshold = rp_data[0]['threshold']
if threshold == 'None':
threshold = None
# init
result, result_norm = initialize_data()
result_resample = exec_ts_resampler(result_norm, rp_data[0]['image_size'])
#(242,28,1)
result_ = result_resample.reshape(result_resample.shape[0], 1,
result_resample.shape[1])
#(242,28,28)
X = toRPdata(result_, rp_data[0]['dimension'], rp_data[0]['time_delay'],
threshold, rp_data[0]['percentage'] / 100)
X_expand = np.expand_dims(X, axis=3)
all_feature = fit_autoencoder(
X_expand, rp_data[0]['image_size'], ae_data[0]['dimension_feature'],
ae_data[0]['optimizer'], (3e-7) * (10**ae_data[0]['learning_rate']),
ae_data[0]['activation_function'], ae_data[0]['loss_function'],
ae_data[0]['batch_size'], ae_data[0]['epoch'])
cluster = dbscan(all_feature,
eps=dbs_data[0]['epsilon'],
min_samples=dbs_data[0]['min_sample'])
cluster_num = max(cluster.labels_) + 1
send_result_data(result, cluster_num, "RP Autoencoder & DBSCAN",
cluster.labels_, all_feature)
return []
def exct_ts_sample_tskmeans(n_clikcs, tsk_data, tsre_data):
print("TimeSeriesResampler & TimeSeriesKMeans 실행중 입니다...")
# init
result, result_norm = initialize_data()
min = result.dropna(axis='columns')
min_len = len(
min.columns
) if tsre_data[0]['dimension'] is None else tsre_data[0]['dimension']
result_ = exec_ts_resampler(result_norm, min_len)
cluster = ts_kmeans_clustering(result_, tsk_data[0]['number_of_cluster'],
tsk_data[0]['try_n_init'],
tsk_data[0]['distance_algorithm'])
send_result_data(result, tsk_data[0]['number_of_cluster'],
"TimeSeriesResampler & TimeSeriesKMeans ",
cluster.labels_, result_.reshape(result_.shape[0],
min_len))
def exct_wavelet_hierarchy(n_clicks, wav_data, hrc_data):
print("DWT & Hierarchy 중 입니다...")
# init
result, result_norm = initialize_data()
min = result.dropna(axis='columns')
min_len = len(min.columns)
result_ = exec_ts_resampler(result_norm, min_len)
result_ = result_.reshape(result_.shape[0], min_len)
result_ = exec_wavelet(result_, wav_data[0]['wavelet_func'],
wav_data[0]['iter_to_half'])
cluster = hierarchicalClustering(result_, hrc_data[0]['number_of_cluster'],
hrc_data[0]['linkage'])
send_result_data(result, hrc_data[0]['number_of_cluster'],
"DWT & Hierarchy", cluster.labels_, result_)
return []
def exct_ts_sample_dbscan(n_clicks, dbs_data, tsre_data):
print("Time Series Resampler & DBSCAN 중 입니다...")
# init
result, result_norm = initialize_data()
min = result.dropna(axis='columns')
min_len = len(
min.columns
) if tsre_data[0]['dimension'] is None else tsre_data[0]['dimension']
result_ = exec_ts_resampler(result_norm, min_len)
result_ = result_.reshape(result_.shape[0], min_len)
cluster = dbscan(result_,
eps=dbs_data[0]['epsilon'],
min_samples=dbs_data[0]['min_sample'])
cluster_num = max(cluster.labels_) + 1
send_result_data(result, cluster_num, "Time Series Resampler & DBSCAN ",
cluster.labels_, result_)
return []
def exct_ts_sample_hierarchy(n_clicks, hrc_data, tsre_data):
print("Time Series Resampler & Hierarchy 중 입니다...")
print(hrc_data[0])
# init
result, result_norm = initialize_data()
min = result.dropna(axis='columns')
min_len = len(
min.columns
) if tsre_data[0]['dimension'] is None else tsre_data[0]['dimension']
result_ = exec_ts_resampler(result_norm, min_len)
result_ = result_.reshape(result_.shape[0], min_len)
cluster = hierarchicalClustering(result_, hrc_data[0]['number_of_cluster'],
hrc_data[0]['linkage'])
send_result_data(result, hrc_data[0]['number_of_cluster'],
"Time Series Resampler & Hierarchy", cluster.labels_,
result_)
return 0
def exct_ts_sample_kmeans(n_clicks, km_data, tsre_data):
print("Time Series Resampler & Kmeans 중 입니다...")
# init
result, result_norm = initialize_data()
min = result.dropna(axis='columns')
min_len = len(
min.columns
) if tsre_data[0]['dimension'] is None else tsre_data[0]['dimension']
result_ = exec_ts_resampler(result_norm, min_len)
result_ = result_.reshape(result_.shape[0], min_len)
cluster = kmeans(result_, km_data[0]['number_of_cluster'],
km_data[0]['tolerance'], km_data[0]['try_n_init'],
km_data[0]['try_n_kmeans'])
send_result_data(result, km_data[0]['number_of_cluster'],
"Time Series Resampler & Kmeans", cluster.labels_,
result_)
return 0
def exct_wavelet_dbscan(n_clicks, wav_data, dbs_data):
print("DWT & Dbscan 중 입니다...")
# init
result, result_norm = initialize_data()
min = result.dropna(axis='columns')
min_len = len(min.columns)
result_ = exec_ts_resampler(result_norm, min_len)
result_ = result_.reshape(result_.shape[0], min_len)
result_ = exec_wavelet(result_, wav_data[0]['wavelet_func'],
wav_data[0]['iter_to_half'])
cluster = dbscan(result_,
eps=dbs_data[0]['epsilon'],
min_samples=dbs_data[0]['min_sample'])
cluster_num = max(cluster.labels_) + 1
send_result_data(result, cluster_num, "DWT & Dbscan", cluster.labels_,
result_)
return []
def exct_wavelet_kmeans(n_clicks, wav_data, km_data):
print(wav_data)
print(km_data)
print("DWT & Kmeans 중 입니다...")
# init
result, result_norm = initialize_data()
min = result.dropna(axis='columns')
min_len = len(min.columns)
result_ = exec_ts_resampler(result_norm, min_len)
result_ = result_.reshape(result_.shape[0], min_len)
result_ = exec_wavelet(result_, wav_data[0]['wavelet_func'],
wav_data[0]['iter_to_half'])
cluster = kmeans(result_, km_data[0]['number_of_cluster'],
km_data[0]['tolerance'], km_data[0]['try_n_init'],
km_data[0]['try_n_kmeans'])
send_result_data(result, km_data[0]['number_of_cluster'], "DWT & Kmeans",
cluster.labels_, result_)
return []