def getWindows(timeseries,window_size=20,vectorize=True):
conn_mat = []
for i in range(0,timeseries.shape[1]-window_size+1,1):
if vectorize:
tmp_conn_mat = np.corrcoef(timeseries[:,i:i+window_size])
if i==0:
conn_mat = ts.mat2vec(tmp_conn_mat)[np.newaxis,:]
else:
conn_mat = np.vstack((conn_mat, ts.mat2vec(tmp_conn_mat)[np.newaxis,:]))
else:
tmp_conn_mat = np.corrcoef(timeseries[:,i:i+window_size])
conn_mat.append(tmp_conn_mat)
conn_mat = np.array(conn_mat)
#print conn_mat.shape
return conn_mat
def getWindowCluster(timeseries,nclusters=12,window_size=20):
binary_mat = np.array([])
for i in range(0,timeseries.shape[1]-window_size+1,1):
clust_ind = hclustering(timeseries[:,i:i+window_size],nclusters)
tmp_mat = np.array(ind2matrix(clust_ind)>0,dtype=int)
if i==0:
binary_mat = ts.mat2vec(tmp_mat)[np.newaxis,:]
else:
binary_mat = np.vstack((binary_mat, ts.mat2vec(tmp_mat)[np.newaxis,:]))
print binary_mat.shape
return binary_mat
def getWindowCluster(timeseries,nclusters=12,window_size=20):
binary_mat = np.array([])
for i in range(0,timeseries.shape[1]-window_size+1,1):
clust_ind = hclustering(timeseries[:,i:i+window_size],nclusters)
tmp_mat = np.array(ind2matrix(clust_ind)>0,dtype=int)
if i==0:
binary_mat = ts.mat2vec(tmp_mat)[np.newaxis,:]
else:
binary_mat = np.vstack((binary_mat, ts.mat2vec(tmp_mat)[np.newaxis,:]))
print binary_mat.shape
return binary_mat
def getWindows(timeseries,window_size=20,vectorize=True):
conn_mat = []
for i in range(0,timeseries.shape[1]-window_size+1,1):
if vectorize:
tmp_conn_mat = np.corrcoef(timeseries[:,i:i+window_size])
if i==0:
conn_mat = ts.mat2vec(tmp_conn_mat)[np.newaxis,:]
else:
conn_mat = np.vstack((conn_mat, ts.mat2vec(tmp_conn_mat)[np.newaxis,:]))
else:
tmp_conn_mat = np.corrcoef(timeseries[:,i:i+window_size])
conn_mat.append(tmp_conn_mat)
conn_mat = np.array(conn_mat)
#print conn_mat.shape
return conn_mat