def save():
from context.resource_manager import Properties
from pandas import DataFrame, Series
path = os.path.join(Properties.getRootPath(),
Properties.getImageXmlResource())
from xml.dom.minidom import parse, parseString
images = parse(path)
id = []
data = []
for node in images.getElementsByTagName("Image"):
idNode = node.getElementsByTagName("id")[0].childNodes[0].data
id.append(idNode)
dataNode = node.getElementsByTagName("data")[0].childNodes[0].data
dataNode = dataNode[1:-1].split(',')
data.append(dataNode)
id = np.asarray(id)
id = Series(id)
data = np.asarray(list(map(conv, data)), dtype=np.float)
if not os.path.exists(Properties.getDefaultDataFold() + "/cache/image7"):
#f=open(Properties.getDefaultDataFold()+"/csv/threshold.csv","w")
#f.close()
os.makedirs(Properties.getDefaultDataFold() + "/cache/image7")
np.save(Properties.getRootPath() + "/data/cache/image7/id.npy", id)
np.save(Properties.getRootPath() + "/data/cache/image7/data.npy", data)
def loadCacheData(self, name='default', tag="Image", relative=True):
"""
返回xml数据
:param name:
:param tag:
:param relative:
:return:
"""
if relative:
path = os.path.join(Properties.getRootPath() + "/data/cache/" + name)
else:
path = name
try:
id = np.load(path + "/id.npy")
data = np.load(path + "/data.npy")
except:
return self.getXmlData(name=name, tag=tag, save=True)
return id, data
def compute_distance(data=np.array([])):
"""
:param data:
:return:
the numpy array 按行进行查看
"""
if data.size <= 0:
raise Exception
log.critical("cluster need the data, and there has no data in numpy array collection.")
else:
log.info("start running compute distance. data count:" + str(data.shape[0]) + " point has to computing.")
row = data.shape[0]
result = np.zeros((row, row))
i = 0
for i in range(row):
j = i
for j in range(row):
k = compute_point_distance(data[i], data[j])
result[i][j] = k;
result[j][i] = k;
result[i][i] = np.nan
np.save(Properties.getRootPath() + "/data/cache/distance/data.npy", result)
return result
rho_id=Series(rho,index=id)
log.critical(rho)
"""
from cluster import density_cluster
from pandas import Series
from pandas import Series, DataFrame
from context.resource_manager import Properties
from view import shape_view
from cluster import density_cluster
name = 'path'
distance_c = 12.3972318748
m = '3_44'
pile = 0
id = np.load(Properties.getRootPath() + "/data/cache/" + name + "/id.npy")
data = np.load(Properties.getRootPath() + "/data/cache/" + name + "/data.npy")
id_index = Series(id.tolist())
from cluster import density_cluster
index_id = Series(id_index.index, index=id_index.values)
distance = density_cluster.compute_distance(data)
pile_id = DataFrame([], columns=['pile', 'size'])
rho_id = density_cluster.rho_function(index_id, distance, distance_c=distance_c)
rho_id = Series(rho_id, index=index_id.index)
rho_id = rho_id.sort_values(ascending=False)
#delta_id, data_id = density_cluster.delta_function(id_index, index_id, rho_id, distance)
log.debug(rho_id)
pile=['3_44']
pile_max=14
pile = density_cluster.pile_brother(index_id, id_index, distance, distance_c, pile,pile_max)
log.debug("pile info:")
def removeConfigFiles(cls):
if os.path.isfile(Properties.getRootPath()+'conf' + resource_manager.getSeparator() + 'directory.ini'):
os.remove(Properties.getRootPath()+'conf' + resource_manager.getSeparator() + 'directory.ini')
if os.path.isfile(Properties.getRootPath()+'conf' + resource_manager.getSeparator() + 'document.ini'):
os.remove(Properties.getRootPath()+'conf' + resource_manager.getSeparator() + 'document.ini')
def reload(self):
if type == 'D':
self.configParse.read(Properties.getRootPath()+'conf' + resource_manager.getSeparator() + 'directory.ini')
# self.f = open(Properties.getRootPath()+'conf' + resource_manager.getSeparator() + 'directory.ini', 'w+')
elif type == 'F':
self.configParse.read(Properties.getRootPath()+'conf' + resource_manager.getSeparator() + 'document.ini')
def load_data():
id = np.load(Properties.getRootPath() + "/data/cache/flame/id.npy")
data = np.load(Properties.getRootPath() + "/data/cache/flame/data.npy")
return id, data