def instantiate_graph(self):
graph = Graph()
try:
with open(self.__file_name) as f:
for line in f:
# check if the line defines nodes or edges
if line[1] is ':':
nodes = line.split(' ')
for node in nodes:
name = node.split(':')[0]
x, y = node.split(':')[1].split(',')
graph.add_node(name.strip(), {
'X': x.strip(),
'Y': y.strip()
})
elif line[1] is ',':
edges = line.split(' ')
for edge in edges:
source = edge.split(',')[0]
destination, weight = edge.split(',')[1].split(':')
graph.add_edge(source.strip(), destination.strip(),
weight.strip())
else:
raise SyntaxError("Unknown line type in %s" %
(this.file_name))
except Exception as e:
raise e
finally:
f.close()
return graph
def kMean(clusterNum):
g = Graph(r"E:\ds2018")
vocaDir = r"temp/vocabulary/"
if not os.path.exists(vocaDir):
os.makedirs(vocaDir)
sift = SIFT()
centers = []
for i in range(g.getTypeNum()):
print("[kmeans]:" + str(i))
imgPaths = g.getTrainSet(i)
features = np.float32([]).reshape((0, 128))
for imgPath, type in imgPaths:
imgMat = g.getGreyGraph(imgPath)
if imgMat is None:
print("[kmean]:" + imgPath + " is None")
continue
feature = sift.getFeature(imgMat)
features = np.append(features, feature, axis=0)
kmeans = KMeans(n_clusters=clusterNum).fit(features)
filename = os.path.join(vocaDir, str(i) + ".npy")
np.save(filename, kmeans.cluster_centers_)
centers.append(kmeans.cluster_centers_)
return centers
def loadVoca():
g = Graph(r"E:\dataset")
vocaDir = r"temp/vocabulary/"
centers = []
for i in range(g.getTypeNum()):
filePath = os.path.join(vocaDir, str(i) + '.npy')
_, center = np.load(filePath)
centers.append(centers)
return centers
def prepare_clusters(rfpath, wfpath):
basic_net = load_data(rfpath)
pid_dict = load_data(settings.PID_INDEX)
pubs = load_json(settings.PUBS_JSON)
components = {}
for name, pairs in basic_net.items():
pid_index = pid_dict[name]
num_papers = len(pubs[name])
G = Graph()
G.add_nodes_from(range(num_papers))
G.add_edges_from(pairs)
C = Connectivity(G)
components[name] = [list(map(pid_index.get(), compo)) for compo in C.connected_components().values()]
print('prepare clusters', name, 'done')
dump_data(components, wfpath)
def getFeatVecForSvm(self, imgList, load=0):
if load == 1:
feats = np.load(r"temp/featVectHog.npy")
return feats
g = Graph(r"E:\ds2018")
feats = np.float32([]).reshape((0, self.getVecLength()))
for imgPath, type in imgList:
mat = g.getGreyGraph(imgPath)
if mat is None:
continue
feat = self.getFeature(mat)
feats = np.append(feats, feat, axis=0)
np.save(r"temp/featVectHog.npy", feats)
return feats
def calcVectorForSvm(self, imgList, n_cluster, load=0):
if load == 1:
types = np.load(r"temp/types.npy")
featVec = np.load(r"temp/featVectSift.npy")
centers = np.load(r"temp/centersSift.npy")
return (types, featVec, centers)
g = Graph(r"E:\ds2018")
featMat = np.float32([]).reshape((0, 128))
featList = []
featVec = np.float32([]).reshape((0, n_cluster))
types = np.float32([])
i = 0
for imgPath, type in imgList:
print("[kmeans before]:" + str(i))
i += 1
mat = g.getGreyGraph(imgPath)
if mat is None:
continue
feat = self.getFeature(mat)
featList.append(feat)
featMat = np.append(featMat, feat, axis=0)
types = np.append(types, np.float32([type]))
kmeans = KMeans(n_cluster)
kmeans.fit(featMat)
centers = kmeans.cluster_centers_
i = 0
for feature in featList:
print("[kmeans after]:" + str(i))
i += 1
featVec = np.append(featVec,
self._calcFeatVec(feature, kmeans, n_cluster,
centers),
axis=0)
np.save(r"temp/types.npy", types)
np.save(r"temp/featVectSift.npy", featVec)
np.save(r"temp/centersSift.npy", centers)
return (types, featVec, centers)
def getFeatVecForSvm(self, imgList, load=0):
if load == 1:
feats = np.load(r"temp/featVectLbp.npy")
return feats
g = Graph(r"E:\ds2018")
feats = np.float32([]).reshape((0, self.getVecLength()))
i = 0
for imgPath, type in imgList:
print("[lbp]:" + str(i))
i += 1
mat = g.getGreyGraph(imgPath)
if mat is None:
continue
feat = self.getFeature(mat)
feats = np.append(feats, feat.reshape((1, -1)), axis=0)
np.save(r"temp/featVectLbp.npy", feats)
return feats
from Graph.Graph import Graph
from Graph.exception import CantReach
graph = [[1, 2, -2], [1, 3, 4], [3, 2, 3], [3, 4, 1], [4, 3, 2], [5, 5, None]]
g = Graph(st=graph)
#print(g.fw_path(start = 4, end=3))
print('Інциденція:\n')
m = g.matrix_incidence()
for n, i in enumerate(m):
print(n + 1, ":", i)
print('Суміжність:\n')
mm = g.matrix_adjacency()
for n, i in enumerate(mm):
print(n + 1, ":", i)
hamilton = g.check_hamilton_cycle(1)
print("Без циклів" if hamilton is None else (
"Цикл з вершини {} на шляху {}".format(hamilton[0], hamilton[1])))
print("Степені точок: ", g.get_nodes_power())
print("Ізольовані точки: ", g.get_isolated())
dejkstra_path = g.dejkstra_path(1, end=2)
bellman_ford_path = g.bf_path(1)
topological_sorted_list = g.topological_sort()
print(dejkstra_path)
print(bellman_ford_path)
print("Топологічне сортування: ", topological_sorted_list)
from sklearn import svm
from Graph.Graph import Graph
from Features.LBP import LBP
from sklearn.decomposition import PCA
import numpy as np
g = Graph(r"E:\ds2018")
if not g.isDivided():
g.divideTrainTest("ds2018")
trainList = g.readTrainCSV()
features = np.float32([]).reshape(0, 128 * 128)
types = []
for imgPath, type in trainList:
lbp = LBP([8], [1], "default")
imgMatrix = g.getGreyGraph(imgPath)
if imgMatrix is None:
continue
lbpFeatures = lbp.getFeature(imgMatrix)
for j in lbpFeatures:
print(features.shape)
print(j.shape)
np.append(features, j.reshape(1, 128 * 128), axis=0)
types.append(type)
clf = svm.SVC(C=0.8, kernel='rbf', gamma=20, decision_function_shape='ovr')
clf.fit(features, types)
print("done")
import os
from openpyxl import load_workbook
from Cut.Cut import Cut
from Format.Format import Format
from Graph.Graph import Graph
from TimeModel.TimeModel import Timemodel
DATA_DIR = "C:\\Users\\User\\Google 雲端硬碟\\畢業專題\\漢翔\\src"
exe_file = []
plan_file = []
print("****File in " + DATA_DIR + "****")
for filename in os.listdir(DATA_DIR):
print(' ' + filename)
if filename.endswith("txt"):
file = open(os.path.join(DATA_DIR, filename), 'r')
exe_file.append(file)
elif filename.endswith("xlsx"):
file = load_workbook(os.path.join(DATA_DIR, filename))
plan_file.append(file)
else:
print('!!!! ' + filename + ' is not available!!!!')
cut = Cut(exe_file, plan_file)
format = Format(cut.load_data, cut.plan_data)
graph = Graph(format.machine_name, format.execute_format)
timemodel = Timemodel(format.machine_name, format.match, format.execute_format,
format.plan_format, format.status_format)
