diff --git a/.idea/encodings.xml b/.idea/encodings.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d30021a68c1faf9e2b9de4101a54e7513d310a9b
--- /dev/null
+++ b/.idea/encodings.xml
@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+ <component name="Encoding">
+ <file url="file://$PROJECT_DIR$/node2vec/src/graph/test.npy" charset="windows-1252" />
+ <file url="file://$PROJECT_DIR$/node2vec/src/graph/test_Cliques.npy" charset="UTF-16" />
+ </component>
+</project>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000000000000000000000000000000000000..94a25f7f4cb416c083d265558da75d457237d671
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+ <component name="VcsDirectoryMappings">
+ <mapping directory="$PROJECT_DIR$" vcs="Git" />
+ </component>
+</project>
\ No newline at end of file
diff --git a/ANN_CLIQUES.py b/ANN_CLIQUES.py
index 141a0b4e0a7f05ab3577074a3ff2a8e3b3870498..7eb392f5ef5262067cfadb1128d3a83a5a962c4d 100644
--- a/ANN_CLIQUES.py
+++ b/ANN_CLIQUES.py
@@ -80,14 +80,17 @@ def PatternFinding(dat,lab):
adj = np.count_nonzero(lab == 1)
size = len(lab)
for i in range(1):
- Ufin = np.random.uniform(-19, -1, size) * 0
+ Ufin = np.random.uniform(-1, 0, size) * 0
+ #print("ufin",Ufin)
+ #print(len(dat) * 2 / ((size-1) * (size - 1)))
x = 1
- U = np.random.uniform(-19, -1, size)
+ U = np.random.uniform(-1, 0, size)
V = np.random.randint(1, size=size)
- B = (adj / (size * (len(dat) * 2 / (size * (size - 1))))) * 20
+ B = (adj / (size * (len(list(O.edges)) * 2 / (size * (size - 1)))))
+ #print("B",B)
Arrange(lab,Ufin,B,V)
- print(np.count_nonzero(V == 1))
- print(np.count_nonzero(lab == 1))
+ #print(np.count_nonzero(V == 1))
+ #print(np.count_nonzero(lab == 1))
"""
while len(dat) > 0:
x = x + 1
diff --git a/Generate.py b/Generate.py
index 29fc0b7d846590d5eda21123768f4b5eb75fbbf6..835327053b01c0cca753a48f08f3bd03b6dff4a6 100644
--- a/Generate.py
+++ b/Generate.py
@@ -16,7 +16,7 @@ def generate_clique(nb,size,total_size):
cliques=np.asarray(([y for x in cliques for y in x if len(x)>=4]))
nodes_cliques = np.unique(cliques)
x = len(nodes_cliques)
- print("nodes_cliques",x)
+ #print("nodes_cliques",x)
output=np.zeros(total_size)
output[nodes_cliques]=1
return G_new,output,x,nodes_cliques,size,nb
@@ -51,7 +51,7 @@ total_size = 100
max_size_clique = 10
max_clique_count = 10
outputs = []
-Gr_size = 1
+Gr_size = 1000
graph = []
data = []
lab = []
@@ -91,24 +91,27 @@ for id in range(Gr_size):
for k in range(len(t)):
if (t[r, 0] == t[k, 0]) and r != k and w != t[r, 0]:
w = t[r, 0]
- print(w)
+ #print(w)
P = np.delete(T,w,axis=0)
- print(len(P),E[i])
T=P
-
+ print("id",id)
sz.append(T)
-np.save(os.path.join(PATH, "size.npy"), np.asarray(sz[0])) ###########################
-np.save(os.path.join(PATH, "data.npy"), np.asarray(graph)) ############################
-np.save(os.path.join(PATH, "data2.npy"), np.asarray(data)) ##########################
+np.save(os.path.join(PATH, "size.npy"), np.asarray(sz)) ###########################
+#np.save(os.path.join(PATH, "data.npy"), np.asarray(graph)) ############################
+#np.save(os.path.join(PATH, "data2.npy"), np.asarray(data)) ##########################
+#print("out",sz[0])
+#print("out",graph[0])
+#print("out",data[0])
+
output = np.asarray(outputs)
-np.save(os.path.join(PATH,"output.npy"),output) #generation des outputs #######################
-print("out",output[0])
+#np.save(os.path.join(PATH,"output.npy"),output) #generation des outputs #######################
+#print("out",output[0])
labs = np.asarray(lab)
np.save(os.path.join(PATH,"labels2.npy"),labs) #generation des outputs ##########################
-print("labs",labs[0])
-print(s)
+#print("labs",labs[0])
+#print(s)
print(len(sz[0]))
#nx.draw(G,with_labels=True)
diff --git a/node2vec/.gitignore b/PatternRecognition/.gitignore
similarity index 100%
rename from node2vec/.gitignore
rename to PatternRecognition/.gitignore
diff --git a/node2vec/.vs/ProjectSettings.json b/PatternRecognition/.vs/ProjectSettings.json
similarity index 100%
rename from node2vec/.vs/ProjectSettings.json
rename to PatternRecognition/.vs/ProjectSettings.json
diff --git a/node2vec/.vs/VSWorkspaceState.json b/PatternRecognition/.vs/VSWorkspaceState.json
similarity index 100%
rename from node2vec/.vs/VSWorkspaceState.json
rename to PatternRecognition/.vs/VSWorkspaceState.json
diff --git a/node2vec/.vs/node2vec/v16/.suo b/PatternRecognition/.vs/node2vec/v16/.suo
similarity index 100%
rename from node2vec/.vs/node2vec/v16/.suo
rename to PatternRecognition/.vs/node2vec/v16/.suo
diff --git a/node2vec/.vs/slnx.sqlite b/PatternRecognition/.vs/slnx.sqlite
similarity index 100%
rename from node2vec/.vs/slnx.sqlite
rename to PatternRecognition/.vs/slnx.sqlite
diff --git a/node2vec/LICENSE.md b/PatternRecognition/LICENSE.md
similarity index 100%
rename from node2vec/LICENSE.md
rename to PatternRecognition/LICENSE.md
diff --git a/node2vec/README.md b/PatternRecognition/README.md
similarity index 100%
rename from node2vec/README.md
rename to PatternRecognition/README.md
diff --git a/node2vec/emb/karate.emb b/PatternRecognition/emb/karate.emb
similarity index 100%
rename from node2vec/emb/karate.emb
rename to PatternRecognition/emb/karate.emb
diff --git a/node2vec/node2vec_spark/README.md b/PatternRecognition/node2vec_spark/README.md
similarity index 100%
rename from node2vec/node2vec_spark/README.md
rename to PatternRecognition/node2vec_spark/README.md
diff --git a/node2vec/node2vec_spark/pom.xml b/PatternRecognition/node2vec_spark/pom.xml
similarity index 100%
rename from node2vec/node2vec_spark/pom.xml
rename to PatternRecognition/node2vec_spark/pom.xml
diff --git a/node2vec/node2vec_spark/src/main/resources/log4j2.properties b/PatternRecognition/node2vec_spark/src/main/resources/log4j2.properties
similarity index 100%
rename from node2vec/node2vec_spark/src/main/resources/log4j2.properties
rename to PatternRecognition/node2vec_spark/src/main/resources/log4j2.properties
diff --git a/node2vec/node2vec_spark/src/main/scala/com/navercorp/Main.scala b/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/Main.scala
similarity index 100%
rename from node2vec/node2vec_spark/src/main/scala/com/navercorp/Main.scala
rename to PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/Main.scala
diff --git a/node2vec/node2vec_spark/src/main/scala/com/navercorp/Node2vec.scala b/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/Node2vec.scala
similarity index 100%
rename from node2vec/node2vec_spark/src/main/scala/com/navercorp/Node2vec.scala
rename to PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/Node2vec.scala
diff --git a/node2vec/node2vec_spark/src/main/scala/com/navercorp/Word2vec.scala b/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/Word2vec.scala
similarity index 100%
rename from node2vec/node2vec_spark/src/main/scala/com/navercorp/Word2vec.scala
rename to PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/Word2vec.scala
diff --git a/node2vec/node2vec_spark/src/main/scala/com/navercorp/graph/GraphOps.scala b/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/graph/GraphOps.scala
similarity index 100%
rename from node2vec/node2vec_spark/src/main/scala/com/navercorp/graph/GraphOps.scala
rename to PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/graph/GraphOps.scala
diff --git a/node2vec/node2vec_spark/src/main/scala/com/navercorp/graph/package.scala b/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/graph/package.scala
similarity index 100%
rename from node2vec/node2vec_spark/src/main/scala/com/navercorp/graph/package.scala
rename to PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/graph/package.scala
diff --git a/node2vec/node2vec_spark/src/main/scala/com/navercorp/lib/AbstractParams.scala b/PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/lib/AbstractParams.scala
similarity index 100%
rename from node2vec/node2vec_spark/src/main/scala/com/navercorp/lib/AbstractParams.scala
rename to PatternRecognition/node2vec_spark/src/main/scala/com/navercorp/lib/AbstractParams.scala
diff --git a/node2vec/requirements.txt b/PatternRecognition/requirements.txt
similarity index 100%
rename from node2vec/requirements.txt
rename to PatternRecognition/requirements.txt
diff --git a/node2vec/src/Bipartie_convert.py b/PatternRecognition/src/Bipartie_convert.py
similarity index 92%
rename from node2vec/src/Bipartie_convert.py
rename to PatternRecognition/src/Bipartie_convert.py
index 324e66180a1ae3b8fd5c6380cad8923220d16aa7..017bfe1048d2ec82ec1f0355190c1c0a36023058 100644
--- a/node2vec/src/Bipartie_convert.py
+++ b/PatternRecognition/src/Bipartie_convert.py
@@ -4,11 +4,13 @@ import numpy as np
import time
from multiprocessing import Pool
from itertools import repeat
+from ANN_CLIQUES import PatternFinding
+from train import Training
def localisation(G,i):
X = []
a = list(G.neighbors(i))
- if len(G.edges(i)) > 1:
+ if len(G.edges(i)) > 10:
for j in sorted(G.nodes()):
"""
if j > i and G.number_of_edges(i, j) == 0:
@@ -16,7 +18,7 @@ def localisation(G,i):
else:
Y.append(j)
"""
- if j > i and G.number_of_edges(i,j) == 0 and len(G.edges(j)) > 1:
+ if j > i and G.number_of_edges(i,j) == 0 and len(G.edges(j)) > 10:
V = []
V.append(i)
V.append(j)
@@ -88,17 +90,6 @@ if __name__ == "__main__":
#print("flat 2",len(flat_list))#,flat_list)
np.save('graph/test_Bip.npy', flat_list)
- fo = open("example1.model", "a")
- stri = "bc "
- for i in flat_list:
- x = True
- strt = ' '.join(map(str, i[0:2]))
- stry = ' '.join(map(str, i[-(len(i) - 2):]))
- fo.write(stri + strt + ',' + stry + "\n")
- fo.close()
- np.save('graph/test_Star.npy', flat_list)
-
-
new_k = []
k_new = []
for elem in flat_list:
@@ -117,8 +108,19 @@ if __name__ == "__main__":
R = np.unique(flat_list)
#print("R",np.unique(R),len(np.unique(R)))
- node_bip = np.zeros(37000)
+ node_bip = np.zeros(300)
node_bip[np.unique(R)] = 1
+ #print(node_bip)
+ PatternFinding(G.edges, node_bip)
+ Training()
+ fo = open("example1.model", "a")
+ stri = "bc "
+ for i in flat_list:
+ x = True
+ strt = ' '.join(map(str, i[0:2]))
+ stry = ' '.join(map(str, i[-(len(i) - 2):]))
+ fo.write(stri + strt + ',' + stry + "\n")
+ fo.close()
fh = open("C:/Users/LENOVO/Desktop/karate.edgelist", "rb")
G = nx.read_edgelist(fh)
T = nx.edges(G)
diff --git a/PatternRecognition/src/Clique-Adapt.py b/PatternRecognition/src/Clique-Adapt.py
new file mode 100644
index 0000000000000000000000000000000000000000..2c7fc0f32d493b351f563e6816182afa21da755c
--- /dev/null
+++ b/PatternRecognition/src/Clique-Adapt.py
@@ -0,0 +1,14 @@
+import itertools
+
+L = [[ 269 , 240 , 232 ], [ 269 , 240 , 293 ], [ 269 , 268 , 178 ], [ 269 , 30 ], [ 269 , 56 , 154 ], [ 269 , 154 , 232 ], [ 269 , 293 , 258 , 246 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 22 , 238 , 239 , 202 , 232 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 22 , 238 , 239 , 202 , 247 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 171 , 249 , 238 , 202 , 232 , 239 , 219 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 171 , 249 , 238 , 202 , 232 , 239 , 250 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 171 , 249 , 238 , 202 , 232 , 241 , 219 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 171 , 249 , 238 , 202 , 232 , 241 , 250 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 171 , 249 , 238 , 202 , 258 , 247 , 239 , 219 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 171 , 249 , 238 , 202 , 258 , 247 , 239 , 250 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 171 , 249 , 238 , 202 , 258 , 247 , 241 , 219 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 171 , 249 , 238 , 202 , 258 , 247 , 241 , 250 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 171 , 249 , 238 , 246 , 239 , 232 , 219 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 171 , 249 , 238 , 246 , 239 , 232 , 250 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 171 , 249 , 238 , 246 , 239 , 247 , 258 , 219 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 171 , 249 , 238 , 246 , 239 , 247 , 258 , 250 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 171 , 249 , 164 , 258 , 247 , 250 , 239 , 246 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 171 , 249 , 164 , 258 , 247 , 250 , 241 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 62 , 246 , 249 , 232 , 239 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 62 , 246 , 249 , 232 , 217 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 62 , 246 , 249 , 247 , 239 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 62 , 246 , 249 , 247 , 217 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 261 , 219 , 238 , 249 , 232 , 202 , 179 , 239 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 261 , 219 , 238 , 249 , 232 , 202 , 179 , 241 ], [ 269 , 282 , 283 , 278 , 279 , 262 , 261 , 219 , 238 , 249 , 232 , 202 , 217 , 241 ], [ 269 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298 , 204 , 130 , 280 , 284 , 242 , 294 , 267 ], [ 299 , 298 , 204 , 297 , 105 , 280 , 293 , 270 ], [ 299 , 298 , 204 , 297 , 105 , 280 , 293 , 267 ], [ 299 , 298 , 204 , 297 , 105 , 280 , 284 , 259 , 270 ], [ 299 , 298 , 204 , 297 , 105 , 280 , 284 , 267 ], [ 299 , 298 , 204 , 297 , 289 , 240 , 213 , 242 , 259 , 227 ], [ 299 , 298 , 204 , 297 , 289 , 240 , 213 , 242 , 259 , 280 , 270 , 284 ], [ 299 , 298 , 204 , 297 , 289 , 240 , 213 , 242 , 293 , 227 ], [ 299 , 298 , 204 , 297 , 289 , 240 , 213 , 242 , 293 , 280 , 270 ], [ 299 , 298 , 204 , 297 , 289 , 259 , 271 , 227 ], [ 299 , 298 , 204 , 297 , 289 , 259 , 271 , 270 , 284 ], [ 299 , 298 , 204 , 297 , 289 , 242 , 267 , 284 , 280 ], [ 299 , 298 , 204 , 297 , 289 , 242 , 267 , 293 , 227 ], [ 299 , 298 , 204 , 297 , 289 , 242 , 267 , 293 , 280 ], [ 299 , 298 , 204 , 297 , 294 , 240 , 213 , 242 , 259 , 227 ], [ 299 , 298 , 204 , 297 , 294 , 240 , 213 , 242 , 259 , 280 , 270 , 284 ], [ 299 , 298 , 204 , 297 , 294 , 240 , 213 , 242 , 293 , 227 ], [ 299 , 298 , 204 , 297 , 294 , 240 , 213 , 242 , 293 , 280 , 270 ], [ 299 , 298 , 204 , 297 , 294 , 259 , 271 , 227 ], [ 299 , 298 , 204 , 297 , 294 , 259 , 271 , 270 , 284 ], [ 299 , 298 , 204 , 297 , 294 , 242 , 267 , 284 , 280 ], [ 299 , 298 , 204 , 297 , 294 , 242 , 267 , 293 , 227 ], [ 299 , 298 , 204 , 297 , 294 , 242 , 267 , 293 , 280 ], [ 299 , 298 , 148 , 196 , 264 , 243 , 274 , 297 , 271 ], [ 299 , 298 , 148 , 196 , 264 , 243 , 274 , 297 , 293 ], [ 299 , 298 , 148 , 196 , 264 , 243 , 296 , 271 ], [ 299 , 298 , 148 , 196 , 264 , 243 , 296 , 293 ], [ 299 , 298 , 148 , 196 , 264 , 294 , 274 , 297 , 271 ], [ 299 , 298 , 148 , 196 , 264 , 294 , 274 , 297 , 293 ], [ 299 , 298 , 148 , 196 , 264 , 294 , 296 , 271 ], [ 299 , 298 , 148 , 196 , 264 , 294 , 296 , 293 ], [ 299 , 298 , 148 , 259 , 271 , 294 , 274 , 297 , 284 ], [ 299 , 298 , 148 , 259 , 271 , 294 , 274 , 297 , 264 ], [ 299 , 298 , 148 , 259 , 271 , 294 , 296 , 284 ], [ 299 , 298 , 148 , 259 , 271 , 294 , 296 , 264 ], [ 299 , 170 , 284 , 240 ], [ 299 , 170 , 284 , 267 ], [ 299 , 210 ], [ 299 , 31 , 286 , 297 , 274 , 172 , 270 , 289 , 271 , 284 ], [ 299 , 31 , 286 , 297 , 274 , 172 , 270 , 289 , 293 ], [ 299 , 31 , 286 , 297 , 274 , 172 , 270 , 294 , 271 , 284 ], [ 299 , 31 , 286 , 297 , 274 , 172 , 270 , 294 , 293 ], [ 299 , 31 , 286 , 297 , 150 , 289 ], [ 299 , 31 , 286 , 297 , 150 , 294 ], [ 299 , 83 , 227 , 271 ], [ 299 , 237 , 55 ], [ 299 , 237 , 220 ], [ 299 , 220 , 254 ], [ 299 , 220 , 250 ], [ 299 , 172 , 295 , 274 , 289 , 130 , 242 , 280 , 270 , 284 ], [ 299 , 172 , 295 , 274 , 289 , 286 , 297 , 242 , 280 , 270 , 293 ], [ 299 , 172 , 295 , 274 , 289 , 286 , 297 , 242 , 280 , 270 , 284 ], [ 299 , 172 , 295 , 274 , 289 , 286 , 297 , 242 , 230 , 284 ], [ 299 , 172 , 295 , 274 , 289 , 286 , 297 , 242 , 230 , 293 ], [ 299 , 172 , 295 , 274 , 289 , 286 , 297 , 243 , 293 , 280 , 270 ], [ 299 , 172 , 295 , 274 , 289 , 286 , 297 , 243 , 293 , 230 ], [ 299 , 172 , 295 , 274 , 289 , 286 , 297 , 243 , 284 , 271 , 270 ], [ 299 , 172 , 295 , 274 , 289 , 286 , 297 , 243 , 284 , 271 , 230 ], [ 299 , 172 , 295 , 274 , 289 , 286 , 297 , 243 , 284 , 280 , 270 ], [ 299 , 172 , 295 , 274 , 294 , 130 , 242 , 280 , 270 , 284 ], [ 299 , 172 , 295 , 274 , 294 , 286 , 297 , 293 , 242 , 280 , 270 ], [ 299 , 172 , 295 , 274 , 294 , 286 , 297 , 293 , 242 , 230 ], [ 299 , 172 , 295 , 274 , 294 , 286 , 297 , 284 , 270 , 242 , 280 ], [ 299 , 172 , 295 , 274 , 294 , 286 , 297 , 284 , 270 , 271 ], [ 299 , 172 , 295 , 274 , 294 , 286 , 297 , 284 , 230 , 242 ], [ 299 , 172 , 295 , 274 , 294 , 286 , 297 , 284 , 230 , 271 ], [ 299 , 172 , 295 , 228 , 286 , 297 , 289 , 242 , 280 , 270 , 293 ], [ 299 , 172 , 295 , 228 , 286 , 297 , 289 , 242 , 280 , 270 , 284 ], [ 299 , 172 , 295 , 228 , 286 , 297 , 289 , 242 , 230 , 284 ], [ 299 , 172 , 295 , 228 , 286 , 297 , 289 , 242 , 230 , 293 ], [ 299 , 172 , 295 , 228 , 286 , 297 , 289 , 243 , 293 , 280 , 270 ], [ 299 , 172 , 295 , 228 , 286 , 297 , 289 , 243 , 293 , 230 ], [ 299 , 172 , 295 , 228 , 286 , 297 , 289 , 243 , 284 , 271 , 270 ], [ 299 , 172 , 295 , 228 , 286 , 297 , 289 , 243 , 284 , 271 , 230 ], [ 299 , 172 , 295 , 228 , 286 , 297 , 289 , 243 , 284 , 280 , 270 ], [ 299 , 172 , 295 , 228 , 286 , 297 , 294 , 293 , 242 , 280 , 270 ], [ 299 , 172 , 295 , 228 , 286 , 297 , 294 , 293 , 242 , 230 ], [ 299 , 172 , 295 , 228 , 286 , 297 , 294 , 284 , 270 , 242 , 280 ], [ 299 , 172 , 295 , 228 , 286 , 297 , 294 , 284 , 270 , 271 ], [ 299 , 172 , 295 , 228 , 286 , 297 , 294 , 284 , 230 , 242 ], [ 299 , 172 , 295 , 228 , 286 , 297 , 294 , 284 , 230 , 271 ], [ 299 , 172 , 29 , 286 , 297 , 294 , 271 ], [ 299 , 172 , 29 , 286 , 297 , 294 , 293 ], [ 299 , 51 , 250 , 297 ], [ 299 , 51 , 295 , 286 , 271 , 294 , 297 , 227 , 211 ], [ 299 , 51 , 295 , 286 , 271 , 294 , 297 , 227 , 267 , 230 , 264 ], [ 299 , 51 , 295 , 286 , 271 , 294 , 297 , 284 , 267 , 230 ], [ 299 , 51 , 295 , 286 , 271 , 294 , 296 , 227 , 211 ], [ 299 , 51 , 295 , 286 , 271 , 294 , 296 , 227 , 267 , 230 , 264 ], [ 299 , 51 , 295 , 286 , 271 , 294 , 296 , 284 , 267 , 230 ], [ 299 , 24 , 240 , 280 , 259 , 213 , 284 , 297 ], [ 299 , 24 , 240 , 280 , 259 , 213 , 284 , 296 ], [ 299 , 24 , 240 , 280 , 259 , 213 , 264 , 297 ], [ 299 , 24 , 240 , 280 , 259 , 213 , 264 , 296 ], [ 299 , 24 , 240 , 280 , 259 , 286 , 284 , 297 ], [ 299 , 24 , 240 , 280 , 259 , 286 , 284 , 296 ], [ 299 , 24 , 240 , 280 , 259 , 286 , 264 , 297 ], [ 299 , 24 , 240 , 280 , 259 , 286 , 264 , 296 ], [ 299 , 24 , 240 , 280 , 293 , 264 , 213 , 297 ], [ 299 , 24 , 240 , 280 , 293 , 264 , 213 , 296 ], [ 299 , 24 , 240 , 280 , 293 , 264 , 286 , 297 ], [ 299 , 24 , 240 , 280 , 293 , 264 , 286 , 296 ], [ 299 , 128 , 254 , 204 , 297 , 271 , 270 , 243 ], [ 299 , 128 , 254 , 204 , 297 , 271 , 270 , 294 ], [ 299 , 128 , 254 , 204 , 297 , 271 , 267 , 294 ], [ 299 , 128 , 254 , 204 , 297 , 280 , 293 , 270 , 243 ], [ 299 , 128 , 254 , 204 , 297 , 280 , 293 , 270 , 294 ], [ 299 , 128 , 254 , 204 , 297 , 280 , 293 , 267 , 294 ], [ 299 , 128 , 254 , 264 , 286 , 274 , 297 , 271 , 270 , 243 ], [ 299 , 128 , 254 , 264 , 286 , 274 , 297 , 271 , 270 , 294 ], [ 299 , 128 , 254 , 264 , 286 , 274 , 297 , 271 , 267 , 294 ], [ 299 , 128 , 254 , 264 , 286 , 274 , 297 , 280 , 293 , 270 , 243 ], [ 299 , 128 , 254 , 264 , 286 , 274 , 297 , 280 , 293 , 270 , 294 ], [ 299 , 128 , 254 , 264 , 286 , 274 , 297 , 280 , 293 , 267 , 294 ], [ 299 , 128 , 254 , 264 , 286 , 296 , 271 , 270 , 243 ], [ 299 , 128 , 254 , 264 , 286 , 296 , 271 , 270 , 294 ], [ 299 , 128 , 254 , 264 , 286 , 296 , 271 , 267 , 294 ], [ 299 , 128 , 254 , 264 , 286 , 296 , 280 , 293 , 270 , 243 ], [ 299 , 128 , 254 , 264 , 286 , 296 , 280 , 293 , 270 , 294 ], [ 299 , 128 , 254 , 264 , 286 , 296 , 280 , 293 , 267 , 294 ], [ 299 , 128 , 295 , 264 , 286 , 274 , 297 , 271 , 270 , 243 ], [ 299 , 128 , 295 , 264 , 286 , 274 , 297 , 271 , 270 , 294 ], [ 299 , 128 , 295 , 264 , 286 , 274 , 297 , 271 , 267 , 294 ], [ 299 , 128 , 295 , 264 , 286 , 274 , 297 , 280 , 293 , 270 , 243 ], [ 299 , 128 , 295 , 264 , 286 , 274 , 297 , 280 , 293 , 270 , 294 ], [ 299 , 128 , 295 , 264 , 286 , 274 , 297 , 280 , 293 , 267 , 294 ], [ 299 , 128 , 295 , 264 , 286 , 296 , 271 , 270 , 243 ], [ 299 , 128 , 295 , 264 , 286 , 296 , 271 , 270 , 294 ], [ 299 , 128 , 295 , 264 , 286 , 296 , 271 , 267 , 294 ], [ 299 , 128 , 295 , 264 , 286 , 296 , 280 , 293 , 270 , 243 ], [ 299 , 128 , 295 , 264 , 286 , 296 , 280 , 293 , 270 , 294 ], [ 299 , 128 , 295 , 264 , 286 , 296 , 280 , 293 , 267 , 294 ], [ 200 , 286 , 260 ], [ 200 , 286 , 296 ], [ 202 , 241 , 11 ], [ 202 , 280 , 249 ], [ 202 , 210 ], [ 3 , 275 , 68 ], [ 3 , 275 , 292 ], [ 117 , 208 , 227 ], [ 62 , 297 , 230 ], [ 244 , 237 , 276 ], [ 244 , 280 , 286 ], [ 244 , 286 , 292 , 229 ], [ 136 , 259 , 274 ], [ 190 , 5 ], [ 190 , 148 ], [ 99 , 115 ], [ 99 , 151 ], [ 14 , 128 ], [ 14 , 246 ], [ 209 , 242 ], [ 27 , 277 ], [ 27 , 295 ], [ 218 , 267 ], [ 161 , 15 ], [ 161 , 298 , 227 , 286 ], [ 187 , 30 , 68 , 140 , 144 ], [ 187 , 121 , 292 ], [ 187 , 295 ], [ 281 , 180 , 277 ], [ 281 , 176 , 275 ], [ 281 , 176 , 210 ], [ 281 , 195 ], [ 98 , 228 , 97 ], [ 282 , 271 ], [ 260 , 254 , 274 , 286 , 227 ], [ 260 , 254 , 274 , 286 , 280 ], [ 260 , 227 , 292 , 286 ], [ 43 , 102 ], [ 223 , 70 ], [ 223 , 298 , 289 , 150 ], [ 223 , 172 , 289 ], [ 88 , 150 ], [ 88 , 250 ], [ 97 , 75 , 295 ], [ 97 , 228 , 295 ], [ 97 , 140 , 126 , 90 ], [ 97 , 140 , 30 , 68 ], [ 97 , 140 , 30 , 90 ], [ 151 , 229 ], [ 151 , 293 , 297 ], [ 140 , 52 ], [ 169 , 163 , 270 ], [ 208 , 149 , 220 ], [ 126 , 234 ], [ 144 , 280 ], [ 144 , 229 ], [ 229 , 105 , 270 , 286 , 296 ], [ 115 , 254 ], [ 277 , 254 , 180 ], [ 277 , 254 , 148 ], [ 96 , 29 ], [ 195 , 31 ], [ 182 , 264 , 298 ], [ 182 , 246 ], [ 153 , 213 , 75 ], [ 249 , 259 , 289 , 242 , 280 ], [ 249 , 259 , 246 ], [ 249 , 289 , 102 , 280 ], [ 234 , 71 , 174 ], [ 246 , 259 , 270 ], [ 246 , 293 , 270 ], [ 30 , 189 , 294 ], [ 75 , 298 , 295 , 259 , 274 , 297 , 289 , 227 , 213 ], [ 75 , 298 , 295 , 259 , 274 , 297 , 289 , 227 , 271 ], [ 75 , 298 , 295 , 259 , 274 , 297 , 289 , 284 , 213 , 280 ], [ 75 , 298 , 295 , 259 , 274 , 297 , 289 , 284 , 271 ], [ 75 , 298 , 295 , 259 , 274 , 297 , 294 , 227 , 213 ], [ 75 , 298 , 295 , 259 , 274 , 297 , 294 , 227 , 271 ], [ 75 , 298 , 295 , 259 , 274 , 297 , 294 , 284 , 213 , 280 ], [ 75 , 298 , 295 , 259 , 274 , 297 , 294 , 284 , 271 ], [ 75 , 298 , 295 , 259 , 296 , 289 , 227 , 213 ], [ 75 , 298 , 295 , 259 , 296 , 289 , 227 , 271 ], [ 75 , 298 , 295 , 259 , 296 , 289 , 284 , 213 , 280 ], [ 75 , 298 , 295 , 259 , 296 , 289 , 284 , 271 ], [ 75 , 298 , 295 , 259 , 296 , 294 , 227 , 213 ], [ 75 , 298 , 295 , 259 , 296 , 294 , 227 , 271 ], [ 75 , 298 , 295 , 259 , 296 , 294 , 284 , 213 , 280 ], [ 75 , 298 , 295 , 259 , 296 , 294 , 284 , 271 ], [ 75 , 298 , 295 , 267 , 274 , 297 , 289 , 271 , 227 ], [ 75 , 298 , 295 , 267 , 274 , 297 , 289 , 271 , 284 ], [ 75 , 298 , 295 , 267 , 274 , 297 , 289 , 280 , 284 ], [ 75 , 298 , 295 , 267 , 274 , 297 , 294 , 271 , 227 ], [ 75 , 298 , 295 , 267 , 274 , 297 , 294 , 271 , 284 ], [ 75 , 298 , 295 , 267 , 274 , 297 , 294 , 280 , 284 ], [ 75 , 298 , 295 , 267 , 296 , 289 , 271 , 227 ], [ 75 , 298 , 295 , 267 , 296 , 289 , 271 , 284 ], [ 75 , 298 , 295 , 267 , 296 , 289 , 280 , 284 ], [ 75 , 298 , 295 , 267 , 296 , 294 , 271 , 227 ], [ 75 , 298 , 295 , 267 , 296 , 294 , 271 , 284 ], [ 75 , 298 , 295 , 267 , 296 , 294 , 280 , 284 ], [ 116 , 254 , 29 ], [ 116 , 254 , 264 ], [ 116 , 29 , 172 ], [ 241 , 11 , 270 ], [ 241 , 170 ], [ 219 , 52 ], [ 292 , 227 , 83 , 271 ], [ 292 , 227 , 286 , 271 ], [ 292 , 227 , 286 , 293 ], [ 275 , 71 ], [ 275 , 135 , 257 ], [ 134 , 240 , 289 , 274 ], [ 134 , 240 , 289 , 296 ], [ 257 , 267 , 170 ], [ 186 , 264 , 270 ], [ 276 , 228 , 296 ], [ 71 , 230 ], [ 90 , 293 , 242 ]]
+K = L
+for i in L:
+ for j in L:
+ if i[0] == j[0]:
+ for k in j:
+ if k not in i:
+ i.append(k)
+ i.sort()
+L.sort()
+K = list(L for L,_ in itertools.groupby(L))
+print(K)
\ No newline at end of file
diff --git a/node2vec/src/Compact_Clique.py b/PatternRecognition/src/Compact_Clique.py
similarity index 100%
rename from node2vec/src/Compact_Clique.py
rename to PatternRecognition/src/Compact_Clique.py
diff --git a/node2vec/src/Edge.py b/PatternRecognition/src/Edge.py
similarity index 100%
rename from node2vec/src/Edge.py
rename to PatternRecognition/src/Edge.py
diff --git a/node2vec/src/EdgeConert_Star.py b/PatternRecognition/src/EdgeConert_Star.py
similarity index 97%
rename from node2vec/src/EdgeConert_Star.py
rename to PatternRecognition/src/EdgeConert_Star.py
index 600bbfd00c24786397e6986cbe845e4e4aa3e643..130418b6c4920d9e2226456eb3657f5845a7f551 100644
--- a/node2vec/src/EdgeConert_Star.py
+++ b/PatternRecognition/src/EdgeConert_Star.py
@@ -7,8 +7,8 @@ from compact import Compact
import matplotlib.pyplot as plt
fh = open("C:/Users/LENOVO/Desktop/karate.edgelist", "rb")
G = nx.read_edgelist(fh,nodetype=int)
-nx.draw(G, node_size = 4000)
-plt.show()
+#nx.draw(G, node_size = 900)
+#plt.show()
W = G.number_of_nodes()
x = list(set(list(G.nodes)))
size = x[len(x)-1]
@@ -19,15 +19,15 @@ if y != 0:
G.add_node(W+i)
#print("n",x+i)
total_size = G.number_of_nodes()
-print(total_size)
+#print(total_size)
outU = np.zeros(size + 1 + 100 -y)
U = list(sorted(list(G.nodes)))
start = time.time()
for i in U:
- if len(list(G.neighbors(i))) > 25:
+ if len(list(G.neighbors(i))) > 1:
outU[i] = 1
outU[list(G.neighbors(i))] = 1
-print(outU)
+#print(outU)
PatternFinding(G.edges,outU)
Training()
#print(type(G.edges))
diff --git a/node2vec/src/EdgeConvert.py b/PatternRecognition/src/EdgeConvert.py
similarity index 76%
rename from node2vec/src/EdgeConvert.py
rename to PatternRecognition/src/EdgeConvert.py
index c9fe75e4f3eef4c0200e4c717fdde8e84691ccda..4c970bfb06c3bac89dc504ec54cdb2356b10b489 100644
--- a/node2vec/src/EdgeConvert.py
+++ b/PatternRecognition/src/EdgeConvert.py
@@ -4,29 +4,31 @@ import numpy as np
import copy
from networkx.algorithms import clique
import time
+from ANN_CLIQUES import PatternFinding
+from train import Training
def Filewrite(E):
stri = "fc "
fo = open("example1.model", "a")
R = []
for i in E:
- x = True
- for j in E:
- if set(i).issubset(set(j)) and i != j:
- x = False
- if x:
- R.append(set(i))
+ #x = True
+ #for j in E:
+ # if set(i).issubset(set(j)) and i != j:
+ # x = False
+ #if x:
+ #R.append(set(i))
strt = ' '.join(map(str, i))
fo.write(stri + strt + "\n")
fo.close()
import os
- print(os.path.abspath("example1.model"))
-print("lancement")
+ #print(os.path.abspath("example1.model"))
+#print("lancement")
fh = open("C:/Users/LENOVO/Desktop/karate.edgelist", "rb")
start = time.time()
G = nx.read_edgelist(fh)
x = G.number_of_nodes()
-print(x)
+#print(x)
print("====== Start Cliques ======")
y = x % 100
if y != 0:
@@ -35,12 +37,14 @@ if y != 0:
total_size = G.number_of_nodes()
cliques = list(clique.find_cliques(G))
-flat_list = [sublist for sublist in cliques if len(sublist) > 2]
-cliques = np.asarray(([y for x in flat_list for y in x if len(x) >= 4]))
+flat_list = [sublist for sublist in cliques if len(sublist) > 5]
+cliques = np.asarray(([y for x in flat_list for y in x if len(x) >= 5]))
+
+#print("cliques",flat_list,len(flat_list))
+fo = open("example1.model", "w")
+#fo.write(str(flat_list))
+#fo.close()
-print("cliques",flat_list,len(flat_list))
-Filewrite(flat_list)
-np.save('graph/test_Cliques.npy', flat_list)
node_cliques = np.unique(cliques)
nodes_cliques = node_cliques.astype(int)
@@ -60,7 +64,7 @@ key[nodes_cliques]=1
"""
output = np.zeros(G.number_of_nodes())
output[nodes_cliques] = 1
-
+#print(output)
end = time.time()
print("====== End of first step ======")
print("Time",end-start)
@@ -70,7 +74,11 @@ for a,b in G.edges():
data.append([a,b])
data = np.asarray(data)
data = np.expand_dims(data,axis=0)
+PatternFinding(G.edges,output)
+Training()
+Filewrite(flat_list)
+np.save('graph/test_Cliques.npy', flat_list)
sz = []
T = nx.edges(G)
@@ -83,6 +91,7 @@ for i in range(len(E)):
T = nx.edges(G)
T = np.asarray(T)
sz.append(T)
+#print(sz)
end = time.time()
print("====== End of second step ======")
print("Time",end-start)
diff --git a/node2vec/src/File_addaptation.py b/PatternRecognition/src/File_addaptation.py
similarity index 95%
rename from node2vec/src/File_addaptation.py
rename to PatternRecognition/src/File_addaptation.py
index b8d115cbaf9d7a6ce33a6b3f1bfc215ff8282135..e66fd892fd6f75055e325c5ca2820118f9dcbb95 100644
--- a/node2vec/src/File_addaptation.py
+++ b/PatternRecognition/src/File_addaptation.py
@@ -7,15 +7,18 @@ r = []
for line in paragraph:
#f1.write(line.strip()+",1\n")
- tmp = line.strip().split(' ')
- #print(tmp[0])
- #print(tmp[1])
+ tmp = line.strip().split(',')
+ print(tmp[0])
+ print(tmp[1])
#x = []
#x.append(int(tmp[0]))
#x.append(int(tmp[1]))
#if tmp[0] != tmp[1] and sorted(x) not in r:
#r.append(sorted(x))
- f1.write(str(int(tmp[0]))+" "+str(int(tmp[1]))+"\n")
+ f1.write(str(int(tmp[0]))+","+str(int(tmp[1]))+"\n")
+
+ #f1.write("x["+str(int(tmp[0]))+","+str(int(tmp[1]))+"] <- NA\n")
+"""
import networkx as nx
import matplotlib.pyplot as plt
@@ -130,4 +133,4 @@ nx.draw(G,
with_labels=True,node_size = 800)
#nx.draw(G, with_labels='True',node_size = 1000, edge_color = ['red','red','red','red','red','red','red','red','red','red','red','red','red','red','red','red','red','red','red','red','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','green','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black','black'])
-plt.show()
\ No newline at end of file
+plt.show()"""
\ No newline at end of file
diff --git a/node2vec/src/Final_model.h5 b/PatternRecognition/src/Final_model.h5
similarity index 100%
rename from node2vec/src/Final_model.h5
rename to PatternRecognition/src/Final_model.h5
diff --git a/PatternRecognition/src/INoutput_data_val.npy b/PatternRecognition/src/INoutput_data_val.npy
new file mode 100644
index 0000000000000000000000000000000000000000..88379bba7a83d0501c5f2b464643063ad9c85f16
Binary files /dev/null and b/PatternRecognition/src/INoutput_data_val.npy differ
diff --git a/node2vec/src/compact.py b/PatternRecognition/src/compact.py
similarity index 93%
rename from node2vec/src/compact.py
rename to PatternRecognition/src/compact.py
index 78f793511a21200481c5d2da91a792bebf860877..46447c7fec41a2a1d553e154e2bf9b6ca5606125 100644
--- a/node2vec/src/compact.py
+++ b/PatternRecognition/src/compact.py
@@ -27,9 +27,9 @@ def Compact(flat_list):
new_L = []
L2 = copy.deepcopy(flat_list)
for i in range(len(flat_list)):
- if len(flat_list[i]) > 15:
+ if len(flat_list[i]) > 2:
for j in range(len(flat_list)):
- if len(flat_list[j]) > 15:
+ if len(flat_list[j]) > 2:
x = flat_list[i]
if flat_list[i] != flat_list[j] and x[0] in L2[j]:
y = flat_list[j]
diff --git a/node2vec/src/compact2.py b/PatternRecognition/src/compact2.py
similarity index 90%
rename from node2vec/src/compact2.py
rename to PatternRecognition/src/compact2.py
index 6e7b25417397729f09ed8cb861e4b235bee262a8..2210fa83c045ae7e4d673452a3ff219c86addef5 100644
--- a/node2vec/src/compact2.py
+++ b/PatternRecognition/src/compact2.py
@@ -1,8 +1,12 @@
def Filewrite(E):
fo = open("example1.model", "w")
+ #f = open("patterns.txt","w")
for i in E:
- if len(i) > 15:
+ print(type(i))
+ #f.write(i)
+ print(i)
+ if len(i) > 1:
stri = "st "
stri = stri + str(i[0]) + ", "
for j in i:
diff --git a/node2vec/src/compacte3 b/PatternRecognition/src/compacte3
similarity index 100%
rename from node2vec/src/compacte3
rename to PatternRecognition/src/compacte3
diff --git a/node2vec/src/compacte3.py b/PatternRecognition/src/compacte3.py
similarity index 100%
rename from node2vec/src/compacte3.py
rename to PatternRecognition/src/compacte3.py
diff --git a/PatternRecognition/src/example1.model b/PatternRecognition/src/example1.model
new file mode 100644
index 0000000000000000000000000000000000000000..8614af85f655ab16bdb52f04a85be9c44c12c213
--- /dev/null
+++ b/PatternRecognition/src/example1.model
@@ -0,0 +1,448 @@
+fc 94 143 262 255 187 110
+fc 94 143 262 255 187 206
+fc 94 269 255 140 187 213 206
+fc 94 269 255 140 187 110
+fc 94 269 255 262 187 213 206
+fc 94 269 255 262 187 110
+fc 37 288 283 290 219 50
+fc 37 288 283 290 219 281
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diff --git a/node2vec/src/graph/Graphes.npy b/PatternRecognition/src/graph/Graphes.npy
similarity index 100%
rename from node2vec/src/graph/Graphes.npy
rename to PatternRecognition/src/graph/Graphes.npy
diff --git a/node2vec/src/graph/data.npy b/PatternRecognition/src/graph/data.npy
similarity index 100%
rename from node2vec/src/graph/data.npy
rename to PatternRecognition/src/graph/data.npy
diff --git a/node2vec/src/graph/data_test.npy b/PatternRecognition/src/graph/data_test.npy
similarity index 100%
rename from node2vec/src/graph/data_test.npy
rename to PatternRecognition/src/graph/data_test.npy
diff --git a/node2vec/src/graph/data_train.npy b/PatternRecognition/src/graph/data_train.npy
similarity index 100%
rename from node2vec/src/graph/data_train.npy
rename to PatternRecognition/src/graph/data_train.npy
diff --git a/node2vec/src/graph/data_val.npy b/PatternRecognition/src/graph/data_val.npy
similarity index 100%
rename from node2vec/src/graph/data_val.npy
rename to PatternRecognition/src/graph/data_val.npy
diff --git a/node2vec/src/graph/label_test.npy b/PatternRecognition/src/graph/label_test.npy
similarity index 70%
rename from node2vec/src/graph/label_test.npy
rename to PatternRecognition/src/graph/label_test.npy
index 7240a1562be2a97e794ef233c27ed21ba5616464..7c5eed2deee252caa0905148031bcee54946924d 100644
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diff --git a/node2vec/src/graph/label_train.npy b/PatternRecognition/src/graph/label_train.npy
similarity index 76%
rename from node2vec/src/graph/label_train.npy
rename to PatternRecognition/src/graph/label_train.npy
index 2cb850304db48502d0fe4fcb879cacd9e0057c6b..675b98c61cdde7845ddfd51f35eff93da496e405 100644
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diff --git a/node2vec/src/graph/label_val.npy b/PatternRecognition/src/graph/label_val.npy
similarity index 70%
rename from node2vec/src/graph/label_val.npy
rename to PatternRecognition/src/graph/label_val.npy
index 8eb75050de61c71905198098d4b32e6d3970fbc1..dbdb15ffb6f98f486071ad367f2d41588b803932 100644
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diff --git a/PatternRecognition/src/graph/labfin.npy b/PatternRecognition/src/graph/labfin.npy
new file mode 100644
index 0000000000000000000000000000000000000000..99696c9684d5315cf8a929164e6c77f0e376e4f2
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diff --git a/PatternRecognition/src/graph/sam.npy b/PatternRecognition/src/graph/sam.npy
new file mode 100644
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diff --git a/node2vec/src/graph/sample.npy b/PatternRecognition/src/graph/sample.npy
similarity index 100%
rename from node2vec/src/graph/sample.npy
rename to PatternRecognition/src/graph/sample.npy
diff --git a/PatternRecognition/src/graph/size.npy b/PatternRecognition/src/graph/size.npy
new file mode 100644
index 0000000000000000000000000000000000000000..067196f5d080b7745e3431abec4b85b20a3693f2
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diff --git a/PatternRecognition/src/graph/size_test.npy b/PatternRecognition/src/graph/size_test.npy
new file mode 100644
index 0000000000000000000000000000000000000000..98bb0429448a470fb4f83eb79fb0bf79daf232df
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diff --git a/PatternRecognition/src/graph/size_train.npy b/PatternRecognition/src/graph/size_train.npy
new file mode 100644
index 0000000000000000000000000000000000000000..e3061063cafb67af6ce85e57852c6369944232a7
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diff --git a/PatternRecognition/src/graph/size_val.npy b/PatternRecognition/src/graph/size_val.npy
new file mode 100644
index 0000000000000000000000000000000000000000..99b4e1d1d2b75ce2f58ca855bdb08bc8efd3d3f6
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diff --git a/PatternRecognition/src/graph/test.npy b/PatternRecognition/src/graph/test.npy
new file mode 100644
index 0000000000000000000000000000000000000000..2d269243661bd5259f84662b8cb490f1c0abfe1f
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diff --git a/node2vec/src/graph/test/data_test.npy b/PatternRecognition/src/graph/test/data_test.npy
similarity index 100%
rename from node2vec/src/graph/test/data_test.npy
rename to PatternRecognition/src/graph/test/data_test.npy
diff --git a/node2vec/src/graph/test/data_train.npy b/PatternRecognition/src/graph/test/data_train.npy
similarity index 100%
rename from node2vec/src/graph/test/data_train.npy
rename to PatternRecognition/src/graph/test/data_train.npy
diff --git a/node2vec/src/graph/test/data_val.npy b/PatternRecognition/src/graph/test/data_val.npy
similarity index 100%
rename from node2vec/src/graph/test/data_val.npy
rename to PatternRecognition/src/graph/test/data_val.npy
diff --git a/node2vec/src/graph/test/transformed_0.npy b/PatternRecognition/src/graph/test/transformed_0.npy
similarity index 100%
rename from node2vec/src/graph/test/transformed_0.npy
rename to PatternRecognition/src/graph/test/transformed_0.npy
diff --git a/PatternRecognition/src/graph/test_Bip.npy b/PatternRecognition/src/graph/test_Bip.npy
new file mode 100644
index 0000000000000000000000000000000000000000..d27790ab5e4cd93926f0c85c6210b2f3811704b0
Binary files /dev/null and b/PatternRecognition/src/graph/test_Bip.npy differ
diff --git a/PatternRecognition/src/graph/test_Cliques.npy b/PatternRecognition/src/graph/test_Cliques.npy
new file mode 100644
index 0000000000000000000000000000000000000000..ed61107589a2d76e5d4fd9389059ae0b5f7bd163
Binary files /dev/null and b/PatternRecognition/src/graph/test_Cliques.npy differ
diff --git a/PatternRecognition/src/graph/test_Star.npy b/PatternRecognition/src/graph/test_Star.npy
new file mode 100644
index 0000000000000000000000000000000000000000..d27790ab5e4cd93926f0c85c6210b2f3811704b0
Binary files /dev/null and b/PatternRecognition/src/graph/test_Star.npy differ
diff --git a/node2vec/src/main.py b/PatternRecognition/src/main.py
similarity index 97%
rename from node2vec/src/main.py
rename to PatternRecognition/src/main.py
index 385bd5c08ee8db3c6e41a6153ddfea4013bf5d08..1405ac8928a9e5d93d7429f571cfe4cac07c1f82 100644
--- a/node2vec/src/main.py
+++ b/PatternRecognition/src/main.py
@@ -16,7 +16,7 @@ import node2vec
from gensim.models import Word2Vec
-GRAPH_SIZE = 80000
+GRAPH_SIZE = 100
def parse_args():
'''
Parses the node2vec arguments.
@@ -24,7 +24,7 @@ def parse_args():
print("Parses the node2vec arguments.")
parser = argparse.ArgumentParser(description="Run node2vec.")
#graph/dataset/data_train.npy
- parser.add_argument('--input', nargs='?', default='graph/test_Bip.npy',
+ parser.add_argument('--input', nargs='?', default='graph/size_val.npy',
help='Input graph path')
parser.add_argument('--output', nargs='?', default='emb/karate.emb',
@@ -81,6 +81,7 @@ def read_all():
print("input",args.input)
data = np.load(args.input,allow_pickle=True)
id=0
+ print(data)
for x in data:
nx_G=read(x)
G = node2vec.Graph(nx_G, args.directed, args.p, args.q)
diff --git a/node2vec/src/model_train.h5 b/PatternRecognition/src/model_train.h5
similarity index 100%
rename from node2vec/src/model_train.h5
rename to PatternRecognition/src/model_train.h5
diff --git a/node2vec/src/node2vec.py b/PatternRecognition/src/node2vec.py
similarity index 100%
rename from node2vec/src/node2vec.py
rename to PatternRecognition/src/node2vec.py
diff --git a/node2vec/src/example1.model b/PatternRecognition/src/patterns.txt
similarity index 100%
rename from node2vec/src/example1.model
rename to PatternRecognition/src/patterns.txt
diff --git a/node2vec/src/soc-tribes.edges b/PatternRecognition/src/soc-tribes.edges
similarity index 100%
rename from node2vec/src/soc-tribes.edges
rename to PatternRecognition/src/soc-tribes.edges
diff --git a/Skit-Cluster.py b/Skit-Cluster.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7d6b96cdfaf75eac0f1223e39651d37845c5e89
--- /dev/null
+++ b/Skit-Cluster.py
@@ -0,0 +1,175 @@
+from sknetwork.clustering import Louvain
+from sknetwork.data import karate_club
+import networkx as nx
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy import sparse
+import time
+from sknetwork.clustering import PropagationClustering
+from sknetwork.data import karate_club
+from sknetwork.clustering import modularity
+from collections import Counter
+
+#f = np.load('node2vec/src/graph/test_Star.npy',allow_pickle=True)
+#M = [[1,0,0],[1,0,0],[1,0,0],[1,0,0],[0,1,0],[0,1,0],[0,1,0],[0,1,0],[0,0,1],[0,0,1],[0,0,1],[0,0,1]]
+fh = open("C:/Users/LENOVO/Desktop/karate.edgelist", "rb")
+
+
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192, 273], [35, 138, 169, 275], [295, 42, 116, 259, 250], [295, 42, 230, 186, 259], [295, 42, 230, 250, 253, 272, 259], [295, 42, 230, 250, 253, 272, 166], [295, 208, 33, 250], [295, 208, 33, 80], [295, 208, 231, 259, 123, 116], [295, 208, 231, 259, 123, 186], [295, 208, 231, 259, 116, 39], [295, 208, 231, 259, 116, 80], [295, 208, 231, 259, 230, 39], [295, 208, 231, 259, 230, 186, 80], [295, 208, 231, 4, 116], [295, 208, 231, 4, 186], [295, 208, 250, 259, 123, 116], [295, 208, 250, 259, 230], [295, 80, 272, 33, 118], [295, 80, 272, 230, 231, 259], [295, 80, 272, 230, 231, 118], [295, 80, 118, 231, 116], [295, 80, 118, 231, 230, 186], [295, 123, 231, 259, 253, 272], [295, 123, 231, 166, 253, 272], [295, 123, 231, 166, 186], [295, 123, 250, 259, 253, 272], [295, 123, 250, 166, 253, 272], [295, 253, 272, 33, 118, 250], [295, 253, 272, 230, 231, 39, 259], [295, 253, 272, 230, 231, 118], [295, 253, 272, 230, 231, 166], [295, 253, 272, 230, 250, 118], [295, 253, 272, 4, 231], [109, 259, 208, 39, 231], [109, 259, 208, 250], [109, 259, 253, 39, 231], [109, 259, 253, 231, 86], [109, 259, 253, 250, 42], [109, 259, 253, 250, 86], [109, 166, 253, 231, 86], [109, 166, 253, 250, 42], [109, 166, 253, 250, 86], [109, 4, 231, 208], [109, 4, 231, 253], [205, 235, 282, 266, 237, 245, 256, 217, 174, 52], [205, 235, 282, 266, 237, 245, 256, 217, 174, 5], [205, 235, 282, 266, 237, 245, 256, 217, 150], [205, 235, 282, 266, 237, 245, 256, 13], [205, 235, 282, 266, 237, 245, 256, 134, 52, 174], [205, 235, 282, 266, 237, 245, 256, 134, 150], [205, 235, 282, 266, 19, 217, 5], [205, 235, 282, 266, 19, 217, 150], [205, 235, 282, 266, 19, 134, 150], [140, 269, 187, 213, 255, 161], [140, 269, 187, 213, 255, 120], [140, 269, 187, 213, 255, 94, 206], [140, 269, 187, 213, 44, 161], [140, 269, 187, 213, 44, 120], [140, 269, 187, 213, 44, 206], [296, 183, 192, 189], [296, 183, 192, 277, 273], [296, 183, 216, 214, 180], [296, 183, 216, 214, 273, 277], [296, 183, 216, 214, 273, 169], [296, 183, 216, 214, 189], [296, 183, 216, 202, 273, 277], [296, 183, 216, 202, 189], [296, 275, 137, 138, 180], [296, 275, 137, 234, 162, 180], [296, 275, 137, 234, 162, 273, 277], [296, 275, 137, 234, 189], [296, 275, 137, 177, 273, 277], [296, 275, 234, 180, 214], [296, 275, 234, 277, 273, 214], [296, 275, 234, 277, 273, 202], [296, 275, 234, 122, 273, 202], [296, 275, 234, 122, 273, 169, 214], [296, 275, 234, 122, 273, 169, 162], [296, 275, 234, 122, 189, 214], [296, 275, 234, 122, 189, 202], [296, 275, 169, 216, 214, 273], [296, 192, 234, 162, 273, 277], [296, 192, 234, 189], [255, 150, 19, 282], [255, 262, 269, 161, 18, 213], [255, 262, 269, 161, 18, 69], [255, 262, 269, 161, 187, 213], [255, 262, 269, 206, 213, 18], [255, 262, 269, 206, 213, 187, 94], [255, 262, 269, 206, 69, 18], [255, 262, 143, 94, 187, 206], [255, 120, 269, 213, 18], [143, 187, 44, 161], [143, 187, 44, 139, 206], [143, 187, 44, 120], [39, 158, 230, 259, 208], [39, 158, 230, 259, 272, 253], [151, 29, 173, 113, 11, 10], [151, 29, 173, 113, 2], [151, 29, 173, 6], [151, 29, 199, 113, 11, 10], [151, 29, 199, 113, 2], [10, 11, 153, 29, 113, 173], [10, 11, 153, 29, 113, 199], [135, 243, 190, 215], [135, 104, 54, 182], [135, 104, 190, 130, 215], [61, 268, 164, 225, 271], [61, 268, 274, 197, 225, 271], [61, 268, 274, 223], [61, 294, 147, 276, 223], [61, 294, 147, 276, 271], [61, 294, 223, 274], [61, 294, 225, 62, 276], [61, 294, 225, 62, 274], [61, 294, 225, 271, 276], [61, 294, 225, 271, 164], [61, 294, 225, 271, 197, 274], [150, 235, 282, 217, 237, 245, 256, 266, 49], [150, 235, 282, 217, 237, 245, 256, 90], [150, 235, 282, 217, 19, 266, 49], [282, 235, 256, 237, 245, 266, 115, 217], [282, 235, 256, 237, 245, 266, 115, 13], [282, 235, 256, 237, 245, 266, 115, 134], [282, 235, 256, 237, 245, 266, 49, 217, 52, 174], [282, 235, 256, 237, 245, 90, 217, 115], [282, 235, 256, 237, 245, 90, 217, 174], [282, 235, 256, 237, 245, 90, 13, 115], [282, 235, 19, 266, 217, 115], [282, 235, 19, 266, 134, 115], [20, 248, 298, 210, 263], [20, 248, 298, 210, 293], [20, 229, 185, 293, 236, 210], [20, 229, 185, 293, 236, 207], [20, 229, 185, 293, 298, 210], [20, 229, 263, 236, 210], [20, 229, 263, 236, 207], [20, 229, 263, 298, 210], [294, 276, 223, 92], [294, 276, 62, 92], [294, 276, 240, 60, 147], [294, 276, 240, 60, 142], [294, 276, 240, 142, 225], [294, 276, 240, 271, 147], [294, 276, 240, 271, 165], [294, 276, 240, 271, 225], [294, 276, 92, 60], [294, 276, 92, 165, 271], [294, 147, 196, 223], [294, 147, 196, 240, 60], [294, 147, 196, 240, 271], [294, 147, 171, 60, 240], [294, 147, 171, 223], [294, 274, 223, 196], [294, 274, 223, 171], [294, 274, 34, 62], [294, 274, 34, 271, 89], [294, 274, 62, 171, 225], [294, 274, 240, 60, 196], [294, 274, 240, 60, 171], [294, 274, 240, 165, 197, 196, 271], [294, 274, 240, 165, 171], [294, 274, 240, 225, 197, 271], [294, 274, 240, 225, 171], [294, 274, 89, 197, 271], [294, 164, 142, 225], [294, 164, 271, 165, 196], [294, 164, 271, 89], [294, 142, 197, 225, 240], [294, 92, 171, 60], [294, 92, 171, 223], [294, 92, 171, 62], [294, 92, 171, 165], [294, 92, 89, 271], [92, 179, 60, 276], [92, 179, 60, 171], [92, 179, 223, 276], [92, 179, 223, 268], [92, 179, 223, 171], [92, 179, 89, 268], [92, 268, 165, 271], [92, 268, 271, 89], [169, 40, 183, 216], [241, 238, 209, 284], [241, 238, 129, 136], [241, 238, 129, 8], [241, 286, 209, 284], [241, 286, 136, 57], [55, 270, 289, 278, 252, 93, 156], [55, 270, 289, 278, 252, 155, 168], [55, 270, 289, 278, 163, 93, 156], [55, 270, 289, 278, 163, 155], [55, 270, 289, 228, 252, 93, 156], [160, 45, 264, 144, 280], [160, 45, 264, 283], [160, 101, 283, 141], [160, 101, 283, 264], [160, 101, 283, 288], [160, 101, 280, 264, 144], [160, 101, 280, 288], [82, 100, 149, 265], [82, 194, 226, 83], [82, 194, 226, 195], [82, 149, 265, 221, 195], [82, 149, 265, 244], [82, 83, 226, 265], [194, 145, 226, 178, 83], [194, 297, 178, 226, 83], [194, 297, 178, 98], [194, 297, 100, 251], [194, 297, 100, 198], [194, 297, 257, 226, 198], [194, 297, 257, 244, 98], [185, 15, 293, 38], [185, 15, 293, 298, 247], [40, 183, 146, 192], [40, 183, 146, 216], [40, 183, 189, 192], [40, 183, 189, 216], [172, 76, 264, 74], [172, 76, 264, 144], [172, 74, 299, 264, 280], [172, 74, 299, 264, 124], [172, 124, 299, 288], [244, 191, 198, 257, 265], [244, 191, 198, 211, 227], [244, 191, 149, 265], [244, 145, 265, 149], [244, 297, 251, 227, 211], [244, 297, 198, 257, 265], [244, 297, 198, 211, 227], [244, 297, 149, 265], [99, 167, 215, 246], [99, 167, 215, 23], [176, 252, 278, 93, 156, 289, 291], [176, 252, 278, 156, 70], [176, 252, 228, 93, 156, 289, 291], [176, 252, 228, 156, 70], [176, 91, 291, 289, 278, 156], [176, 91, 291, 289, 278, 168], [176, 91, 291, 289, 228, 156], [127, 285, 30, 107], [127, 285, 30, 59], [127, 285, 209, 107], [127, 285, 209, 284, 59], [149, 265, 100, 191], [149, 265, 100, 297], [149, 265, 221, 178, 297], [149, 265, 221, 178, 145], [149, 265, 220, 195], [286, 107, 117, 30], [286, 107, 117, 239], [286, 203, 170, 57, 125], [286, 203, 170, 117], [286, 117, 170, 239], [286, 170, 57, 136], [286, 170, 284, 239], [286, 0, 284, 239], [286, 0, 284, 209], [139, 269, 187, 206, 213, 44], [139, 269, 187, 206, 213, 262], [53, 15, 248, 38], [53, 15, 248, 298, 263], [53, 15, 248, 298, 247], [53, 232, 263, 248, 298], [53, 232, 263, 229, 236], [53, 232, 263, 229, 298], [208, 158, 259, 123, 186], [208, 158, 259, 123, 250], [208, 158, 259, 230, 186, 80], [208, 158, 259, 230, 250], [234, 146, 180, 162], [234, 146, 180, 214], [234, 146, 277, 273, 192, 162], [234, 146, 277, 273, 214], [234, 146, 277, 273, 202], [226, 265, 83, 178, 297], [226, 265, 83, 178, 112, 145], [226, 265, 83, 191, 79], [226, 265, 257, 198, 191, 79], [226, 265, 257, 198, 297], [226, 265, 195, 112], [226, 227, 178, 297], [226, 227, 178, 145], [226, 227, 198, 191, 257, 79], [226, 227, 198, 191, 211], [226, 227, 198, 297, 257], [226, 227, 198, 297, 211], [156, 270, 70, 252, 278], [156, 270, 70, 252, 228], [156, 270, 291, 289, 93, 252, 278], [156, 270, 291, 289, 93, 252, 17, 228], [156, 270, 291, 289, 93, 163, 278], [156, 270, 291, 289, 91, 278, 163], [156, 270, 291, 289, 91, 228], [144, 95, 45, 119], [144, 76, 45, 264], [182, 54, 104, 246], [182, 54, 246, 167], [123, 158, 186, 166], [123, 158, 250, 253, 272, 259], [123, 158, 250, 253, 272, 166], [116, 86, 80, 231, 259], [116, 86, 80, 231, 118], [60, 179, 147, 276], [60, 179, 147, 196], [60, 179, 147, 171], [60, 85, 276, 142], [60, 85, 171, 274], [220, 291, 270, 289, 252, 278, 168], [220, 291, 270, 289, 252, 17, 228], [220, 291, 270, 289, 163, 278], [107, 287, 117, 239], [107, 285, 117, 30], [107, 285, 117, 239], [281, 59, 284, 285], [281, 111, 24, 288, 280], [281, 111, 24, 288, 95], [281, 111, 95, 37, 288], [281, 285, 239, 284], [281, 283, 95, 45], [281, 283, 95, 288, 24], [281, 283, 95, 288, 37], [281, 283, 299, 45, 264], [281, 283, 299, 101, 141], [281, 283, 299, 101, 264], [281, 283, 299, 101, 288], [281, 76, 264, 74], [281, 76, 264, 45], [281, 76, 264, 108], [281, 124, 24, 95, 288], [281, 124, 299, 219, 288], [281, 124, 299, 101, 74, 264], [281, 124, 299, 101, 288], [281, 74, 299, 101, 141], [281, 74, 299, 101, 280, 264], [281, 280, 299, 45, 264], [281, 280, 299, 101, 288], [291, 289, 270, 155, 278, 252, 168], [291, 289, 270, 155, 278, 163], [291, 289, 270, 168, 278, 91], [133, 298, 248, 263, 210, 233], [133, 298, 248, 263, 210, 66], [133, 298, 248, 263, 15], [133, 298, 248, 293, 66, 210], [133, 298, 248, 293, 247, 233, 210], [133, 298, 248, 293, 247, 15], [133, 298, 229, 233, 210, 263], [133, 298, 229, 233, 210, 293, 247], [133, 207, 229, 233, 263], [133, 207, 229, 233, 293, 247], [155, 70, 278, 168, 270, 252], [239, 105, 287, 170], [239, 229, 287, 170], [239, 284, 0, 238], [239, 284, 170, 238], [239, 117, 9, 285], [239, 117, 238, 285], [239, 117, 238, 170], [202, 183, 146, 216, 273, 277], [230, 158, 272, 250, 253, 259], [230, 158, 272, 250, 253, 118], [230, 158, 272, 250, 253, 166], [230, 158, 272, 80, 259], [230, 158, 272, 80, 118], [230, 158, 186, 80, 118], [46, 79, 257, 181], [146, 183, 192, 273, 277], [146, 183, 214, 216, 180], [146, 183, 214, 216, 273, 277], [170, 287, 203, 117], [170, 287, 203, 125], [170, 287, 203, 105], [170, 57, 105, 203], [157, 81, 215, 243, 167], [157, 215, 190, 243], [22, 97, 152, 87, 28, 1], [251, 100, 297, 211], [251, 100, 297, 32], [251, 178, 221, 297], [251, 178, 221, 112], [251, 297, 221, 32], [251, 297, 32, 257], [251, 181, 257, 32], [251, 181, 257, 79, 227], [251, 181, 32, 221], [251, 181, 227, 211], [238, 175, 285, 117], [238, 175, 285, 209, 284], [238, 175, 285, 136], [238, 175, 285, 8], [238, 175, 125, 8], [238, 175, 129, 136], [238, 175, 129, 8], [238, 30, 285, 117], [238, 0, 284, 209], [243, 81, 215, 246, 47, 167], [243, 81, 215, 246, 47, 188], [243, 215, 190, 246], [299, 131, 74, 141], [299, 131, 74, 124], [299, 131, 219, 124, 288], [2, 153, 29, 113, 173], [2, 153, 29, 113, 199], [246, 104, 130, 215, 81, 188], [246, 104, 130, 215, 190], [287, 209, 203, 121], [287, 209, 203, 105], [287, 209, 284, 59], [287, 121, 203, 125], [175, 203, 285, 117], [175, 203, 285, 209], [175, 59, 285, 284, 209], [175, 59, 285, 136], [195, 265, 221, 112, 98], [211, 100, 198, 191], [211, 100, 198, 297], [211, 181, 191, 227, 198], [280, 119, 24, 288], [292, 274, 268, 223, 196], [292, 274, 268, 271, 197, 196, 165], [292, 274, 268, 271, 197, 225], [292, 274, 268, 271, 197, 89], [292, 274, 268, 271, 34, 89], [292, 274, 41, 196, 223], [292, 274, 41, 196, 271], [292, 274, 41, 225, 271], [292, 274, 85, 197, 225], [292, 274, 85, 223], [292, 34, 142, 224], [292, 34, 268, 179, 89], [292, 34, 268, 224], [292, 165, 276, 271], [292, 165, 268, 164, 196, 271], [292, 165, 268, 164, 224], [292, 165, 268, 224, 197], [292, 197, 142, 85, 224], [292, 197, 142, 85, 225], [292, 196, 164, 64, 268], [292, 196, 223, 179, 147], [292, 196, 223, 179, 64, 268], [292, 196, 223, 179, 41], [292, 196, 271, 147], [292, 261, 276, 223, 179, 147], [292, 261, 276, 223, 179, 64], [292, 261, 276, 223, 179, 41], [292, 261, 276, 223, 85], [292, 261, 276, 142, 64], [292, 261, 276, 142, 225, 85], [292, 261, 276, 225, 41, 271], [292, 261, 276, 271, 147], [292, 261, 164, 142, 64, 224], [292, 261, 164, 142, 225], [292, 261, 164, 268, 64, 224], [292, 261, 164, 268, 271, 225], [292, 261, 164, 268, 271, 89], [292, 261, 268, 223, 64, 179], [292, 261, 268, 223, 64, 224], [292, 261, 268, 179, 89], [292, 261, 224, 223, 147], [292, 261, 224, 223, 41], [292, 261, 224, 223, 85], [292, 261, 224, 142, 85], [1, 87, 152, 43, 28, 21], [1, 87, 152, 43, 28, 97], [33, 86, 272, 118, 253, 250], [33, 86, 272, 118, 80], [209, 285, 121, 203], [166, 253, 86, 272, 231], [166, 253, 86, 272, 250], [288, 131, 95, 24, 124], [288, 131, 95, 24, 111], [288, 119, 283, 24, 95], [250, 86, 259, 253, 272], [57, 121, 203, 125], [57, 121, 203, 9], [247, 15, 248, 126, 298, 293], [247, 126, 298, 293, 210, 232, 248], [247, 126, 298, 293, 210, 232, 229], [247, 126, 298, 293, 210, 233, 248], [247, 126, 298, 293, 210, 233, 229], [285, 9, 203, 117], [285, 9, 203, 121], [171, 274, 85, 223], [171, 274, 85, 62, 225], [171, 62, 224, 85], [171, 165, 224, 240], [171, 261, 147, 223, 179], [171, 261, 147, 223, 224], [171, 261, 147, 240, 224], [171, 261, 225, 240], [171, 261, 225, 85], [171, 261, 85, 223, 224], [178, 265, 221, 297, 98], [178, 265, 221, 112, 98], [178, 265, 221, 112, 145], [297, 100, 198, 32], [297, 100, 198, 265], [297, 198, 32, 257], [66, 263, 236, 210, 232], [66, 263, 248, 298, 63], [66, 263, 248, 298, 210, 232], [32, 191, 198, 100], [32, 191, 198, 257, 181], [15, 248, 263, 298, 126], [15, 248, 38, 293], [265, 100, 191, 198], [181, 79, 198, 257, 227, 191], [173, 29, 6, 153], [63, 263, 229, 233, 298], [63, 263, 229, 233, 207], [80, 86, 272, 231, 259], [80, 86, 272, 231, 118], [232, 263, 248, 210, 298, 126], [232, 263, 229, 126, 210, 236], [232, 263, 229, 126, 210, 298], [232, 263, 229, 207, 236], [232, 126, 293, 236, 229, 210], [248, 263, 210, 233, 298, 126], [126, 229, 233, 210, 263, 236], [126, 229, 233, 210, 263, 298], [126, 229, 233, 210, 293, 236], [76, 50, 219, 131], [119, 283, 45, 95], [118, 86, 231, 253, 272], [272, 231, 86, 253, 259], [263, 236, 229, 233, 207], [62, 85, 225, 276], [240, 142, 224, 197], [240, 142, 224, 261], [240, 142, 225, 261, 276], [240, 224, 268, 197, 165], [240, 224, 268, 261], [240, 224, 41, 261], [240, 271, 41, 196, 274], [240, 271, 41, 225, 276, 261], [240, 271, 41, 225, 274], [240, 271, 268, 261, 225], [240, 271, 268, 274, 197, 196, 165], [240, 271, 268, 274, 197, 225], [240, 271, 261, 276, 147]]
+print(len(j))
+G = nx.read_edgelist(fh, nodetype=int)
+fh.close()
+M = np.empty((len(G),len(j)))
+print(M.shape)
+l = list(G.nodes)
+v = 0
+for i in l:
+ print(v)
+ v = v + 1
+ for line in range(len(j)):
+ #print(f[line])
+ if i in j[line]:
+ M[l.index(i), line] = 1
+ else:
+ M[l.index(i), line] = 0
+
+print(M)
+adjacency = nx.adjacency_matrix(G)
+#print(adjacency.shape)
+#print(adjacency)
+adjacency = adjacency.todense()
+#print(adjacency)
+adjacency = sparse.csr_matrix(adjacency)
+louvain = Louvain()
+#print(adjacency)
+
+#adjacency = karate_club()
+start = time.time()
+labels = louvain.fit_transform(adjacency)
+print("Time",time.time()-start)
+print(len(labels), np.asarray(labels))
+print("modularity Louvain",np.round(modularity(adjacency, labels), 10))
+
+M = sparse.csr_matrix(M)
+#print(M)
+start = time.time()
+labels2 = louvain.fit_transform(M)
+k = False
+#for i in range(len(labels)):
+# print(i,labels[i],labels2[i])
+print(k)
+print("Time",time.time()-start)
+print(len(labels), labels2)
+
+print("modularity ta3na",np.round(modularity(adjacency, labels2), 10))
+
+
+p = []
+for i in range(len(labels)):
+ p.append(i)
+Y = []
+for i in range(len(set(labels))):
+ Y.append(i)
+print(Y)
+fo = open("example1.model", "w")
+strin = "{\"n_elements\": " + str(len(labels)) + ", \"n_clusters\": " + str(len(set(labels))) + ", \"elements\": " + str(p) + ", \"clusters\": " + str(Y)
+strin = strin + ", \"elm2clu_dict\": {"
+for i in range(len(labels)):
+ strin = strin + "\"" + str(i) + "\"" + ": [" + str(labels[i]) + "],"
+strin = strin[:-1] + "}, \"clu2elm_dict\": {"
+O = []
+for i in Y:
+ indices = []
+
+ for j in range(len(labels)):
+ if labels[j] == i:
+ indices.append(j)
+ O.append(len(indices))
+ strin = strin + "\"" + str(i) + "\": " + str(indices) +", "
+strin = strin[:-2] + "}" + ", \"hier_graph\": {\"directed\": true, \"multigraph\": false, \"graph\": {}, \"nodes\":[],\"links\": []}, \"clu_size_seq\": "+ str(O) +", \"is_disjoint\": true,\"is_hierarchical\": false, \"hierclusdict\": null}"
+fo.write(strin + "\n")
+fo.close()
+
+
+
+
+
+fo = open("example1.model", "a")
+strin = "{\"n_elements\": " + str(len(labels2)) + ", \"n_clusters\": " + str(len(set(labels2))) + ", \"elements\": " + str(p) + ", \"clusters\": " + str(Y)
+strin = strin + ", \"elm2clu_dict\": {"
+for i in range(len(labels2)):
+ strin = strin + "\"" + str(i) + "\"" + ": [" + str(labels2[i]) + "],"
+strin = strin[:-1] + "}, \"clu2elm_dict\": {"
+O = []
+for i in Y:
+ indices = []
+
+ for j in range(len(labels2)):
+ if labels2[j] == i:
+ indices.append(j)
+ O.append(len(indices))
+ strin = strin + "\"" + str(i) + "\": " + str(indices) +", "
+strin = strin[:-2] + "}" + ", \"hier_graph\": {\"directed\": true, \"multigraph\": false, \"graph\": {}, \"nodes\":[],\"links\": []}, \"clu_size_seq\": "+ str(O) +", \"is_disjoint\": true,\"is_hierarchical\": false, \"hierclusdict\": null}"
+fo.write(strin + "\n")
+fo.close()
+################ Propagation
+
+propagation = PropagationClustering()
+#graph = karate_club(metadata=True)
+#adjacency = graph.adjacency
+#print(adjacency)
+start = time.time()
+labels = propagation.fit_transform(M)
+print("Time",time.time()-start)
+print("PropagationClustering",len(set(labels)),labels)
+print("modularity ta3na",np.round(modularity(adjacency, labels), 10))
+start = time.time()
+labels = propagation.fit_transform(adjacency)
+print("Time",time.time()-start)
+print("PropagationClustering",len(set(labels)),labels)
+print("modularity propagation",np.round(modularity(adjacency, labels), 10))
+labels = propagation.fit_transform(M)
+print("modularity propagation ta3na",np.round(modularity(adjacency, labels), 10))
+
+
+
+
+
+
+fo = open("example1.model", "a")
+strin = "{\"n_elements\": " + str(len(labels)) + ", \"n_clusters\": " + str(len(set(labels))) + ", \"elements\": " + str(p) + ", \"clusters\": " + str(Y)
+strin = strin + ", \"elm2clu_dict\": {"
+for i in range(len(labels)):
+ strin = strin + "\"" + str(i) + "\"" + ": [" + str(labels[i]) + "],"
+strin = strin[:-1] + "}, \"clu2elm_dict\": {"
+O = []
+for i in Y:
+ indices = []
+
+ for j in range(len(labels)):
+ if labels[j] == i:
+ indices.append(j)
+ O.append(len(indices))
+ strin = strin + "\"" + str(i) + "\": " + str(indices) +", "
+strin = strin[:-2] + "}" + ", \"hier_graph\": {\"directed\": true, \"multigraph\": false, \"graph\": {}, \"nodes\":[],\"links\": []}, \"clu_size_seq\": "+ str(O) +", \"is_disjoint\": true,\"is_hierarchical\": false, \"hierclusdict\": null}"
+fo.write(strin + "\n")
+fo.close()
+
+
+from sklearn import datasets
+X = datasets.load_iris(return_X_y=False)
+print(X)
+from sklearn import metrics
+print(metrics.silhouette_score(adjacency, labels, metric='euclidean'))
+
+from sknetwork.data import house
+#adjacency = house()
+#labels = louvain.fit_transform(adjacency)
+#print("louvain",labels)
+#print(adjacency)
+#M = [[0,0,0,1],[0,0,0,1],[0,0,0,1],[1,1,1,0]]#,[0,0,0,0,0,1,1],[0,0,0,0,1,0,1],[0,0,0,0,1,1,0]]
+#M = sparse.csr_matrix(M)
+#labels = louvain.fit_transform(M)
+#print(M.shape,labels)
+nx.draw(G, with_labels=True, node_size=100)
+plt.show()
\ No newline at end of file
diff --git a/Train2.py b/Train2.py
index 7d5bc5e6c208a26ad86f37f1216a183055d107d5..356f545c66c4ec3e9aec2eadb6940f3d186eb976 100644
--- a/Train2.py
+++ b/Train2.py
@@ -8,6 +8,8 @@ from keras.utils.generic_utils import get_custom_objects
from tensorflow.keras.models import load_model
import sklearn.preprocessing as s
import time
+import matplotlib.pyplot as plt
+
def step(x):
return K.relu(K.sigmoid(x))
get_custom_objects().update({'step': Activation(step)})
@@ -43,17 +45,36 @@ def model(input_shape):
return model
train_x,train_y,test_x,test_y,val_x,val_y = load_data()
-#model=model((100,65))
+model=model((100,6))
print(train_x.shape)
print(test_x.shape)
print(val_x.shape)
-#model.fit(train_x,train_y,validation_data=(val_x,val_y),epochs=30,batch_size=2)
+history = model.fit(train_x,train_y,validation_data=(val_x,val_y),epochs=300,batch_size=8)
-#model.save('model_test.h5')
+model.save('model_new.h5')
-model = load_model('model_test.h5')
+model = load_model('model_new.h5')
print(model.evaluate(test_x,test_y))
+
+print(history.history.keys())
+# summarize history for accuracy
+plt.plot(history.history['binary_accuracy'])
+plt.plot(history.history['val_binary_accuracy'])
+plt.title('model accuracy')
+plt.ylabel('accuracy')
+plt.xlabel('epoch')
+plt.legend(['train', 'test'], loc='upper left')
+plt.show()
+# summarize history for loss
+plt.plot(history.history['loss'])
+plt.plot(history.history['val_loss'])
+plt.title('model loss')
+plt.ylabel('loss')
+plt.xlabel('epoch')
+plt.legend(['train', 'test'], loc='upper left')
+plt.show()
+"""
print( 2900 % 100)
data_train=np.load('INoutput_data_val.npy')
print("data_train",type(data_train),data_train.shape,data_train)
@@ -73,5 +94,4 @@ for i in range(2900):
tab = []
end = time.time()
-
-print("Time",end - start)
+"""
\ No newline at end of file
diff --git a/__pycache__/ANN_CLIQUES.cpython-37.pyc b/__pycache__/ANN_CLIQUES.cpython-37.pyc
index ccd84637e7a1e4c055c35462a0f12f608477b4e7..f7a3c24071fc776ba4114622b40a1e1d653f8cac 100644
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diff --git a/__pycache__/train.cpython-37.pyc b/__pycache__/train.cpython-37.pyc
index 606756cc841978fa11588d380a9a4bf12110db51..28ef0d668c128617576e1378a00faac33fe37811 100644
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diff --git a/billets.pdf b/billets.pdf
deleted file mode 100644
index e0bf2aa3dac87d5b0245f4d9d5d5a01d83d2ab71..0000000000000000000000000000000000000000
Binary files a/billets.pdf and /dev/null differ
diff --git a/data/clique_1/labels2.npy b/data/clique_1/labels2.npy
new file mode 100644
index 0000000000000000000000000000000000000000..0d1fff892cd33347ee2bbb9d4dd10d19a5f3973e
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diff --git a/data/clique_1/size.npy b/data/clique_1/size.npy
new file mode 100644
index 0000000000000000000000000000000000000000..067196f5d080b7745e3431abec4b85b20a3693f2
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diff --git a/data_test.npy b/data_test.npy
new file mode 100644
index 0000000000000000000000000000000000000000..d04c65f88a57aad9e9b5743950cacd374e78bcc2
Binary files /dev/null and b/data_test.npy differ
diff --git a/data_train.npy b/data_train.npy
new file mode 100644
index 0000000000000000000000000000000000000000..2726cae9f1912869bcb9c73c7f89a2ac45bcd9e6
Binary files /dev/null and b/data_train.npy differ
diff --git a/data_val.npy b/data_val.npy
new file mode 100644
index 0000000000000000000000000000000000000000..194abf01b5530f68769b3a964552afaf6da39af9
Binary files /dev/null and b/data_val.npy differ
diff --git a/example1.model b/example1.model
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..f2a378ae1afe0a5e32da0f55e73a88dde65fe43a 100644
--- a/example1.model
+++ b/example1.model
@@ -0,0 +1,3 @@
+{"n_elements": 300, "n_clusters": 12, "elements": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299], "clusters": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], "elm2clu_dict": {"0": [1],"1": [1],"2": [1],"3": [2],"4": [3],"5": [1],"6": [1],"7": [2],"8": [1],"9": [3],"10": [1],"11": [1],"12": [1],"13": [3],"14": [1],"15": [1],"16": [0],"17": [0],"18": [0],"19": [0],"20": [0],"21": [0],"22": [0],"23": [0],"24": [3],"25": [0],"26": [0],"27": [0],"28": [8],"29": [0],"30": [0],"31": [3],"32": [11],"33": [7],"34": [11],"35": [11],"36": [10],"37": [11],"38": [11],"39": [11],"40": [11],"41": [11],"42": [11],"43": [3],"44": [6],"45": [11],"46": [11],"47": 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92, 93, 95, 96, 100, 101, 103, 104, 107, 109, 113, 135, 187, 228, 268, 286, 287], "2": [3, 7, 87, 94, 116, 144, 150, 172, 195, 196, 223, 230, 232, 233, 234, 235, 236, 237, 275, 276, 277, 281, 288, 289, 294, 295, 296, 297], "3": [4, 9, 13, 24, 31, 43, 83, 97, 108, 125, 136, 155, 156, 157, 159, 160, 164, 165, 204, 205, 206, 207, 208, 211, 231, 252, 254, 274], "4": [72, 86, 98, 115, 118, 126, 142, 162, 169, 182, 189, 197, 201, 209, 226, 227, 229, 241, 242, 243, 244, 253, 257, 269, 282, 290, 291, 292], "5": [55, 69, 90, 120, 122, 123, 124, 127, 128, 129, 130, 131, 132, 134, 138, 140, 198, 200, 202, 203, 218, 271, 279, 283], "6": [44, 51, 78, 84, 106, 110, 161, 190, 245, 246, 247, 248, 249, 250, 251, 261, 263, 264, 265, 266, 267, 293, 298, 299], "7": [33, 47, 89, 99, 145, 146, 147, 149, 151, 152, 153, 154, 188, 191, 192, 193, 194, 210, 240, 255, 256, 278, 280], "8": [28, 52, 53, 56, 57, 58, 59, 60, 61, 62, 63, 64, 114, 117, 141, 143, 238, 239, 258, 259, 260, 262, 270], "9": [67, 85, 119, 121, 139, 163, 166, 168, 170, 171, 173, 175, 176, 217, 219, 220, 221, 222, 224, 225, 284, 285], "10": [36, 54, 65, 66, 68, 70, 71, 73, 74, 75, 76, 77, 79, 102, 105, 133, 137, 185, 186], "11": [32, 34, 35, 37, 38, 39, 40, 41, 42, 45, 46, 80, 81, 82, 111, 112]}, "hier_graph": {"directed": true, "multigraph": false, "graph": {}, "nodes":[],"links": []}, "clu_size_seq": [35, 30, 28, 28, 28, 24, 24, 23, 23, 22, 19, 16], "is_disjoint": true,"is_hierarchical": false, "hierclusdict": null}
+{"n_elements": 300, "n_clusters": 13, "elements": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 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176, 217, 219, 220, 221, 222, 224, 225, 284, 285], "10": [32, 34, 35, 37, 38, 39, 40, 41, 42, 45, 46, 80, 81, 82, 111, 112], "11": [36, 54, 65, 66, 68, 70, 71, 73, 74, 75, 76, 77, 79, 102, 105, 133, 137, 185, 186]}, "hier_graph": {"directed": true, "multigraph": false, "graph": {}, "nodes":[],"links": []}, "clu_size_seq": [30, 28, 28, 23, 28, 23, 24, 24, 21, 22, 16, 19], "is_disjoint": true,"is_hierarchical": false, "hierclusdict": null}
+{"n_elements": 300, "n_clusters": 16, "elements": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 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"hier_graph": {"directed": true, "multigraph": false, "graph": {}, "nodes":[],"links": []}, "clu_size_seq": [21, 14, 23, 16, 24, 22, 23, 28, 14, 30, 19, 1], "is_disjoint": true,"is_hierarchical": false, "hierclusdict": null}
diff --git a/merge.py b/merge.py
index 803e814638beb537f0c01f9dbcf3eda98a2f8204..7e045152aa8227d17a21f4f57373a13079f1525b 100644
--- a/merge.py
+++ b/merge.py
@@ -1,7 +1,7 @@
import os,numpy as np
-BASE_PATH = "data"
-DIR ="clique_1"
-PATH = os.path.join(BASE_PATH,DIR)
+BASE_PATH = "data_val"
+#DIR ="clique_1"
+PATH = os.path.join(BASE_PATH)
#merger les files dans un seule file "data.npy"
def read(file):
@@ -22,6 +22,6 @@ for _,_,files in os.walk(os.path.join(PATH)):
data.append(read(file))
print(data[-1].shape)
data = np.asarray(data)
-#np.save(os.path.join(PATH,"data.npy"),data)
-data = np.load(os.path.join(PATH,"data.npy"),allow_pickle=True)
+np.save(os.path.join(PATH,"data.npy"),data)
+#data = np.load(os.path.join(PATH,"data.npy"),allow_pickle=True)
print(data.shape)
diff --git a/merge_embaded.py b/merge_embaded.py
index 8f08aa03f533ce6e085684e7d5088394424fbecc..85b0a6830f0fd2700a4a656d944acaa0d91af229 100644
--- a/merge_embaded.py
+++ b/merge_embaded.py
@@ -6,7 +6,7 @@ import numpy as np
DIRS = ['data_test','data_val','data_train']
for DIR in DIRS:
for _,_,files in os.walk(DIR):
- data = np.zeros((len(files),2900,64))
+ data = np.zeros((len(files),100,6))
i = 0
for file in files:
t = np.load(os.path.join(DIR,file),allow_pickle=True)
@@ -14,4 +14,5 @@ for DIR in DIRS:
data[i]=t
i+=1
np.save(os.path.join(DIR,DIR),data)
- print(data.shape)
\ No newline at end of file
+ print(data.shape)
+ print(DIR,data)
\ No newline at end of file
diff --git a/model_new.h5 b/model_new.h5
new file mode 100644
index 0000000000000000000000000000000000000000..92073bfa077d2365e71269b41c7ece0b6d8da957
Binary files /dev/null and b/model_new.h5 differ
diff --git a/node2vec/src/INoutput_data_val.npy b/node2vec/src/INoutput_data_val.npy
deleted file mode 100644
index 62aa5cad5ec63ca561a307d7518e0c849fd4ce42..0000000000000000000000000000000000000000
Binary files a/node2vec/src/INoutput_data_val.npy and /dev/null differ
diff --git a/node2vec/src/graph/labfin.npy b/node2vec/src/graph/labfin.npy
deleted file mode 100644
index a23809f038747c95f99dc8c48ca26c1e9ab3334d..0000000000000000000000000000000000000000
Binary files a/node2vec/src/graph/labfin.npy and /dev/null differ
diff --git a/node2vec/src/graph/sam.npy b/node2vec/src/graph/sam.npy
deleted file mode 100644
index a227693cc98d87fd58ff9253638946b5661830e4..0000000000000000000000000000000000000000
Binary files a/node2vec/src/graph/sam.npy and /dev/null differ
diff --git a/node2vec/src/graph/test.npy b/node2vec/src/graph/test.npy
deleted file mode 100644
index 87f0574dd1422b8f24975983ef129173258dfb4b..0000000000000000000000000000000000000000
Binary files a/node2vec/src/graph/test.npy and /dev/null differ
diff --git a/node2vec/src/graph/test_Bip.npy b/node2vec/src/graph/test_Bip.npy
deleted file mode 100644
index 142939e6a6db11e74ac7be5f7a7082f13f96e4b8..0000000000000000000000000000000000000000
Binary files a/node2vec/src/graph/test_Bip.npy and /dev/null differ
diff --git a/node2vec/src/graph/test_Cliques.npy b/node2vec/src/graph/test_Cliques.npy
deleted file mode 100644
index 4653c5af6f49e217badb15222353e0326c3f8a9a..0000000000000000000000000000000000000000
Binary files a/node2vec/src/graph/test_Cliques.npy and /dev/null differ
diff --git a/node2vec/src/graph/test_Star.npy b/node2vec/src/graph/test_Star.npy
deleted file mode 100644
index 1b7ce3f1dd9fec901aa24daa2528dc2d250b047a..0000000000000000000000000000000000000000
Binary files a/node2vec/src/graph/test_Star.npy and /dev/null differ
diff --git a/node2vec/tnaket.emd b/node2vec/tnaket.emd
deleted file mode 100644
index e40188a7e7281cb01f85c145e235e459350b4b69..0000000000000000000000000000000000000000
Binary files a/node2vec/tnaket.emd and /dev/null differ
diff --git a/split_data.py b/split_data.py
index 5a61337742b0df0aed5f4aa1279d63b9b0f149d6..82c7580203019f9db99619d543ee8eaa068be612 100644
--- a/split_data.py
+++ b/split_data.py
@@ -4,80 +4,80 @@ import numpy as np
#data = np.load('data/clique_1/data.npy',allow_pickle=True)
#matrix = np.load('data/clique_1/data2.npy',allow_pickle=True)
#out = np.load('data/clique_1/output.npy',allow_pickle=True)
-#size = np.load('data/clique_1/size.npy',allow_pickle=True)
-labels = np.load('data/clique_2/labels.npy',allow_pickle=True)
-data = np.load('INoutput_data.npy')
+size = np.load('data/clique_1/size.npy',allow_pickle=True)
+labels = np.load('data/clique_1/labels2.npy',allow_pickle=True)
+#data = np.load('INoutput_data.npy')
-print(data.shape)
+#print(data.shape)
print(labels.shape)
#print(matrix.shape)
#print(out.shape)
-#print(size.shape)
+print(size.shape)
-print(data[2])
+#print(data[2])
print(labels[0])
#print(matrix[0])
#print(out[0])
-#print(size[0])
+print(size[0])
print("DONE")
#définition du ratio train / test
split_rate=0.8
-k = int(len(data)*split_rate)
+k = int(len(size)*split_rate)
#données d'apprentissage
-data_train = data[:k]
+#data_train = data[:k]
#matrix_train = matrix[:k]
#out_train = out[:k]
-#size_train = size[:k]
+size_train = size[:k]
label_train = labels[:k]
#données de test
-data_test = data[k:]
+#data_test = data[k:]
#matrix_test = matrix[k:]
-#size_test = size[k:]
+size_test = size[k:]
#out_test = out[k:]
label_test = labels[k:]
#définition du ratio train / validation
split_rate=0.8
-k = int(len(data_train)*split_rate)
+k = int(len(size_train)*split_rate)
#données de validation
-data_val = data_train[k:]
+#data_val = data_train[k:]
#matrix_val = matrix_train[k:]
#out_val = out_train[k:]
-#size_val = size_train[k:]
+size_val = size_train[k:]
label_val = label_train[k:]
#donnée d'apprentissage
-data_train = data_train[:k]
+#data_train = data_train[:k]
#matrix_train = matrix_train[:k]
#out_train = out_train[:k]
-#size_train = size_train[:k]
+size_train = size_train[:k]
label_train = label_train[:k]
print(label_train.shape,label_test.shape,label_val.shape)
-print(data_train.shape,data_test.shape,data_val.shape)
+#print(data_train.shape,data_test.shape,data_val.shape)
print(label_train[0])
-print(data_train[0])
+#print(data_train[0])
#print(out_train.shape,out_test.shape,out_val.shape)
-#print(size_train.shape,size_test.shape,size_val.shape)
+print(size_train.shape,size_test.shape,size_val.shape)
#sauvegarde
-np.save("node2vec/src/graph/data_train.npy", data_train)
-np.save("node2vec/src/graph/data_test.npy", data_test)
-np.save("node2vec/src/graph/data_val.npy", data_val)
+#np.save("node2vec/src/graph/data_train.npy", data_train)
+#np.save("node2vec/src/graph/data_test.npy", data_test)
+#np.save("node2vec/src/graph/data_val.npy", data_val)
#np.save("matrix_train.npy", matrix_train)
#np.save("matrix_test.npy", matrix_test)
#np.save("matrix_val.npy", matrix_val)
#np.save("out_train.npy", out_train)
#np.save("out_test.npy", out_test)
#np.save("out_val.npy", out_val)
-#np.save("size_train.npy", size_train)
-#np.save("size_test.npy", size_test)
-#np.save("size_val.npy", size_val)
+np.save("node2vec/src/graph/size_train.npy", size_train)
+np.save("node2vec/src/graph/size_test.npy", size_test)
+np.save("node2vec/src/graph/size_val.npy", size_val)
np.save("node2vec/src/graph/label_val.npy",label_val)
np.save("node2vec/src/graph/label_train.npy",label_train)
np.save("node2vec/src/graph/label_test.npy",label_test)
\ No newline at end of file
diff --git a/train.py b/train.py
index a49a2748c87734784b8a9ae2d94d4b3ebb21cc40..62967ff762d5039b90a951d968b5911bff12acce 100644
--- a/train.py
+++ b/train.py
@@ -83,6 +83,7 @@ def Training():
#print(data_tr.shape)
pred = model.predict(data_tr)
pred = np.round(pred)
+ print("pred",pred)
#for i in range(len(pred)):
# print(pred[i], np.count_nonzero(pred[i]))
# print("data_train", type(data_tr), data_train.shape, data_tr[0].shape) # ,data_train,data_train[1])
diff --git a/transformation_clus.py b/transformation_clus.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ba93dcf10410e0bf6393fcadebe498cbe515a54
--- /dev/null
+++ b/transformation_clus.py
@@ -0,0 +1,119 @@
+import networkx as nx
+import matplotlib.pyplot as plt
+import numpy as np
+import copy
+from networkx.algorithms import clique
+import time
+f = open("C:/Users/LENOVO/Desktop/karate.edgelist", "r")
+
+"""
+x = [[2,7,10,19,30,42,47,53,61],[1,5,6,9,13,17,22,25,26,27,31,32,41,48,54,56,57,60],[3,8,28,36,39,59],[12,14,16,20,33,34,37,38,40,43,44,46,49,50,52,58]]
+for i in x:
+ for j in i:
+ for k in i:
+ if j < k:
+ print("x[",j,",",k,"] <- NA")
+
+f = open("C:/Users/LENOVO/Desktop/karate.edgelist", "r")
+G = nx.read_edgelist(f,nodetype=int)
+x = []
+Y = []
+for i in range(len(G.nodes)):
+ x.append(list(G.neighbors(i)))
+ Y.append(i)
+ #print(i,list(G.neighbors(i)))
+print(x)
+c = []
+for i in range(len(x)):
+ c = []
+ for j in x[i]:
+ if j > i:
+ c.append(j)
+ x[i] = c
+print(x)
+for i in range(len(x)):
+ for j in range(len(x)):
+ v = []
+ t = 0
+ if len(x[i]) > len(x[j]):
+ #print(x[i],x[j])
+ v.append(x[i])
+ t = Y[i]
+ x[i] = x[j]
+ x[j] = v[0]
+ Y[i] = Y [j]
+ Y[j] = t
+ #print(x[i], x[j])
+print(x)
+print(Y,len(Y))
+for i in x:
+ for j in range(len(i)):
+ i[j] = i[j] + 1
+print(x)
+
+R = np.zeros(len(Y))
+for i in range(len(R)):
+ for j in x:
+ if i + 1 in j:
+ R[i] = R[i] + 1
+print(R,len(R))
+print(len(G.edges))
+P =[]
+for i in range(len(Y)):
+ for j in x:
+ if i+1 in j:
+ j[j.index(i+1)] = Y[i]
+ print("index ",i+1,j)
+for i in range(len(Y)):
+ for j in x[i]:
+ print("x[",i+1,",",j,"] <- NA")
+"""
+
+f1 = open("C:/Users/LENOVO/Desktop/o.txt","r")
+
+paragraph = f1.readlines()
+print("hna")
+r = []
+x = []
+for line in paragraph:
+ #f1.write(line.strip()+",1\n")
+ tmp = line.strip().split(' ')
+ x.append(tmp[1])
+ if int(tmp[1]) not in r:
+ r.append(int(tmp[1]))
+ #print(tmp[0])
+ #print(tmp[1])
+ #x = []
+ #x.append(int(tmp[0]))
+ #x.append(int(tmp[1]))
+ #if tmp[0] != tmp[1] and sorted(x) not in r:
+ #r.append(sorted(x))
+print(r,len(x))
+# f1.write(str(int(tmp[0]))+","+str(int(tmp[1]))+"\n")
+p = []
+for i in range(len(x)):
+ p.append(i)
+Y = []
+for i in range(len(set(x))):
+ Y.append(i)
+print(Y)
+fo = open("example1.model", "w")
+strin = "{\"n_elements\": " + str(len(x)) + ", \"n_clusters\": " + str(len(set(x))) + ", \"elements\": " + str(p) + ", \"clusters\": " + str(Y)
+strin = strin + ", \"elm2clu_dict\": {"
+for i in range(len(x)):
+ strin = strin + "\"" + str(i) + "\"" + ": [" + str(x[i]) + "],"
+strin = strin[:-1] + "}, \"clu2elm_dict\": {"
+O = []
+print("fffff",len(x))
+for i in Y:
+ indices = []
+
+ for j in range(len(x)):
+ if int(x[j]) == i+1:
+ indices.append(j)
+ O.append(len(indices))
+ print("oooooooooo",O)
+ strin = strin + "\"" + str(i+1) + "\": " + str(indices) +","
+strin = strin[:-2] + "}" + ", \"hier_graph\": {\"directed\": true, \"multigraph\": false, \"graph\": {}, \"nodes\":[],\"links\": []}, \"clu_size_seq\": "+ str(O) +", \"is_disjoint\": true,\"is_hierarchical\": false, \"hierclusdict\": null}"
+fo.write(strin + "\n")
+fo.close()