prepared qpu solving

util/graph.py

@@ -16,3 +16,12 @@ def negate_qubo(qubo):
         negative_qubo[coupler] = -1 * energy
         
     return  negative_qubo
+
+def create_qpu_solver_nxgraph(solver):
+    graph = nx.Graph()
+    
+    graph.name = solver.id
+    
+    graph.add_edges_from(solver.edges)
+    
+    return graph

util/queries.py

@@ -398,6 +398,9 @@ def read_raw_qubo(raw_qubo):
 def read_raw_embedding(raw_embedding):
     emb = {}
     
+    if "embedding" in raw_embedding:
+        raw_embedding = raw_embedding["embedding"]
+        
     for entry in raw_embedding:
         emb[tuple(entry[0])] = entry[1]
         

util/random_instance_pool.py

@@ -116,12 +116,12 @@ class Random_logistic_variable_distribution:
                  min_variables,
                  max_variables,
                  alpha_point_of_interest,
-                 steepnesss):
+                 steepness):
         self.__number_of_clauses = number_of_clauses
         self.__min_variables = min_variables
         self.__max_variables = max_variables
         self.__alpha_point_of_interest = alpha_point_of_interest
-        self.__steepnesss = steepnesss
+        self.__steepness = steepness
         
     def random(self):
         number_of_variables = 0
@@ -131,7 +131,7 @@ class Random_logistic_variable_distribution:
             
             alpha = inv_logistic(random.random(),
                                  1,
-                                 self.__steepnesss,
+                                 self.__steepness,
                                  self.__alpha_point_of_interest)
             
             number_of_variables = int(self.__number_of_clauses / alpha)
@@ -140,6 +140,17 @@ class Random_logistic_variable_distribution:
 
 def inv_logistic(x, L, k, x0):
     return math.log(math.pow(x / (L-x), 1/k)) + x0
+
+def create_random_logistic_pool(logistic_variable_distr_params, number_of_instances,
+                                instance_params):
+    
+    logist_distr = Random_logistic_variable_distribution(**logistic_variable_distr_params)
+    
+    rnd_range = Random_range(logist_distr, number_of_instances)
+    
+    param_range = Instance_parameter_variable_range(instance_params, rnd_range)
+    
+    return Random_instance_pool(param_range)
     
 def test():
     sns.set()

util/script.py

@@ -7,12 +7,14 @@ import mysql.connector
 import networkx as nx
 from . import queries
 from . import graph
+from . import SAT2QUBO
 import minorminer
 from tqdm import tqdm
 import numpy as np
 import random
 import sys
 
+
 def readConfig(configFilePath):
     config = configparser.ConfigParser()
     
@@ -177,6 +179,15 @@ def write_qubo_to_pool_db(collection, qubo, sat_instance_id):
     
     collection.insert_one(doc)
     
+def create_wmis_qubos_for_scope(db, scope):
+    instances = queries.Instance_scope_query(db)
+    instances.query(scope)
+    
+    for instance, instance_id in instances:
+        qubo = SAT2QUBO.WMISdictQUBO(instance)
+        
+        write_qubo_to_pool_db(db["wmis_qubos"], qubo, instance_id)
+    
 def __qubo_to_JSON(qubo):
     quboJSON = []
     
@@ -295,7 +306,7 @@ def find_wmis_embeddings_for_scope(db, scope, solver_graph):
                                                    percentage))
     print("{} new embeddigns found".format(new_embeddings_found))
 
-def save_simulated_annealing_result(collection, result, solver_input, emb_list_index):
+def save_sample_set(collection, result, solver_input, emb_list_index, run):
     doc = {}
     
     doc["data"] = result.to_serializable()
@@ -304,9 +315,13 @@ def save_simulated_annealing_result(collection, result, solver_input, emb_list_i
         "embedding_id": solver_input["embeddings_id"],
         "list_index": emb_list_index
     }
+    doc["run"] = run
     
     collection.insert_one(doc)
     
+def save_qpu_result():
+    doc = {}
+    
 def analyze_wmis_sample(sample):
     data = {}