def constructDecisionTree(self):
# initialize
nsfdb = MySQLdb.connect(host="localhost", user="******", db="nsfdb")
nsfcur = nsfdb.cursor()
self.capabilitylist = []
self.learning_input = []
self.learning_output = []
self.nsflist = []
self.nsf_capability = []
# find all registered capabilities
nsfcur.execute("SELECT DISTINCT cname FROM capabilitytable")
rows = nsfcur.fetchall()
rowlen = len(rows)
for ptr in rows:
self.capabilitylist.append(ptr[0])
self.learning_output.append([])
# construct database for decision tree
nsfcur.execute("SELECT DISTINCT nname FROM nsftable")
rows = nsfcur.fetchall()
for ptr in rows:
self.nsflist.append(ptr[0])
for nsf in self.nsflist:
temp_capalist = []
nsfcur.execute("SELECT cname FROM capabilitytable WHERE nname='" +
nsf + "'")
rows = nsfcur.fetchall()
for ptr in rows:
temp_capalist.append(ptr[0])
self.nsf_capability.append(temp_capalist)
"""
learning_temp = []
for i in range(len(self.capabilitylist)):
learning_temp.append(False)
self.learning_input.append(learning_temp)
for i in range(len(self.nsf_capability)):
self.learning_output[i].append(False)
"""
for x in range(len(self.nsf_capability)):
learning_temp = []
for i in range(len(self.capabilitylist)):
if self.capabilitylist[i] in self.nsf_capability[x]:
learning_temp.append(True)
else:
learning_temp.append(False)
self.learning_input.append(learning_temp)
for y in range(len(self.nsf_capability)):
self.learning_output[y].append(x == y)
for i in range(len(self.nsf_capability)):
capa_temp = []
for j in range(len(self.nsf_capability[i])):
capa_temp.append(self.nsf_capability[i][j])
for j in range(len(self.nsf_capability)):
if i != j:
for k in range(len(self.nsf_capability[j])):
if self.nsf_capability[j][k] in capa_temp:
capa_temp.remove(self.nsf_capability[j][k])
learning_temp = []
for j in range(len(self.capabilitylist)):
if self.capabilitylist[j] in capa_temp:
learning_temp.append(True)
else:
learning_temp.append(False)
self.learning_input.append(learning_temp)
for y in range(len(self.nsf_capability)):
self.learning_output[y].append((i == y and len(capa_temp) > 0))
# construct Decision Tree
self.dtlist = []
for i in range(len(self.nsf_capability)):
self.dtlist.append(
DecisionTree.DecisionTreeNode(self.learning_input,
self.learning_output[i], 0.005,
self.capabilitylist))
nsfcur.close()
nsfdb.close()
