Input : Net Training and Testing Data
Output : PGCM Matrix
'''
PGCM_0 = {}
N = len(train_X[0]) - 1
print "Number of Features ", N
for i in xrange(1, N+1):
for j in xrange(i + 1, N+1):
(new_tr_X, new_tr_y, new_ts_X, new_ts_y) = makeData(
train_X, train_y, test_X, test_y, i, j)
classifier = mushrooms_bfgs.train_decision_tree(
new_tr_X, new_tr_y)
PGCM_0[(i,j)] = nltk.classify.accuracy(classifier,
mushrooms_bfgs.test_decision_tree(
new_ts_X, new_ts_y))*100
print "Finding Accuracy for", i, j
return PGCM_0
if __name__ == "__main__":
timeStart = datetime.datetime.now()
(train_X, train_y, test_X, test_y) = mushrooms_bfgs.initialize(float(1)/2)
print "Number of Training Data =",len(train_y)
print "Number of Testing Data =",len(test_y)
PGCM_0 = makePairs(train_X, train_y, test_X, test_y)
print "Total time taken = ", datetime.datetime.now() - timeStart
f = open("PGCM_0", 'w')
f.write(str(PGCM_0))
f.close()
print "Written to PGCM File"
Input : Net Training and Testing Data
Output : PGCM Matrix
'''
PGCM_0 = {}
N = len(train_X[0]) - 1
print ("Number of Features ", N)
for i in xrange(1, N+1):
for j in xrange(i + 1, N+1):
(new_tr_X, new_tr_y, new_ts_X, new_ts_y) = makeData(
train_X, train_y, test_X, test_y, i, j)
classifier = mushrooms_bfgs.train_decision_tree(
new_tr_X, new_tr_y)
PGCM_0[(i,j)] = nltk.classify.accuracy(classifier,
mushrooms_bfgs.test_decision_tree(
new_ts_X, new_ts_y))*100
print ("Finding Accuracy for", i, j)
return PGCM_0
if __name__ == "__main__":
timeStart = datetime.datetime.now()
(train_X, train_y, test_X, test_y) = mushrooms_bfgs.initialize(float(1)/2)
print ("Number of Training Data =",len(train_y))
print ("Number of Testing Data =",len(test_y))
PGCM_0 = makePairs(train_X, train_y, test_X, test_y)
print ("Total time taken = ", datetime.datetime.now() - timeStart)
f = open("PGCM_0", 'w')
f.write(str(PGCM_0))
f.close()
print ("Written to PGCM File")
z_stat_for_symbiotic, p_val_for_symbiotic = stats.ranksums(
Symbiotic_output, Original_input)
z_stat_for_GA, p_val_for_GA = stats.ranksums(Modified_GA_output,
Original_input)
print(p_val_for_symbiotic, p_val_for_GA)
if max(p_val_for_GA, p_val_for_symbiotic) < 1e-300:
return -1
if (p_val_for_GA > p_val_for_symbiotic):
print("Forest one is better")
return 1
else:
print("Symbiotic is better")
return 0
if __name__ == '__main__':
(train_X, train_y, test_X,
test_y) = mushrooms_bfgs.initialize(float(80) / 100)
(train_X_Sym,
test_X_Sym) = mushrooms_bfgs.featureRemoval(train_X, test_X, [3])
(train_X_For,
test_X_For) = mushrooms_bfgs.featureRemoval(train_X, test_X, [16])
#~ print train_X[0], train_X_Sym[0], train_X_For[0]
if WilcoxonTest(train_X.ravel(), train_X_Sym.ravel(), train_X_For.ravel()):
newData_Tr = train_X_For
newData_Ts = test_X_For
else:
newData_Tr = train_X_Sym
newData_Ts = test_X_Sym
#~ print WilcoxonTest([1,2,3,4], [1,2,3,5],[0,9,10,11])
Input : Net Training and Testing Data
Output : PGCM Matrix
'''
PGCM_0 = {}
N = len(train_X[0]) - 1
print "Number of Features ", N
for i in xrange(1, N + 1):
for j in xrange(i + 1, N + 1):
(new_tr_X, new_tr_y, new_ts_X,
new_ts_y) = makeData(train_X, train_y, test_X, test_y, i, j)
classifier = mushrooms_bfgs.train_decision_tree(new_tr_X, new_tr_y)
PGCM_0[(i, j)] = nltk.classify.accuracy(
classifier,
mushrooms_bfgs.test_decision_tree(new_ts_X, new_ts_y)) * 100
print "Finding Accuracy for", i, j
return PGCM_0
if __name__ == "__main__":
timeStart = datetime.datetime.now()
(train_X, train_y, test_X,
test_y) = mushrooms_bfgs.initialize(float(1) / 2)
print "Number of Training Data =", len(train_y)
print "Number of Testing Data =", len(test_y)
PGCM_0 = makePairs(train_X, train_y, test_X, test_y)
print "Total time taken = ", datetime.datetime.now() - timeStart
f = open("PGCM_0", 'w')
f.write(str(PGCM_0))
f.close()
print "Written to PGCM File"