def train_func(data):
now = time.time()
# clustering and save
clusters = Clustering(data).clusters
csv.write_file(cf.clusters_path, clusters)
# make_rule and save
rules = Rule(data, clusters).colection_rules()
csv.write_file(cf.rule_path, rules)
# Predict and save data
x = Predict(data, clusters, rules)
corrects = 0
edit = []
editCollection = []
for ix, record in enumerate(data):
if int(record[0]) == x.predict(record, rules)["predict"]:
corrects += 1
else:
pr_rules = x.predict(record, rules)["rule"]
cr_rules = x.get_rule_truth(record)["rule"]
edit += x.detect_cluster(pr_rules, cr_rules, record[1:len(record)])
print(' Training Time: {:.2f}s'.format(time.time() - now))
print(" Correct: {}, Total: {}, Accuracy: {:.2f}%".format(
corrects, len(data), 100 * corrects / len(data)))
return 100 * corrects / len(data)
def predict(data):
now = time.time()
x = Predict(data, clusters, rules)
corrects = 0
edit = []
editCollection = []
for ix, record in enumerate(data):
if int(record[0]) == x.predict(record, rules)["predict"]:
corrects += 1
else:
pr_rules = x.predict(record, rules)["rule"]
cr_rules = x.get_rule_truth(record)["rule"]
edit += x.detect_cluster(pr_rules, cr_rules, record[1: len(record) ])
return 100*corrects / len(data)
def predict(data, slug):
now = time.time()
x = Predict(data, clusters, rules)
corrects = 0
edit = []
editCollection = []
for ix, record in enumerate(data):
if int(record[0]) == x.predict(record, rules)["predict"]:
corrects += 1
else:
pr_rules = x.predict(record, rules)["rule"]
cr_rules = x.get_rule_truth(record)["rule"]
edit += x.detect_cluster(pr_rules, cr_rules, record[1:len(record)])
print('\n __{} >> Predict time: {:.2f}s'.format(slug, time.time() - now))
print(" __Correct: {}, Total: {}, Accuracy: {:.2f}%\n".format(
corrects, len(data), 100 * corrects / len(data)))
data = csv.read_file(cf.train_path, 'float')
# clustering and save
clusters = Clustering(data).clusters
csv.write_file(cf.clusters_path, clusters)
_2nd = time.time()
print(' __Clustering time: {:.2f}s'.format(_2nd - _1st))
# make_rule and save
rules = Rule(data, clusters).colection_rules()
csv.write_file(cf.rule_path, rules)
_3rd = time.time()
print(' __Making rule time: {:.2f}s'.format(_3rd - _2nd))
# Predict and save data
x = Predict(data, clusters, rules)
corrects = 0
edit = []
editCollection = []
for ix, record in enumerate(data):
if int(record[0]) == x.predict(record, rules)["predict"]:
corrects += 1
else:
pr_rules = x.predict(record, rules)["rule"]
cr_rules = x.get_rule_truth(record)["rule"]
edit += x.detect_cluster(pr_rules, cr_rules, record[1: len(record) ])
_4th = time.time()
print(' __Predict time: {:.2f}s'.format(_4th - _3rd))
def train_func(data):
now = time.time()
# clustering and save
clusters = Clustering(data).clusters
csv.write_file(cf.clusters_path, clusters)
# make_rule and save
rules = Rule(data, clusters).colection_rules()
csv.write_file(cf.rule_path, rules)
# Predict and save data
x = Predict(data, clusters, rules)
corrects = 0
edit = []
editCollection = []
for ix, record in enumerate(data):
if int(record[0]) == x.predict(record, rules)["predict"]:
corrects += 1
else:
pr_rules = x.predict(record, rules)["rule"]
cr_rules = x.get_rule_truth(record)["rule"]
edit += x.detect_cluster(pr_rules, cr_rules, record[1: len(record) ])
for e in edit:
if 0.5 < e[2] and e[2] < 0.99 and e[2] + e[4] == 1:
editCollection.append(e)
def editCollectionSort(x):
return x[2]
editCollection.sort(key=editCollectionSort)
# default fcm_path
fmc = [[0.5 for i in range(2*cf.k_mean)] for i in range(len(data[0]) - 1)]
csv.write_file(cf.fmc_path, fmc)
edit = editCollection
x = ha_predict.Predict(data, clusters, rules, fmc)
correct = x.num_corrects()
train_old = correct*100/len(data)
for attr, flase, u_fasle, true, u_true in edit:
fmc = csv.read_file(cf.fmc_path, 'float')
if true > flase:
fmc[attr][2*true] = u_true/u_fasle*fmc[attr][2*true - 1]
else:
fmc[attr][2*true + 1] = u_true/u_fasle*fmc[attr][2*true + 2]
x = ha_predict.Predict(data, clusters, rules, fmc)
y = x.num_corrects()
if correct <= y:
print(" -----> edited", (attr, flase, u_fasle, true, u_true ), "\n")
correct = y
csv.write_file(cf.fmc_path, fmc)
else:
print(" -----> rejected", (attr, flase, u_fasle, true, u_true ), "\n")
print(" Time: {:.2f}s\n T1FS Train Accuracy: {:.3f}%\n HA_T2FS Accuracy: {:.3f}%".format(time.time() - now, train_old, correct*100/len(data)))
return train_old, correct*100/len(data)