def main():
filename = "SPECT.csv"
attributes = []
rows = []
with open(filename, 'r') as csvfile:
csvreader = csv.reader(csvfile)
attributes = next(csvreader)
for row in csvreader:
rows.append(row)
k = 10
accuracy = []
summ = 0
for i in range(1, k + 1):
after_fold = fold(rows, i, k)
train_set = after_fold[0]
test_set = after_fold[1]
acc = train(train_set, attributes, test_set)
accuracy.append(acc)
print("The Accuracy for each fold is as follows : ")
for i in accuracy:
summ += i
print(math.ceil(i))
summ = summ / k
print("Average Accuracy : ", summ)
def main():
filename = "IRIS.csv"
attributes = []
rows = []
with open(filename, 'r') as csvfile:
csvreader = csv.reader(csvfile)
attributes = next(csvreader)
for row in csvreader:
rows.append(row)
print(len(attributes))
k = 10
#n=int(input("Enter the number of nodes at hidden layer : "))
n = 5
max_acc = 0
threshold = 0.1
for j in range(k):
for i in range(1, k + 1):
constant = i / 10
Rows = shuffle(rows)
accuracy = []
Pre = []
Re = []
for i in range(1, k + 1):
after_fold = fold(Rows, i, k)
train_set = after_fold[0]
test_set = after_fold[1]
intermediate = train(train_set, test_set, attributes, constant,
n, threshold)
acc = intermediate[0]
Precision = intermediate[1]
Recall = intermediate[2]
accuracy.append(acc)
Pre.append(Precision)
Re.append(Recall)
acc_sum = 0
pre_sum = 0
re_sum = 0
for i in accuracy:
print(i)
for i in accuracy:
acc_sum += i
for i in Pre:
pre_sum += i
for i in accuracy:
re_sum += i
if (acc_sum / k) > max_acc:
max_acc = acc_sum / k
max_constant = constant
print("For Learning Rate : ", constant, " Accuracy : ",
acc_sum / k)
print("Precision : ", pre_sum / k, " Recall : ", re_sum / k)
print("--------------------------------------------")
print("Maximum Accuracy : ", max_acc, " For learning rate : ",
max_constant, " For Threshold : ", threshold)
threshold += 0.1
def naive_bayes(k, rows, attributes):
accuracy = []
for i in range(1, k + 1):
after_fold = fold(rows, i, k)
train_set = after_fold[0]
test_set = after_fold[1]
#print(len(train_set),"------------------")
acc = train(train_set, attributes, test_set)
accuracy.append(acc)
summ = 0
for i in accuracy:
summ += i
return (summ / k)
def main():
filename="IRIS.csv"
attributes=[]
rows=[]
with open(filename,'r') as csvfile:
csvreader=csv.reader(csvfile)
attributes=next(csvreader)
for row in csvreader:
rows.append(row)
print(len(attributes))
k=10
acc_final=[]
for i in range(1,k+1):
constant=i/10
#constant=0.2
Rows=shuffle(rows)
accuracy=[]
Pre=[]
Re=[]
for i in range(1,k+1):
after_fold=fold(rows,i,k)
train_set=after_fold[0]
test_set=after_fold[1]
intermediate=train(train_set,test_set,attributes,constant)
acc=intermediate[0]
Precision=intermediate[1]
Recall=intermediate[2]
accuracy.append(acc)
Pre.append(Precision)
Re.append(Recall)
#print(accuracy)
acc_sum=0
pre_sum=0
re_sum=0
for i in accuracy:
print(i)
for i in accuracy:
acc_sum+=i
for i in Pre:
pre_sum+=i
for i in accuracy:
re_sum+=i
acc_final.append(acc_sum/k)
print("For Learning Rate : ",constant," Accuracy : ",acc_sum/k)
print("Precision : ",pre_sum/k," Recall : ",re_sum/k)