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"""""
data=pd.DataFrame( data[data.iloc[:, 1]!=84].values)
linear_result=[]
rbf_result=[]
poly_result=[]
sig_result=[]
#for index in range(len(data)):
# if SVM_result[index][84]==SVM_result[index][1]:Percent_SVM=Percent_SVM+1
for testIndex in range( len(data)):
train=data.drop([testIndex])
test=data.iloc[testIndex]
x = train.iloc[:, 2:].values
y = train.iloc[:, 1].values
xt = test.iloc[ 2:].values
yt = test.iloc[ 1]
linear = svm.SVC(kernel='linear', C=1, decision_function_shape='ovo').fit(x, y)
rbf = svm.SVC(kernel='rbf', gamma=1, C=1, decision_function_shape='ovo').fit(x, y)
poly = svm.SVC(kernel='poly', degree=3, C=1, decision_function_shape='ovo').fit(x, y)
sig = svm.SVC(kernel='sigmoid', C=1, decision_function_shape='ovo').fit(x, y)
linear_result.append([linear.predict([xt])[0],yt])
rbf_result.append([rbf.predict([xt])[0],yt])
poly_result.append([poly.predict([xt])[0],yt])
sig_result.append([sig.predict([xt])[0],yt])
pd.DataFrame(linear_result).to_csv('multi_class_linear.csv')
pd.DataFrame(rbf_result).to_csv('multi_class_rbf.csv')
pd.DataFrame(poly_result).to_csv('multi_class_poly.csv')
pd.DataFrame(sig_result).to_csv('multi_class_sig.csv')
Percent_linear=Percent_SVM/len(data)
data.iloc[:,1]=data.iloc[:,1].replace(2,0)
data.iloc[:,1]=data.iloc[:,1].replace(103,0.25)
data.iloc[:,1]=data.iloc[:,1].replace(7,0.75)
data.iloc[:,1]=data.iloc[:,1].replace(84,1)
scaler = MinMaxScaler(feature_range=(0, 1))
data = pd.DataFrame(scaler.fit_transform(data))
ANN_result=[]
for testIndex in range( len(data)):
train=data.drop([testIndex])
test=data.iloc[testIndex]
x = train.iloc[:, 2:].values
y = train.iloc[:, 1].values
xt = test.iloc[ 2:].values
yt = test.iloc[ 1]
pyt= quarter(ANN(x,y,xt,yt))
ANN_result.append([pyt,yt])
Percent_linear=0
Percent_poly=0
Percent_ANN=0
for index in range(len(data)):
if linear_result[index][0]==linear_result[index][1]:Percent_linear=Percent_linear+1
if poly_result[index][0]==poly_result[index][1]:Percent_poly=Percent_poly+1
if ANN_result[index][0]==ANN_result[index][1]:Percent_ANN=Percent_ANN+1
Percent_linear=Percent_linear/len(data)
Percent_poly=Percent_poly/len(data)
Percent_ANN=Percent_ANN/len(data)
#2 103 7 84
class1=data.drop(np.where(data.iloc[:,1] != 2)[0])
class2=data.drop(np.where(data.iloc[:,1] != 103)[0])
class3=data.drop(np.where(data.iloc[:,1] != 7)[0])
class4=data.drop(np.where(data.iloc[:,1] != 84)[0])
""" |
