import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
def display(img):
cv2.imshow("show",img)
cv2.waitKey(0)
cv2.destroyAllWindows()
b=edges.shape[0]
c=edges.shape[1]
#for i in range(b):
# for j in range(c):
# D=edges[i][j]
# #print(str(i)+","+str(j)+"="+str(D))
return img
def myresize(img,b,c):
dim = (b, c)
resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
return resized
def resshape(img):
width = int(img.shape[0])
height = int(img.shape[1])
wh=width * height
dim = (wh, 1)
resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
return resized
def uk(img):
width = int(img.shape[0])
height = int(img.shape[1])
print (width ,' x ' , height)
return (width ,' x ' , height)
def uk2(img):
width =len(img[0])
height = len(img[1])
print (width ,' x ' , height)
return (width ,' x ' , height)
def uk3(img):
s =img.shape()
return s
def model(img):
lap=type(img)
print(lap)
return lap
bankdata = pd.read_csv("data/wavelet.csv")
bankdata.head() #<class 'pandas.core.frame.DataFrame'>
train=bankdata
model(train)
#X = bankdata.drop('Class', axis=1)
#y = bankdata['Class']
X = train.iloc[:, :-1].values.astype('int32') #:[:, :-1], [:, 4]=just c4,[:, 1:]c1 dst=1371 x 4
y = train.iloc[:, 4].values.astype('int32')
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30)
from sklearn.svm import SVC
svclassifier = SVC(kernel='linear')
svclassifier.fit(X_train, y_train)
print(X_test)
y_pred = svclassifier.predict(X_test)
print(y_test)
print(y_pred)
correct = 0
total = len(y_test)
for i in range(total):
predicted = y_pred[i] #np.argmax(y_pred[i])
test = y_test[i] #np.argmax(y_test[i])
#print(predicted ,'=',test)
correct = correct + (1 if predicted == test else 0)
print('Accuracy: {:f}'.format(correct/total))
hasil:
[[-3 -8 10 0] [ 1 6 0 0] [-1 9 1 -5] ... [ 3 1 1 1] [ 0 -1 1 0] [ 0 3 4 0]] [1 0 0 1 1 1 1 0 0 0 1 0 1 1 0 1 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 0 1 1 1 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 0 0 0 0 1 0 1 1 0 1 1 0 0 1 0 1 0 1 1 0 0 0 1 1 1 1 1 0 1 0 1 0 1 0 0 1 0 1 1 1 0 0 1 1 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 0 0 1 0 0 1 0 0 1 1 0 1 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 1 1 1 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 1 1 1 1 1 1 1 0 1 1 0 1 0 0 1 1 1 0 1 0 1 1 0 0 0 0 1 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 1 1 0 1 1 1 1 0 0 1 1 1 1 1 0 0 0 0 1 1 1 1 1 0 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 1 1 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 0 0 1 0 1 1 1 1 0 0 1 0 1 1 1 0 1 0 0 0 1 1 0 0 1 0 1 1 1 1 1 0 0 0 0 1 1 0 0 1 0 1 1 0 0 1 1 1 1 0 0 1 0 0 0 0 0 1 0 0 1 1 1 1 1 1 1 1 0 0 1 0 0 0 0 0 1 0 0 1 0 1 1 0 1 0] [1 0 0 1 1 1 0 0 0 0 1 0 1 1 0 1 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 0 0 0 0 1 0 1 1 0 1 1 0 0 1 0 1 0 1 1 0 0 0 1 1 1 1 1 0 1 0 1 1 1 0 0 1 0 1 1 1 0 0 1 1 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 0 0 1 0 0 1 0 0 1 1 0 1 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 1 1 1 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 0 1 0 0 1 1 1 1 1 1 1 0 1 1 0 1 0 0 1 1 1 0 1 0 1 1 0 0 0 0 1 1 1 1 0 0 1 1 0 0 0 1 0 0 0 0 1 1 0 1 0 1 1 0 0 0 0 0 0 0 1 1 0 1 1 1 1 0 0 1 1 1 1 1 0 0 0 0 1 1 1 1 1 0 1 0 0 1 1 1 0 0 0 1 1 0 1 0 0 0 0 1 1 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 0 0 1 0 1 1 1 1 0 0 1 0 1 1 1 0 1 0 0 0 1 1 0 0 1 0 1 1 1 1 1 0 0 0 0 1 1 0 0 1 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 0 0 1 0 0 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 1 0 0 1 0 1 1 0 1 0] Accuracy: 0.975728
from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test,y_pred))
print(classification_report(y_test,y_pred))
hasil
[[210 9] [ 1 192]] precision recall f1-score support 0 1.00 0.96 0.98 219 1 0.96 0.99 0.97 193 accuracy 0.98 412 macro avg 0.98 0.98 0.98 412 weighted avg 0.98 0.98 0.98 412#pengujian dataUji=[3.6216,8.6661,-2.8073,-0.44699] uji=list() uji.append(dataUji) y_pred = svclassifier.predict(uji) print("Hasil Pengujian ") print(y_pred)
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