svm_5.py

from sklearn.metrics import accuracy_score

## Clip the weights 
index = list(range(10,90))
w1 = np.delete(w1,index)
w2 = np.delete(w2,index)

w1 = w1.reshape(10,1)
w2 = w2.reshape(10,1)
## Extract the test data features 
test_f1 = x_test[:,0]
test_f2 = x_test[:,1]

test_f1 = test_f1.reshape(10,1)
test_f2 = test_f2.reshape(10,1)
## Predict
y_pred = w1 * test_f1 + w2 * test_f2
predictions = []
for val in y_pred:
    if(val > 1):
        predictions.append(1)
    else:
        predictions.append(-1)

print(accuracy_score(y_test,predictions))