else:
return class2
def apply_potentials(learn1, learn2, exam1, exam2, data, a):
class1 = learn1[0]
class2 = learn2[0]
print(class1," vs ", class2)
X_train, y_train, X_test, y_test = split_by_labels(learn1, learn2, exam1, exam2, data)
X_train = X_train.as_matrix()
X_test = X_test.as_matrix()
predicted = []
for x in X_test:
predicted.append(classify(x,a,X_train,y_train,class1,class2))
predicted = np.array(predicted)
print(sum(predicted == y_test)/len(y_test))
print(confusion_matrix(y_test,predicted))
data = pd.read_csv("data.csv")
data['class'] = make_pure_label(data['label'])
apply_potentials("alearn","blearn","aexam","bexam",data,0.01)
apply_potentials("alearn","clearn","aexam","cexam",data,0.01)
apply_potentials("blearn","clearn","bexam","cexam",data,0.01)
apply_potentials("alearn","blearn","aexam","bexam",data,1)
apply_potentials("alearn","clearn","aexam","cexam",data,1)
apply_potentials("blearn","clearn","bexam","cexam",data,1)
def main():
data = pd.read_csv("data.csv")
data['class'] = make_pure_label(data['label'])
apply_perceptron("alearn","blearn","aexam","bexam",data)
apply_perceptron("alearn","clearn","aexam","cexam",data)
apply_perceptron("blearn","clearn","bexam","cexam",data)
