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))
def apply_perceptron(learn1, learn2, exam1, exam2, data):
class1 = learn1[0]
class2 = learn2[0]
print(class1," vs ", class2)
d = 4 # number of dimensions
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()
y_train = scale_y(y_train,class1)
y_test = scale_y(y_test,class1)
step = 0
tolerance = 25
previous_count = 10**5
w = np.ones(d+1)
while True:
# print(previous_count)
# print("w",w)
step += 1
grad = np.zeros(d+1)
if tolerance < 0:
break
count = 0
for x,y in zip(X_train, y_train):
if y*multiply(x,w) <= 0:
grad -= y*np.array(list(chain([1],x)))
count += 1
if previous_count == count or count < np.ceil(len(y_train)*0.05):
tolerance -= 1
previous_count = count
normalized = normalize(grad)[0]
# print("grad",normalized)
w -= normalized
print(count)
print(w)
test_count = 0
predicted = []
for x,y in zip(X_test,y_test):
if multiply(x,w) > 0:
predicted.append(1)
else:
predicted.append(-1)
if multiply(x,w)*y < 0:
test_count += 1
print(1-test_count/len(y_test))
print(confusion_matrix(y_test,predicted))
