def cluster_border_plot(centers):
rand_data_number = 1500
rand_data = create_rand_data(dataa, rand_data_number, dimension_size)
rand_data = rand_data.T[:len(rand_data.T) - 1].T
membership = FCM.cal_membership(rand_data, centers, len(centers),
len(rand_data), 2)
FCM.plot(membership, centers, rand_data)
def test(test_dataa, test_dataa_size, cluster_size, m, centers, W, gama):
test_data = test_dataa.T[:len(test_dataa.T) - 1].T
membership_test = FCM.cal_membership(test_data, centers, cluster_size,
test_dataa_size, m)
G = cal_G(test_data, centers, membership_test, gama, test_dataa_size)
G = np.array(G)
yh = G @ W
yhat = []
for r in range(0, test_dataa_size):
argmax = np.argmax(yh[r])
yhat.append(argmax)
# test_data[r].append(argmax)
scatter(test_dataa)
# scatter(test_data)
plt.figure()
f1 = f2 = 0
for uo in range(0, len(test_data)):
if yhat[uo] == 1:
if f1 == 0:
plt.scatter(test_data[uo][0],
test_data[uo][1],
color='red',
label='Label=1')
f1 = 1
else:
plt.scatter(test_data[uo][0], test_data[uo][1], color='red')
else:
if f2 == 0:
plt.scatter(test_data[uo][0],
test_data[uo][1],
color='blue',
label='Label=0')
f2 = 1
else:
plt.scatter(test_data[uo][0], test_data[uo][1], color='blue')
plt.title('Scattered data!')
plt.legend(loc="upper left")
plt.show()
accuracy = 0
for ty in range(0, test_dataa_size):
if (test_dataa[ty][2] == 1.0
and yhat[ty] == 1.0) or (test_dataa[ty][2] == -1.0
and yhat[ty] == 0):
accuracy = accuracy + 1
accuracy = accuracy / test_dataa_size * 100
print('accuracy is ', accuracy)
