def my_model_k1():
"""
Initialization of the ANFIS regressor for k1 prediction. Single output.
Fuzzy reasoning and rules can be modified according to human-expertise.
:return:
model: Initialized ANFIS
"""
invardefs = [
('x0', make_gauss_mfs(0.5, np.linspace(0, 1, 2))),
('x1', make_gauss_mfs(0.5, np.linspace(0, 1, 4))),
('x2', make_gauss_mfs(0.5, np.linspace(0, 1, 3))),
('x3', make_gauss_mfs(0.5, np.linspace(0, 1, 3))),
('x4', make_gauss_mfs(0.5, np.linspace(0, 1, 6))),
('x5', make_gauss_mfs(0.5, np.linspace(0, 1, 3))),
('x6', make_gauss_mfs(0.5, np.linspace(0, 1, 6))),
('x7', make_gauss_mfs(0.5, np.linspace(0, 1, 5))),
('x8', make_gauss_mfs(0.5, np.linspace(0, 1, 5))),
('x9', make_gauss_mfs(0.5, np.linspace(0, 1, 7))),
]
outvars = ['k1']
model = anfis.AnfisNet('My_Anfis', invardefs, outvars, grid=False)
rules = [[1, 1, 2, 2, 4, 1, 4, 0, 1, 6], [0, 0, 0, 0, 0, 0, 0, 3, 4, 2],
[1, 3, 2, 1, 5, 2, 5, 2, 0, 0], [0, 0, 1, 1, 1, 0, 1, 1, 3, 1],
[1, 3, 2, 2, 5, 2, 5, 0, 0, 3], [0, 2, 0, 0, 2, 1, 2, 4, 3, 4],
[0, 2, 1, 1, 3, 1, 3, 1, 2, 4], [1, 1, 2, 1, 4, 1, 4, 2, 1, 5]]
model.set_rules(rules)
return model
def my_model_class1():
"""
Initialization of the ANFIS classifier for virtual generic model. Multi-output.
Fuzzy reasoning and rules can be modified according to human-expertise.
:return:
model: Initialized ANFIS
"""
invardefs = [('x0', make_gauss_mfs(0.5, np.linspace(0, 1, 5))),
('x1', make_gauss_mfs(0.5, np.linspace(0, 1, 4))),
('x2', make_gauss_mfs(0.5, np.linspace(0, 1, 3))),
('x3', make_gauss_mfs(0.5, np.linspace(0, 1, 3))),
('x4', make_gauss_mfs(0.5, np.linspace(0, 1, 7))),
('x5', make_gauss_mfs(0.5, np.linspace(0, 1, 3))),
('x6', make_gauss_mfs(0.5, np.linspace(0, 1, 5))),
('x7', make_gauss_mfs(0.5, np.linspace(0, 1, 5))),
('x8', make_gauss_mfs(0.5, np.linspace(0, 1, 2))),
('x9', make_gauss_mfs(0.5, np.linspace(0, 1, 7)))]
outvars = ['dc1', 'dc2', 'dc3', 'dc4', 'dc5', 'dc6', 'dc7', 'dc8']
model = anfis.AnfisNet('My_Anfis',
invardefs,
outvars,
hybrid=False,
grid=False)
# 20.07
rules = [[0, 0, 1, 2, 6, 0, 0, 0, 1, 3], [3, 0, 0, 0, 2, 0, 3, 2, 0, 0],
[2, 3, 2, 1, 1, 2, 2, 2, 1, 4], [1, 0, 0, 1, 5, 0, 1, 1, 0, 2],
[0, 2, 2, 2, 4, 1, 0, 0, 1, 1], [4, 1, 1, 0, 0, 0, 4, 2, 0, 5],
[1, 1, 1, 1, 2, 0, 1, 4, 0, 6], [2, 0, 1, 1, 3, 0, 2, 3, 1, 3]]
model.set_rules(rules, hybrid=False)
return model
def vignette_ex3():
'''
These are the original (untrained) MFS for Vignette example 3.
Like example 1, but now using 5 Gaussian MFs.
'''
invardefs = [
('x0', make_gauss_mfs(2, [-10, -5, 0, 5, 10])),
('x1', make_gauss_mfs(2, [-10, -5, 0, 5, 10]))
]
outvars = ['y0']
anf = anfis.AnfisNet('Vignette Example 3', invardefs, outvars)
return anf
def model():
invardefs = [
# ainda n escolhi os centros e sigma
('x(t-18)', make_gauss_mfs(sigma=0.2, mu_list=[0.4, 1.2])),
('x(t-12)', make_gauss_mfs(sigma=0.2, mu_list=[0.4, 1.2])),
('x(t-6)', make_gauss_mfs(sigma=0.2, mu_list=[0.4, 1.2])),
('x(t)', make_gauss_mfs(sigma=0.2, mu_list=[0.4, 1.2]))
]
outvars = ['ys']
model = anfis.AnfisNet('mackey-glass', invardefs, outvars)
return model
def vignette_ex5():
'''
These are the original (untrained) MFS for Vignette example 5
Same MFs as for example 3, but now there will be two outputs.
These will be: y0 = sinc(x0, x1) and y1 = 1 - sinc(x0,x1).
'''
invardefs = [
('x0', make_gauss_mfs(2, [-10, -5, 0, 5, 10])),
('x1', make_gauss_mfs(2, [-10, -5, 0, 5, 10]))
]
outvars = ['y0', 'y1']
anf = anfis.AnfisNet('Vignette Example 5', invardefs, outvars)
return anf
def fuzzy_classifier(num_in, num_mfs=5):
'''
Make a fuzzy classifier with 5 MFS per input, and one output
'''
sigma = 10 / num_mfs
mulist = torch.linspace(-5, 5, num_mfs).tolist()
invardefs = [('x{}'.format(i), membership.make_gauss_mfs(sigma, mulist))
for i in range(num_in)]
outvars = ['y0']
model = anfis.AnfisNet('Simple classifier', invardefs, outvars)
return model
def ex1_model():
'''
Define modelo e parametros para funcoes de pertinencia
'''
# invardefs = [
# ('x0', make_bell_mfs(3.33333, 2, list(np.linspace(0, 2*np.pi, 3))))
# ]
invardefs = [('x0',
make_gauss_mfs(sigma=1.2,
mu_list=np.linspace(0, 2 * np.pi, 3)))]
outvars = ['y0']
anf = anfis.AnfisNet('Aproximacao senoidal', invardefs, outvars)
return anf
def mk_var(name):
return (name, make_gauss_mfs(1, [-5, 0.08, 1.67]))
def mk_var(name, centros, i):
return (name, make_gauss_mfs(3,
[centros[n, i] for n in range(n_rules)]))
def classifier_rig(window="small"):
"""
Initialization of the ANFIS classifier for test rig. Multi-output.
Fuzzy reasoning and rules can be modified according to human-expertise.
:param window: "small", or "large" window size
:return:
model: Initialized ANFIS
"""
if window == "small":
invardefs = [
('x0', make_gauss_mfs(0.5, np.linspace(0, 1, 2))),
('x1', make_gauss_mfs(0.5, np.linspace(0, 1, 6))),
('x2', make_gauss_mfs(0.5, np.linspace(0, 1, 4))),
('x3', make_gauss_mfs(0.5, np.linspace(0, 1, 3))),
('x4', make_gauss_mfs(0.5, np.linspace(0, 1, 3))),
('x5', make_gauss_mfs(0.5, np.linspace(0, 1, 3))),
('x6', make_gauss_mfs(0.5, np.linspace(0, 1, 2))),
('x7', make_gauss_mfs(0.5, np.linspace(0, 1, 3))),
('x8', make_gauss_mfs(0.5, np.linspace(0, 1, 2))),
('x9', make_gauss_mfs(0.5, np.linspace(0, 1, 3))),
]
# 22.07 small
rules = [[1, 0, 0, 2, 2, 0, 0, 2, 0,
2], [0, 2, 2, 1, 0, 1, 1, 1, 1, 1],
[0, 5, 1, 0, 1, 2, 1, 0, 1,
0], [0, 1, 2, 0, 1, 2, 1, 0, 1, 0],
[0, 4, 3, 1, 0, 1, 1, 1, 1, 1],
[0, 3, 3, 1, 0, 1, 1, 1, 1, 1]]
else:
invardefs = [
('x0', make_gauss_mfs(0.5, np.linspace(0, 1, 3))),
('x1', make_gauss_mfs(0.5, np.linspace(0, 1, 3))),
('x2', make_gauss_mfs(0.5, np.linspace(0, 1, 4))),
('x3', make_gauss_mfs(0.5, np.linspace(0, 1, 4))),
('x4', make_gauss_mfs(0.5, np.linspace(0, 1, 4))),
('x5', make_gauss_mfs(0.5, np.linspace(0, 1, 4))),
('x6', make_gauss_mfs(0.5, np.linspace(0, 1, 2))),
('x7', make_gauss_mfs(0.5, np.linspace(0, 1, 3))),
('x8', make_gauss_mfs(0.5, np.linspace(0, 1, 4))),
('x9', make_gauss_mfs(0.5, np.linspace(0, 1, 3))),
]
# 22.07 Large
rules = [[2, 0, 0, 3, 0, 0, 0, 2, 0,
2], [0, 2, 3, 0, 1, 1, 1, 1, 3, 1],
[1, 1, 1, 2, 3, 2, 1, 0, 1,
0], [1, 2, 2, 0, 2, 3, 1, 0, 2, 0],
[0, 2, 3, 1, 1, 1, 1, 1, 3, 1]]
outvars = ['nc', 'dc1', 'dc2', 'dc3', 'dc4', 'dc5', 'dc6']
model = anfis.AnfisNet('My_Anfis',
invardefs,
outvars,
hybrid=False,
grid=False)
model.set_rules(rules, hybrid=False)
return model
def my_model_c1(rules='less'):
"""
Initialization of the ANFIS regressor for c1 prediction. Single output.
Fuzzy reasoning and rules can be modified according to human-expertise.
:param rules: one rule for each case (less) or three rules for each case (more)
:return:
model: Initialized ANFIS
"""
if rules == 'less':
invardefs = [
('x0', make_gauss_mfs(0.5, np.linspace(0, 1, 3))),
('x1', make_gauss_mfs(0.5, np.linspace(0, 1, 7))),
('x2', make_gauss_mfs(0.5, np.linspace(0, 1, 4))),
('x3', make_gauss_mfs(0.5, np.linspace(0, 1, 7))),
('x4', make_gauss_mfs(0.5, np.linspace(0, 1, 6))),
('x5', make_gauss_mfs(0.5, np.linspace(0, 1, 6))),
('x6', make_gauss_mfs(0.5, np.linspace(0, 1, 7))),
('x7', make_gauss_mfs(0.5, np.linspace(0, 1, 5))),
('x8', make_gauss_mfs(0.5, np.linspace(0, 1, 7))),
('x9', make_gauss_mfs(0.5, np.linspace(0, 1, 7))),
]
# 14.07
rules = [[1, 0, 0, 6, 5, 1, 6, 0, 0,
6], [0, 4, 0, 2, 2, 0, 2, 0, 3, 2],
[2, 5, 3, 1, 1, 5, 1, 4, 5,
1], [0, 1, 0, 5, 4, 0, 5, 0, 1, 5],
[2, 2, 2, 4, 0, 4, 4, 3, 3,
4], [1, 6, 1, 0, 2, 3, 0, 1, 6, 0],
[1, 4, 1, 2, 3, 3, 2, 2, 4, 2],
[1, 3, 0, 3, 4, 2, 3, 0, 2, 3]]
elif rules == 'more':
invardefs = [
('x0', make_gauss_mfs(0.5, np.linspace(0, 1, 5))),
('x1', make_gauss_mfs(0.5, np.linspace(0, 1, 6))),
('x2', make_gauss_mfs(0.5, np.linspace(0, 1, 10))),
('x3', make_gauss_mfs(0.5, np.linspace(0, 1, 5))),
('x4', make_gauss_mfs(0.5, np.linspace(0, 1, 8))),
('x5', make_gauss_mfs(0.5, np.linspace(0, 1, 5))),
('x6', make_gauss_mfs(0.5, np.linspace(0, 1, 8))),
('x7', make_gauss_mfs(0.5, np.linspace(0, 1, 9))),
('x8', make_gauss_mfs(0.5, np.linspace(0, 1, 5))),
('x9', make_gauss_mfs(0.5, np.linspace(0, 1, 11))),
]
rules = [[4, 1, 6, 4, 0, 4, 0, 1, 4,
4], [3, 0, 8, 4, 3, 4, 3, 4, 4, 7],
[4, 5, 8, 4, 5, 4, 5, 6, 4,
8], [3, 0, 7, 4, 2, 4, 2, 3, 4, 6],
[4, 4, 8, 4, 4, 4, 4, 5, 4,
7], [4, 3, 9, 4, 7, 4, 7, 8, 4, 10],
[4, 3, 9, 4, 6, 4, 6, 7, 4,
9], [4, 2, 6, 4, 1, 4, 1, 2, 4, 5],
[0, 2, 5, 0, 0, 0, 1, 0, 0,
0], [4, 2, 1, 4, 0, 4, 1, 1, 4, 3],
[2, 2, 4, 2, 0, 2, 1, 1, 2,
2], [0, 2, 2, 0, 0, 0, 1, 1, 0, 2],
[0, 2, 5, 0, 0, 0, 1, 0, 0,
1], [3, 2, 0, 3, 0, 3, 1, 1, 3, 3],
[0, 2, 3, 0, 0, 0, 1, 0, 0, 2],
[1, 2, 4, 1, 0, 1, 1, 1, 1, 2]]
outvars = ['c1']
model = anfis.AnfisNet('My_Anfis', invardefs, outvars, grid=False)
model.set_rules(rules)
return model
def mk_var(name, centros, i): # de iris_example
return (name, make_gauss_mfs(1,
[centros[n, i] for n in range(n_rules)]))
def mk_var(name, centros, i): # de iris_example
return (name,
make_gauss_mfs(1,
[centros[0, i], centros[1, i], centros[2, i]]))
