def test_fuzzy_not():
x = np.r_[0:10.1:0.1]
mfx = trimf(x, [0, 5, 10])
expected = np.fmax(trimf(x, [0, 0, 5]), trimf(x, [5, 10, 10]))
# Not exactly equal due to expected float error ~1e-16
assert_allclose(fuzzy_not(mfx), expected)
def _plot_mf_for(x):
'''Given the x-values, plot a series of example membership functions'''
tests = OrderedDict([
# Gaussians:
('gauss', skmemb.gaussmf(x, 50, 10)),
('left gauss', leftgaussmf(x, 50, 10)),
('right gauss', rightgaussmf(x, 50, 10)),
# Triangles:
('triangular', skmemb.trimf(x, [25, 50, 75])),
('left linear', leftlinearmf(x, 25, 75)),
('right linear', rightlinearmf(x, 25, 75)),
# fuzzylite:
('cosine', cosinemf(x, 50, 50)),
('inc concave', concavemf(x, 50, 75)),
('dec concave', concavemf(x, 50, 25)),
('spike', spikemf(x, 50, 50)),
('inc ramp', rampmf(x, 25, 75)),
('dec ramp', rampmf(x, 75, 25)),
# Rectangle-ish
('trapezoid', skmemb.trapmf(x, [20, 40, 60, 80])),
('rectangle', rectanglemf(x, 25, 75)),
('singleton', singletonmf(x, 50)),
])
# Example point sets:
ps_tests = [[(40, 0.5), (60, 1)],
[(10, 0.5), (25, 0.25), (40, 0.75), (80, .5)],
[(0, 1), (40, 0.25), (50, .5), (99, 0)]]
# Now try some interpolation methods on these:
for method in ['linear', 'lagrange', 'spline', 'cubic']:
tests.update([('{} ex{}'.format(method, i), pointsetmf(x, ps, method))
for i, ps in enumerate(ps_tests)])
return tests
def generate_mf_group(self, G, x):
mf_group = {}
for (k, v) in G.iteritems():
shp = v['shp']
mf = v['mf']
if mf == 'trap':
mf_group[k] = trapmf(x, shp)
if mf == 'tri':
mf_group[k] = trimf(x, shp)
if mf == 'gbell':
mf_group[k] = gbellmf(x, shp[0], shp[1], shp[2])
if mf == 'gauss':
mf_group[k] = gaussmf(x, shp[0], shp[1])
if mf == 'gauss2':
mf_group[k] = gauss2mf(x, shp[0], shp[1])
if mf == 'sig':
mf_group[k] = sigmf(x, shp[0], shp[1])
if mf == 'psig':
mf_group[k] = psigmf(x, shp[0], shp[1], shp[2], shp[3])
if mf == 'zmf':
mf_group[k] = zmf(x, shp[0], shp[1], shp[2], shp[3])
if mf == 'smf':
mf_group[k] = smf(x, shp[0], shp[1], shp[2], shp[3])
if mf == 'pimf':
mf_group[k] = pimf(x, shp[0], shp[1], shp[2], shp[3])
if mf == 'piecemf':
mf_group[k] = piecemf(x, shp[0], shp[1], shp[2], shp[3])
return mf_group
def generate_mf_group(self, G, x):
mf_group = {}
for (k, v) in G.iteritems():
shp = v['shp']
mf = v['mf']
if mf == 'trap':
mf_group[k] = trapmf(x, shp)
if mf == 'tri':
mf_group[k] = trimf(x, shp)
if mf == 'gbell':
mf_group[k] = gbellmf(x, shp[0], shp[1], shp[2])
if mf == 'gauss':
mf_group[k] = gaussmf(x, shp[0], shp[1])
if mf == 'gauss2':
mf_group[k] = gauss2mf(x, shp[0], shp[1])
if mf == 'sig':
mf_group[k] = sigmf(x, shp[0], shp[1])
if mf == 'psig':
mf_group[k] = psigmf(x, shp[0], shp[1], shp[2], shp[3])
if mf == 'zmf':
mf_group[k] = zmf(x, shp[0], shp[1], shp[2], shp[3])
if mf == 'smf':
mf_group[k] = smf(x, shp[0], shp[1], shp[2], shp[3])
if mf == 'pimf':
mf_group[k] = pimf(x, shp[0], shp[1], shp[2], shp[3])
if mf == 'piecemf':
mf_group[k] = piecemf(x, shp[0], shp[1], shp[2], shp[3])
return mf_group
def test_fuzzy_or():
x = np.r_[0:10.1:0.1]
mfx = trimf(x, [0, 5, 10])
mfy = np.ones_like(mfx) - 0.5
expected = np.fmax(mfy, mfx)
z, mfz = fuzzy_or(x, mfx, x, mfy)
# Not exactly equal due to expected float error ~1e-16
assert_allclose(mfz, expected)
assert_array_equal(z, x)
def test_trimf():
x = np.arange(-4, 4.1, 0.1)
abc = (-np.pi, -np.e / 2., np.pi / 5.)
expected = np.zeros_like(x)
idx = np.logical_and(x > abc[0], x < abc[1])
expected[idx] = np.interp(x[idx], np.r_[abc[0], abc[1]], np.r_[0, 1])
expected[x == abc[1]] = 1
idx = np.logical_and(x > abc[1], x < abc[2])
expected[idx] = np.interp(x[idx], np.r_[abc[1], abc[2]], np.r_[1, 0])
test = trimf(x, abc)
assert_allclose(test, expected)
def test_trimf():
x = np.arange(-4, 4.1, 0.1)
abc = (-np.pi, -np.e / 2., np.pi / 5.)
expected = np.zeros_like(x)
idx = np.logical_and(x > abc[0], x < abc[1])
expected[idx] = np.interp(x[idx], np.r_[abc[0], abc[1]], np.r_[0, 1])
expected[x == abc[1]] = 1
idx = np.logical_and(x > abc[1], x < abc[2])
expected[idx] = np.interp(x[idx], np.r_[abc[1], abc[2]], np.r_[1, 0])
test = trimf(x, abc)
assert_allclose(test, expected)
def test_universe_resampling():
x = np.r_[0:10.1:0.1]
y = np.r_[-2:12.1:0.25]
mfx = trimf(x, [0, 5, 10])
mfy = trimf(y, [-2, 2, 12])
# Tests for `fuzzy_and` resampling
z, mfz = fuzzy_and(x, mfx, y, mfy)
z_expected = np.r_[-2:12.1:0.1]
assert_allclose(z, z_expected, atol=1e-14)
mfz_expected = np.fmin(trimf(z_expected, [0, 5, 10]),
trimf(z_expected, [-2, 2, 12]))
assert_allclose(mfz, mfz_expected, atol=1e-14)
# Tests for `fuzzy_or` resampling
z, mfz = fuzzy_or(x, mfx, y, mfy)
z_expected = np.r_[-2:12.1:0.1]
assert_allclose(z, z_expected, atol=1e-14)
mfz_expected = np.fmax(trimf(z_expected, [0, 5, 10]),
trimf(z_expected, [-2, 2, 12]))
assert_allclose(mfz, mfz_expected, atol=1e-14)
ax[2].set_title('Fuzzy logic control action u')
fig.suptitle('Simulation. Gain K = ' + str(K))
plt.show()
return xx1, xx2, uu, tt
if __name__ == '__main__':
"""
These functions represent an inverted pendulum.
"""
# Membership Matrices
x1 = np.arange(-10, 10.1, 0.1) # Angle range, 1d array, length N, Fuzzy state variable #1.
N1 = trapmf(x1, [-10, -10, -2, 0]) # Negative membership function
Z1 = trimf(x1, [-2, 0, 2]) # Zero membership function
P1 = trapmf(x1, [0, 2, 10, 10]) # Positive membership function
M1 = np.vstack((N1, Z1, P1)) # Input 1 membership matrix, 2d array, shape (Q1, N), Matrix with Q1 membership functions for x1.
x2 = np.arange(-10, 10.1, 0.1) # Angular velocity range, 1d array, length M, Fuzzy state variable #2.
N2 = trapmf(x2, [-10, -10, -2, 0]) # Negative membership function
Z2 = trimf(x2, [-2, 0, 2]) # Zero membership function
P2 = trapmf(x2, [0, 2, 10, 10]) # Positive membership function
M2 = np.vstack((N2, Z2, P2)) # Input 2 membership matrix, 2d array, shape (Q2, M), Matrix with Q2 membership functions for x2.
# Fuzzy control (u) and member functions
u = np.arange(-25, 25.25, 0.25) # Fuzzy control output variable, 1d array, length U, Feedback control action variable.
NBu = trapmf(u, [-25, -25, -16, -8]) # Big Negative` membership function
Nu = trimf(u, [-16, -8, 0]) # Negative` membership function
Zu = trimf(u, [-8, 0, 8]) # Zero` membership function
Pu = trimf(u, [0, 8, 16]) # Positive` membership function
import numpy as np import skfuzzy as fuzz import skfuzzy.membership as mf import matplotlib.pyplot as plt x_tempTest = np.arange(0, 101, 1) x_firmTest = np.arange(0, 101, 1) y_quality = np.arange(0, 101, 1) temp_low = mf.trapmf(x_tempTest, [0, 0, 10, 40]) temp_med = mf.trimf(x_tempTest, [20, 50, 80]) temp_hig = mf.trapmf(x_tempTest, [60, 90, 100, 100]) firm_low = mf.trimf(x_firmTest, [0, 0, 40]) firm_med = mf.trimf(x_firmTest, [30, 50, 70]) firm_hig = mf.trimf(x_firmTest, [60, 100, 100]) #result_no = mf.trimf(y_quality, [0, 0, 100]) #result_ok = mf.trimf(y_quality, [0, 100, 100]) quality_verylow = mf.trimf(y_quality, [0, 0, 25]) quality_bitlow = mf.trimf(y_quality, [0, 25, 50]) quality_middle = mf.trimf(y_quality, [25, 50, 75]) quality_bithigh = mf.trimf(y_quality, [50, 75, 100]) quality_veryhig = mf.trimf(y_quality, [75, 100, 100]) fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, figsize=(6, 10)) ax0.plot(x_tempTest, temp_low, 'r', linewidth=2, label='Düşük') ax0.plot(x_tempTest, temp_med, 'g', linewidth=2, label='Orta') ax0.plot(x_tempTest, temp_hig, 'b', linewidth=2, label='Yüksek')
def fuzzy_calculate(input_ph, input_zf, input_ai, input_i, input_kc, input_pd,
input_ed, input_gb, input_gs):
# zf kisminda kullanici tarafindan girilen zaman dilimi degerini alan
# bu degeri araliktaki kismini bulan method yazilacak
# Değişkenlerin oluşturulması
# 1- ph(Proje Surecine hakimiyet)
# 2- zf(Zaman Dilimi Farkliligi)
# 3- ai(Adim Adim Ilerleme)
# 4- i(iletisim)
# 5- pd(Proje Deneyimi)
# 6- ed(ekibin deneyimmli olmasi)
# 7- gb(Gorevlerin Birbirine Bagliligi)
# 8- gs(Gerekliliklerin Sabit Kalmasi)
# 9- kc(Kaynak Cesitliligi),
plt.close()
ph = np.arange(0, 101, 1)
zf = np.arange(0, 101, 1)
ai = np.arange(0, 101, 1)
i = np.arange(0, 101, 1)
pd = np.arange(0, 101, 1)
ed = np.arange(0, 101, 1)
gb = np.arange(0, 101, 1)
gs = np.arange(0, 101, 1)
kc = np.arange(0, 101, 1)
risk = np.arange(0, 101, 1)
# Üyelik fonksiyonlarının oluşturulması
ph_low = mf.trimf(ph, [0, 0, 50])
ph_med = mf.trimf(ph, [0, 50, 100])
ph_hig = mf.trimf(ph, [50, 100, 100])
zf_low = mf.trimf(zf, [0, 0, 50])
zf_med = mf.trimf(zf, [0, 50, 100])
zf_hig = mf.trimf(zf, [50, 100, 100])
ai_very_low = mf.trimf(ai, [0, 0, 100])
ai_very_hig = mf.trimf(ai, [0, 100, 100])
i_low = mf.trimf(i, [0, 0, 50])
i_med = mf.trimf(i, [0, 50, 100])
i_hig = mf.trimf(i, [50, 100, 100])
pd_very_low = mf.trimf(pd, [0, 0, 25])
pd_low = mf.trimf(pd, [0, 25, 50])
pd_med = mf.trimf(pd, [25, 50, 75])
pd_hig = mf.trimf(pd, [50, 75, 100])
pd_very_hig = mf.trimf(pd, [75, 100, 100])
ed_very_low = mf.trimf(ed, [0, 0, 25])
ed_low = mf.trimf(ed, [0, 25, 50])
ed_med = mf.trimf(ed, [25, 50, 75])
ed_hig = mf.trimf(ed, [50, 75, 100])
ed_very_hig = mf.trimf(ed, [75, 100, 100])
gb_low = mf.trimf(gb, [0, 0, 50])
gb_med = mf.trimf(gb, [0, 50, 100])
gb_hig = mf.trimf(gb, [50, 100, 100])
gs_low = mf.trimf(gs, [0, 0, 50])
gs_med = mf.trimf(gs, [0, 50, 100])
gs_hig = mf.trimf(gs, [50, 100, 100])
kc_very_low = mf.trimf(kc, [0, 0, 25])
kc_low = mf.trimf(kc, [0, 25, 50])
kc_med = mf.trimf(kc, [25, 50, 75])
kc_hig = mf.trimf(kc, [50, 75, 100])
kc_very_hig = mf.trimf(kc, [75, 100, 100])
risk_very_hig = mf.trimf(risk, [0, 0, 25])
risk_hig = mf.trimf(risk, [0, 25, 50])
risk_med = mf.trimf(risk, [25, 50, 75])
risk_low = mf.trimf(risk, [50, 75, 100])
risk_very_low = mf.trimf(risk, [75, 100, 100])
# Veri görselleştirme
#ax3, ax4, ax5, ax6, ax7, ax8, ax9
fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, figsize=(6, 10))
ax0.plot(ph, ph_low, 'r', linewidth=2, label='Düşük')
ax0.plot(ph, ph_med, 'g', linewidth=2, label='Orta')
ax0.plot(ph, ph_hig, 'b', linewidth=2, label='Yüksek')
ax0.set_title('Proje Süreci Hakimiyeti')
ax0.legend()
ax1.plot(zf, zf_low, 'r', linewidth=2, label='Düşük')
ax1.plot(zf, zf_med, 'g', linewidth=2, label='Orta')
ax1.plot(zf, zf_hig, 'b', linewidth=2, label='Yüksek')
ax1.set_title('Zaman Dilimi Farklılığı')
ax1.legend()
ax2.plot(ai, ai_very_low, 'r', linewidth=2, label='Düşük')
ax2.plot(ai, ai_very_hig, 'b', linewidth=2, label='Yüksek')
ax2.set_title('Adım Adım İlerleme')
ax2.legend()
plt.tight_layout()
fig, (ax3, ax4, ax5) = plt.subplots(nrows=3, figsize=(6, 10))
ax3.plot(i, i_low, 'r', linewidth=2, label='Düşük')
ax3.plot(i, i_med, 'g', linewidth=2, label='Orta')
ax3.plot(i, i_hig, 'b', linewidth=2, label='Yüksek')
ax3.set_title('İletişim')
ax3.legend()
ax4.plot(pd, pd_very_low, 'y', linewidth=2, label='Çok Düşük')
ax4.plot(pd, pd_low, 'r', linewidth=2, label='Düşük')
ax4.plot(pd, pd_med, 'g', linewidth=2, label='Orta')
ax4.plot(pd, pd_hig, 'b', linewidth=2, label='Yüksek')
ax4.plot(pd, pd_very_hig, 'k', linewidth=2, label='Çok Yüksek')
ax4.set_title('Proje Deneyimi')
ax4.legend()
ax5.plot(ed, ed_very_low, 'y', linewidth=2, label='Çok Düşük')
ax5.plot(ed, ed_low, 'r', linewidth=2, label='Düşük')
ax5.plot(ed, ed_med, 'g', linewidth=2, label='Orta')
ax5.plot(ed, ed_hig, 'b', linewidth=2, label='Yüksek')
ax5.plot(ed, ed_very_hig, 'k', linewidth=2, label='Çok Yüksek')
ax5.set_title('Ekip Deneyimi')
ax5.legend()
plt.tight_layout()
fig, (ax6, ax7, ax8, ax9) = plt.subplots(nrows=4, figsize=(6, 10))
ax6.plot(gb, gb_low, 'r', linewidth=2, label='Düşük')
ax6.plot(gb, gb_med, 'g', linewidth=2, label='Orta')
ax6.plot(gb, gb_hig, 'b', linewidth=2, label='Yüksek')
ax6.set_title('Görevlerin Birbirine Bağımlılığı')
ax6.legend()
ax7.plot(gs, gs_low, 'r', linewidth=2, label='Düşük')
ax7.plot(gs, gs_med, 'g', linewidth=2, label='Orta')
ax7.plot(gs, gs_hig, 'b', linewidth=2, label='Yüksek')
ax7.set_title('Gerekliliklerin Sabit kalması')
ax7.legend()
ax8.plot(kc, kc_very_low, 'y', linewidth=2, label='Çok Düşük')
ax8.plot(kc, kc_low, 'r', linewidth=2, label='Düşük')
ax8.plot(kc, kc_med, 'g', linewidth=2, label='Orta')
ax8.plot(kc, kc_hig, 'b', linewidth=2, label='Yüksek')
ax8.plot(kc, kc_very_hig, 'k', linewidth=2, label='Çok Yüksek')
ax8.set_title('Kaynak Çeşitliliği')
ax8.legend()
ax9.plot(risk, risk_very_low, 'y', linewidth=2, label='Çok Düşük')
ax9.plot(risk, risk_low, 'r', linewidth=2, label='Düşük')
ax9.plot(risk, risk_med, 'g', linewidth=2, label='Orta')
ax9.plot(risk, risk_hig, 'b', linewidth=2, label='Yüksek')
ax9.plot(risk, risk_very_hig, 'k', linewidth=2, label='Çok Yüksek')
ax9.set_title('Risk Oranı')
ax9.legend()
plt.tight_layout()
# input_ph
# input_zf
# input_ai
# input_i
# input_dp
# input_ed
# input_gb
# input_gs
# input_kc
# Üyelik derecelerinin hesaplanması
ph_fit_low = fuzz.interp_membership(ph, ph_low, input_ph)
ph_fit_med = fuzz.interp_membership(ph, ph_med, input_ph)
ph_fit_hig = fuzz.interp_membership(ph, ph_hig, input_ph)
zf_fit_low = fuzz.interp_membership(zf, zf_low, input_zf)
zf_fit_med = fuzz.interp_membership(zf, zf_med, input_zf)
zf_fit_hig = fuzz.interp_membership(zf, zf_hig, input_zf)
ai_fit_very_low = fuzz.interp_membership(ai, ai_very_low, input_ai)
ai_fit_very_hig = fuzz.interp_membership(ai, ai_very_hig, input_ai)
i_fit_low = fuzz.interp_membership(i, i_low, input_i)
i_fit_med = fuzz.interp_membership(i, i_med, input_i)
i_fit_hig = fuzz.interp_membership(i, i_hig, input_i)
pd_fit_very_low = fuzz.interp_membership(pd, pd_very_low, input_pd)
pd_fit_low = fuzz.interp_membership(pd, pd_low, input_pd)
pd_fit_med = fuzz.interp_membership(pd, pd_med, input_pd)
pd_fit_hig = fuzz.interp_membership(pd, pd_hig, input_pd)
pd_fit_very_hig = fuzz.interp_membership(pd, pd_very_hig, input_pd)
ed_fit_very_low = fuzz.interp_membership(ed, ed_very_low, input_ed)
ed_fit_low = fuzz.interp_membership(ed, ed_low, input_ed)
ed_fit_med = fuzz.interp_membership(ed, ed_med, input_ed)
ed_fit_hig = fuzz.interp_membership(ed, ed_hig, input_ed)
ed_fit_very_hig = fuzz.interp_membership(ed, ed_very_hig, input_ed)
gb_fit_low = fuzz.interp_membership(gb, gb_low, input_gb)
gb_fit_med = fuzz.interp_membership(gb, gb_med, input_gb)
gb_fit_hig = fuzz.interp_membership(gb, gb_hig, input_gb)
gs_fit_low = fuzz.interp_membership(gs, gs_low, input_gs)
gs_fit_med = fuzz.interp_membership(gs, gs_med, input_gs)
gs_fit_hig = fuzz.interp_membership(gs, gs_hig, input_gs)
kc_fit_very_low = fuzz.interp_membership(kc, kc_very_low, input_kc)
kc_fit_low = fuzz.interp_membership(kc, kc_low, input_kc)
kc_fit_med = fuzz.interp_membership(kc, kc_med, input_kc)
kc_fit_hig = fuzz.interp_membership(kc, kc_hig, input_kc)
kc_fit_very_hig = fuzz.interp_membership(kc, kc_very_hig, input_kc)
# Kuralların tabanları
# PH GB GS ZF
rule11 = np.fmin(ph_fit_hig, risk_very_low)
rule12 = np.fmin(ph_fit_med, risk_med)
rule13 = np.fmin(ph_fit_low, risk_very_hig)
rule14 = np.fmin(zf_fit_hig, risk_hig)
rule15 = np.fmin(zf_fit_med, risk_med)
rule16 = np.fmin(zf_fit_low, risk_low)
rule17 = np.fmin(gs_fit_hig, risk_low)
rule18 = np.fmin(gs_fit_med, risk_med)
rule19 = np.fmin(gs_fit_low, risk_hig)
rule110 = np.fmin(gb_fit_hig, risk_very_hig)
rule111 = np.fmin(gb_fit_med, risk_med)
rule112 = np.fmin(gb_fit_low, risk_very_low)
rule1_1 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_hig), gb_fit_hig),
zf_fit_hig), risk_med)
rule1_2 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_hig), gb_fit_hig),
zf_fit_med), risk_med)
rule1_3 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_hig), gb_fit_hig),
zf_fit_low), risk_med)
rule1_4 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_hig), gb_fit_med),
zf_fit_hig), risk_med)
rule1_5 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_hig), gb_fit_med),
zf_fit_med), risk_med)
rule1_6 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_hig), gb_fit_med),
zf_fit_low), risk_med)
rule1_7 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_hig), gb_fit_low),
zf_fit_hig), risk_med)
rule1_8 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_hig), gb_fit_low),
zf_fit_med), risk_med)
rule1_9 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_hig), gb_fit_low),
zf_fit_low), risk_low)
rule1_10 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_med), gb_fit_hig),
zf_fit_hig), risk_hig)
rule1_11 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_med), gb_fit_hig),
zf_fit_med), risk_med)
rule1_12 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_med), gb_fit_hig),
zf_fit_low), risk_med)
rule1_13 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_med), gb_fit_med),
zf_fit_hig), risk_med)
rule1_14 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_med), gb_fit_med),
zf_fit_med), risk_med)
rule1_15 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_med), gb_fit_med),
zf_fit_low), risk_med)
rule1_16 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_med), gb_fit_low),
zf_fit_hig), risk_med)
rule1_17 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_med), gb_fit_low),
zf_fit_med), risk_med)
rule1_18 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_med), gb_fit_low),
zf_fit_low), risk_med)
rule1_19 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_low), gb_fit_hig),
zf_fit_hig), risk_hig)
rule1_20 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_low), gb_fit_hig),
zf_fit_med), risk_hig)
rule1_21 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_low), gb_fit_hig),
zf_fit_low), risk_hig)
rule1_22 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_low), gb_fit_med),
zf_fit_hig), risk_hig)
rule1_23 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_low), gb_fit_med),
zf_fit_med), risk_hig)
rule1_24 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_low), gb_fit_med),
zf_fit_low), risk_hig)
rule1_25 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_low), gb_fit_low),
zf_fit_hig), risk_hig)
rule1_26 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_low), gb_fit_low),
zf_fit_med), risk_hig)
rule1_27 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_hig, gs_fit_low), gb_fit_low),
zf_fit_low), risk_med)
rule1_28 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_hig), gb_fit_hig),
zf_fit_hig), risk_hig)
rule1_29 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_hig), gb_fit_hig),
zf_fit_med), risk_med)
rule1_30 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_hig), gb_fit_hig),
zf_fit_low), risk_med)
rule1_31 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_hig), gb_fit_med),
zf_fit_hig), risk_hig)
rule1_32 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_hig), gb_fit_med),
zf_fit_med), risk_med)
rule1_33 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_hig), gb_fit_med),
zf_fit_low), risk_med)
rule1_34 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_hig), gb_fit_low),
zf_fit_hig), risk_med)
rule1_35 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_hig), gb_fit_low),
zf_fit_med), risk_med)
rule1_36 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_hig), gb_fit_low),
zf_fit_low), risk_med)
rule1_37 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_med), gb_fit_hig),
zf_fit_hig), risk_hig)
rule1_38 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_med), gb_fit_hig),
zf_fit_med), risk_hig)
rule1_39 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_med), gb_fit_hig),
zf_fit_low), risk_hig)
rule1_40 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_med), gb_fit_med),
zf_fit_hig), risk_hig)
rule1_41 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_med), gb_fit_med),
zf_fit_med), risk_hig)
rule1_42 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_med), gb_fit_med),
zf_fit_low), risk_hig)
rule1_43 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_med), gb_fit_low),
zf_fit_hig), risk_hig)
rule1_44 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_med), gb_fit_low),
zf_fit_med), risk_hig)
rule1_45 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_med), gb_fit_low),
zf_fit_low), risk_med)
rule1_46 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_low), gb_fit_hig),
zf_fit_hig), risk_very_hig)
rule1_47 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_low), gb_fit_hig),
zf_fit_med), risk_hig)
rule1_48 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_low), gb_fit_hig),
zf_fit_low), risk_hig)
rule1_49 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_low), gb_fit_med),
zf_fit_hig), risk_hig)
rule1_50 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_low), gb_fit_med),
zf_fit_med), risk_hig)
rule1_51 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_low), gb_fit_med),
zf_fit_low), risk_hig)
rule1_52 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_low), gb_fit_low),
zf_fit_hig), risk_hig)
rule1_53 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_low), gb_fit_low),
zf_fit_med), risk_hig)
rule1_54 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_med, gs_fit_low), gb_fit_low),
zf_fit_low), risk_hig)
rule1_55 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_hig), gb_fit_hig),
zf_fit_hig), risk_hig)
rule1_56 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_hig), gb_fit_hig),
zf_fit_med), risk_hig)
rule1_57 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_hig), gb_fit_hig),
zf_fit_low), risk_hig)
rule1_58 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_hig), gb_fit_med),
zf_fit_hig), risk_hig)
rule1_59 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_hig), gb_fit_med),
zf_fit_med), risk_hig)
rule1_60 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_hig), gb_fit_med),
zf_fit_low), risk_hig)
rule1_61 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_hig), gb_fit_low),
zf_fit_hig), risk_hig)
rule1_62 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_hig), gb_fit_low),
zf_fit_med), risk_hig)
rule1_63 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_hig), gb_fit_low),
zf_fit_low), risk_med)
rule1_64 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_med), gb_fit_hig),
zf_fit_hig), risk_very_hig)
rule1_65 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_med), gb_fit_hig),
zf_fit_med), risk_hig)
rule1_66 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_med), gb_fit_hig),
zf_fit_low), risk_hig)
rule1_67 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_med), gb_fit_med),
zf_fit_hig), risk_hig)
rule1_68 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_med), gb_fit_med),
zf_fit_med), risk_hig)
rule1_69 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_med), gb_fit_med),
zf_fit_low), risk_hig)
rule1_70 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_med), gb_fit_low),
zf_fit_hig), risk_hig)
rule1_71 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_med), gb_fit_low),
zf_fit_med), risk_hig)
rule1_72 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_med), gb_fit_low),
zf_fit_low), risk_hig)
rule1_73 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_low), gb_fit_hig),
zf_fit_low), risk_hig)
rule1_74 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_low), gb_fit_hig),
zf_fit_hig), risk_very_hig)
rule1_75 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_low), gb_fit_hig),
zf_fit_med), risk_very_hig)
rule1_76 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_low), gb_fit_med),
zf_fit_hig), risk_very_hig)
rule1_77 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_low), gb_fit_med),
zf_fit_med), risk_very_hig)
rule1_78 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_low), gb_fit_med),
zf_fit_low), risk_hig)
rule1_79 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_low), gb_fit_low),
zf_fit_hig), risk_very_hig)
rule1_80 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_low), gb_fit_low),
zf_fit_med), risk_very_hig)
rule1_81 = np.fmin(
np.fmin(np.fmin(np.fmin(ph_fit_low, gs_fit_low), gb_fit_low),
zf_fit_low), risk_hig)
#ED KÇ I
rule2_1 = np.fmin(
np.fmin(np.fmin(ed_fit_very_low, kc_fit_very_low), i_fit_low),
risk_hig)
rule2_2 = np.fmin(
np.fmin(np.fmin(ed_fit_very_low, kc_fit_very_low), i_fit_med),
risk_hig)
rule2_3 = np.fmin(
np.fmin(np.fmin(ed_fit_very_low, kc_fit_very_low), i_fit_hig),
risk_hig)
rule2_4 = np.fmin(np.fmin(np.fmin(ed_fit_very_low, kc_fit_low), i_fit_low),
risk_hig)
rule2_5 = np.fmin(np.fmin(np.fmin(ed_fit_very_low, kc_fit_low), i_fit_med),
risk_med)
rule2_6 = np.fmin(np.fmin(np.fmin(ed_fit_very_low, kc_fit_low), i_fit_hig),
risk_med)
rule2_7 = np.fmin(np.fmin(np.fmin(ed_fit_very_low, kc_fit_med), i_fit_low),
risk_med)
rule2_8 = np.fmin(np.fmin(np.fmin(ed_fit_very_low, kc_fit_med), i_fit_med),
risk_med)
rule2_9 = np.fmin(np.fmin(np.fmin(ed_fit_very_low, kc_fit_med), i_fit_hig),
risk_med)
rule2_10 = np.fmin(
np.fmin(np.fmin(ed_fit_very_low, kc_fit_hig), i_fit_low), risk_med)
rule2_11 = np.fmin(
np.fmin(np.fmin(ed_fit_very_low, kc_fit_hig), i_fit_med), risk_med)
rule2_12 = np.fmin(
np.fmin(np.fmin(ed_fit_very_low, kc_fit_hig), i_fit_hig), risk_low)
rule2_13 = np.fmin(
np.fmin(np.fmin(ed_fit_very_low, kc_fit_very_hig), i_fit_low),
risk_med)
rule2_14 = np.fmin(
np.fmin(np.fmin(ed_fit_very_low, kc_fit_very_hig), i_fit_med),
risk_low)
rule2_15 = np.fmin(
np.fmin(np.fmin(ed_fit_very_low, kc_fit_very_hig), i_fit_hig),
risk_low)
rule2_16 = np.fmin(
np.fmin(np.fmin(ed_fit_low, kc_fit_very_low), i_fit_low), risk_hig)
rule2_17 = np.fmin(
np.fmin(np.fmin(ed_fit_low, kc_fit_very_low), i_fit_med), risk_hig)
rule2_18 = np.fmin(
np.fmin(np.fmin(ed_fit_low, kc_fit_very_low), i_fit_hig), risk_med)
rule2_19 = np.fmin(np.fmin(np.fmin(ed_fit_low, kc_fit_low), i_fit_low),
risk_hig)
rule2_20 = np.fmin(np.fmin(np.fmin(ed_fit_low, kc_fit_low), i_fit_med),
risk_med)
rule2_21 = np.fmin(np.fmin(np.fmin(ed_fit_low, kc_fit_low), i_fit_hig),
risk_med)
rule2_22 = np.fmin(np.fmin(np.fmin(ed_fit_low, kc_fit_med), i_fit_low),
risk_med)
rule2_23 = np.fmin(np.fmin(np.fmin(ed_fit_low, kc_fit_med), i_fit_med),
risk_med)
rule2_24 = np.fmin(np.fmin(np.fmin(ed_fit_low, kc_fit_med), i_fit_hig),
risk_med)
rule2_25 = np.fmin(np.fmin(np.fmin(ed_fit_low, kc_fit_hig), i_fit_low),
risk_med)
rule2_26 = np.fmin(np.fmin(np.fmin(ed_fit_low, kc_fit_hig), i_fit_med),
risk_low)
rule2_27 = np.fmin(np.fmin(np.fmin(ed_fit_low, kc_fit_hig), i_fit_hig),
risk_low)
rule2_28 = np.fmin(
np.fmin(np.fmin(ed_fit_low, kc_fit_very_hig), i_fit_low), risk_low)
rule2_29 = np.fmin(
np.fmin(np.fmin(ed_fit_low, kc_fit_very_hig), i_fit_med), risk_low)
rule2_30 = np.fmin(
np.fmin(np.fmin(ed_fit_low, kc_fit_very_hig), i_fit_hig), risk_low)
rule2_31 = np.fmin(
np.fmin(np.fmin(ed_fit_med, kc_fit_very_low), i_fit_low), risk_hig)
rule2_32 = np.fmin(
np.fmin(np.fmin(ed_fit_med, kc_fit_very_low), i_fit_med), risk_med)
rule2_33 = np.fmin(
np.fmin(np.fmin(ed_fit_med, kc_fit_very_low), i_fit_hig), risk_med)
rule2_34 = np.fmin(np.fmin(np.fmin(ed_fit_med, kc_fit_low), i_fit_low),
risk_med)
rule2_35 = np.fmin(np.fmin(np.fmin(ed_fit_med, kc_fit_low), i_fit_med),
risk_med)
rule2_36 = np.fmin(np.fmin(np.fmin(ed_fit_med, kc_fit_low), i_fit_hig),
risk_med)
rule2_37 = np.fmin(np.fmin(np.fmin(ed_fit_med, kc_fit_med), i_fit_low),
risk_med)
rule2_38 = np.fmin(np.fmin(np.fmin(ed_fit_med, kc_fit_med), i_fit_med),
risk_med)
rule2_39 = np.fmin(np.fmin(np.fmin(ed_fit_med, kc_fit_med), i_fit_hig),
risk_low)
rule2_40 = np.fmin(np.fmin(np.fmin(ed_fit_med, kc_fit_hig), i_fit_low),
risk_med)
rule2_41 = np.fmin(np.fmin(np.fmin(ed_fit_med, kc_fit_hig), i_fit_med),
risk_low)
rule2_42 = np.fmin(np.fmin(np.fmin(ed_fit_med, kc_fit_hig), i_fit_hig),
risk_low)
rule2_43 = np.fmin(
np.fmin(np.fmin(ed_fit_med, kc_fit_very_hig), i_fit_low), risk_low)
rule2_44 = np.fmin(
np.fmin(np.fmin(ed_fit_med, kc_fit_very_hig), i_fit_med), risk_low)
rule2_45 = np.fmin(
np.fmin(np.fmin(ed_fit_med, kc_fit_very_hig), i_fit_hig),
risk_very_low)
rule2_46 = np.fmin(
np.fmin(np.fmin(ed_fit_hig, kc_fit_very_low), i_fit_low), risk_med)
rule2_47 = np.fmin(
np.fmin(np.fmin(ed_fit_hig, kc_fit_very_low), i_fit_med), risk_med)
rule2_48 = np.fmin(
np.fmin(np.fmin(ed_fit_hig, kc_fit_very_low), i_fit_hig), risk_med)
rule2_49 = np.fmin(np.fmin(np.fmin(ed_fit_hig, kc_fit_low), i_fit_low),
risk_med)
rule2_50 = np.fmin(np.fmin(np.fmin(ed_fit_hig, kc_fit_low), i_fit_med),
risk_med)
rule2_51 = np.fmin(np.fmin(np.fmin(ed_fit_hig, kc_fit_low), i_fit_hig),
risk_low)
rule2_52 = np.fmin(np.fmin(np.fmin(ed_fit_hig, kc_fit_med), i_fit_low),
risk_med)
rule2_53 = np.fmin(np.fmin(np.fmin(ed_fit_hig, kc_fit_med), i_fit_med),
risk_low)
rule2_54 = np.fmin(np.fmin(np.fmin(ed_fit_hig, kc_fit_med), i_fit_hig),
risk_low)
rule2_55 = np.fmin(np.fmin(np.fmin(ed_fit_hig, kc_fit_hig), i_fit_low),
risk_low)
rule2_56 = np.fmin(np.fmin(np.fmin(ed_fit_hig, kc_fit_hig), i_fit_med),
risk_low)
rule2_57 = np.fmin(np.fmin(np.fmin(ed_fit_hig, kc_fit_hig), i_fit_hig),
risk_low)
rule2_58 = np.fmin(
np.fmin(np.fmin(ed_fit_hig, kc_fit_very_hig), i_fit_low), risk_low)
rule2_59 = np.fmin(
np.fmin(np.fmin(ed_fit_hig, kc_fit_very_hig), i_fit_med), risk_low)
rule2_60 = np.fmin(
np.fmin(np.fmin(ed_fit_hig, kc_fit_very_hig), i_fit_hig),
risk_very_low)
rule2_61 = np.fmin(
np.fmin(np.fmin(ed_fit_very_hig, kc_fit_very_low), i_fit_low),
risk_med)
rule2_62 = np.fmin(
np.fmin(np.fmin(ed_fit_very_hig, kc_fit_very_low), i_fit_med),
risk_med)
rule2_63 = np.fmin(
np.fmin(np.fmin(ed_fit_very_hig, kc_fit_very_low), i_fit_hig),
risk_med)
rule2_64 = np.fmin(
np.fmin(np.fmin(ed_fit_very_hig, kc_fit_low), i_fit_low), risk_med)
rule2_65 = np.fmin(
np.fmin(np.fmin(ed_fit_very_hig, kc_fit_low), i_fit_med), risk_low)
rule2_66 = np.fmin(
np.fmin(np.fmin(ed_fit_very_hig, kc_fit_low), i_fit_hig), risk_low)
rule2_67 = np.fmin(
np.fmin(np.fmin(ed_fit_very_hig, kc_fit_med), i_fit_low), risk_low)
rule2_68 = np.fmin(
np.fmin(np.fmin(ed_fit_very_hig, kc_fit_med), i_fit_med), risk_low)
rule2_69 = np.fmin(
np.fmin(np.fmin(ed_fit_very_hig, kc_fit_med), i_fit_hig), risk_low)
rule2_70 = np.fmin(
np.fmin(np.fmin(ed_fit_very_hig, kc_fit_hig), i_fit_low), risk_low)
rule2_71 = np.fmin(
np.fmin(np.fmin(ed_fit_very_hig, kc_fit_hig), i_fit_med), risk_low)
rule2_72 = np.fmin(
np.fmin(np.fmin(ed_fit_very_hig, kc_fit_hig), i_fit_hig),
risk_very_low)
rule2_73 = np.fmin(
np.fmin(np.fmin(ed_fit_very_hig, kc_fit_very_hig), i_fit_low),
risk_low)
rule2_74 = np.fmin(
np.fmin(np.fmin(ed_fit_very_hig, kc_fit_very_hig), i_fit_med),
risk_very_low)
rule2_75 = np.fmin(
np.fmin(np.fmin(ed_fit_very_hig, kc_fit_very_hig), i_fit_hig),
risk_very_low)
rule2_111 = np.fmin(ed_fit_very_hig, risk_very_low)
rule2_112 = np.fmin(ed_fit_hig, risk_low)
rule2_113 = np.fmin(ed_fit_med, risk_med)
rule2_114 = np.fmin(ed_fit_low, risk_hig)
rule2_115 = np.fmin(ed_fit_very_low, risk_very_hig)
rule2_211 = np.fmin(kc_fit_very_hig, risk_very_low)
rule2_212 = np.fmin(kc_fit_very_hig, risk_low)
rule2_213 = np.fmin(kc_fit_med, risk_med)
rule2_214 = np.fmin(kc_fit_low, risk_hig)
rule2_215 = np.fmin(kc_fit_very_low, risk_very_hig)
rule2_311 = np.fmin(i_fit_hig, risk_low)
rule2_312 = np.fmin(i_fit_med, risk_med)
rule2_313 = np.fmin(i_fit_low, risk_hig)
#PD GS Aİ
rule3_1 = np.fmin(
np.fmin(np.fmin(pd_fit_very_low, gs_fit_low), ai_fit_very_hig),
risk_med)
rule3_2 = np.fmin(
np.fmin(np.fmin(pd_fit_very_low, gs_fit_low), ai_fit_very_low),
risk_hig)
rule3_3 = np.fmin(
np.fmin(np.fmin(pd_fit_very_low, gs_fit_med), ai_fit_very_hig),
risk_med)
rule3_4 = np.fmin(
np.fmin(np.fmin(pd_fit_very_low, gs_fit_med), ai_fit_very_low),
risk_med)
rule3_5 = np.fmin(
np.fmin(np.fmin(pd_fit_very_low, gs_fit_hig), ai_fit_very_hig),
risk_med)
rule3_6 = np.fmin(
np.fmin(np.fmin(pd_fit_very_low, gs_fit_hig), ai_fit_very_low),
risk_med)
rule3_7 = np.fmin(
np.fmin(np.fmin(pd_fit_low, gs_fit_low), ai_fit_very_hig), risk_med)
rule3_8 = np.fmin(
np.fmin(np.fmin(pd_fit_low, gs_fit_low), ai_fit_very_low), risk_med)
rule3_9 = np.fmin(
np.fmin(np.fmin(pd_fit_low, gs_fit_med), ai_fit_very_hig), risk_med)
rule3_10 = np.fmin(
np.fmin(np.fmin(pd_fit_low, gs_fit_med), ai_fit_very_low), risk_med)
rule3_11 = np.fmin(
np.fmin(np.fmin(pd_fit_low, gs_fit_hig), ai_fit_very_hig), risk_low)
rule3_12 = np.fmin(
np.fmin(np.fmin(pd_fit_low, gs_fit_hig), ai_fit_very_low), risk_med)
rule3_13 = np.fmin(
np.fmin(np.fmin(pd_fit_med, gs_fit_low), ai_fit_very_hig), risk_med)
rule3_14 = np.fmin(
np.fmin(np.fmin(pd_fit_med, gs_fit_low), ai_fit_very_low), risk_med)
rule3_15 = np.fmin(
np.fmin(np.fmin(pd_fit_med, gs_fit_med), ai_fit_very_hig), risk_low)
rule3_16 = np.fmin(
np.fmin(np.fmin(pd_fit_med, gs_fit_med), ai_fit_very_low), risk_med)
rule3_17 = np.fmin(
np.fmin(np.fmin(pd_fit_med, gs_fit_hig), ai_fit_very_hig), risk_low)
rule3_18 = np.fmin(
np.fmin(np.fmin(pd_fit_med, gs_fit_hig), ai_fit_very_low), risk_low)
rule3_19 = np.fmin(
np.fmin(np.fmin(pd_fit_hig, gs_fit_low), ai_fit_very_hig), risk_low)
rule3_20 = np.fmin(
np.fmin(np.fmin(pd_fit_hig, gs_fit_low), ai_fit_very_low), risk_med)
rule3_21 = np.fmin(
np.fmin(np.fmin(pd_fit_hig, gs_fit_med), ai_fit_very_hig), risk_low)
rule3_22 = np.fmin(
np.fmin(np.fmin(pd_fit_hig, gs_fit_med), ai_fit_very_low), risk_low)
rule3_23 = np.fmin(
np.fmin(np.fmin(pd_fit_hig, gs_fit_hig), ai_fit_very_hig), risk_low)
rule3_24 = np.fmin(
np.fmin(np.fmin(pd_fit_hig, gs_fit_hig), ai_fit_very_low), risk_low)
rule3_25 = np.fmin(
np.fmin(np.fmin(pd_fit_very_hig, gs_fit_low), ai_fit_very_hig),
risk_low)
rule3_26 = np.fmin(
np.fmin(np.fmin(pd_fit_very_hig, gs_fit_low), ai_fit_very_low),
risk_med)
rule3_27 = np.fmin(
np.fmin(np.fmin(pd_fit_very_hig, gs_fit_med), ai_fit_very_hig),
risk_low)
rule3_28 = np.fmin(
np.fmin(np.fmin(pd_fit_very_hig, gs_fit_med), ai_fit_very_low),
risk_low)
rule3_29 = np.fmin(
np.fmin(np.fmin(pd_fit_very_hig, gs_fit_hig), ai_fit_very_hig),
risk_very_low)
rule3_30 = np.fmin(
np.fmin(np.fmin(pd_fit_very_hig, gs_fit_hig), ai_fit_very_low),
risk_low)
rule31 = np.fmin(pd_fit_very_low, risk_very_hig)
rule32 = np.fmin(pd_fit_low, risk_hig)
rule33 = np.fmin(pd_fit_med, risk_med)
rule34 = np.fmin(pd_fit_hig, risk_low)
rule35 = np.fmin(pd_fit_very_hig, risk_very_low)
rule36 = np.fmin(gs_fit_low, risk_hig)
rule37 = np.fmin(gs_fit_med, risk_med)
rule38 = np.fmin(gs_fit_hig, risk_low)
rule39 = np.fmin(ai_fit_very_low, risk_very_hig) # adim adim ilerleme
rule310 = np.fmin(ai_fit_very_hig, risk_very_low)
# Birleşim kümelerinin oluşturulması
list_very_hig = [
rule1_46, rule1_64, rule1_74, rule1_75, rule1_76, rule1_77, rule1_79,
rule1_80, rule1_78, rule2_19, rule31, rule39, rule2_115, rule2_215
]
list_hig = [
rule1_10, rule1_19, rule1_20, rule1_21, rule1_22, rule1_23, rule1_24,
rule1_25, rule1_26, rule1_28, rule1_31, rule1_37, rule1_38, rule1_39,
rule1_40, rule1_41, rule1_42, rule1_43, rule1_44, rule1_47, rule1_48,
rule1_49, rule1_50, rule1_51, rule1_52, rule1_53, rule1_54, rule1_55,
rule1_56, rule1_57, rule1_58, rule1_59, rule1_60, rule1_61, rule1_62,
rule1_65, rule1_66, rule1_67, rule1_68, rule1_69, rule1_70, rule1_71,
rule1_72, rule1_73, rule1_79, rule1_81, rule2_1, rule2_2, rule2_3,
rule2_4, rule2_16, rule2_17, rule2_31, rule3_2, rule32, rule36,
rule2_114, rule2_214, rule2_313, rule13, rule14, rule19, rule110
]
list_med = [
rule1_1, rule1_2, rule1_3, rule1_4, rule1_5, rule1_6, rule1_7, rule1_8,
rule1_11, rule1_12, rule1_13, rule1_14, rule1_15, rule1_16, rule1_17,
rule1_18, rule1_27, rule1_29, rule1_30, rule1_32, rule1_33, rule1_34,
rule1_35, rule1_36, rule1_45, rule1_63, rule2_5, rule2_6, rule2_7,
rule2_8, rule2_9, rule2_10, rule2_11, rule2_13, rule2_18, rule2_20,
rule2_21, rule2_22, rule2_23, rule2_24, rule2_25, rule2_32, rule2_33,
rule2_34, rule2_35, rule2_36, rule2_37, rule2_38, rule2_40, rule2_46,
rule2_47, rule2_48, rule2_49, rule2_50, rule2_52, rule2_61, rule2_62,
rule2_63, rule2_64, rule3_1, rule3_3, rule3_4, rule3_5, rule3_6,
rule3_7, rule3_8, rule3_9, rule3_10, rule3_12, rule3_13, rule3_14,
rule3_16, rule3_20, rule3_26, rule33, rule37, rule2_113, rule2_213,
rule2_312, rule12, rule15, rule18, rule111
]
list_low = [
rule2_12, rule2_14, rule2_15, rule2_26, rule2_27, rule2_28, rule2_29,
rule2_30, rule2_39, rule2_41, rule2_42, rule2_43, rule2_44, rule2_51,
rule2_53, rule2_54, rule2_55, rule2_56, rule2_57, rule2_58, rule2_59,
rule2_65, rule2_66, rule2_67, rule2_68, rule2_69, rule2_70, rule2_71,
rule2_73, rule3_11, rule3_15, rule3_17, rule3_18, rule3_19, rule3_21,
rule3_22, rule3_23, rule3_24, rule3_25, rule3_27, rule3_28, rule3_30,
rule1_9, rule34, rule38, rule2_112, rule2_212, rule2_311, rule16,
rule17, rule112
]
list_very_low = [
rule2_45, rule2_60, rule2_72, rule2_74, rule2_75, rule3_29, rule35,
rule310, rule2_111, rule2_211, rule11
]
out_very_hig = []
out_hig = []
out_med = []
out_low = []
out_very_low = []
n = 0
first = list_very_hig[n]
for n in range(len(list_very_hig) - 1):
out_very_hig = np.fmax(first, list_very_hig[n + 1])
first = out_very_hig
n = 0
first = list_hig[n]
for n in range(len(list_hig) - 1):
out_hig = np.fmax(first, list_hig[n + 1])
first = out_hig
n = 0
first = list_med[n]
for n in range(len(list_med) - 1):
out_med = np.fmax(first, list_med[n + 1])
first = out_med
n = 0
first = list_low[n]
for n in range(len(list_low) - 1):
out_low = np.fmax(first, list_low[n + 1])
first = out_low
n = 0
first = list_very_low[n]
for n in range(len(list_very_low) - 1):
out_very_low = np.fmax(first, list_very_low[n + 1])
first = out_very_low
# Veri görselleştirme
risk0 = np.zeros_like(risk)
fig, ax11 = plt.subplots(figsize=(10, 4))
ax11.fill_between(risk, risk0, out_very_hig, facecolor='r', alpha=0.7)
ax11.plot(risk, out_very_hig, 'r', linestyle='--')
ax11.fill_between(risk, risk0, out_hig, facecolor='g', alpha=0.7)
ax11.plot(risk, out_hig, 'k', linestyle='--')
ax11.fill_between(risk, risk0, out_med, facecolor='g', alpha=0.7)
ax11.plot(risk, out_med, 'y', linestyle='--')
ax11.fill_between(risk, risk0, out_low, facecolor='g', alpha=0.7)
ax11.plot(risk, out_low, 'y', linestyle='--')
ax11.fill_between(risk, risk0, out_very_low, facecolor='g', alpha=0.7)
ax11.plot(risk, out_very_low, 'y', linestyle='--')
ax11.set_title('Risk Çıkışı')
plt.tight_layout()
# Durulaştırma
out_list = [out_very_hig, out_hig, out_med, out_low, out_very_low]
n = 0
first = out_list[n]
for n in range(len(out_list) - 1):
out_risk = np.fmax(first, out_list[n + 1])
first = out_risk
defuzzified = fuzz.defuzz(risk, out_risk, 'centroid')
result = fuzz.interp_membership(risk, out_risk, defuzzified)
# Sonuç
print("Başarı Değeri:", defuzzified)
myLabel = tk.Label(root, text=100 - defuzzified, font=("Calibri", 13))
myLabel.place(relwidth=0.22, relheight=0.08, relx=0.75, rely=0.8)
# Veri Görselleştirme
risk0 = np.zeros_like(risk)
fig, ax10 = plt.subplots(figsize=(7, 4))
ax10.plot(risk, out_very_hig, 'b', linewidth=0.5, linestyle='--')
ax10.plot(risk, out_hig, 'b', linewidth=0.5, linestyle='--')
ax10.plot(risk, out_med, 'b', linewidth=0.5, linestyle='--')
ax10.plot(risk, out_low, 'b', linewidth=0.5, linestyle='--')
ax10.plot(risk, out_very_low, 'b', linewidth=0.5, linestyle='--')
ax10.fill_between(risk, risk0, out_risk, facecolor='Orange', alpha=0.7)
ax10.plot([defuzzified, defuzzified], [0, result],
'k',
linewidth=1.5,
alpha=0.9)
ax10.set_title('Ağırlık Merkezi ile Durulaştırma')
plt.tight_layout()
plt.show()
return defuzzified
def __init__(self):
# Create input and output variables
self.lsonar = ctrl.Antecedent(self.sonar_interval, 'lsonar')
self.rsonar = ctrl.Antecedent(self.sonar_interval, 'rsonar')
self.last_theta = ctrl.Antecedent(self.theta_interval, 'last_theta')
self.theta = ctrl.Consequent(self.theta_interval, 'theta')
self.v_x = ctrl.Consequent(self.v_interval, 'v_x')
# we need membership functions!
distance_levels = ['dangerous', 'unsafe', 'safe']
theta_directions = ['left', 'centre', 'right']
velocity_levels = ['slow', 'medium', 'fast']
self.lsonar.automf(names=distance_levels)
self.rsonar.automf(names=distance_levels)
self.last_theta.automf(names=theta_directions)
self.theta.automf(names=theta_directions)
self.v_x.automf(names=velocity_levels)
low_mshp = mshp.trimf(self.sonar_interval, [0.25, 0.25, 0.31])
mid_mshp = mshp.trimf(self.sonar_interval, [0.28, 0.35, 0.45])
high_mshp = mshp.trapmf(self.sonar_interval, [0.4, 0.6, 2.55, 2.55])
self.lsonar['dangerous'] = low_mshp
self.lsonar['unsafe'] = mid_mshp
self.lsonar['safe'] = high_mshp
self.rsonar['dangerous'] = low_mshp
self.rsonar['unsafe'] = mid_mshp
self.rsonar['safe'] = high_mshp
left_angle_mshp = mshp.gaussmf(self.theta_interval, 0.7, 0.2)
centre_angle_mshp = mshp.gaussmf(self.theta_interval, 0, 0.1)
right_angle_mshp = mshp.gaussmf(self.theta_interval, -0.7, 0.2)
self.theta['left'] = left_angle_mshp
self.theta['centre'] = centre_angle_mshp
self.theta['right'] = right_angle_mshp
self.last_theta['left'] = left_angle_mshp
self.last_theta['centre'] = centre_angle_mshp
self.last_theta['right'] = right_angle_mshp
self.v_x['slow'] = mshp.trimf(self.v_interval, [0, 0, 0.32])
self.v_x['medium'] = mshp.gaussmf(self.v_interval, 0.5, 0.36)
self.v_x['fast'] = mshp.trapmf(self.v_interval, [0.75, 0.95, 1.0, 1.0])
# Here the rules are defined!
# look at http://pythonhosted.org/scikit-fuzzy/auto_examples/plot_control_system_advanced.html#set-up-the-fuzzy-control-system
# and http://pythonhosted.org/scikit-fuzzy/auto_examples/plot_tipping_problem_newapi.html
# for good examples
rules = []
# Sonars are NOT dangerous, go fast and forward
rules.append(ctrl.Rule(label="FastAndForward",
antecedent=(~self.lsonar['dangerous'] & ~self.rsonar['dangerous']),
consequent=(self.theta['centre'], self.v_x['fast'])))
# Right sonar reads dangerous, left does not, steer left
rules.append(ctrl.Rule(label="SteerLeftCaution",
antecedent=(~self.lsonar['dangerous'] & self.rsonar['dangerous']),
consequent=(self.theta['left'], self.v_x['slow'])))
# Left sonar reads dangerous, right does not, steer left
rules.append(ctrl.Rule(label="SteerRightCaution",
antecedent=(self.lsonar['dangerous'] & ~self.rsonar['dangerous']),
consequent=(self.theta['right'], self.v_x['slow'])))
# Right sonar is NOT safe, left reads safe, turn left full speed
rules.append(ctrl.Rule(label="SteerLeftReckless",
antecedent=(self.lsonar['safe'] & ~self.rsonar['safe']),
consequent=(self.theta['left'], self.v_x['fast'])))
# Left sonar is NOT safe, right reads safe, turn right full speed
rules.append(ctrl.Rule(label="SteerRightReckless",
antecedent=(~self.lsonar['safe'] & self.rsonar['safe']),
consequent=(self.theta['right'], self.v_x['fast'])))
# Left sonar is NOT dangerous and right is NOT safe, turn left medium speed
rules.append(ctrl.Rule(label="SteerLeftWatchSpeed",
antecedent=(~self.lsonar['dangerous'] & ~self.rsonar['safe']),
consequent=(self.theta['left'], self.v_x['medium'])))
# Right sonar is NOT dangerous and left is NOT safe, turn right medium speed
rules.append(ctrl.Rule(label="SteerRightWatchSpeed",
antecedent=(~self.lsonar['safe'] & ~self.rsonar['dangerous']),
consequent=(self.theta['right'], self.v_x['medium'])))
# Last theta was right, right sonar is dangerous, turn left slow speed
rules.append(ctrl.Rule(label="SteerLeftMedSpeed",
antecedent=(self.rsonar['dangerous'] & self.last_theta['right']),
consequent=(self.theta['left'], self.v_x['medium'])))
# Last theta was left, left sonar is dangerous, turn right slow speed
rules.append(ctrl.Rule(label="SteerRightMedSpeed",
antecedent=(self.lsonar['dangerous'] & self.last_theta['left']),
consequent=(self.theta['right'], self.v_x['medium'])))
# Last theta was left, rsonar reads not dangerous, turn centre, full speed
rules.append(ctrl.Rule(label="OutOfDangerFromLeft",
antecedent=(~self.rsonar['dangerous'] & self.last_theta['left']),
consequent=(self.theta['centre'], self.v_x['fast'])))
# Last theta was right, lsonar reads not dangerous, turn centre, full speed
rules.append(ctrl.Rule(label="OutOfDangerFromRight",
antecedent=(~self.lsonar['dangerous'] & self.last_theta['right']),
consequent=(self.theta['centre'], self.v_x['fast'])))
# Create the control system
self.system = ctrl.ControlSystem(rules=rules)
# System is created, just create a simulation, input values and call compute()
self.sim = ctrl.ControlSystemSimulation(self.system)
import numpy as np from skfuzzy import control as ctrl from skfuzzy import membership as mf speed = ctrl.Antecedent(np.arange(0, 85, 0.1), 'speed') distance = ctrl.Antecedent(np.arange(0, 3000, 0.5), 'distance') brake = ctrl.Consequent(np.arange(0, 100, 0.5), 'brake') throttle = ctrl.Consequent(np.arange(0, 100, 0.5), 'throttle') speed['stopped'] = mf.trimf(speed.universe, [0, 0, 2]) speed['very slow'] = mf.trimf(speed.universe, [1, 2.5, 4]) speed['slow'] = mf.trimf(speed.universe, [2.5, 6.5, 10.5]) speed['medium fast'] = mf.trimf(speed.universe, [6.5, 26.5, 46.5]) speed['fast'] = mf.trapmf(speed.universe, [26.5, 70, 85, 85]) distance['at'] = mf.trimf(distance.universe, [0, 0, 2]) distance['very near'] = mf.trimf(distance.universe, [1, 3, 5]) distance['near'] = mf.trimf(distance.universe, [3, 101.5, 200]) distance['medium far'] = mf.trimf(distance.universe, [100, 1550, 3000]) distance['far'] = mf.trapmf(distance.universe, [1500, 2250, 3000, 3000]) brake['no'] = mf.trimf(brake.universe, [0, 0, 40]) brake['very slight'] = mf.trimf(brake.universe, [20, 50, 80]) brake['slight'] = mf.trimf(brake.universe, [70, 83.5, 97]) brake['medium'] = mf.trimf(brake.universe, [95, 97, 99]) brake['full'] = mf.trimf(brake.universe, [98, 100, 100]) throttle['no'] = mf.trimf(throttle.universe, [0, 0, 2]) throttle['very slight'] = mf.trimf(throttle.universe, [1, 3, 5]) throttle['slight'] = mf.trimf(throttle.universe, [3, 16.5, 30]) throttle['medium'] = mf.trimf(throttle.universe, [20, 50, 80])
plt.scatter(df_date, df_avg, color='red')
plt.plot(df_date, lr.predict(df_date), color='blue')
plt.show()
egim = tahmin[1] - tahmin[0]
print('Eğim:', egim)
egim = egim + 1
egim100 = (100 * egim) / 2
print(egim100)
#4le çarptıkkkkk
x_std = np.arange(0, 101, 1)
std_low = mf.trimf(x_std, [0, 28, 56])
std_med = mf.trimf(x_std, [28, 56, 84])
std_high = mf.trimf(x_std, [56, 84, 100000])
x_egim = np.arange(0, 101, 1)
egim_inc = mf.trimf(x_egim, [0, 100, 100])
egim_dec = mf.trimf(x_egim, [0, 0, 100])
x_risk = np.arange(0, 101, 1)
risk_az = mf.trimf(x_risk, [0, 0, 50])
risk_orta = mf.trimf(x_risk, [30, 50, 70])
risk_cok = mf.trimf(x_risk, [50, 100, 100])
plt.set_title = 'Egim'
plt.plot(x_egim, egim_inc, 'r', linewidth=2, label="Artan")
plt.plot(x_egim, egim_dec, 'b', linewidth=2, label="Azalan")
plt.scatter(df_date, df_avg, color='red')
plt.plot(df_date, lr.predict(df_date), color='blue')
plt.show()
# calculate slope with predicted data
slope = prediction[1] - prediction[0]
print('Slope:', slope)
# formula for linear regression slope in stock market
slope = slope + 1
slope100 = (100 * slope) / 2
#print(slope100)
# computes standart deviation values using a triangular membership function
x_std = np.arange(0, 101, 1)
std_low = mf.trimf(x_std, [0, 28, 56])
std_med = mf.trimf(x_std, [28, 56, 84])
std_high = mf.trimf(x_std, [56, 84, 100000])
# computes slope values using a triangular membership function
x_slope = np.arange(0, 101, 1)
slope_inc = mf.trimf(x_slope, [0, 100, 100])
slope_dec = mf.trimf(x_slope, [0, 0, 100])
# computes risk values using a triangular membership function
x_risk = np.arange(0, 101, 1)
low_risk = mf.trimf(x_risk, [0, 0, 50])
med_risk = mf.trimf(x_risk, [30, 50, 70])
high_risk = mf.trimf(x_risk, [50, 100, 100])
plt.set_title = 'Slope'
# -*- coding: utf-8 -*- import numpy as np import skfuzzy as fuzz import skfuzzy.membership as mf import matplotlib.pyplot as plt # Değişkenlerin oluşturulması x_pedal = np.arange(0, 101, 1) x_speed = np.arange(0, 101, 1) y_brake = np.arange(0, 101, 1) # Üyelik fonksiyonlarının oluşturulması pedal_low = mf.trimf(x_pedal, [0, 0, 50]) pedal_med = mf.trimf(x_pedal, [0, 50, 100]) pedal_hig = mf.trimf(x_pedal, [50, 100, 100]) speed_low = mf.trimf(x_pedal, [0, 0, 60]) speed_med = mf.trimf(x_pedal, [20, 50, 80]) speed_hig = mf.trimf(x_pedal, [40, 100, 100]) brake_poor = mf.trimf(y_brake, [0, 0, 100]) brake_strong = mf.trimf(y_brake, [0, 100, 100]) # Veri görselleştirme fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, figsize=(6, 10)) ax0.plot(x_pedal, pedal_low, 'r', linewidth=2, label='Düşük')
def __estimate(self, abc):
euro_courses = np.asarray([self.euro_course])
response = trimf(euro_courses, abc)
return response[0]
# In[45]: x_bodytemp = np.arange(20, 50 , 1) x_respiratory = np.arange(0, 50 , 1) x_pulse = np.arange(0, 150 , 1) y_areacolour = np.arange(0, 4, 1) # In[46]: bodytemp_verylow = mf.trapmf(x_bodytemp, [-100, 0, 30, 34.5]) bodytemp_low = mf.trapmf(x_bodytemp, [0, 34.5, 35 ,36 ]) bodytemp_normal = mf.trimf(x_bodytemp, [36, 36.5, 37]) bodytemp_high = mf.trapmf(x_bodytemp, [36, 38, 41, 42]) bodytemp_veryhigh = mf.trapmf(x_bodytemp, [41, 42,45, 100]) # In[47]: respiratory_low = mf.trimf(x_respiratory, [-100, 0, 12]) respiratory_normal = mf.trapmf(x_respiratory, [8, 12, 20, 24]) respiratory_high = mf.trimf(x_respiratory, [20, 50, 100]) # In[48]:
import numpy as np from skfuzzy import control as ctrl from skfuzzy import membership as mf import matplotlib.pyplot as plt service = ctrl.Antecedent(np.arange(0, 11, 1), 'service') food = ctrl.Antecedent(np.arange(0, 11, 1), 'food') tips = ctrl.Consequent(np.arange(0, 31, 1), 'tips') # service['poor'] = mf.trimf(service.universe, [0, 0, 5]) # service['average'] = mf.trimf(service.universe, [0, 5, 10]) # service['good'] = mf.trimf(service.universe, [5, 10, 10]) service['poor'] = mf.trimf(service.universe, [0, 0, 3]) service['average'] = mf.trimf(service.universe, [2, 5, 8]) service['good'] = mf.trimf(service.universe, [6, 10, 10]) food['poor'] = mf.trimf(food.universe, [0, 0, 5]) food['average'] = mf.trimf(food.universe, [0, 5, 10]) food['good'] = mf.trimf(food.universe, [5, 10, 10]) tips['low'] = mf.trimf(tips.universe, [0, 0, 15]) tips['medium'] = mf.trimf(tips.universe, [0, 15, 30]) tips['high'] = mf.trimf(tips.universe, [15, 30, 30]) rule1 = ctrl.Rule(service['poor'] & food['poor'], tips['low']) rule2 = ctrl.Rule(service['poor'] & food['average'], tips['low']) rule3 = ctrl.Rule(service['poor'] & food['good'], tips['medium']) rule4 = ctrl.Rule(service['average'] & food['poor'], tips['low']) rule5 = ctrl.Rule(service['average'] & food['average'], tips['medium']) rule6 = ctrl.Rule(service['average'] & food['good'], tips['high']) rule7 = ctrl.Rule(service['good'] & food['poor'], tips['medium'])