def __init__(self, lowest=0, highest=40, step=0.5):
super(ControlTemperature, self).__init__()
self.lowest = lowest
self.highest = highest
self.step = step
self.temp = np.arange(lowest, highest, step)
self.temp_err = np.arange(-50, 50, 0.1)
self.temp_roc = np.arange(-30, 30, 0.1) # degress c per minute
self.chill_out = np.arange(-100, 100, 1)
# self.heat_out = np.arange(0, 100, 1)
#Current Temperature
self.te_too_cold = fuzz.trapmf(self.temp_err, [-40, -30, -1.5, -1])
self.te_cold = fuzz.trimf(self.temp_err, [-1.5, -1, -0.5])
self.te_optimal = fuzz.trimf(self.temp_err, [-0.5, 0, 0.5])
self.te_hot = fuzz.trimf(self.temp_err, [0.5, 1, 1.5])
self.te_too_hot = fuzz.trapmf(self.temp_err, [1, 1.5, 30, 40])
#Temperature Rate of Change (deg C per minute)
self.tr_cooling_quickly = fuzz.trapmf(self.temp_roc, [-20, -10, -0.5, -0.25])
# Output - Chiller
self.co_off = fuzz.trimf(self.chill_out, [0, 0, 5])
self.co_low = fuzz.trimf(self.chill_out, [5, 20, 40])
self.co_medium = fuzz.trimf(self.chill_out, [20, 40, 60])
self.co_high = fuzz.trapmf(self.chill_out, [40, 60, 100, 100])
# Output - Heater
self.ho_off = fuzz.trimf(self.chill_out, [-5, 0, 0])
self.ho_low = fuzz.trimf(self.chill_out, [-40, -20, -5])
self.ho_medium = fuzz.trimf(self.chill_out, [-60, -40, -20])
self.ho_high = fuzz.trapmf(self.chill_out, [-100, -100, -60, -40])
def test_tipping_problem():
# The full tipping problem uses many of these methods
food = ctrl.Antecedent(np.linspace(0, 10, 11), 'quality')
service = ctrl.Antecedent(np.linspace(0, 10, 11), 'service')
tip = ctrl.Consequent(np.linspace(0, 25, 26), 'tip')
food.automf(3)
service.automf(3)
# Manual membership function definition
tip['bad'] = fuzz.trimf(tip.universe, [0, 0, 13])
tip['middling'] = fuzz.trimf(tip.universe, [0, 13, 25])
tip['lots'] = fuzz.trimf(tip.universe, [13, 25, 25])
# Define fuzzy rules
rule1 = ctrl.Rule(food['poor'] | service['poor'], tip['bad'])
rule2 = ctrl.Rule(service['average'], tip['middling'])
rule3 = ctrl.Rule(service['good'] | food['good'], tip['lots'])
# The control system - defined both possible ways
tipping = ctrl.ControlSystem([rule1, rule2, rule3])
tipping2 = ctrl.ControlSystem()
tipping2.addrule(rule2)
tipping2.addrule(rule3)
tipping2.addrule(rule1)
tip_sim = ctrl.ControlSystemSimulation(tipping)
tip_sim2 = ctrl.ControlSystemSimulation(tipping2)
# Inputs added both possible ways
inputs = {'quality': 6.5, 'service': 9.8}
for key, value in inputs.items():
tip_sim.input[key] = value
tip_sim2.inputs(inputs)
# Compute the system
tip_sim.compute()
tip_sim2.compute()
# Ensure both methods of defining rules yield the same results
for val0, val1 in zip(tip_sim.output.values(),
tip_sim2.output.values()):
tst.assert_allclose(val0, val1)
# Verify against manual computation
tst.assert_allclose(tip_sim.output['tip'], 19.8578, atol=1e-2, rtol=1e-2)
def rangeToMF(range, type):
"""
Translate the range into a list of x_values and an MF function.
range : list/tuple
range to translate to MF
type : string
type of MF:
'sing' - singleton MF
'gauss' - gaussian MF function (mean, standard deviation)
'tri' - triangular MF function
'trap' - trapezoidal MF function
"""
c = 100 #number of x points
minR = float(min(range))
maxR = float(max(range))
if type == 'sing': #singleton
c = 10 #special short mf for singleton
if maxR == minR:
#print "SAME R"
ran = max(abs(0.15*minR),0.0001)
xrange = [minR-ran, minR+ran, ran/c]
Xs = [minR-2.*ran, minR-1.*ran, minR, minR+1.*ran, minR+2.*ran,]
Ys = [0.0, 0.0, 1.0, 0.0, 0.0]
else:
ran = abs(maxR-minR)
xrange = [minR, maxR, ran/c]
Xs, Ys = singleton_to_fuzzy(sum(range)/len(range), xrange)
elif type == 'gauss': #gaussian MF
std_range = (1./4.) #0.25
if minR == maxR: ran = max(0.0001,abs(0.05*minR))#0.05
else: ran = abs(maxR - minR) #check for min=max and get range
Xs = np.arange(minR - 0.5*ran, maxR + 0.5*ran, 2*ran/c)
Ys = fuzz.gaussmf(Xs, sum(range)/len(range), std_range*(ran)) #gaussian mf with 4sigma = range (to 97.7% "certainty")
elif type == 'tri': #triangular MF
if minR == maxR:
ran = max(abs(0.2*minR),0.001)
xrange = [0.9*minR, 1.1*maxR, ran/c,]
Xs, Ys = singleton_to_fuzzy(sum(range)/len(range), xrange)
else:
Xs = np.arange(0.9*minR, 1.1*maxR, (1.1*maxR-0.9*minR)/c) #create fuzzy MF for output
Ys = fuzz.trimf(Xs, [minR, sum(range)/len(range), maxR])
elif type == 'trap': #trapezoidal MF
if minR == maxR:
ran = max(abs(0.2*minR),0.001)
xrange = [0.9*minR, 1.1*maxR, ran/c,]
Xs, Ys = singleton_to_fuzzy(sum(range)/len(range), xrange)
else:
Xs = np.arange(0.9*minR, 1.1*maxR, (1.1*maxR-0.9*minR)/c) #create fuzzy MF for output
Ys = fuzz.trapmf(Xs, [minR, minR, maxR, maxR])
else:
raise StandardError("Unknown type of membership function: %s" % type)
return [np.asarray(Xs), np.asarray(Ys)] #create MF
def __init__(self):
# Generate universe functions
self.distance = np.arange(0.,181.,1.)
self.acceleration = np.arange(0.,0.1,0.01)
# Generate Distance membership functions
self.near = fuzz.trapmf(self.distance, (-1.,-1.,20.,65.))
self.medium = fuzz.trapmf(self.distance,(35.,80.,120.,135.))
self.far = fuzz.trapmf(self.distance,(105.,170.,180.,200.))
# Generate Acceleration membership functions
self.slow = fuzz.trimf(self.acceleration, (-1.,0.,0.05))
self.normal = fuzz.trapmf(self.acceleration,(0.02,0.035,0.04,0.07))
self.fast = fuzz.trapmf(self.acceleration,(0.06,0.085,0.1,0.2))
# Fuzzy relation
self.R1 = fuzz.relation_product(self.near,self.slow)
self.R2 = fuzz.relation_product(self.medium,self.normal)
self.R3 = fuzz.relation_product(self.far,self.fast)
# Combine the fuzzy relation
self.R_combined = np.fmax(self.R1, np.fmax(self.R2, self.R3))
self.thetaOne = 0.0
self.thetaTwo = 0.0
self.InputDistanceAngle = 0.0
self.OutputAcceleration = 0.0
self.visualize = True
def membership_f(mf, x, abcd):
"""
Returns y values corresponding to type of type of Membership fn.
arguments:
mf - string containing type of Membership function
x - x axis values
"""
if mf == "zmf":
return fuzz.zmf(x, abcd[0], abcd[1]) # zmf(x, a, b)
elif mf == "trimf":
return fuzz.trimf(x, abcd[0:3]) # trimf(x, abc)
elif mf == "dsigmf":
return fuzz.dsigmf(x, abcd[0], abcd[1], abcd[2], abcd[3]) # dsigmf(x, b1, c1, b2, c2)
elif mf == "gauss2mf":
return fuzz.gauss2mf(x, abcd[0], abcd[1], abcd[2], abcd[3]) # gauss2mf(x, mean1, sigma1, mean2, sigma2)
elif mf == "gaussmf":
return fuzz.gaussmf(x, abcd[0], abcd[1]) # gaussmf(x, mean, sigma)
elif mf == "gbellmf":
return fuzz.gbellmf(x, abcd[0], abcd[1], abcd[2]) # gbellmf(x, a, b, c)
elif mf == "piecemf":
return fuzz.piecemf(x, abcd[0:3]) # piecemf(x, abc)
elif mf == "pimf":
return fuzz.pimf(x, abcd[0], abcd[1], abcd[2], abcd[3]) # pimf(x, a, b, c, d)
elif mf == "psigmf":
return fuzz.psigmf(x, abcd[0], abcd[1], abcd[2], abcd[3]) # psigmf(x, b1, c1, b2, c2)
elif mf == "sigmf":
return fuzz.sigmf(x, abcd[0], abcd[1]) # sigmf(x, b, c)
elif mf == "smf":
return fuzz.smf(x, abcd[0], abcd[1]) # smf(x, a, b)
elif mf == "trapmf":
return fuzz.trapmf(x, abcd) # trapmf(x, abcd)
def test_tipping_problem():
# The full tipping problem uses many of these methods
food = Antecedent(np.linspace(0, 10, 11), 'quality')
service = Antecedent(np.linspace(0, 10, 11), 'service')
tip = Consequent(np.linspace(0, 25, 26), 'tip')
food.automf(3)
service.automf(3)
# Manual membership function definition
tip['bad'] = trimf(tip.universe, [0, 0, 13])
tip['middling'] = trimf(tip.universe, [0, 13, 25])
tip['lots'] = trimf(tip.universe, [13, 25, 25])
# Define fuzzy rules
rule1 = Rule(food['poor'] | service['poor'], tip['bad'])
rule2 = Rule(service['average'], tip['middling'])
rule3 = Rule(service['good'] | food['good'], tip['lots'])
# The control system - defined both possible ways
tipping = ControlSystem([rule1, rule2, rule3])
tipping2 = ControlSystem()
tipping2.addrule(rule2)
tipping2.addrule(rule3)
tipping2.addrule(rule1)
tip_sim = ControlSystemSimulation(tipping)
tip_sim2 = ControlSystemSimulation(tipping2)
# Inputs added both possible ways
inputs = {'quality': 6.5, 'service': 9.8}
for key, value in inputs.items():
tip_sim.input[key] = value
tip_sim2.inputs(inputs)
# Compute the system
tip_sim.compute()
tip_sim2.compute()
assert tip_sim.output == tip_sim2.output
tst.assert_allclose(tip_sim.output['tip'], 20.244508118433625)
def test_lambda_cut_series():
x = np.arange(21) - 10
mfx = fuzz.trimf(x, [-2, 3, 5])
expected = np.array([[ 0. , -2., 5.],
[ 0.25, 0., 4.],
[ 0.5 , 1., 4.],
[ 0.75, 2., 3.],
[ 1. , 3., 3.]])
assert_allclose(expected, fuzz.lambda_cut_series(x, mfx, 5))
def __init__(self):
# Generate universe variables
distance = ctrl.Antecedent(np.arange(-440, 441, 1), 'distance') # paddle.x - ball.x
paddle_speed = ctrl.Consequent(np.arange(-12,13,4), 'speed') # paddle.x +=paddle_speed
# Auto-membership function population is possible with .automf(3, 5, or 7)
# Generate fuzzy membership functions
distance['far right'] = fuzz.trimf(distance.universe, [-440, -250, -110])
distance['close right'] = fuzz.trimf(distance.universe, [-200, -10, 0])
distance['close left'] = fuzz.trimf(distance.universe, [0, 10, 200])
distance['far left'] = fuzz.trimf(distance.universe, [200, 440, 440])
paddle_speed.automf(7)
rule1 = ctrl.Rule(distance['far left'], paddle_speed['dismal'])
rule2 = ctrl.Rule(distance['close left'], paddle_speed['dismal'])
rule3 = ctrl.Rule(distance['close right'], paddle_speed['excellent'])
rule4 = ctrl.Rule(distance['far right'], paddle_speed['excellent'])
# rule5 = ctrl.Rule(distance['above'], paddle_speed['average'])
paddle_ctrl = ctrl.ControlSystem([rule1, rule2, rule3, rule4])
self.agent = ctrl.ControlSystemSimulation(paddle_ctrl)
def __init__(self):
# Generate universe variables
distance = ctrl.Antecedent(np.arange(-8, 9, 1), 'distance')
trajectory = ctrl.Consequent(np.arange(-2, 3, 1), 'trajectory')
# Auto-membership function population is possible with .automf(3, 5, or 7)
distance.automf(3)
# Custom membership functions can be built interactively with a familiar,
# Pythonic API
trajectory['low'] = fuzz.trimf(trajectory.universe, [-2, -2, -2])
trajectory['medium'] = fuzz.trimf(trajectory.universe, [0, 0, 2])
trajectory['high'] = fuzz.trimf(trajectory.universe, [0, 2, 2])
rule1 = ctrl.Rule(distance['poor'], trajectory['low'])
rule2 = ctrl.Rule(distance['average'], trajectory['medium'])
rule3 = ctrl.Rule(distance['good'], trajectory['high'])
# Pass inputs to the ControlSystem using Antecedent labels with Pythonic API
# Note: if you like passing many inputs all at once, use .inputs(dict_of_data)
trajectoryping_ctrl = ctrl.ControlSystem([rule1, rule2, rule3])
self.agent = ctrl.ControlSystemSimulation(trajectoryping_ctrl)
def test_lambda_cut():
x = np.arange(21) - 10
mfx = fuzz.trimf(x, [-2, 3, 5])
# fuzz.lambda_cut test
expected = np.r_[0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]
result = fuzz.lambda_cut(mfx, 0.33)
assert_allclose(expected, result)
# fuzz.arglcut test
expected = np.r_[10, 11, 12, 13, 14]
result = fuzz.arglcut(mfx, 0.33)
assert len(result) == 1
assert_allclose(expected, result[0])
def membership_f(mf, x, abc = [0,0,0], a = 1, b = 2, c = 3, d = 4, abcd = [0,0,0,0]):
return {
'trimf' : fuzz.trimf(x, abc), # trimf(x, abc)
'dsigmf' : fuzz.dsigmf(x, a, b, c, d), # dsigmf(x, b1, c1, b2, c2)
'gauss2mf': fuzz.gauss2mf(x, a, b, c, d), # gauss2mf(x, mean1, sigma1, mean2, sigma2)
'gaussmf' : fuzz.gaussmf(x, a, b), # gaussmf(x, mean, sigma)
'gbellmf' : fuzz.gbellmf(x, a, b, c), # gbellmf(x, a, b, c)
'piecemf' : fuzz.piecemf(x, abc), # piecemf(x, abc)
'pimf' : fuzz.pimf(x, a, b, c, d), # pimf(x, a, b, c, d)
'psigmf' : fuzz.psigmf(x, a, b, c, d), # psigmf(x, b1, c1, b2, c2)
'sigmf' : fuzz.sigmf(x, a, b), # sigmf(x, b, c)
'smf' : fuzz.smf(x, a, b), # smf(x, a, b)
'trapmf' : fuzz.trapmf(x, abcd), # trapmf(x, abcd)
'zmf' : fuzz.zmf(x, a, b), # zmf(x, a, b)
}[mf]
def throttle_command(speed_read):
# Universe of discourse
speed = np.arange(60,150,.1)
throttle_value = np.arange(0,1,0.01)
# Input membership functions
too_slow = fuzz.trapmf(speed,[60,60,80,85])
slow = fuzz.trimf(speed,[83,90,97])
cruise = fuzz.trimf(speed,[95,100,105])
fast = fuzz.trimf(speed,[103,110,117])
too_fast = fuzz.trapmf(speed,[115,120,150,150])
# Output membership functions
very_low = fuzz.trimf(throttle_value,[0,0,0.15])
low = fuzz.trimf(throttle_value,[0.1,0.25,0.4])
medium = fuzz.trimf(throttle_value,[0.35,0.5,0.65])
high = fuzz.trimf(throttle_value,[0.6,0.75,0.9])
very_high = fuzz.trimf(throttle_value,[0.85,1,1])
# membership values
speed_cat_too_slow = fuzz.interp_membership(speed,too_slow,speed_read)
speed_cat_slow = fuzz.interp_membership(speed,slow,speed_read)
speed_cat_cruise = fuzz.interp_membership(speed,cruise,speed_read)
speed_cat_fast = fuzz.interp_membership(speed,fast,speed_read)
speed_cat_too_fast = fuzz.interp_membership(speed,too_fast,speed_read)
# If part of rules
rule1 = speed_cat_too_slow
rule2 = speed_cat_slow
rule3 = speed_cat_cruise
rule4 = speed_cat_fast
rule5 = speed_cat_too_fast
# Then part of rules
imp1 = np.fmin(rule1,very_high)
imp2 = np.fmin(rule2,high)
imp3 = np.fmin(rule3,medium)
imp4 = np.fmin(rule4,low)
imp5 = np.fmin(rule5,very_low)
aggregate_membership = np.fmax(imp1,np.fmax(imp2,np.fmax(imp3,np.fmax(imp4,imp5))))
return fuzz.defuzz(throttle_value,aggregate_membership,'centroid')
def test_bisector():
x = np.arange(6)
mfx = fuzz.trimf(x, [0, 5, 5])
expected = 3.53553390593274
# Test both triangle code paths
assert_allclose(expected, fuzz.defuzz(x, mfx, 'bisector'))
assert_allclose(5 - expected, fuzz.defuzz(x, 1 - mfx, 'bisector'))
# Test singleton input
y = np.r_[2]
mfy = np.r_[0.33]
assert_allclose(y, fuzz.defuzz(y, mfy, 'bisector'))
# Test rectangle code path
mfx = fuzz.trapmf(x, [2, 2, 4, 4])
assert_allclose(3., fuzz.defuzz(x, mfx, 'bisector'))
def load_rules(self, filename):
with open(filename, 'r') as jsonfile:
rules = json.loads(jsonfile.read())
self.actions = rules['actions']
self.state = rules['state']
self.rules = rules['results']
self.x = np.arange(self.state['min'], self.state['max'])
for subset, parts in self.rules.items():
aggregated = []
points = []
for n, v in parts.items():
print n
if v['mf'] == 'tri':
mf = fuzz.trimf(self.x, v['shp'])
if v['mf'] == 'trap':
mf = fuzz.trapmf(self.x, v['shp'])
elif v['mf'] == 'gbell':
mf = fuzz.gbellmf(self.x, v['shp'])
aggregated.append(mf)
points.append(v['pts'])
self.rules[subset] = (aggregated, points)
def membership_f(mf, x, abc=[0, 0, 0], a=1, b=2, c=3, d=4, abcd=[0, 0, 0, 0]):
"""
Returns y values corresponding to type of type of Membership fn.
arguments:
mf - string containing type of Membership function
x - x axis values
abc - list containing triangular edge point x-values
"""
return {
"trimf": fuzz.trimf(x, abc), # trimf(x, abc)
"dsigmf": fuzz.dsigmf(x, a, b, c, d), # dsigmf(x, b1, c1, b2, c2)
"gauss2mf": fuzz.gauss2mf(x, a, b, c, d), # gauss2mf(x, mean1, sigma1, mean2, sigma2)
"gaussmf": fuzz.gaussmf(x, a, b), # gaussmf(x, mean, sigma)
"gbellmf": fuzz.gbellmf(x, a, b, c), # gbellmf(x, a, b, c)
"piecemf": fuzz.piecemf(x, abc), # piecemf(x, abc)
"pimf": fuzz.pimf(x, a, b, c, d), # pimf(x, a, b, c, d)
"psigmf": fuzz.psigmf(x, a, b, c, d), # psigmf(x, b1, c1, b2, c2)
"sigmf": fuzz.sigmf(x, a, b), # sigmf(x, b, c)
"smf": fuzz.smf(x, a, b), # smf(x, a, b)
"trapmf": fuzz.trapmf(x, abcd), # trapmf(x, abcd)
"zmf": fuzz.zmf(x, a, b), # zmf(x, a, b)
}[mf]
def paramsToMF(params):
"""
Translate the piecewise params list of x_values to an MF function. Assumes a
list of length:
1 - singleton MF
2 - gaussian MF function (mean, standard deviation)
3 - triangular MF function
4 - trapezoidal MF function
"""
c = 100.0
if len(params) == 1: #singleton
c = 50 #special short MF for singleton
xrange = [0.9*params[0], 1.1*params[0], 2*0.2*params[0]/c]
x, y = singleton_to_fuzzy(params[0], xrange)
if len(params) == 2: #gaussian MF
#print "PARAMS:", params
if params[1] == 0.0: v = 0.01*params[0]
else: v = params[1]
x = np.arange( params[0] - 6*v,
params[0] + 6*v,
(14.0*v/c) ) #use 6 sigmas
y = fuzz.gaussmf(x, params[0], params[1])
elif len(params) == 3: #triangular MF
if max(params) == min(params): prange = max(params)
else: prange = max(params) - min(params)
x = np.arange( min(params), max(params),prange/c)
y = fuzz.trimf(x, params)
elif len(params) == 4: #trapezoidal MF
if max(params) == min(params): prange = max(params)
else: prange = max(params) - min(params)
x = np.arange( min(params), max(params),prange/c)
y = fuzz.trapmf(x, params)
return [np.asarray(x), np.asarray(y)] #create MF
def test_lambda_cut_boundaries_degenerate():
x = np.arange(11)
mfx = fuzz.trimf(x, [0, 7, 10])
assert_allclose(fuzz.lambda_cut_boundaries(x, mfx, 1), np.r_[7])
def test_multiple_rules_same_consequent_term():
# 2 input variables, 1 output variable and 7 instances.
x1_inputs = [0.6, 0.2, 0.4, 0.7, 1, 1.2, 1.8]
x2_inputs = [0.9, 1, 0.8, 0, 1.2, 0.6, 1.8]
dom = np.arange(0, 2.1, 0.01)
x1 = ctrl.Antecedent(dom, "x1")
x1['label0'] = fuzz.trimf(x1.universe, (0.2, 0.2, 0.6))
x1['label1'] = fuzz.trimf(x1.universe, (0.2, 0.6, 1.0))
x1['label2'] = fuzz.trimf(x1.universe, (0.6, 1.0, 1.4))
x1['label3'] = fuzz.trimf(x1.universe, (1.0, 1.4, 1.8))
x1['label4'] = fuzz.trimf(x1.universe, (1.4, 1.8, 1.8))
x2 = ctrl.Antecedent(dom, "x2")
x2['label0'] = fuzz.trimf(x2.universe, (0.0, 0.0, 0.45))
x2['label1'] = fuzz.trimf(x2.universe, (0.0, 0.45, 0.9))
x2['label2'] = fuzz.trimf(x2.universe, (0.45, 0.9, 1.35))
x2['label3'] = fuzz.trimf(x2.universe, (0.9, 1.35, 1.8))
x2['label4'] = fuzz.trimf(x2.universe, (1.35, 1.8, 1.8))
y = ctrl.Consequent(dom, "y")
y['label0'] = fuzz.trimf(y.universe, (0.3, 0.3, 0.725))
y['label1'] = fuzz.trimf(y.universe, (0.3, 0.725, 1.15))
y['label2'] = fuzz.trimf(y.universe, (0.725, 1.15, 1.575))
y['label3'] = fuzz.trimf(y.universe, (1.15, 1.575, 2.0))
y['label4'] = fuzz.trimf(y.universe, (1.575, 2.0, 2.0))
r1 = ctrl.Rule(x1['label0'] & x2['label2'], y['label0'])
r2 = ctrl.Rule(x1['label1'] & x2['label0'], y['label0'])
r3 = ctrl.Rule(x1['label1'] & x2['label2'], y['label0'])
# Equivalent to above 3 rules
r123 = ctrl.Rule((x1['label0'] & x2['label2']) |
(x1['label1'] & x2['label0']) |
(x1['label1'] & x2['label2']), y['label0'])
r4 = ctrl.Rule(x1['label2'] & x2['label1'], y['label2'])
r5 = ctrl.Rule(x1['label2'] & x2['label3'], y['label3'])
r6 = ctrl.Rule(x1['label4'] & x2['label4'], y['label4'])
# Build a system with three rules targeting the same Consequent Term,
# and then an equivalent system with those three rules combined into one.
cs0 = ctrl.ControlSystem([r1, r2, r3, r4, r5, r6])
cs1 = ctrl.ControlSystem([r123, r4, r5, r6])
expected_results = [0.438372093023,
0.443962536855,
0.461436409933,
0.445290345769,
1.575,
1.15,
1.86162790698]
# Ensure the results are equivalent within error
for inst, expected in zip(range(7), expected_results):
sim0 = ctrl.ControlSystemSimulation(cs0)
sim1 = ctrl.ControlSystemSimulation(cs1)
sim0.input["x1"] = x1_inputs[inst]
sim0.input["x2"] = x2_inputs[inst]
sim1.input["x1"] = x1_inputs[inst]
sim1.input["x2"] = x2_inputs[inst]
sim0.compute()
sim1.compute()
tst.assert_allclose(sim0.output['y'], sim1.output['y'])
tst.assert_allclose(expected, sim0.output['y'], atol=1e-4, rtol=1e-4)
import numpy as np import skfuzzy as fuzz # New Antecedent/Consequent objects hold universe variables and membership functions quality = fuzz.Antecedent(np.arange(0, 11, 1), 'quality') service = fuzz.Antecedent(np.arange(0, 11, 1), 'service') tip = fuzz.Consequent(np.arange(0, 26, 1), 'tip') # Auto-membership function population is possible with .automf(3, 5, or 7) quality.automf(3) service.automf(3) # Custom membership functions can be built interactively with a familiar, Pythonic API tip['poor'] = fuzz.trimf(tip.universe, [0, 0, 13]) tip['average'] = fuzz.trimf(tip.universe, [0, 13, 25]) tip['good'] = fuzz.trimf(tip.universe, [13, 25, 25]) # You can see how these look with .view() quality['average'].view() service.view() tip.view() # Rule objects connect one or more antecedent membership functions with # one or more consequent membership functions, using 'or' or 'and' to combine the antecedents. # * rule1: "If food is poor OR services is poor, then tip will be poor # * rule2: "If service is average, then tip will be average # * rule3: "If service is good OR food is good, then tip will be good rule1 = fuzz.Rule([quality['poor'], service['poor']], tip['poor'], kind='or') rule2 = fuzz.Rule(service['average'], tip['average']) rule3 = fuzz.Rule([service['good'], quality['good']], tip['good'])
def __init__(self, intensidade_interacoes_AC, intensidade_interacoes_BC, similaridade_entre_vertices, idade_interacoes_AC, idade_interacoes_BC):
print intensidade_interacoes_AC, intensidade_interacoes_BC, similaridade_entre_vertices, idade_interacoes_AC,idade_interacoes_BC
self.intensidade_interacoes_AC = intensidade_interacoes_AC
self.intensidade_interacoes_BC = intensidade_interacoes_BC
self.similaridade_entre_vertices = similaridade_entre_vertices
self.idade_interacoes_AC = idade_interacoes_AC
self.idade_interacoes_BC = idade_interacoes_BC
# Generate universe variables
self.x_intensidade_interacoes_AC = np.arange(0, 10, 1)
self.x_intensidade_interacoes_BC = np.arange(0, 10, 1)
self.x_similaridade_entre_vertices = np.arange(0, 101, 1)
self.x_idade_interacoes_AC = np.arange(0, 10, 1)
self.x_idade_interacoes_BC = np.arange(0, 10, 1)
self.x_potencial_ligacao = np.arange(0, 100, 1)
# Generate fuzzy membership functions
self.intensidade_interacoes_AC_baixa = fuzz.trimf(self.x_intensidade_interacoes_AC, [0, 0, 6])
self.intensidade_interacoes_AC_alta = fuzz.trapmf(self.x_intensidade_interacoes_AC, [2, 6, 10, 10])
self.intensidade_interacoes_BC_baixa = fuzz.trimf(self.x_intensidade_interacoes_BC, [0, 0, 6])
self.intensidade_interacoes_BC_alta = fuzz.trapmf(self.x_intensidade_interacoes_BC, [2, 6, 10, 10])
self.similaridade_entre_vertices_baixa = fuzz.trimf(self.x_similaridade_entre_vertices, [0, 0, 60])
self.similaridade_entre_vertices_alta = fuzz.trimf(self.x_similaridade_entre_vertices, [40, 100, 100])
self.idade_interacoes_AC_baixa = fuzz.trimf(self.x_idade_interacoes_AC, [0, 0, 6])
self.idade_interacoes_AC_alta = fuzz.trimf(self.x_idade_interacoes_AC, [4, 10, 10])
self.idade_interacoes_BC_baixa = fuzz.trimf(self.x_idade_interacoes_BC, [0, 0, 6])
self.idade_interacoes_BC_alta = fuzz.trimf(self.x_idade_interacoes_BC, [4, 10, 10])
self.potencial_ligacao_baixo = fuzz.trimf(self.x_potencial_ligacao, [0, 0, 60])
self.potencial_ligacao_medio = fuzz.trimf(self.x_potencial_ligacao, [10, 50, 90])
self.potencial_ligacao_alto = fuzz.trimf(self.x_potencial_ligacao, [40, 100, 100])
self.grau_intensidade_interacoes_AC_baixa = fuzz.interp_membership(self.x_intensidade_interacoes_AC, self.intensidade_interacoes_AC_baixa, intensidade_interacoes_AC)
self.grau_intensidade_interacoes_AC_alta = fuzz.interp_membership(self.x_intensidade_interacoes_AC, self.intensidade_interacoes_AC_alta, intensidade_interacoes_AC)
self.grau_intensidade_interacoes_BC_baixa = fuzz.interp_membership(self.x_intensidade_interacoes_BC, self.intensidade_interacoes_BC_baixa, intensidade_interacoes_BC)
self.grau_intensidade_interacoes_BC_alta = fuzz.interp_membership(self.x_intensidade_interacoes_BC, self.intensidade_interacoes_BC_alta, intensidade_interacoes_BC)
self.grau_similaridade_entre_vertices_baixa = fuzz.interp_membership(self.x_similaridade_entre_vertices, self.similaridade_entre_vertices_baixa, similaridade_entre_vertices)
self.grau_similaridade_entre_vertices_alta = fuzz.interp_membership(self.x_similaridade_entre_vertices, self.similaridade_entre_vertices_alta, similaridade_entre_vertices)
self.grau_idade_interacoes_AC_baixa = fuzz.interp_membership(self.x_idade_interacoes_AC, self.idade_interacoes_AC_baixa, self.idade_interacoes_AC)
self.grau_idade_interacoes_AC_alta = fuzz.interp_membership(self.x_idade_interacoes_AC, self.idade_interacoes_AC_alta, self.idade_interacoes_AC)
self.grau_idade_interacoes_BC_baixa = fuzz.interp_membership(self.x_idade_interacoes_BC, self.idade_interacoes_BC_baixa, self.idade_interacoes_BC)
self.grau_idade_interacoes_BC_alta = fuzz.interp_membership(self.x_idade_interacoes_BC, self.idade_interacoes_BC_alta, self.idade_interacoes_BC)
active_rule1 = np.fmin(self.grau_intensidade_interacoes_AC_alta, np.fmin(self.grau_intensidade_interacoes_BC_alta, np.fmin(self.grau_similaridade_entre_vertices_alta, np.fmin(self.grau_idade_interacoes_AC_baixa, self.grau_idade_interacoes_BC_baixa))))
potencial_ligacao_rule1 = np.fmin(active_rule1, self.potencial_ligacao_alto)
print('active_rule1 ', active_rule1)
active_rule2 = np.fmin(self.grau_intensidade_interacoes_AC_baixa, np.fmin(self.grau_intensidade_interacoes_BC_baixa, np.fmin(self.grau_similaridade_entre_vertices_baixa, np.fmin(self.grau_idade_interacoes_AC_alta, self.grau_idade_interacoes_BC_alta))))
potencial_ligacao_rule2 = np.fmin(active_rule2, self.potencial_ligacao_baixo)
print('active_rule2 ', active_rule2)
#print('potencial_ligacao_rule2 ', potencial_ligacao_rule2)
active_rule3 = np.fmin(self.grau_intensidade_interacoes_AC_alta, np.fmin(self.grau_intensidade_interacoes_BC_alta, np.fmin(self.grau_similaridade_entre_vertices_baixa, np.fmin(self.grau_idade_interacoes_AC_baixa, self.grau_idade_interacoes_BC_baixa))))
potencial_ligacao_rule3 = np.fmin(active_rule3, self.potencial_ligacao_medio)
print('active_rule3 ', active_rule3)
#print('potencial_ligacao_rule3 ', potencial_ligacao_rule3)
active_rule4 = np.fmin(self.grau_intensidade_interacoes_AC_baixa, np.fmin(self.grau_intensidade_interacoes_BC_baixa, np.fmin(self.grau_similaridade_entre_vertices_alta, np.fmin(self.grau_idade_interacoes_AC_baixa, self.grau_idade_interacoes_BC_baixa))))
potencial_ligacao_rule4 = np.fmin(active_rule4, self.potencial_ligacao_medio)
print('active_rule4 ', active_rule4)
#print('potencial_ligacao_rule4 ', potencial_ligacao_rule4)
# Aggregate all three output membership functions together
aggregated = np.fmax(potencial_ligacao_rule1,
np.fmax(potencial_ligacao_rule2, np.fmax(potencial_ligacao_rule3, potencial_ligacao_rule4)))
# Calculate defuzzified result
self.potencial_ligacao = fuzz.defuzz(self.x_potencial_ligacao, aggregated, 'centroid')
self.grau_potencial_ligacao = fuzz.interp_membership(self.x_potencial_ligacao, aggregated, self.potencial_ligacao)
import numpy as np
import skfuzzy as sk
import matplotlib.pyplot as plt
# Se defube un array x para el manejo del factor de calidad en un restaurante
x = np.arange(30, 80, 0.1)
# Se define un array para la funcion miembro gauss
lento = sk.trimf(x, [30, 30, 50])
medio = sk.trimf(x, [30, 50, 70])
medio_rapido = sk.trimf(x, [50, 60, 70])
rapido = sk.trimf(x, [60, 80, 780])
# Graficar
plt.figure()
plt.plot(x, rapido, 'b', linewidth=1.5, label='Rapido')
plt.plot(x, medio_rapido, 'k', linewidth=1.5, label='Medio-rapido')
plt.plot(x, medio, 'm', linewidth=1.5, label='Medio')
plt.plot(x, lento, 'r', linewidth=1.5, label='Lento')
plt.title('Pentalti difuso')
plt.ylabel('Membresia')
plt.xlabel('Velocidad (km/h)')
plt.legend(loc='center right',
bbox_to_anchor=(1.25, 0.5),
ncol=1,
fancybox=True,
shadow=True)
at27['b'] = fuzz.trapmf(at27.universe, [0, minb[27], maxb[27], 1])
at26 = ctrl.Antecedent(np.arange(0, 1, 0.0001), 'at26')
at26['m'] = fuzz.trapmf(at26.universe, [0, minm[26], maxm[26], 1])
at26['b'] = fuzz.trapmf(at26.universe, [0, minb[26], maxb[26], 1])
at29 = ctrl.Antecedent(np.arange(0, 1, 0.0001), 'at29')
at29['m'] = fuzz.trapmf(at29.universe, [0, minm[29], maxm[29], 1])
at29['b'] = fuzz.trapmf(at29.universe, [0, minb[29], maxb[29], 1])
at28 = ctrl.Antecedent(np.arange(0, 1, 0.0001), 'at28')
at28['m'] = fuzz.trapmf(at28.universe, [0, minm[28], maxm[28], 1])
at28['b'] = fuzz.trapmf(at28.universe, [0, minb[28], maxb[28], 1])
output = ctrl.Consequent(np.arange(0, 1, 0.01), 'output')
output['m'] = fuzz.trimf(output.universe, [0, 0, 0.6])
# output['idk'] = fuzz.trimf(output.universe, [0, 0.5, 1])
output['b'] = fuzz.trimf(output.universe, [0.4, 1, 1])
rule1 = ctrl.Rule(
at0['m'] | at1['m'] | at2['m'] | at3['m'] | at4['m'] | at5['m'] | at6['m']
| at7['m'] | at8['m'] | at9['m'] | at10['m'] | at11['m'] | at12['m']
| at13['m'] | at14['m'] | at15['m'] | at16['m'] | at17['m'] | at18['m']
| at19['m'] | at20['m'] | at21['m'] | at22['m'] | at23['m'] | at24['m']
| at25['m'] | at26['m'] | at27['m'] | at28['m'] | at29['m'], output['m'])
rule2 = ctrl.Rule(
at0['b'] | at1['b'] | at2['b'] | at3['b'] | at4['b'] | at5['b'] | at6['b']
| at7['b'] | at8['b'] | at9['b'] | at10['b'] | at11['b'] | at12['b']
| at13['b'] | at14['b'] | at15['b'] | at16['b'] | at17['b'] | at18['b']
| at19['b'] | at20['b'] | at21['b'] | at22['b'] | at23['b'] | at24['b']
| at25['b'] | at26['b'] | at27['b'] | at28['b'] | at29['b'], output['b'])
def Fuzzy_Ctrl(Goal_pos,Act_pos):
# New Antecedent/Consequent objects hold universe variables and membership functions
Distance = ctrl.Antecedent(np.arange(0, 100+0.1, 0.1), 'Distance') #Input
Angle = ctrl.Antecedent(np.arange(-np.pi, np.pi+0.001, 0.001), 'Angle') #Input
Velocity = ctrl.Consequent(np.arange(-0.13, 0.26+0.13+0.01, 0.01), 'Velocity') #Output
Omega = ctrl.Consequent(np.arange(-1.82-1.82-0.01, 1.82+1.82+0.01, 0.01), 'Omega') #Output
# Auto-membership function population is possible with .automf(3, 5, or 7)
#Distance.automf(5)
#Velocity.automf(3)
# Custom membership functions can be built interactively with a familiar, Pythonic API
Distance['Near'] = fuzz.trimf(Distance.universe, [0, 0, 0.5])
Distance['Mid'] = fuzz.trimf(Distance.universe, [0, 0.5, 1])
Distance['Far'] = fuzz.trapmf(Distance.universe, [0.5, 1, 100, 100])
Angle['Neg'] = fuzz.trapmf(Angle.universe, [-np.pi, -np.pi, -np.pi/4, 0])
Angle['Zero'] = fuzz.trimf(Angle.universe, [-np.pi/4, 0, np.pi/4])
Angle['Pos'] = fuzz.trapmf(Angle.universe, [0, np.pi/4, np.pi, np.pi])
Velocity['Zero'] = fuzz.trimf(Velocity.universe, [-0.13, 0, 0.13])
Velocity['Half'] = fuzz.trimf(Velocity.universe, [0, 0.13, 0.26])
Velocity['Full'] = fuzz.trimf(Velocity.universe, [0.13, 0.26, 0.26+0.13])
Omega['Negative'] = fuzz.trimf(Omega.universe, [-1.82-1.82, -1.82, 0])
Omega['Zero'] = fuzz.trimf(Omega.universe, [-1.82, 0, 1.82])
Omega['Positive'] = fuzz.trimf(Omega.universe, [0, 1.82, 1.82+1.82])
# You can see how these look with .view()
#Distance.view()
#Angle.view()
#Velocity.view()
#Omega.view()
#Identify Rules
#rule0 = ctrl.Rule(antecedent=((Distance['nb'] & delta['nb']) |
# (Distance['ns'] & delta['nb']) |
# (Distance['nb'] & delta['ns'])),
# consequent=output['nb'], label='rule nb')
rule1 = ctrl.Rule(Angle['Neg'], Velocity['Zero'])
rule2 = ctrl.Rule(Angle['Neg'], Omega['Negative'])
rule3 = ctrl.Rule(Angle['Pos'], Velocity['Zero'])
rule4 = ctrl.Rule(Angle['Pos'], Omega['Positive'])
rule5 = ctrl.Rule(Angle['Zero'], Omega['Zero'])
rule6 = ctrl.Rule(antecedent=((Distance['Near'] & Angle['Zero'])),
consequent=Velocity['Zero'])
rule7 = ctrl.Rule(antecedent=((Distance['Mid'] & Angle['Zero'])),
consequent=Velocity['Half'])
rule8 = ctrl.Rule(antecedent=((Distance['Far'] & Angle['Zero'])),
consequent=Velocity['Full'])
Robot_ctrl = ctrl.ControlSystem([rule1, rule2, rule3, rule4, rule5, rule6, rule7, rule8])
#Robot_ctrl = ctrl.ControlSystem([rule1, rule2, rule3, rule4, rule6, rule7, rule8])
Motion = ctrl.ControlSystemSimulation(Robot_ctrl)
# Pass inputs to the ControlSystem using Antecedent labels with Pythonic API
# Note: if you like passing many inputs all at once, use .inputs(dict_of_data)
dis = np.sqrt((Goal_pos[0]-Act_pos[0])**2+(Goal_pos[1]-Act_pos[1])**2)
print('distance is',dis)
Motion.input['Distance'] = dis
global last
ang = np.arctan2((Goal_pos[1]-Act_pos[1]),(Goal_pos[0]-Act_pos[0]))-Act_pos[2]
while ang < last-np.pi: ang += 2*np.pi
while ang > last+np.pi: ang -= 2*np.pi
last = ang
print('angle is',ang)
Motion.input['Angle'] = ang
# Crunch the numbers
Motion.compute()
print(Motion.output['Velocity'])
#Velocity.view(sim=Motion)
out1 = Motion.output['Velocity']
print(Motion.output['Omega'])
#Omega.view(sim=Motion)
out2 = Motion.output['Omega']
Cont_input = [out1,out2]
print(Cont_input)
return Cont_input
import numpy as np
import skfuzzy as fuzz
from skfuzzy import control as karar
ortamSicakligi = karar.Antecedent(np.arange(20, 50, 1), 'ortam sıcaklığı')
nemOrani = karar.Antecedent(np.arange(0, 101, 1), 'nem oranı')
ortamKisiSayisi = karar.Antecedent(np.arange(1, 21, 1),
'ortamdaki kişi sayısı')
sonuc = karar.Consequent(np.arange(0, 101, 1), 'klimanın durumu')
ortamSicakligi['düşük'] = fuzz.trimf(ortamSicakligi.universe, [20, 23, 25])
ortamSicakligi['orta'] = fuzz.trimf(ortamSicakligi.universe, [25, 30, 35])
ortamSicakligi['yüksek'] = fuzz.trimf(ortamSicakligi.universe, [40, 45, 50])
nemOrani['düşük'] = fuzz.trimf(nemOrani.universe, [0, 20, 30])
nemOrani['orta'] = fuzz.trimf(nemOrani.universe, [30, 50, 70])
nemOrani['yüksek'] = fuzz.trimf(nemOrani.universe, [70, 90, 100])
ortamKisiSayisi['az'] = fuzz.trimf(ortamKisiSayisi.universe, [1, 3, 5])
ortamKisiSayisi['orta'] = fuzz.trimf(ortamKisiSayisi.universe, [5, 7, 10])
ortamKisiSayisi['çok'] = fuzz.trimf(ortamKisiSayisi.universe, [10, 15, 20])
sonuc['sıcaklık derecesini arttır'] = fuzz.trimf(sonuc.universe, [0, 0, 50])
sonuc['soğutmayı arttır'] = fuzz.trimf(sonuc.universe, [0, 50, 100])
sonuc['şuanki dereceyi koru'] = fuzz.trimf(sonuc.universe, [50, 100, 100])
ortamSicakligi.view()
nemOrani.view()
ortamKisiSayisi.view()
sonuc.view()
def get_score(dict):
kopidict = dict
kopi_input = float(kopidict['kopi'])
milk_input = float(kopidict['milk'])
sugar_input = float(kopidict['sugar'])
count = 1
#To set the universe range dynamically
max_level = 11
# print("Max Level and count:")
# print(max_level)
# print(count)
min_level = 0
milk_y = 0
sugar_y = 0
score = 0
sugar_level = 11
print("Milk Value:" + str(milk_input), end="\t")
print("Sugar Value:" + str(sugar_input), end="\t")
print("Kopi Value:" + str(kopi_input))
# max_level=100
"""Kopi Level"""
kopi_bins = np.arange(0, max_level, count)
kopi = ctrl.Antecedent(kopi_bins, 'kopi')
mid_kopi = max_level // 2
kopi_LP_1 = (min_level + mid_kopi) // 5
kopi_LP_2 = (kopi_LP_1 + mid_kopi) // 2
kopi_TR_1 = mid_kopi // 2
kopi_TR_2 = mid_kopi
kopi_TR_3 = kopi_TR_1 + kopi_TR_2
kopi_HP_1 = (max_level + mid_kopi) // 2.5
kopi_HP_2 = (max_level + mid_kopi) // 5 + mid_kopi
# print(kopi_bins)
# print(kopi_HP_2,kopi_HP_1)
kopi_thick = fuzz.smf(kopi.universe, kopi_HP_1, kopi_HP_2)
kopi_norm = fuzz.trimf(kopi.universe, [kopi_TR_1, kopi_TR_2, kopi_TR_3])
kopi_thin = fuzz.zmf(kopi.universe, kopi_LP_1, kopi_LP_2)
kopi['thin'] = kopi_thin
kopi['norm'] = kopi_norm
kopi['thick'] = kopi_thick
# kopi.view()
# pyplot.show()
# print("Printing Kopi MFs:")
# print(fuzz.interp_membership(kopi.universe,kopi_thin,kopi_input))
# print(fuzz.interp_membership(kopi.universe,kopi_norm,kopi_input))
# print(fuzz.interp_membership(kopi.universe,kopi_thick,kopi_input))
"""Milk Level"""
milk_pre_bins = np.arange(0, 1, 0.1)
milk_pre = ctrl.Antecedent(milk_pre_bins, 'milk_pre')
milk_pre_mf = fuzz.sigmf(milk_pre.universe, 0.5, 1)
milk_pre['milk_pre'] = milk_pre_mf
#milk input value being passed as the last argument
val_milk = fuzz.interp_membership(milk_pre.universe, milk_pre_mf,
milk_input)
# print("Milk MF:")
# print(val_milk)
if val_milk == 0.0:
milk_y = 1
# print("Milk Required")
milk_bins = np.arange(0, max_level, count)
milk = ctrl.Antecedent(milk_bins, 'milk')
mid_milk = max_level // 2
milk_LP_1 = (min_level + mid_milk) // 5
milk_LP_2 = (milk_LP_1 + mid_milk) // 2
milk_TR_1 = mid_milk // 2
milk_TR_2 = mid_milk
milk_TR_3 = milk_TR_1 + milk_TR_2
milk_HP_1 = (max_level + mid_milk) // 2.5
milk_HP_2 = (max_level + mid_milk) // 5 + mid_milk
milk_extra = fuzz.smf(milk.universe, milk_HP_1, milk_HP_2)
milk_norm = fuzz.trimf(milk.universe,
[milk_TR_1, milk_TR_2, milk_TR_3])
milk_less = fuzz.zmf(milk.universe, milk_LP_1, milk_LP_2)
milk['less'] = milk_less
milk['norm'] = milk_norm
milk['extra'] = milk_extra
milk_temp = fuzz.smf(milk_pre.universe, 0.5, 1)
milk_pre['milk_pre'] = milk_temp
# milk.view()
# pyplot.show()
# print("Printing Milk MFs:")
# print(fuzz.interp_membership(milk.universe,milk_less,milk_input))
# print(fuzz.interp_membership(milk.universe,milk_norm,milk_input))
# print(fuzz.interp_membership(milk.universe,milk_extra,milk_input))
# print(fuzz.interp_membership(milk_pre.universe,milk_temp,milk_input))
else:
milk_y = 0
# print("No milk")
milk_bins = np.arange(0, max_level, count)
milk = ctrl.Antecedent(milk_bins, 'milk')
milk_mf = fuzz.smf(milk.universe, 0.5, 1)
milk['less'] = milk_mf
milk['norm'] = milk_mf
milk['extra'] = milk_mf
milk_temp = fuzz.zmf(milk_pre.universe, 0.5, 1)
milk_pre['milk_pre'] = milk_temp
# print(fuzz.interp_membership(milk.universe,milk_mf,milk_input))
# print(fuzz.interp_membership(milk.universe,milk_mf,milk_input))
# print(fuzz.interp_membership(milk.universe,milk_mf,milk_input))
# print(fuzz.interp_membership(milk_pre.universe,milk_temp,milk_input))
"""Sugar Membership Function"""
sugar_pre_bins = np.arange(0, 1, 0.1)
sugar_pre = ctrl.Antecedent(sugar_pre_bins, 'sugar_pre')
sugar_pre_mf = fuzz.sigmf(sugar_pre.universe, 0.5, 1)
sugar_pre['sugar_pre'] = sugar_pre_mf
#sugar input value being passed as the last argument
val_sugar = fuzz.interp_membership(sugar_pre.universe, sugar_pre_mf,
sugar_input)
# print("Sugar MF:")
# print(val_sugar)
if val_sugar == 0.0:
sugar_y = 1
sugar_bins = np.arange(0, sugar_level, 1)
sugar = ctrl.Antecedent(sugar_bins, 'sugar')
# print("Sugar Required")
sugar_extra = fuzz.smf(sugar.universe, 6, 8)
sugar_norm = fuzz.trimf(sugar.universe, [3, 5, 7])
sugar_less = fuzz.zmf(sugar.universe, 2, 4)
sugar['norm'] = sugar_norm
sugar['extra'] = sugar_extra
sugar['less'] = sugar_less
sugar_temp = fuzz.smf(sugar_pre.universe, 0.5, 1)
sugar_pre['sugar_pre'] = sugar_temp
# sugar.view()
# pyplot.show()
# print(fuzz.interp_membership(sugar.universe,sugar_less,sugar_input))
# print(fuzz.interp_membership(sugar.universe,sugar_norm,sugar_input))
# print(fuzz.interp_membership(sugar.universe,sugar_extra,sugar_input))
# print(fuzz.interp_membership(sugar_pre.universe,sugar_temp,sugar_input))
else:
sugar_y = 0
sugar_bins = np.arange(0, sugar_level, 1)
sugar = ctrl.Antecedent(sugar_bins, 'sugar')
sugar_mf = fuzz.smf(sugar.universe, 0.5, 1)
sugar['norm'] = sugar_mf
sugar['extra'] = sugar_mf
sugar['less'] = sugar_mf
sugar_temp = fuzz.zmf(sugar_pre.universe, 0.5, 1)
sugar_pre['sugar_pre'] = sugar_temp
# print(fuzz.interp_membership(sugar.universe,sugar_mf,sugar_input))
# print(fuzz.interp_membership(sugar.universe,sugar_mf,sugar_input))
# print(fuzz.interp_membership(sugar.universe,sugar_mf,sugar_input))
# print(fuzz.interp_membership(sugar_pre.universe,sugar_temp,sugar_input))
print("No Sugar")
"""Ouput Membership Function"""
universe = np.linspace(-19, 19, 39)
# print(universe)
output = ctrl.Consequent(universe, 'output')
# names = ['Kopi_O', 'Kopi_O_KoSong', 'Kopi_O_Siew_Dai', 'Kopi_O_Ga_Dai', 'Kopi_C','Kopi_C_Siew_Dai','Kopi_C_Ga_Dai','Kopi','Kopi_Gao','Kopi_C_Gau_Ga_Dai','Kopi_Po']
names = [
'KOPSD', 'KOP', 'KOPGD', 'KOSD', 'KOGD', 'KO', 'KOGSD', 'KOG', 'KOGGD',
'KOPK', 'KOK', 'KOGK', 'KP', 'KG', 'KDL', 'KPSD', 'KD', 'KGSD', 'KP',
'KP', 'K', 'NA', 'KPGD', 'KGD', 'KGGD', 'KCPSD', 'KCSD', 'KCGSD',
'KCP', 'KC', 'KCG', 'KCPGD', 'KCGD', 'KCGGD', 'KCPK', 'KCK', 'KCGK',
'NA', 'NA'
]
# print(len(names))
out = output.automf(names=names)
# Rules for zero milk and various sugar levels
rule_1 = ctrl.Rule(milk_pre['milk_pre'] & sugar['less'] & kopi['thin'],
output['KOPSD'],
label='KOPSD')
rule_2 = ctrl.Rule(milk_pre['milk_pre'] & sugar['norm'] & kopi['thin'],
output['KOP'],
label='KOP')
rule_3 = ctrl.Rule(milk_pre['milk_pre'] & sugar['extra'] & kopi['thin'],
output['KOPGD'],
label='KOPGD') #KOP
rule_4 = ctrl.Rule(milk_pre['milk_pre'] & sugar['less'] & kopi['norm'],
output['KOSD'],
label='KOSD') #KOPGD
rule_5 = ctrl.Rule(milk_pre['milk_pre'] & sugar['extra'] & kopi['norm'],
output['KOGD'],
label='KOGD')
rule_6 = ctrl.Rule(milk_pre['milk_pre'] & sugar['norm'] & kopi['norm'],
output['KO'],
label='KO')
rule_7 = ctrl.Rule(milk_pre['milk_pre'] & sugar['less'] & kopi['thick'],
output['KOGSD'],
label='KOGSD')
rule_8 = ctrl.Rule(milk_pre['milk_pre'] & sugar['norm'] & kopi['thick'],
output['KOG'],
label='KOG')
rule_9 = ctrl.Rule(milk_pre['milk_pre'] & sugar['extra'] & kopi['thick'],
output['KOGGD'],
label='KOGGD')
# #Rules for zero sugar and zero milk
rule_10 = ctrl.Rule(milk_pre['milk_pre'] & sugar_pre['sugar_pre']
& kopi['thin'],
output['KOPK'],
label='KOPK')
rule_11 = ctrl.Rule(milk_pre['milk_pre'] & sugar_pre['sugar_pre']
& kopi['norm'],
output['KOK'],
label='KOK')
rule_12 = ctrl.Rule(milk_pre['milk_pre'] & sugar_pre['sugar_pre']
& kopi['thick'],
output['KOGK'],
label='KOGK')
#Rules for no sugar and various milk levels (Normal and Less - Condensed)
rule_13 = ctrl.Rule(
(milk['less'] | milk['norm']) & sugar_pre['sugar_pre'] & kopi['thin'],
output['KP'],
label='KP')
rule_14 = ctrl.Rule(
(milk['less'] | milk['norm']) & sugar_pre['sugar_pre'] & kopi['norm'],
output['KG'],
label='KG')
rule_15 = ctrl.Rule(
(milk['less'] | milk['norm']) & sugar_pre['sugar_pre'] & kopi['thick'],
output['KDL'],
label='KDL')
#Rules for various sugar levels and various milk levels (Normal and Less - Condensed)
rule_16 = ctrl.Rule(
(milk['less'] | milk['norm']) & sugar['less'] & kopi['thin'],
output['KPSD'],
label='KPSD')
rule_17 = ctrl.Rule(
(milk['less'] | milk['norm']) & sugar['less'] & kopi['norm'],
output['KD'],
label='KD')
rule_18 = ctrl.Rule(
(milk['less'] | milk['norm']) & sugar['less'] & kopi['thick'],
output['KGSD'],
label='KGSD')
rule_19 = ctrl.Rule(
(milk['less'] | milk['norm']) & sugar['norm'] & kopi['thin'],
output['KP'],
label='KP_1')
rule_20 = ctrl.Rule(
(milk['less'] | milk['norm']) & sugar['norm'] & kopi['norm'],
output['K'],
label='K')
rule_21 = ctrl.Rule(
(milk['less'] | milk['norm']) & sugar['norm'] & kopi['thick'],
output['KG'],
label='KG_1')
rule_22 = ctrl.Rule(
(milk['less'] | milk['norm']) & sugar['extra'] & kopi['thin'],
output['KPGD'],
label='KPGD') #KPGD
rule_23 = ctrl.Rule(
(milk['less'] | milk['norm']) & sugar['extra'] & kopi['norm'],
output['KGD'],
label='KGD') #KGD
rule_24 = ctrl.Rule(
(milk['less'] | milk['norm']) & sugar['extra'] & kopi['thick'],
output['KGGD'],
label='KGGD') #KGGD
#Rules for various sugar and extra milk levels
rule_25 = ctrl.Rule(milk['extra'] & sugar['less'] & kopi['thin'],
output['KCPSD'],
label='KCPSD')
rule_26 = ctrl.Rule(milk['extra'] & sugar['less'] & kopi['norm'],
output['KCSD'],
label='KCSD')
rule_27 = ctrl.Rule(milk['extra'] & sugar['less'] & kopi['thick'],
output['KCGSD'],
label='KCGSD')
rule_28 = ctrl.Rule(milk['extra'] & sugar['norm'] & kopi['thin'],
output['KCP'],
label='KCP')
rule_29 = ctrl.Rule(milk['extra'] & sugar['norm'] & kopi['norm'],
output['KC'],
label='KC')
rule_30 = ctrl.Rule(milk['extra'] & sugar['norm'] & kopi['thick'],
output['KCG'],
label='KCG')
rule_31 = ctrl.Rule(milk['extra'] & sugar['extra'] & kopi['thin'],
output['KCPGD'],
label='KCPGD')
rule_32 = ctrl.Rule(milk['extra'] & sugar['extra'] & kopi['norm'],
output['KCGD'],
label='KCGD')
rule_33 = ctrl.Rule(milk['extra'] & sugar['extra'] & kopi['thick'],
output['KCGGD'],
label='KCGGD')
#Rules for no sugar and extra milk levels
rule_34 = ctrl.Rule(milk['extra'] & sugar_pre['sugar_pre'] & kopi['thin'],
output['KCPK'],
label='KCPK')
rule_35 = ctrl.Rule(milk['extra'] & sugar_pre['sugar_pre'] & kopi['norm'],
output['KCK'],
label='KCK')
rule_36 = ctrl.Rule(milk['extra'] & sugar_pre['sugar_pre'] & kopi['thick'],
output['KCGK'],
label='KCGK')
# rule_1.view()
# print("Milk_y and Sugar_y:"+str(milk_y)+" "+str(sugar_y))
if milk_y == 0 and sugar_y == 1:
# print("Rules for kopi without milk and various sugar levels")
ctrl_sys = ctrl.ControlSystem([
rule_1, rule_2, rule_3, rule_4, rule_5, rule_6, rule_7, rule_8,
rule_9
])
# sim = ctrl.ControlSystemSimulation(ctrl_sys, flush_after_run=35 * 35 + 1)
sim = ctrl.ControlSystemSimulation(ctrl_sys)
sim.input['kopi'] = kopi_input
sim.input['sugar'] = sugar_input
sim.input['milk_pre'] = milk_input
sim.compute()
score = sim.output['output']
# output.view(sim=sim)
# pyplot.show()
elif milk_y == 0 and sugar_y == 0:
# print("Rules for kopi without milk and sugar ")
ctrl_sys = ctrl.ControlSystem([rule_10, rule_11, rule_12])
# sim = ctrl.ControlSystemSimulation(ctrl_sys, flush_after_run=35 * 35 + 1)
sim = ctrl.ControlSystemSimulation(ctrl_sys)
sim.input['kopi'] = kopi_input
sim.input['sugar_pre'] = sugar_input
sim.input['milk_pre'] = milk_input
sim.compute()
score = sim.output['output']
# output.view(sim=sim)
# pyplot.show()
elif milk_y == 1 and sugar_y == 0:
# print("Rules for kopi with milk and no sugar")
ctrl_sys = ctrl.ControlSystem(
[rule_13, rule_14, rule_15, rule_34, rule_35, rule_36])
# sim = ctrl.ControlSystemSimulation(ctrl_sys, flush_after_run=35 * 35 + 1)
sim = ctrl.ControlSystemSimulation(ctrl_sys)
sim.input['kopi'] = kopi_input
sim.input['sugar_pre'] = sugar_input
sim.input['milk'] = milk_input
sim.compute()
score = sim.output['output']
# output.view(sim=sim)
# pyplot.show()
elif milk_y == 1 and sugar_y == 1:
# print("Rules for kopi various levels of milk and sugar")
ctrl_sys = ctrl.ControlSystem([
rule_16, rule_17, rule_18, rule_19, rule_20, rule_21, rule_22,
rule_23, rule_24, rule_25, rule_26, rule_27, rule_28, rule_29,
rule_30, rule_31, rule_32, rule_33
])
# sim = ctrl.ControlSystemSimulation(ctrl_sys, flush_after_run=35 * 35 + 1)
sim = ctrl.ControlSystemSimulation(ctrl_sys)
sim.input['kopi'] = kopi_input
sim.input['sugar'] = sugar_input
sim.input['milk'] = milk_input
sim.compute()
score = sim.output['output']
# output.view(sim=sim)
# pyplot.show()
# #Calculate fuzzy score output for determing the Kopi type
# # score = fuzzyscore()
# ctrl_sys= ctrl.ControlSystem([rule_1,rule_2,rule_3,rule_4,rule_5,rule_6,rule_7,rule_8,rule_9,rule_10,rule_11,rule_12,rule_13,rule_14,rule_15,rule_16,rule_17,rule_18,rule_19,rule_20,rule_21,rule_22,rule_23,rule_24,rule_25,rule_26,rule_27,rule_28,rule_29,rule_30,rule_31,rule_32,rule_33,rule_34,rule_35,rule_36])
# sim = ctrl.ControlSystemSimulation(ctrl_sys, flush_after_run=35 * 35 + 1)
# for i in range(1,11):
# print(fuzz.interp_membership(kopi.universe,kopi_thin,i))
# print(fuzz.interp_membership(kopi.universe,kopi_norm,i))
# print(fuzz.interp_membership(kopi.universe,kopi_thick,i))
# for j in range(1,11):
# print("milk")
# milk_input=j
# sugar_input=9
# print(fuzz.interp_membership(milk.universe,milk_less,milk_input))
# print(fuzz.interp_membership(milk.universe,milk_norm,milk_input))
# print(fuzz.interp_membership(milk.universe,milk_extra,milk_input))
# sim.input['kopi']= i
# sim.input['milk']= milk_input
# sim.input['milk_pre']= milk_input
# sim.input['sugar']= sugar_input
# sim.input['sugar_pre']= sugar_input
# sim.compute()
# score = sim.output['output']
# print(score)
print("score = ", end="\t")
print(score)
#return (score)
return names[int(round(score)) + 19]
print("Exception : ", e)
conn.close()
"""
PM10 = np.array(PM10)
NO2 = np.array(NO2)
CO = np.array(CO)
"""
PM10 = np.arange(0, 531, 1)
NO2 = np.arange(0, 2011, 1)
CO = np.arange(0, 32011, 1)
HKİ = np.arange(0, 501, 1)
#pm10_low = fuzz.trapmf(PM10,[0,0,40,75])
pm10_low = fuzz.trimf(PM10, [0, 0, 75])
pm10_medium = fuzz.trimf(PM10, [40, 150, 260])
pm10_high = fuzz.trimf(PM10, [180, 530, 530])
#pm10_high = fuzz.trapmf(PM10,[180,260,530,530])
#no2_low = fuzz.trapmf(NO2,[0,0,50,150])
no2_low = fuzz.trimf(NO2, [0, 0, 150])
no2_medium = fuzz.trimf(NO2, [50, 275, 550])
no2_high = fuzz.trimf(NO2, [300, 2010, 2010])
#no2_high = fuzz.trapmf(NO2,[300,550,2010,2010])
#co_low = fuzz.trapmf(CO,[0,0,4000,7750])
co_low = fuzz.trimf(CO, [0, 0, 7750])
co_medium = fuzz.trimf(CO, [4000, 10000, 16000])
co_high = fuzz.trimf(CO, [12000, 32010, 32010])
#co_high = fuzz.trapmf(CO,[12000,16000,32010,32010])
# Hugo BERANGER - M2 MIAGE IA import numpy as np import skfuzzy as fuzz import matplotlib.pyplot as plt from skfuzzy import control as ctrl # Q1 res = ctrl.Antecedent(np.arange(0, 21, 1), 'resultat') met = ctrl.Antecedent(np.arange(0, 21, 1), 'methode') pre = ctrl.Antecedent(np.arange(0, 21, 1), 'presentation') eva = ctrl.Consequent(np.arange(0, 21, 1), 'evaluation') # Q2 Q3 res['mediocre'] = fuzz.trimf(res.universe, [0, 0, 10]) res['moyen'] = fuzz.trimf(res.universe, [0, 10, 20]) res['excellent'] = fuzz.trimf(res.universe, [10, 20, 20]) met['mediocre'] = fuzz.trimf(met.universe, [0, 0, 10]) met['moyen'] = fuzz.trimf(met.universe, [0, 10, 20]) met['excellent'] = fuzz.trimf(met.universe, [10, 20, 20]) pre['mediocre'] = fuzz.trimf(pre.universe, [0, 0, 10]) pre['moyen'] = fuzz.trimf(pre.universe, [0, 10, 20]) pre['excellent'] = fuzz.trimf(pre.universe, [10, 20, 20]) eva['mediocre'] = fuzz.trimf(eva.universe, [0, 0, 5]) eva['mauvais'] = fuzz.trimf(eva.universe, [0, 5, 10]) eva['moyen'] = fuzz.trimf(eva.universe, [5, 10, 15]) eva['bon'] = fuzz.trimf(eva.universe, [10, 15, 20]) eva['excellent'] = fuzz.trimf(eva.universe, [15, 20, 20])
from skfuzzy import control as ctrl
degree_dirt = ctrl.Antecedent(np.arange(1, 101, 1), 'degree_dirt')
type_dirt = ctrl.Antecedent(np.arange(1, 101, 1), 'type_dirt')
wash_time = ctrl.Consequent(np.arange(0, 61, 1), 'wash_time')
degree_name = ["Low", "Medium", "High"]
type_name = ["NonFat", "Medium", "Fat"]
degree_dirt.automf(names=degree_name)
type_dirt.automf(names=type_name)
degree_dirt.view()
type_dirt.view()
wash_time['very_short'] = fuzz.trimf(wash_time.universe, [0, 8, 12])
wash_time['short'] = fuzz.trimf(wash_time.universe, [8, 12, 20])
wash_time['medium'] = fuzz.trimf(wash_time.universe, [12, 20, 40])
wash_time['long'] = fuzz.trimf(wash_time.universe, [20, 40, 60])
wash_time['very_long'] = fuzz.trimf(wash_time.universe, [40, 60, 60])
wash_time.view()
rule1 = ctrl.Rule(degree_dirt['High'] | type_dirt['Fat'],
wash_time['very_long'])
rule2 = ctrl.Rule(degree_dirt['Medium'] | type_dirt['Fat'], wash_time['long'])
rule3 = ctrl.Rule(degree_dirt['Low'] | type_dirt['Fat'], wash_time['long'])
rule4 = ctrl.Rule(degree_dirt['High'] | type_dirt['Medium'], wash_time['long'])
rule5 = ctrl.Rule(degree_dirt['Medium'] | type_dirt['Medium'],
wash_time['medium'])
rule6 = ctrl.Rule(degree_dirt['Low'] | type_dirt['Medium'],
# Generate universe variables # * Quality and service on subjective ranges [0, 10] # * Tip has a range of [0, 25] in units of percentage points distance = 80 energy = 0 temperature = 30 x_distance = np.arange(0, 300, 1) x_energy = np.arange(0, 100, 1) x_temperature = np.arange(20, 30, 1) x_speed = np.arange(0, 1, 0.1) # Generate fuzzy membership functions for distance distance_lo = fuzz.trimf(x_distance, [0, 0, 150]) distance_md = fuzz.trimf(x_distance, [0, 150, 300]) distance_hi = fuzz.trimf(x_distance, [150, 300, 300]) energy_lo = fuzz.trimf(x_energy, [0, 0, 50]) energy_md = fuzz.trimf(x_energy, [0, 50, 100]) energy_hi = fuzz.trimf(x_energy, [50, 100, 100]) temperature_lo = fuzz.trimf(x_temperature, [20, 20, 25]) temperature_md = fuzz.trimf(x_temperature, [20, 25, 30]) temperature_hi = fuzz.trimf(x_temperature, [25, 30, 30]) speed_lo = fuzz.trimf(x_speed, [0, 0, 0.5]) speed_md = fuzz.trimf(x_speed, [0, 0.5, 1]) speed_hi = fuzz.trimf(x_speed, [0.5, 1, 1])
import numpy as np import math import skfuzzy as fuzz from skfuzzy import control as ctrl # Cria as variáveis do problema distanciaEsq = ctrl.Antecedent(np.arange(0, 25, 0.01), 'distanciaEsq') distanciaDir = ctrl.Antecedent(np.arange(0, 25, 0.01), 'distanciaDir') distanciaFre = ctrl.Antecedent(np.arange(0, 25, 0.01), 'distanciaFre') velocidadeEsq = ctrl.Consequent(np.arange(-1, 10, 0.5), 'velocidadeEsq') velocidadeDir = ctrl.Consequent(np.arange(-1, 10, 0.5), 'velocidadeDir') # Cria as funções de pertinência usando tipos variados distanciaEsq['perto'] = fuzz.trapmf(distanciaEsq.universe, [0, 0, 0.5, 2]) distanciaEsq['media'] = fuzz.trimf(distanciaEsq.universe, [0.5, 2, 10]) distanciaEsq['longe'] = fuzz.trapmf(distanciaEsq.universe, [2, 10, 25, 25]) distanciaDir['perto'] = fuzz.trapmf(distanciaDir.universe, [0, 0, 0.5, 2]) distanciaDir['media'] = fuzz.trimf(distanciaDir.universe, [0.5, 2, 10]) distanciaDir['longe'] = fuzz.trapmf(distanciaDir.universe, [2, 10, 25, 25]) distanciaFre['perto'] = fuzz.trapmf(distanciaFre.universe, [0, 0, 0.01, 0.05]) distanciaFre['media'] = fuzz.trimf(distanciaFre.universe, [0.01, 2, 10]) distanciaFre['longe'] = fuzz.trapmf(distanciaFre.universe, [2, 10, 25, 25]) velocidadeEsq['negativa'] = fuzz.trimf(velocidadeEsq.universe, [-1, -1, 0]) velocidadeEsq['baixa'] = fuzz.trimf(velocidadeEsq.universe, [0, 0, 0.5]) velocidadeEsq['media'] = fuzz.trimf(velocidadeEsq.universe, [0.5, 2, 5]) velocidadeEsq['alta'] = fuzz.trapmf(velocidadeEsq.universe, [2, 5, 8, 10])
imp4 = np.fmin(rule4,low)
imp5 = np.fmin(rule5,very_low)
aggregate_membership = np.fmax(imp1,np.fmax(imp2,np.fmax(imp3,np.fmax(imp4,imp5))))
return fuzz.defuzz(throttle_value,aggregate_membership,'centroid')
# Self Test locally
speeds = np.arange(60,150,0.1)
y = np.zeros_like(speeds)
for i in range(len(speeds)):
y[i] = throttle_command(speeds[i])
# Input membership functions
too_slow = fuzz.trapmf(speeds,[60,60,80,85])
slow = fuzz.trimf(speeds,[83,90,97])
cruise = fuzz.trimf(speeds,[95,100,105])
fast = fuzz.trimf(speeds,[103,110,117])
too_fast = fuzz.trapmf(speeds,[115,120,150,150])
plt.plot(speeds,y,speeds,too_slow,speeds,slow,speeds,cruise,speeds,fast,speeds,too_fast)
plt.xlabel('Airspeed (KIAS)')
plt.ylabel('Throttle Command/Speed Membership Functions')
plt.title('Throttle Commands vs. Airspeeds')
plt.legend(('Throttle Commands','Too Slow','Slow','Cruise','Fast','Too Fast'),bbox_to_anchor=(1.05, 1),loc=2, borderaxespad=0.)
plt.show()
### Test with x-plane
### Need to make sure that the ip address and send/recieve ports match your setup
""" import numpy as np import skfuzzy as fuzz from skfuzzy import control as ctrl # New Antecedent/Consequent objects hold universe variables and membership # functions quality = ctrl.Antecedent(np.arange(0, 11, 1), 'quality') service = ctrl.Antecedent(np.arange(0, 11, 1), 'service') tip = ctrl.Consequent(np.arange(0, 26, 1), 'tip') # Auto-membership function population is possible with .automf(3, 5, or 7) quality.automf(3) service.automf(3) # Custom membership functions can be built interactively with a familiar, # Pythonic API tip['low'] = fuzz.trimf(tip.universe, [0, 0, 13]) tip['medium'] = fuzz.trimf(tip.universe, [0, 13, 25]) tip['high'] = fuzz.trimf(tip.universe, [13, 25, 25]) ## # You can see how these look with .view() quality['average'].view() service.view() tip.view() ## rule1 = ctrl.Rule(quality['poor'] | service['poor'], tip['low']) rule2 = ctrl.Rule(service['average'], tip['medium']) rule3 = ctrl.Rule(service['good'] | quality['good'], tip['high']) rule1.view() # tipping_ctrl = ctrl.ControlSystem([rule1, rule2, rule3])
cpu_medium = fuzz.trapmf(x_cpu, [0.1, 0.45, 0.55, 0.9 ])
cpu_high = fuzz.trapmf(x_cpu, [0.4, 0.7, 0.8, 1.1 ])
cpu_veryhigh = fuzz.trapmf(x_cpu, [0.6, 0.95, 1.05, 1.4 ])
mem_verylow = fuzz.trapmf(x_mem, [-0.2, -0.1, 0.1, 0.4 ])
mem_low = fuzz.trapmf(x_mem, [-0.1, 0.2, 0.3, 0.6 ])
mem_medium = fuzz.trapmf(x_mem, [0.1, 0.45, 0.55, 0.9 ])
mem_high = fuzz.trapmf(x_mem, [0.4, 0.7, 0.8, 1.1 ])
mem_veryhigh = fuzz.trapmf(x_mem, [0.6, 0.95, 1.05, 1.4 ])
rsptmstddev_low = fuzz.trapmf(x_rsptmstddev, [-0.1, -0.01, 0.2, 0.6 ])
rsptmstddev_medium = fuzz.trapmf(x_rsptmstddev, [0.1, 0.4, 0.6, 1 ])
rsptmstddev_high = fuzz.trapmf(x_rsptmstddev, [0.5, 0.8, 1, 1.3 ])
load_extdec = fuzz.trapmf(x_load, [-0.7, -0.65, 0.55, 0.5 ])
load_veryfastdec = fuzz.trimf(x_load, [ -0.6, -0.5, -0.4 ])
load_fastdec = fuzz.trimf(x_load, [ -0.5, -0.4, -0.3 ])
load_dec = fuzz.trimf(x_load, [ -0.4, -0.3, -0.2 ])
load_smalldec = fuzz.trimf(x_load, [ -0.3, -0.2, -0.1 ])
load_verysmalldec = fuzz.trimf(x_load, [ -0.2, -0.1, 0 ])
load_nochange = fuzz.trimf(x_load, [-0.1, 0, 0.1 ])
load_smallincrease = fuzz.trimf(x_load, [ 0, 0.1, 0.2 ])
load_increase = fuzz.trimf(x_load, [ 0.1, 0.2, 0.3 ])
load_fastincrease = fuzz.trimf(x_load, [ 0.2, 0.3, 0.4 ])
load_veryfastincrease = fuzz.trapmf(x_load, [0.3, 0.35, 0.45, 0.5 ])
# Input: service score and food score
cpu_score = cpu_val / 100
mem_score = mem_val / 100
rsptime_score = rsp_time_val
# membership function of the distance in region left d_L = ctrl.Antecedent(np.arange(0, 8, 0.1), 'd_L') d_L['N'] = fuzz.zmf(d_L.universe, 1.2, 8) d_L['F'] = fuzz.smf(d_L.universe, 1.2, 8) d_R = ctrl.Antecedent(np.arange(0, 4, 0.1), 'd_R') d_R['N'] = fuzz.zmf(d_R.universe, 1.2, 8) d_R['F'] = fuzz.smf(d_R.universe, 1.2, 8) d_F = ctrl.Antecedent(np.arange(0, 4, 0.1), 'd_F') d_F['N'] = fuzz.zmf(d_F.universe, 1.2, 8) d_F['F'] = fuzz.smf(d_F.universe, 1.2, 8) # d_R.view() v = ctrl.Consequent(np.arange(0, 1.1, 0.1), 'v') v['VL'] = fuzz.trimf(v.universe, [0, 0, 0.8]) v['L'] = fuzz.trimf(v.universe, [0, 0.8, 1]) v['H'] = fuzz.trimf(v.universe, [0.8, 1, 1]) # v.view() theta = ctrl.Consequent(np.arange(0, 90.001, 0.1), 'theta') theta['VS'] = fuzz.trimf(theta.universe, [0, 0, 22.5]) theta['S'] = fuzz.trimf(theta.universe, [0, 22.5, 45]) theta['M'] = fuzz.trimf(theta.universe, [22.5, 45, 67.5]) theta['B'] = fuzz.trimf(theta.universe, [45, 67.5, 90]) theta['VB'] = fuzz.trimf(theta.universe, [67.5, 90, 90]) # d_F.view() # One Direction # L rule1 = ctrl.Rule(d_L['F'], v['VL'])
#control difuso
# jonatan hernandez henao
#1053864927
#encontrar valor de la propina a partir de la calidad del servicio
#importar librerias
import numpy as np
import skfuzzy as fuzz
import matplotlib.pyplot as plt
x_calidad = np.arange(0, 11, 1)
x_servicio = np.arange(0, 11, 1)
x_propina = np.arange(0, 26, 1)
calidad_baja = fuzz.trimf(x_calidad, [0, 0, 5])
calidad_media = fuzz.trimf(x_calidad, [0, 5, 10])
calidad_alta = fuzz.trimf(x_calidad, [5, 10, 10])
servicio_bajo = fuzz.trimf(x_servicio, [0, 0, 5])
servicio_medio = fuzz.trimf(x_servicio, [0, 5, 10])
servicio_alto = fuzz.trimf(x_servicio, [5, 10, 10])
propina_baja = fuzz.trimf(x_propina, [0, 0, 13])
propina_media = fuzz.trimf(x_propina, [0, 13, 25])
propina_alta = fuzz.trimf(x_propina, [13, 25, 25])
fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, figsize=(8, 9))
ax0.plot(x_calidad, calidad_baja, 'b', linewidth=1.5, label='Mala')
ax0.plot(x_calidad, calidad_media, 'g', linewidth=1.5, label='Aceptable')
ax0.plot(x_calidad, calidad_alta, 'r', linewidth=1.5, label='Buena')
ax0.set_title('Calidad de la comida')
import numpy as np import skfuzzy as fuzz from skfuzzy import control as ctrl # input vaiable cgpa ,industry_knowledge , extra_co activties #generating Universe variables for student performance to calculate CGPA attendence=ctrl.Antecedent(np.arange(0,6,1),'attendence') #(Attendence) CA=ctrl.Antecedent(np.arange(0,26,1),'CA') #(CA-Continous Assignment) MTE=ctrl.Antecedent(np.arange(0,21,1),'MTE') #MTE=(MID TERM EXAMINATIONN) ETE=ctrl.Antecedent(np.arange(0,51,1),'ETE') #ETE(END TERM EXAMINATION) CGPA=ctrl.Consequent(np.arange(0,11,1),'CGPA') #CGPA OF STUDENT (OUPUT) #generate fuzzy membership function for above variables attendence['low']=fuzz.trimf(attendence.universe,[0,0,2]) attendence['average']=fuzz.trimf(attendence.universe,[0,2,5]) attendence['high']=fuzz.trimf(attendence.universe,[2,5,5]) '''attendence.view()''' # generating fuzzy membership function for CA Variables CA['low']=fuzz.trimf(CA.universe,[0,0,12]) CA["average"]=fuzz.trimf(CA.universe,[0,12,25]) CA["high"]=fuzz.trimf(CA.universe,[12,25,25]) '''CA.view()''' #view CA Graph # Generating fuzzy Membership for MTE Variables MTE["low"]=fuzz.trimf(MTE.universe,[0,0,10]) MTE["average"]=fuzz.trimf(MTE.universe,[0,10,20])
def mamdani_defuzz(dism, disp, dor, cans):
dismenorreia = dism
dispareunia = disp
dorNasCostasPernas = dor
cansaco = cans
def nivelLeve(entrada):
if entrada == 0:
return 1
elif entrada >= 6:
return 0
else:
return 1 - (entrada/6)
def nivelModerado(entrada):
if entrada == 5:
return 1
elif entrada < 5:
return (entrada/6) - (1/6)
else:
return (11/6)-(entrada/6)
def nivelIntenso(entrada):
if entrada == 10:
return 1
elif entrada <=4:
return 0
else:
return (entrada/6) - (4/6)
#Antecedentes
dismenorreia_dominio = np.arange(0,11,0.1) #nivel de dismenorreia (0 a 10)
dispareunia_dominio = np.arange(0,11,1) #nivel de dispareunia (0 a 10)
dorNasCostasPernas_dominio = np.arange(0,11,1) #nivel de dor nas costas/pernas (0 a 10)
cansaco_dominio = np.arange(0,11,1) #nivel de cansaco (0 a 10)
#Consequente
risco = np.arange(0,100,1) #nivel de risco de endometriose (0 a 10)
#Funcoes de pertinencia de dismenorreia
dismenorreia_leve = fuzz.trimf(dismenorreia_dominio, [0,0,5]) #dismenorreia leve
dismenorreia_moderada = fuzz.trimf(dismenorreia_dominio, [0,5,10]) #dismenorreia moderada
dismenorreia_intensa = fuzz.trimf(dismenorreia_dominio, [5,10,10]) #dismenorreia intensa
#Funcoes de pertinencia de dispareunia
dispareunia_leve = fuzz.trimf(dispareunia_dominio, [0,0,5]) #dispareunia leve
dispareunia_moderada = fuzz.trimf(dispareunia_dominio, [0,5,10]) #dispareunia moderada
dispareunia_intensa = fuzz.trimf(dispareunia_dominio, [5,10,10]) #dispareunia intensa
#Funcoes de pertinencia de Dor Nas Costas e Pernas
dorCP_leve = fuzz.trimf(dorNasCostasPernas_dominio, [0,0,5]) #dor nas costas e/ou pernas leve
dorCP_moderada = fuzz.trimf(dorNasCostasPernas_dominio, [0,5,10]) #dor nas costas e/ou pernas moderada
dorCP_intensa = fuzz.trimf(dorNasCostasPernas_dominio, [5,10,10]) #dor nas costas e/ou pernas intensa
#Funcoes de pertinencia de cansaco
cansaco_leve = fuzz.trimf(cansaco_dominio, [0,0,5]) #cansaco leve
cansaco_moderado = fuzz.trimf(cansaco_dominio, [0,5,10]) #cansaco moderada
cansaco_intenso = fuzz.trimf(cansaco_dominio, [5,10,10]) #cansaco intensa
#Funcoes de pertinencia do Risco de Endometriose
risco_improvavel = fuzz.trimf(risco, [0,0,33]) #risco de endometriose baixo
risco_poucoprovavel = fuzz.trimf(risco, [0,33,66]) #risco de endometriose medio
risco_provavel = fuzz.trimf(risco, [33,66,100]) #risco de endometriose alto
risco_muitoprovavel = fuzz.trimf(risco, [66,100,100]) #risco de endometriose alto
## Simulando uma entrada
#dismenorreia_nivel_leve = fuzz.interp_membership(dismenorreia_dominio, dismenorreia_leve, dismenorreia) #faz a intersecao da entrada (10) com a funcao de pertinencia da dismenorreia leve
dismenorreia_nivel_leve = nivelLeve(dismenorreia)
#dismenorreia_nivel_moderada = fuzz.interp_membership(dismenorreia_dominio, dismenorreia_moderada, dismenorreia) #faz a intersecao da entrada (10) com a funcao de pertinencia da dismenorreia moderada
dismenorreia_nivel_moderada = nivelModerado(dismenorreia)
#dismenorreia_nivel_intensa = fuzz.interp_membership(dismenorreia_dominio, dismenorreia_intensa, dismenorreia) #faz a intersecao da entrada (10) com a funcao de pertinencia da dismenorreia intensa
dismenorreia_nivel_intensa = nivelIntenso(dismenorreia)
#dispareunia_nivel_leve = fuzz.interp_membership(dispareunia_dominio, dispareunia_leve, dispareunia) #faz a intersecao da entrada (8) com a funcao de pertinencia da dispareunia leve
dispareunia_nivel_leve = nivelLeve(dispareunia)
#dispareunia_nivel_moderada = fuzz.interp_membership(dispareunia_dominio, dispareunia_moderada, dispareunia) #faz a intersecao da entrada (8) com a funcao de pertinencia da dispareunia moderada
dispareunia_nivel_moderada = nivelModerado(dispareunia)
#dispareunia_nivel_intensa = fuzz.interp_membership(dispareunia_dominio, dispareunia_intensa, dispareunia) #faz a intersecao da entrada (8) com a funcao de pertinencia da dispareunia instensa
dispareunia_nivel_intensa = nivelIntenso(dispareunia)
#dorCP_nivel_leve = fuzz.interp_membership(dorNasCostasPernas_dominio, dorCP_leve, dorNasCostasPernas) #faz a intersecao da entrada (8) com a funcao de pertinencia da dor nas costas/pernas leve
dorCP_nivel_leve = nivelLeve(dorNasCostasPernas)
#dorCP_nivel_moderada = fuzz.interp_membership(dorNasCostasPernas_dominio, dorCP_moderada, dorNasCostasPernas) #faz a intersecao da entrada (8) com a funcao de pertinencia da dor nas costas/pernas moderada
dorCP_nivel_moderada = nivelModerado(dorNasCostasPernas)
#dorCP_nivel_intensa = fuzz.interp_membership(dorNasCostasPernas_dominio, dorCP_intensa, dorNasCostasPernas) #faz a intersecao da entrada (8) com a funcao de pertinencia da dor nas costas/pernas intensa
dorCP_nivel_intensa = nivelIntenso(dorNasCostasPernas)
#cansaco_nivel_leve = fuzz.interp_membership(cansaco_dominio, cansaco_leve, cansaco) #faz a intersecao da entrada (9) com a funcao de pertinencia de cansaco leve
cansaco_nivel_leve = nivelLeve(cansaco)
#cansaco_nivel_moderado = fuzz.interp_membership(cansaco_dominio, cansaco_moderado, cansaco) #faz a intersecao da entrada (9) com a funcao de pertinencia de cansaco moderado
cansaco_nivel_moderado = nivelModerado(cansaco)
#cansaco_nivel_intenso = fuzz.interp_membership(cansaco_dominio, cansaco_intenso, cansaco) #faz a intersecao da entrada (9) com a funcao de pertinencia de cansaco intenso
cansaco_nivel_intenso = nivelIntenso(cansaco)
regras_ativas = []
## Base de regras
#Regra 1: dismenorreia leve; dispareunia leve; dor costas/pernas leve; cansaco leve => risco improvavel
ativa_regra1 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra1 = np.fmin(ativa_regra1, risco_improvavel) #implicacao
if regra1.any() != 0:
regras_ativas.append(1)
#Regra 2: dismenorreia leve; dispareunia leve; dor costas/pernas leve; cansaco moderado => risco improvavel
ativa_regra2 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra2 = np.fmin(ativa_regra2, risco_improvavel) #implicacao
if regra2.any() != 0:
regras_ativas.append(2)
#Regra 3: dismenorreia leve; dispareunia leve; dor costas/pernas leve; cansaco intenso => risco pouco provavel
ativa_regra3 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra3 = np.fmin(ativa_regra3, risco_poucoprovavel) #implicacao
if regra3.any() != 0:
regras_ativas.append(3)
#Regra 4: dismenorreia leve; dispareunia leve; dor costas/pernas moderado; cansaco leve => risco improvavel
ativa_regra4 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra4 = np.fmin(ativa_regra4, risco_improvavel) #implicacao
if regra4.any() != 0:
regras_ativas.append(4)
#Regra 5: dismenorreia leve; dispareunia leve; dor costas/pernas moderado; cansaco moderado => risco improvavel
ativa_regra5 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra5 = np.fmin(ativa_regra5, risco_improvavel) #implicacao
if regra5.any() != 0:
regras_ativas.append(5)
#Regra 6: dismenorreia leve; dispareunia leve; dor costas/pernas moderado; cansaco intenso => risco pouco provavel
ativa_regra6 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra6 = np.fmin(ativa_regra6, risco_poucoprovavel) #implicacao
if regra6.any() != 0:
regras_ativas.append(6)
#Regra 7: dismenorreia leve; dispareunia leve; dor costas/pernas intenso; cansaco leve => risco pouco provavel
ativa_regra7 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra7 = np.fmin(ativa_regra7, risco_poucoprovavel) #implicacao
if regra7.any() != 0:
regras_ativas.append(7)
#Regra 8: dismenorreia leve; dispareunia leve; dor costas/pernas intenso; cansaco moderado => risco pouco provavel
ativa_regra8 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra8 = np.fmin(ativa_regra8, risco_poucoprovavel) #implicacao
if regra8.any() != 0:
regras_ativas.append(8)
#Regra 9: dismenorreia leve; dispareunia leve; dor costas/pernas intenso; cansaco intenso => risco provavel
ativa_regra9 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra9 = np.fmin(ativa_regra9, risco_provavel) #implicacao
if regra9.any() != 0:
regras_ativas.append(9)
#Regra 10: dismenorreia leve; dispareunia moderado; dor costas/pernas leve; cansaco leve => risco improvavel
ativa_regra10 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra10 = np.fmin(ativa_regra10, risco_improvavel) #implicacao
if regra10.any() != 0:
regras_ativas.append(10)
#Regra 11: dismenorreia leve; dispareunia moderado; dor costas/pernas leve; cansaco moderado => risco improvavel
ativa_regra11 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra11 = np.fmin(ativa_regra11, risco_improvavel) #implicacao
if regra11.any() != 0:
regras_ativas.append(11)
#Regra 12: dismenorreia leve; dispareunia moderado; dor costas/pernas leve; cansaco intenso => risco pouco provavel
ativa_regra12 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra12 = np.fmin(ativa_regra12, risco_poucoprovavel) #implicacao
if regra12.any() != 0:
regras_ativas.append(12)
#Regra 13: dismenorreia leve; dispareunia moderado; dor costas/pernas moderado; cansaco leve => risco improvavel
ativa_regra13 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra13 = np.fmin(ativa_regra13, risco_improvavel) #implicacao
if regra13.any() != 0:
regras_ativas.append(13)
#Regra 14: dismenorreia leve; dispareunia moderado; dor costas/pernas moderado; cansaco moderado => risco pouco provavel
ativa_regra14 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra14 = np.fmin(ativa_regra14, risco_poucoprovavel) #implicacao
if regra14.any() != 0:
regras_ativas.append(14)
#Regra 15: dismenorreia leve; dispareunia moderado; dor costas/pernas moderado; cansaco intenso => risco provavel
ativa_regra15 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra15 = np.fmin(ativa_regra15, risco_provavel) #implicacao
if regra15.any() != 0:
regras_ativas.append(15)
#Regra 16: dismenorreia leve; dispareunia moderado; dor costas/pernas intenso; cansaco leve => risco provavel
ativa_regra16 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra16 = np.fmin(ativa_regra16, risco_provavel) #implicacao
if regra16.any() != 0:
regras_ativas.append(16)
#Regra 17: dismenorreia leve; dispareunia moderado; dor costas/pernas intenso; cansaco moderado => risco provavel
ativa_regra17 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra17 = np.fmin(ativa_regra17, risco_provavel) #implicacao
if regra17.any() != 0:
regras_ativas.append(17)
#Regra 18: dismenorreia leve; dispareunia moderado; dor costas/pernas intenso; cansaco intenso => risco muito provavel
ativa_regra18 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra18 = np.fmin(ativa_regra18, risco_muitoprovavel) #implicacao
if regra18.any() != 0:
regras_ativas.append(18)
#Regra 19: dismenorreia leve; dispareunia intenso; dor costas/pernas leve; cansaco leve => risco improvavel
ativa_regra19 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra19 = np.fmin(ativa_regra19, risco_improvavel) #implicacao
if regra19.any() != 0:
regras_ativas.append(19)
#Regra 20: dismenorreia leve; dispareunia intenso; dor costas/pernas leve; cansaco moderado => risco pouco provavel
ativa_regra20 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra20 = np.fmin(ativa_regra20, risco_poucoprovavel) #implicacao
if regra20.any() != 0:
regras_ativas.append(20)
#Regra 21: dismenorreia leve; dispareunia intenso; dor costas/pernas leve; cansaco intenso => risco provavel
ativa_regra21 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra21 = np.fmin(ativa_regra21, risco_provavel) #implicacao
if regra21.any() != 0:
regras_ativas.append(21)
#Regra 22: dismenorreia leve; dispareunia intenso; dor costas/pernas moderado; cansaco leve => risco pouco provavel
ativa_regra22 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra22 = np.fmin(ativa_regra22, risco_poucoprovavel) #implicacao
if regra22.any() != 0:
regras_ativas.append(22)
#Regra 23: dismenorreia leve; dispareunia intenso; dor costas/pernas moderado; cansaco moderado => risco provavel
ativa_regra23 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra23 = np.fmin(ativa_regra23, risco_provavel) #implicacao
if regra23.any() != 0:
regras_ativas.append(23)
#Regra 24: dismenorreia leve; dispareunia intenso; dor costas/pernas moderado; cansaco intenso => risco muito provavel
ativa_regra24 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra24 = np.fmin(ativa_regra24, risco_muitoprovavel) #implicacao
if regra24.any() != 0:
regras_ativas.append(24)
#Regra 25: dismenorreia leve; dispareunia intenso; dor costas/pernas intenso; cansaco leve => risco pouco provavel
ativa_regra25 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra25 = np.fmin(ativa_regra25, risco_poucoprovavel) #implicacao
if regra25.any() != 0:
regras_ativas.append(25)
#Regra 26: dismenorreia leve; dispareunia intenso; dor costas/pernas intenso; cansaco moderado => risco provavel
ativa_regra26 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra26 = np.fmin(ativa_regra26, risco_provavel) #implicacao
if regra26.any() != 0:
regras_ativas.append(26)
#Regra 27: dismenorreia leve; dispareunia intenso; dor costas/pernas intenso; cansaco intenso => risco muito provavel
ativa_regra27 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_leve, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra27 = np.fmin(ativa_regra27, risco_muitoprovavel) #implicacao
if regra27.any() != 0:
regras_ativas.append(27)
#Regra 28: dismenorreia moderado; dispareunia leve; dor costas/pernas leve; cansaco leve => risco improvavel
ativa_regra28 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra28 = np.fmin(ativa_regra28, risco_improvavel) #implicacao
if regra28.any() != 0:
regras_ativas.append(28)
#Regra 29: dismenorreia moderado; dispareunia leve; dor costas/pernas leve; cansaco moderado => risco improvavel
ativa_regra29 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra29 = np.fmin(ativa_regra29, risco_improvavel) #implicacao
if regra29.any() != 0:
regras_ativas.append(29)
#Regra 30: dismenorreia moderado; dispareunia leve; dor costas/pernas leve; cansaco intenso => risco improvavel
ativa_regra30 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra30 = np.fmin(ativa_regra30, risco_improvavel) #implicacao
if regra30.any() != 0:
regras_ativas.append(30)
#Regra 31: dismenorreia moderado; dispareunia leve; dor costas/pernas moderado; cansaco leve => risco pouco provavel
ativa_regra31 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra31 = np.fmin(ativa_regra31, risco_poucoprovavel) #implicacao
if regra31.any() != 0:
regras_ativas.append(31)
#Regra 32: dismenorreia moderado; dispareunia leve; dor costas/pernas moderado; cansaco moderado => risco pouco provavel
ativa_regra32 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra32 = np.fmin(ativa_regra32, risco_poucoprovavel) #implicacao
if regra32.any() != 0:
regras_ativas.append(32)
#Regra 33: dismenorreia moderado; dispareunia leve; dor costas/pernas moderado; cansaco intenso => risco provavel
ativa_regra33 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra33 = np.fmin(ativa_regra33, risco_provavel) #implicacao
if regra33.any() != 0:
regras_ativas.append(33)
#Regra 34: dismenorreia moderado; dispareunia leve; dor costas/pernas intenso; cansaco leve => risco provavel
ativa_regra34 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra34 = np.fmin(ativa_regra34, risco_provavel) #implicacao
if regra34.any() != 0:
regras_ativas.append(34)
#Regra 35: dismenorreia moderado; dispareunia leve; dor costas/pernas intenso; cansaco moderado => risco provavel
ativa_regra35 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra35 = np.fmin(ativa_regra35, risco_provavel) #implicacao
if regra35.any() != 0:
regras_ativas.append(35)
#Regra 36: dismenorreia moderado; dispareunia leve; dor costas/pernas intenso; cansaco intenso => risco muito provavel
ativa_regra36 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra36 = np.fmin(ativa_regra36, risco_muitoprovavel) #implicacao
if regra36.any() != 0:
regras_ativas.append(36)
#Regra 37: dismenorreia moderado; dispareunia moderado; dor costas/pernas leve; cansaco leve => risco improvavel
ativa_regra37 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra37 = np.fmin(ativa_regra37, risco_improvavel) #implicacao
if regra37.any() != 0:
regras_ativas.append(37)
#Regra 38: dismenorreia moderado; dispareunia moderado; dor costas/pernas leve; cansaco moderado => risco pouco provavel
ativa_regra38 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra38 = np.fmin(ativa_regra38, risco_poucoprovavel) #implicacao
if regra38.any() != 0:
regras_ativas.append(38)
#Regra 39: dismenorreia moderado; dispareunia moderado; dor costas/pernas leve; cansaco intenso => risco provavel
ativa_regra39 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra39 = np.fmin(ativa_regra39, risco_provavel) #implicacao
if regra39.any() != 0:
regras_ativas.append(39)
#Regra 40: dismenorreia moderado; dispareunia moderado; dor costas/pernas moderado; cansaco leve => risco improvavel
ativa_regra40 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra40 = np.fmin(ativa_regra40, risco_improvavel) #implicacao
if regra40.any() != 0:
regras_ativas.append(40)
#Regra 41: dismenorreia moderado; dispareunia moderado; dor costas/pernas moderado; cansaco moderado => risco pouco provavel
ativa_regra41 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra41 = np.fmin(ativa_regra41, risco_poucoprovavel) #implicacao
if regra41.any() != 0:
regras_ativas.append(41)
#Regra 42: dismenorreia moderado; dispareunia moderado; dor costas/pernas moderado; cansaco intenso => risco provavel
ativa_regra42 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra42 = np.fmin(ativa_regra42, risco_provavel) #implicacao
if regra42.any() != 0:
regras_ativas.append(42)
#Regra 43: dismenorreia moderado; dispareunia moderado; dor costas/pernas intenso; cansaco leve => risco provavel
ativa_regra43 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra43 = np.fmin(ativa_regra43, risco_provavel) #implicacao
if regra43.any() != 0:
regras_ativas.append(43)
#Regra 44: dismenorreia moderado; dispareunia moderado; dor costas/pernas intenso; cansaco moderado => risco provavel
ativa_regra44 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra44 = np.fmin(ativa_regra44, risco_provavel) #implicacao
if regra44.any() != 0:
regras_ativas.append(44)
#Regra 45: dismenorreia moderado; dispareunia moderado; dor costas/pernas intenso; cansaco intenso => risco muito provavel
ativa_regra45 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra45 = np.fmin(ativa_regra45, risco_muitoprovavel) #implicacao
if regra45.any() != 0:
regras_ativas.append(45)
#Regra 46: dismenorreia moderado; dispareunia intenso; dor costas/pernas leve; cansaco leve => risco pouco provavel
ativa_regra46 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra46 = np.fmin(ativa_regra46, risco_poucoprovavel) #implicacao
if regra46.any() != 0:
regras_ativas.append(46)
#Regra 47: dismenorreia moderado; dispareunia intenso; dor costas/pernas leve; cansaco moderado => risco provavel
ativa_regra47 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra47 = np.fmin(ativa_regra47, risco_provavel) #implicacao
if regra47.any() != 0:
regras_ativas.append(47)
#Regra 48: dismenorreia moderado; dispareunia intenso; dor costas/pernas leve; cansaco intenso => risco provavel
ativa_regra48 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra48 = np.fmin(ativa_regra48, risco_provavel) #implicacao
if regra48.any() != 0:
regras_ativas.append(48)
#Regra 49: dismenorreia moderado; dispareunia intenso; dor costas/pernas moderado; cansaco leve => risco provavel
ativa_regra49 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra49 = np.fmin(ativa_regra49, risco_provavel) #implicacao
if regra49.any() != 0:
regras_ativas.append(49)
#Regra 50: dismenorreia moderado; dispareunia intenso; dor costas/pernas moderado; cansaco moderado => risco provavel
ativa_regra50 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra50 = np.fmin(ativa_regra50, risco_provavel) #implicacao
if regra50.any() != 0:
regras_ativas.append(50)
#Regra 51: dismenorreia moderado; dispareunia intenso; dor costas/pernas moderado; cansaco intenso => risco muito provavel
ativa_regra51 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra51 = np.fmin(ativa_regra51, risco_muitoprovavel) #implicacao
if regra51.any() != 0:
regras_ativas.append(51)
#Regra 52: dismenorreia moderado; dispareunia intenso; dor costas/pernas intenso; cansaco leve => risco provavel
ativa_regra52 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra52 = np.fmin(ativa_regra52, risco_provavel) #implicacao
if regra52.any() != 0:
regras_ativas.append(52)
#Regra 53: dismenorreia moderado; dispareunia intenso; dor costas/pernas intenso; cansaco moderado => risco muito provavel
ativa_regra53 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra53 = np.fmin(ativa_regra53, risco_muitoprovavel) #implicacao
if regra53.any() != 0:
regras_ativas.append(53)
#Regra 54: dismenorreia moderado; dispareunia intenso; dor costas/pernas intenso; cansaco intenso => risco muito provavel
ativa_regra54 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_moderada, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra54 = np.fmin(ativa_regra54, risco_muitoprovavel) #implicacao
if regra54.any() != 0:
regras_ativas.append(54)
#Regra 55: dismenorreia intenso; dispareunia leve; dor costas/pernas leve; cansaco leve => risco improvavel
ativa_regra55 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra55 = np.fmin(ativa_regra55, risco_improvavel) #implicacao
if regra55.any() != 0:
regras_ativas.append(55)
#Regra 56: dismenorreia intenso; dispareunia leve; dor costas/pernas leve; cansaco moderado => risco improvavel
ativa_regra56 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra56 = np.fmin(ativa_regra56, risco_improvavel) #implicacao
if regra56.any() != 0:
regras_ativas.append(56)
#Regra 57: dismenorreia intenso; dispareunia leve; dor costas/pernas leve; cansaco intenso => risco pouco provavel
ativa_regra57 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra57 = np.fmin(ativa_regra57, risco_poucoprovavel) #implicacao
if regra57.any() != 0:
regras_ativas.append(57)
#Regra 58: dismenorreia intenso; dispareunia leve; dor costas/pernas moderado; cansaco leve => risco pouco provavel
ativa_regra58 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra58 = np.fmin(ativa_regra58, risco_poucoprovavel) #implicacao
if regra58.any() != 0:
regras_ativas.append(58)
#Regra 59: dismenorreia intenso; dispareunia leve; dor costas/pernas moderado; cansaco moderado => risco pouco provavel
ativa_regra59 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra59 = np.fmin(ativa_regra59, risco_poucoprovavel) #implicacao
if regra59.any() != 0:
regras_ativas.append(59)
#Regra 60: dismenorreia intenso; dispareunia leve; dor costas/pernas moderado; cansaco intenso => risco provavel
ativa_regra60 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra60 = np.fmin(ativa_regra60, risco_provavel) #implicacao
if regra60.any() != 0:
regras_ativas.append(60)
#Regra 61: dismenorreia intenso; dispareunia leve; dor costas/pernas intenso; cansaco leve => risco pouco provavel
ativa_regra61 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra61 = np.fmin(ativa_regra61, risco_poucoprovavel) #implicacao
if regra61.any() != 0:
regras_ativas.append(61)
#Regra 62: dismenorreia intenso; dispareunia leve; dor costas/pernas intenso; cansaco moderado => risco provavel
ativa_regra62 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra62 = np.fmin(ativa_regra62, risco_provavel) #implicacao
if regra62.any() != 0:
regras_ativas.append(62)
#Regra 63: dismenorreia intenso; dispareunia leve; dor costas/pernas intenso; cansaco intenso => risco muito provavel
ativa_regra63 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_leve))) #composicao usando operador AND (minimo)
regra63 = np.fmin(ativa_regra63, risco_muitoprovavel) #implicacao
if regra63.any() != 0:
regras_ativas.append(63)
#Regra 64: dismenorreia intenso; dispareunia moderado; dor costas/pernas leve; cansaco leve => risco pouco provavel
ativa_regra64 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra64 = np.fmin(ativa_regra64, risco_poucoprovavel) #implicacao
if regra64.any() != 0:
regras_ativas.append(64)
#Regra 65: dismenorreia intenso; dispareunia moderado; dor costas/pernas leve; cansaco moderado => risco pouco provavel
ativa_regra65 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra65 = np.fmin(ativa_regra65, risco_poucoprovavel) #implicacao
if regra65.any() != 0:
regras_ativas.append(65)
#Regra 66: dismenorreia intenso; dispareunia moderado; dor costas/pernas leve; cansaco intenso => risco provavel
ativa_regra66 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra66 = np.fmin(ativa_regra66, risco_provavel) #implicacao
if regra66.any() != 0:
regras_ativas.append(66)
#Regra 67: dismenorreia intenso; dispareunia moderado; dor costas/pernas moderado; cansaco leve => risco pouco provavel
ativa_regra67 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra67 = np.fmin(ativa_regra67, risco_poucoprovavel) #implicacao
if regra67.any() != 0:
regras_ativas.append(67)
#Regra 68: dismenorreia intenso; dispareunia moderado; dor costas/pernas moderado; cansaco moderado => risco provavel
ativa_regra68 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra68 = np.fmin(ativa_regra68, risco_provavel) #implicacao
if regra68.any() != 0:
regras_ativas.append(68)
#Regra 69: dismenorreia intenso; dispareunia moderado; dor costas/pernas moderado; cansaco intenso => risco provavel
ativa_regra69 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra69 = np.fmin(ativa_regra69, risco_provavel) #implicacao
if regra69.any() != 0:
regras_ativas.append(69)
#Regra 70: dismenorreia intenso; dispareunia moderado; dor costas/pernas intenso; cansaco leve => risco provavel
ativa_regra70 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra70 = np.fmin(ativa_regra70, risco_provavel) #implicacao
if regra70.any() != 0:
regras_ativas.append(70)
#Regra 71: dismenorreia intenso; dispareunia moderado; dor costas/pernas intenso; cansaco moderado => risco provavel
ativa_regra71 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra71 = np.fmin(ativa_regra71, risco_provavel) #implicacao
if regra71.any() != 0:
regras_ativas.append(71)
#Regra 72: dismenorreia intenso; dispareunia moderado; dor costas/pernas intenso; cansaco intenso => risco muito provavel
ativa_regra72 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_moderada))) #composicao usando operador AND (minimo)
regra72 = np.fmin(ativa_regra72, risco_muitoprovavel) #implicacao
if regra72.any() != 0:
regras_ativas.append(72)
#Regra 73: dismenorreia intenso; dispareunia intenso; dor costas/pernas leve; cansaco leve => risco pouco provavel
ativa_regra73 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra73 = np.fmin(ativa_regra73, risco_poucoprovavel) #implicacao
if regra73.any() != 0:
regras_ativas.append(73)
#Regra 74: dismenorreia intenso; dispareunia intenso; dor costas/pernas leve; cansaco moderado => risco provavel
ativa_regra74 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra74 = np.fmin(ativa_regra74, risco_provavel) #implicacao
if regra74.any() != 0:
regras_ativas.append(74)
#Regra 75: dismenorreia intenso; dispareunia intenso; dor costas/pernas leve; cansaco intenso => risco muito provavel
ativa_regra75 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_leve, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra75 = np.fmin(ativa_regra75, risco_muitoprovavel) #implicacao
if regra75.any() != 0:
regras_ativas.append(75)
#Regra 76: dismenorreia intenso; dispareunia intenso; dor costas/pernas moderado; cansaco leve => risco provavel
ativa_regra76 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra76 = np.fmin(ativa_regra76, risco_provavel) #implicacao
if regra76.any() != 0:
regras_ativas.append(76)
#Regra 77: dismenorreia intenso; dispareunia intenso; dor costas/pernas moderado; cansaco moderado => risco provavel
ativa_regra77 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra77 = np.fmin(ativa_regra77, risco_provavel) #implicacao
if regra77.any() != 0:
regras_ativas.append(77)
#Regra 78: dismenorreia intenso; dispareunia intenso; dor costas/pernas moderado; cansaco intenso => risco muito provavel
ativa_regra78 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_moderada, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra78 = np.fmin(ativa_regra78, risco_muitoprovavel) #implicacao
if regra78.any() != 0:
regras_ativas.append(78)
#Regra 79: dismenorreia intenso; dispareunia intenso; dor costas/pernas intenso; cansaco leve => risco muito provavel
ativa_regra79 = np.fmin(cansaco_nivel_leve, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra79 = np.fmin(ativa_regra79, risco_muitoprovavel) #implicacao
if regra79.any() != 0:
regras_ativas.append(79)
#Regra 80: dismenorreia intenso; dispareunia intenso; dor costas/pernas intenso; cansaco moderado => risco muito provavel
ativa_regra80 = np.fmin(cansaco_nivel_moderado, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra80 = np.fmin(ativa_regra80, risco_muitoprovavel) #implicacao
if regra80.any() != 0:
regras_ativas.append(80)
#Regra 81: dismenorreia intenso; dispareunia intenso; dor costas/pernas intenso; cansaco intenso => risco muito provavel
ativa_regra81 = np.fmin(cansaco_nivel_intenso, np.fmin(dorCP_nivel_intensa, np.fmin(dismenorreia_nivel_intensa, dispareunia_nivel_intensa))) #composicao usando operador AND (minimo)
regra81 = np.fmin(ativa_regra81, risco_muitoprovavel) #implicacao
if regra81.any() != 0:
regras_ativas.append(81)
print "regras ativas: "+str(regras_ativas)
## Agregacao das regras
agregacao = np.fmax(regra81,
np.fmax(regra80,
np.fmax(regra79,
np.fmax(regra78,
np.fmax(regra77,
np.fmax(regra76,
np.fmax(regra75,
np.fmax(regra74,
np.fmax(regra73,
np.fmax(regra72,
np.fmax(regra71,
np.fmax(regra70,
np.fmax(regra69,
np.fmax(regra68,
np.fmax(regra67,
np.fmax(regra66,
np.fmax(regra65,
np.fmax(regra64,
np.fmax(regra63,
np.fmax(regra62,
np.fmax(regra61,
np.fmax(regra60,
np.fmax(regra59,
np.fmax(regra58,
np.fmax(regra57,
np.fmax(regra56,
np.fmax(regra55,
np.fmax(regra54,
np.fmax(regra53,
np.fmax(regra52,
np.fmax(regra51,
np.fmax(regra50,
np.fmax(regra49,
np.fmax(regra48,
np.fmax(regra47,
np.fmax(regra46,
np.fmax(regra45,
np.fmax(regra44,
np.fmax(regra43,
np.fmax(regra42,
np.fmax(regra41,
np.fmax(regra40,
np.fmax(regra39,
np.fmax(regra38,
np.fmax(regra37,
np.fmax(regra36,
np.fmax(regra35,
np.fmax(regra34,
np.fmax(regra33,
np.fmax(regra32,
np.fmax(regra31,
np.fmax(regra30,
np.fmax(regra29,
np.fmax(regra28,
np.fmax(regra27,
np.fmax(regra26,
np.fmax(regra25,
np.fmax(regra24,
np.fmax(regra23,
np.fmax(regra22,
np.fmax(regra21,
np.fmax(regra20,
np.fmax(regra19,
np.fmax(regra18,
np.fmax(regra17,
np.fmax(regra16,
np.fmax(regra15,
np.fmax(regra14,
np.fmax(regra13,
np.fmax(regra12,
np.fmax(regra11,
np.fmax(regra10,
np.fmax(regra9,
np.fmax(regra8,
np.fmax(regra7,
np.fmax(regra6,
np.fmax(regra5,
np.fmax(regra4,
np.fmax(regra3,
np.fmax(regra1, regra2)))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))) #agregacao das regras
risco0 = np.zeros_like(risco) #variavel auxiliar para montar o grafico
## Calculo do resultado defuzzificado
risco_def = fuzz.defuzz(risco, agregacao, 'centroid') #defuzzificacao pelo metodo centroide
risco_ativacao = fuzz.interp_membership(risco, agregacao, risco_def) #intersecao do risco defuzzificado com a funcao de pertinencia
## Grafico da funcao de pertinencia resultante
fig, ax0 = plt.subplots(figsize=(9.27,3.23))
ax0.plot(risco, risco_improvavel, 'b', linewidth=0.5, label='I', linestyle='--')
ax0.plot(risco, risco_poucoprovavel, 'g', linewidth=0.5, label='PP', linestyle='--')
ax0.plot(risco, risco_provavel, 'y', linewidth=0.5, label='P', linestyle='--')
ax0.plot(risco, risco_muitoprovavel, 'r', linewidth=1.5, label='MP', linestyle='--')
ax0.legend(loc='upper center',bbox_to_anchor=(0.5, 1.05), ncol=4, fancybox=True, shadow=True)
ax0.fill_between(risco, risco0, agregacao, facecolor='Orange', alpha=0.7)
ax0.plot([risco_def, risco_def], [0, risco_ativacao], 'k', linewidth=1.5, alpha=0.9)
plt.xticks(np.append(plt.xticks()[0],risco_def))
plt.xlabel('risco (%)')
ax0.set_title("Agregacao das regras e resultado defuzzificado")
plt.tight_layout()
plt.show()
return risco_def
are defined in scikit-fuzzy as follows """ import numpy as np import skfuzzy as fuzz import matplotlib.pyplot as plt # Generate universe variables # * Quality and service on subjective ranges [0, 10] # * Tip has a range of [0, 25] in units of percentage points x_qual = np.arange(0, 11, 1) x_serv = np.arange(0, 11, 1) x_tip = np.arange(0, 26, 1) # Generate fuzzy membership functions qual_lo = fuzz.trimf(x_qual, [0, 0, 5]) qual_md = fuzz.trimf(x_qual, [0, 5, 10]) qual_hi = fuzz.trimf(x_qual, [5, 10, 10]) serv_lo = fuzz.trimf(x_serv, [0, 0, 5]) serv_md = fuzz.trimf(x_serv, [0, 5, 10]) serv_hi = fuzz.trimf(x_serv, [5, 10, 10]) tip_lo = fuzz.trimf(x_tip, [0, 0, 13]) tip_md = fuzz.trimf(x_tip, [0, 13, 25]) tip_hi = fuzz.trimf(x_tip, [13, 25, 25]) # Visualize these universes and membership functions fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, figsize=(8, 9)) ax0.plot(x_qual, qual_lo, 'b', linewidth=1.5, label='Bad') ax0.plot(x_qual, qual_md, 'g', linewidth=1.5, label='Decent') ax0.plot(x_qual, qual_hi, 'r', linewidth=1.5, label='Great')
for key in xclasses.keys():
for f in range(0,features_num):
ff = [x[f] for x in xclasses.get(key)]
[f_min,f_max] = [min(ff),max(ff)]
feature[f] = [f_min,f_max] TERM
uni_min[f] = min([f_min,uni_min[f]])
uni_max[f] = max([f_max,uni_max[f]])
uni_step = (uni_max - uni_min) / 1000
mfs = [[np.nan] for f in range(0, features_num)]
mfs_uni = [[np.nan] for f in range(0, features_num)]
for lingvo in range(0, features_num):
mfs_uni[lingvo] = np.arange(uni_min[lingvo],uni_max[lingvo],uni_step[lingvo])
term = 0
for term_key in xclasses.key():
mfs[lingvo][term] = fuzz.trimf(mfs_uni[lingvp],[feature[x][0],feature[x][0]+(feature[x][1]-feature[x][0])/2,feature[x][1]])
term=term+1
#mf1_universum=np.arange(universum_min,universum_max + universum_step,universum_step)
#mf_feature1 = fuzz.trimf(mf1_universum,[min(feature1),min(feature1)+((max(feature1)-min(feature1))/2),max(feature1)])
#mf_feature2 = fuzz.trimf(mf1_universum,[min(feature2),min(feature2)+((max(feature2)-min(feature2))/2),max(feature2)])
#%% visualization
fig, ax = plt.subplots()
x=0
plt.plot( mfs_uni[x],mfs,'r',mf1_universum,mf_feature2,'g')
# -*- coding: utf-8 -*-
"""
Created on Sun Jul 10 22:10:02 2016
@author: meza
"""
import skfuzzy as fuzz
import numpy as np
import matplotlib.pyplot as plt
x=np.arange(11)
print x
mfx=fuzz.trimf(x, [0, 5,10])
print mfx
plt.plot(x,mfx)
plt.grid('on')
plt.show()
import skfuzzy as fuzz from skfuzzy import control as ctrl # New Antecedent/Consequent objects hold universe variables and membership # functions quality = ctrl.Antecedent(np.arange(0, 11, 1), "quality") service = ctrl.Antecedent(np.arange(0, 11, 1), "service") tip = ctrl.Consequent(np.arange(0, 26, 1), "tip") # Auto-membership function population is possible with .automf(3, 5, or 7) quality.automf(3) service.automf(3) # Custom membership functions can be built interactively with a familiar, # Pythonic API tip["low"] = fuzz.trimf(tip.universe, [0, 0, 13]) tip["medium"] = fuzz.trimf(tip.universe, [0, 13, 25]) tip["high"] = fuzz.trimf(tip.universe, [13, 25, 25]) """ To help understand what the membership looks like, use the ``view`` methods. """ # You can see how these look with .view() quality["average"].view() """ .. image:: PLOT2RST.current_figure """ service.view() """ .. image:: PLOT2RST.current_figure
def init_rule_based_system():
# Antecedent/Consequent objects hold universe variables and membership functions
step_meter = 0.02 # If the step are large, the Gaussian MF will regress to Triangular MF
step_meter_per_second = 0.02
step_risk = 0.05
range_type = np.arange(0, 2+1, 1)
range_degree = np.arange(-180, 180+1, 1.0) # Range: -180 degree ~ 180 degree for direction and orientation
range_meter = np.arange(0, 3.0+step_meter, step_meter) # Range: 0 meter ~ 3 meter for distance
range_meter_per_second = np.arange(0, 2.0+step_meter_per_second, step_meter_per_second)#Range: 0 mps ~ 2 mps for speed
range_risk = np.arange(0, 5+step_risk, step_risk) # Range: 0,1,2,3,4 for risk
object_type = ctrl.Antecedent(range_type, 'type')
object_distance = ctrl.Antecedent(range_meter, 'distance')
object_orientation = ctrl.Antecedent(range_degree , 'orientation')#-180~180 degree
object_direction = ctrl.Antecedent(range_degree , 'direction') # -180~180 degree
object_speed = ctrl.Antecedent(range_meter_per_second , 'speed') #0- 3 m/s
object_risk = ctrl.Consequent(range_risk, 'risk')
# Custom membership functions can be built interactively with a familiar Pythonic API
# Type
object_type['StaObj'] = fuzz.trimf(range_type, [0, 0, 0.1])
object_type['DynObj'] = fuzz.trimf(range_type, [0.9, 1, 1.1])
object_type['Human'] = fuzz.trimf(range_type, [1.9, 2, 2])
# Distance
object_distance['Near'] = fuzz.trapmf(range_meter, [0, 0, IZW, 2*IZW])
object_distance['Medium']= fuzz.trimf(range_meter, [IZW, 2*IZW, 4*IZW])
object_distance['Far'] = fuzz.trapmf(range_meter, [2*IZW, 4*IZW, 3, 3])
#object_distance.view()
# Direction -180~180
rear_d_p2 = fuzz.trapmf(range_degree, [-180, -180, -135, -90])
object_direction['Right'] = fuzz.trimf(range_degree, [-135, -90, -45])
object_direction['FrontRight'] = fuzz.trimf(range_degree, [-90, -45, 0])
object_direction['Front'] = fuzz.trimf(range_degree, [-45, 0, 45])
object_direction['FrontLeft']= fuzz.trimf(range_degree, [0, 45, 90])
object_direction['Left']= fuzz.trimf(range_degree, [45, 90, 135])
rear_d_p1 = fuzz.trapmf(range_degree, [90, 135, 180,180])
null,object_direction['BigRear'] =fuzz.fuzzy_or(range_degree,rear_d_p1,range_degree,rear_d_p2)
print("init_fls_common_part")
#object_direction.view()
# Speed
object_speed['Slow'] = fuzz.trapmf(range_meter_per_second, [0, 0, 0.5, 1.0])
object_speed['Medium']= fuzz.trapmf(range_meter_per_second, [0.5, 1.0, 1.0, 1.5])
object_speed['Fast'] = fuzz.trimf(range_meter_per_second,[1.0,1.5,1.5])
#object_speed.view()
# Orientation
object_orientation['Front'] = fuzz.trimf(range_degree, [-45, 0, 45])
object_orientation['FrontLeft']=fuzz.trimf(range_degree, [0, 45, 90])
object_orientation['Left']= fuzz.trimf(range_degree, [45, 90, 135])
object_orientation['RearLeft']= fuzz.trimf(range_degree, [90, 135, 180])
rear_p1 = fuzz.trimf(range_degree, [135, 180,180])
rear_p2 = fuzz.trimf(range_degree, [-180,-180,-135])
null,object_orientation['Rear'] =fuzz.fuzzy_or(range_degree,rear_p1,range_degree,rear_p2)
object_orientation['RearRight'] = fuzz.trimf(range_degree, [-180,-135,-90])
object_orientation['Right'] = fuzz.trimf(range_degree, [-135,-90,-45])
object_orientation['FrontRight'] = fuzz.trimf(range_degree, [-90,-45, 0])
#object_orientation.view()
# Risk
object_risk['VeryLow'] = fuzz.trimf(range_risk, [0, 0, 1])
object_risk['Low'] = fuzz.trimf(range_risk, [0, 1, 2])
object_risk['Medium'] = fuzz.trimf(range_risk, [1, 2, 3])
object_risk['High'] = fuzz.trimf(range_risk, [2, 3, 4])
object_risk['VeryHigh'] = fuzz.trimf(range_risk, [3, 4, 4])
"""
Fuzzy rules
-----------
"""
#time_previous = time.time()
#from rules_demo import rule_list_generator
#from rules import rule_list_generator
#rule_list=rule_list_generator(object_type,object_distance,object_direction, object_speed, object_orientation, object_risk)
#run_time = time.time() - time_previous
#print 'execute time=',one_run_time,'s'
#print 'setting rules time=',run_time,'sec'
"""
Control System Creation and Simulation
---------------------------------------
"""
global ra_fls
import cPickle as pickle
fls_name = "ra_full.data"
if os.path.exists(fls_name):
print("FLS exists!")
f = open(fls_name,'rb')
ra_fls = pickle.load(f)
else:
print("Init FLS")
from assessment_rules import rule_list_generator
assessment_rule_list=rule_list_generator(object_type,object_distance,object_direction, object_speed, object_orientation, object_risk)
ra_fls = ctrl.ControlSystem(assessment_rule_list)
f = open(fls_name,'wb')
pickle.dump(ra_fls,f)
f.close
"""
In order to simulate this control system, we will create a
``ControlSystemSimulation``. Think of this object representing our controller
applied to a specific set of cirucmstances.
"""
global risk_assessment_instance
risk_assessment_instance = ctrl.ControlSystemSimulation(ra_fls)
def initialize():
global input_buffer_occup, total_buffer_occup, inbff_, tobff_, r_cost_
input_buffer_occup = np.arange(0, 9, 1)
total_buffer_occup = np.arange(0, 41, 1)
router_cost = np.arange(0, 41, 1)
inbff_ = [[] for j in range(5)]
tobff_ = [[] for j in range(5)]
r_cost_ = [[] for j in range(5)]
#global inbff_ = [[],[],[],[],[]]
inbff_[0] = fuzz.trimf(input_buffer_occup, [0, 0, 2])
inbff_[1] = fuzz.trimf(input_buffer_occup, [0, 2, 4])
inbff_[2] = fuzz.trimf(input_buffer_occup, [2, 4, 6])
inbff_[3] = fuzz.trimf(input_buffer_occup, [4, 6, 8])
inbff_[4] = fuzz.trimf(input_buffer_occup, [6, 8, 8])
fig, ax = plt.subplots()
mf = ['Zero', 'Very Small', 'Small', 'Medium', 'Large']
for i in range(5):
ax.plot(input_buffer_occup, inbff_[i], label=mf[i])
ax.legend(loc='upper right', shadow=True)
plt.title("Membership Function For Input Buffer Occupied")
plt.xlabel("Number of Input Buffer Occupied")
plt.ylabel("Membership Value")
tobff_[0] = fuzz.trimf(total_buffer_occup, [0, 0, 10])
tobff_[1] = fuzz.trimf(total_buffer_occup, [0, 10, 20])
tobff_[2] = fuzz.trimf(total_buffer_occup, [10, 20, 30])
tobff_[3] = fuzz.trimf(total_buffer_occup, [20, 30, 40])
tobff_[4] = fuzz.trimf(total_buffer_occup, [30, 40, 40])
fig, ax = plt.subplots()
for i in range(5):
ax.plot(total_buffer_occup, tobff_[i], label=mf[i])
ax.legend(loc='upper right', shadow=True)
plt.title("Membership Function For Total Buffer Occupied")
plt.xlabel("Number of Total Buffer Occupied")
plt.ylabel("Membership Value")
r_cost_[0] = fuzz.trimf(router_cost, [0, 0, 10])
r_cost_[1] = fuzz.trimf(router_cost, [0, 10, 20])
r_cost_[2] = fuzz.trimf(router_cost, [10, 20, 30])
r_cost_[3] = fuzz.trimf(router_cost, [20, 30, 40])
r_cost_[4] = fuzz.trimf(router_cost, [30, 40, 40])
global value
value = [0, 10, 20, 30, 40]
plt.show()
def init_fuzzy(self):
"""
Make fuzzy setup by defining membership functions and rules
"""
# Set the difference between the right front sensor and the right back sensor
difference_right_sensors = ctrl.Antecedent(np.arange(-1.0, 1.0, 0.001),
'difference_right_sensors')
difference_right_sensors['negative'] = fuzz.trimf(
difference_right_sensors.universe, [-1.0, -1.0, 0.0])
difference_right_sensors['zero'] = fuzz.trimf(
difference_right_sensors.universe, [-0.015, 0.0, 0.015])
difference_right_sensors['positive'] = fuzz.trimf(
difference_right_sensors.universe, [0.0, 1.0, 1.0])
# Set the distances of the front sensors
distances_front_sensors = list()
for i in range(3):
distances_front_sensors.append(
ctrl.Antecedent(np.arange(0, 5.0, self.steps),
'distance_front_sensor_' + str(i + 1)))
distances_front_sensors[i]['close'] = fuzz.zmf(
distances_front_sensors[i].universe, 0.3, 0.8)
distances_front_sensors[i]['away'] = fuzz.smf(
distances_front_sensors[i].universe, 0.3, 0.8)
distances_front_sensors[0].view()
# Define available velocities for each wheel
v_left = ctrl.Consequent(
np.arange(-self.max_speed, 1.0 + self.max_speed, 0.01), 'vl')
v_right = ctrl.Consequent(
np.arange(-self.max_speed, 1.0 + self.max_speed, 0.01), 'vr')
# Define the membership functions for the wheels speed
v_left['fast'] = fuzz.trimf(v_left.universe,
[2.0, 2.0, self.max_speed])
v_left['regular'] = fuzz.trimf(v_left.universe, [0.0, 2.0, 2.0])
v_left['zero'] = fuzz.trimf(v_left.universe, [-0.1, 0.0, 0.1])
v_right['fast'] = fuzz.trimf(v_right.universe,
[2.0, 2.0, self.max_speed])
v_right['regular'] = fuzz.trimf(v_left.universe, [0.0, 2.0, 2.0])
v_right['zero'] = fuzz.trimf(v_right.universe, [-0.1, 0.0, 0.1])
# Rules to make the robot follow the wall by turning left, right or going forward
# Turn left
rule_l1 = ctrl.Rule(
distances_front_sensors[0]['close']
| distances_front_sensors[1]['close']
| distances_front_sensors[2]['close'], v_left['zero'])
rule_r1 = ctrl.Rule(
distances_front_sensors[0]['close']
| distances_front_sensors[1]['close']
| distances_front_sensors[2]['close'], v_right['fast'])
# Turn right
rule_l2 = ctrl.Rule(
difference_right_sensors['positive']
& distances_front_sensors[0]['away']
& distances_front_sensors[1]['away'], v_left['fast'])
rule_r2 = ctrl.Rule(difference_right_sensors['positive'],
v_right['zero'])
# Go forward, next to the wall
rule_l3 = ctrl.Rule(
difference_right_sensors['zero']
& distances_front_sensors[0]['away']
& distances_front_sensors[1]['away'], v_left['regular'])
rule_r3 = ctrl.Rule(
difference_right_sensors['zero']
& distances_front_sensors[0]['away']
& distances_front_sensors[1]['away'], v_right['regular'])
rule_l4 = ctrl.Rule(
difference_right_sensors['negative']
& distances_front_sensors[0]['away']
& distances_front_sensors[1]['away'], v_left['regular'])
rule_r4 = ctrl.Rule(
difference_right_sensors['negative']
& distances_front_sensors[0]['away']
& distances_front_sensors[1]['away'], v_right['regular'])
vel_ctrl = ctrl.ControlSystem([
rule_l1, rule_l2, rule_l3, rule_l4, rule_r1, rule_r2, rule_r3,
rule_r4
])
self.fuzzy_system = ctrl.ControlSystemSimulation(vel_ctrl)
import numpy as np import skfuzzy as fuzz import matplotlib.pyplot as plt x = np.arange(11) print x mfx = fuzz.trimf(x, [0, 3, 5]) plt.plot(x, mfx) plt.grid() plt.show()
import numpy as np
import skfuzzy as fuzz
try:
import matplotlib.pyplot as plt
except ImportError:
pass
# Generate universe variables
# * Quality and service on subjective ranges [0, 10]
# * Tip has a range of [0, 25] in units of percentage points
x_qual = np.arange(0, 11, 1)
x_serv = np.arange(0, 11, 1)
x_tip = np.arange(0, 26, 1)
# Generate fuzzy membership functions
qual_lo = fuzz.trimf(x_qual, [0, 0, 5])
qual_md = fuzz.trimf(x_qual, [0, 5, 10])
qual_hi = fuzz.trimf(x_qual, [5, 10, 10])
serv_lo = fuzz.trimf(x_serv, [0, 0, 5])
serv_md = fuzz.trimf(x_serv, [0, 5, 10])
serv_hi = fuzz.trimf(x_serv, [5, 10, 10])
tip_lo = fuzz.trimf(x_tip, [0, 0, 13])
tip_md = fuzz.trimf(x_tip, [0, 13, 25])
tip_hi = fuzz.trimf(x_tip, [13, 25, 25])
# Visualize these universes and membership functions
fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, figsize=(8, 9))
ax0.plot(x_qual, qual_lo, 'b', linewidth=1.5, label='Bad')
ax0.plot(x_qual, qual_md, 'g', linewidth=1.5, label='Decent')
ax0.plot(x_qual, qual_hi, 'r', linewidth=1.5, label='Great')
def getRangeError(truthData, system, inMF='sing', outMF='sing'):
"""
Get a system's error against a set of truth data using Range outputs. (MUST
BE SYSTEM WITH FUZZY OUTPUT, NOT CRISP). For crisp (sing) outMF, error is
distance outside alpha-cut at maximum of system's output fuzzy membership fucntion.
For fuzzy outMF (gauss, tri, trap), gets output alpha-cut range at alpha=1.0,
and error is error function by fuzErrorAC.
------INPUTS------
truthData - list
truth data to compare system output to in form:
[Quant Input, Qual Input, Output]
with inputs as {('input'): [values], ('input'): [values], ... }
and outputs as [output value(s)]
system : module
instance of system to get error on !MUST BE FUZZY OUTPUT!
inMF : string
type of MF to use for input data ('sing', 'tri', 'trap', 'gauss')
outMF : string
type of MF to use for output data ('sing', 'tri', 'trap', 'gauss')
sysOutType : string
type of output to use from system ('crisp' or 'fuzzy')
converts fuzzy outputs to crisp via 'centroid' defuzzification
------OUTPUTS------
error : list
list of [truth[min,max], truth[min,max], error]
"""
q = 0 #use quantitative data
error = [] #track error for output
#Turn Data to MFs
for point in truthData:
#create input MFs for each input
for inp in point[q]:
if inMF == 'sing': #create singleton MF
point[q][inp] = sum(point[q][inp])/len(point[q][inp]) #get mean of range (single value)
elif inMF == 'tri': #create triangluar MF (min, avg, max)
x_range = np.arange(point[q][inp][0]*0.9, point[q][inp][1]*1.1,
(point[q][inp][1]*1.1 - point[q][inp][0]*0.9)/150)
y_vals = fuzz.trimf(x_range, [point[q][inp][0], sum(point[q][inp])/len(point[q][inp]), point[q][inp][1]])
point[q][inp] = [x_range, y_vals]
elif inMF == 'trap': #create traoeziodal MF (min, min, max, max)
x_range = np.arange(point[q][inp][0]*0.9, point[q][inp][1]*1.1,
(point[q][inp][1]*1.1 - point[q][inp][0]*0.9)/150)
y_vals = fuzz.trimf(x_range, [dataIt[q][inp][0], point[q][inp][0],
point[q][inp][0], point[q][inp][1]])
point[q][inp] = [x_range, y_vals]
#create output MFs
if outMF == 'sing': #create singleton MF
point[2] = sum(point[2])/len(point[2]) #get average for singleton value
elif outMF == 'tri': #create singleton MF
x_range = np.arange(point[2][0]*0.9, point[2][1]*1.1,
(point[2][1]*1.1 - point[2][0]*0.9)/150)
y_vals = fuzz.trimf(x_range, [point[2][0], sum(point[2])/len(point[2]), point[2][1]])
point[2] = [x_range, y_vals]
elif outMF == 'trap': #create singleton MF
x_range = np.arange(point[2][0]*0.9, point[2][1]*1.1, (point[2][1]*1.1 - point[2][0]*0.9)/150)
y_vals = fuzz.trimf(x_range, [point[2][0], point[2][0], point[2][1], point[2][1]])
point[2] = [x_range, y_vals]
#Get system responses to data
for point in truthData:
inputs = {key[0]+"_"+key[1]:point[0][key] for key in point[0]}
output = system.run(inputs)
if isinstance(output, dict): output = output.itervalues().next()
point.append(output)
for point in truthData:
#get error
if not outMF=='sing': #if data is fuzzy
err = fuzzy_error.fuzErrorAC(point[2], point[3])
truthRange = fuzzyOps.alpha_at_val(point[2][0], point[2][1], alpha=1.0)
outRange = fuzzyOps.alpha_at_val(point[3], point[3])
else:
outRange = fuzzyOps.alpha_at_val(point[3][0], point[3][1])
if None in outRange: err = None
elif point[2] >= min(outRange) and point[2] <= max(outRange):
err = 0
elif point[2] < min(outRange):
err = point[2] - min(outRange)
elif point[2] > max(outRange):
err = point[2] - max(outRange)
truthRange = point[2]
error.append([truthRange, outRange, err])
return error
import numpy as np import skfuzzy as fuzz from skfuzzy import control as ctrl age = ctrl.Antecedent(np.arange(18, 100, 1), 'age') jobs = ctrl.Antecedent(np.arange(0, 10, 1), 'jobs') salary = ctrl.Consequent(np.arange(1500, 10000, 500), 'salary') age['young'] = fuzz.trimf(age.universe, [18, 18, 35]) age['middle aged'] = fuzz.trimf(age.universe, [30, 40, 50]) age['old'] = fuzz.trimf(age.universe, [45, 65, 65]) jobs['few'] = fuzz.trimf(jobs.universe, [0, 0, 2]) jobs['medium'] = fuzz.trimf(jobs.universe, [1, 4, 6]) jobs['many'] = fuzz.trimf(jobs.universe, [5, 10, 10]) salary['low'] = fuzz.trimf(salary.universe, [1500, 1500, 2500]) salary['medium'] = fuzz.trimf(salary.universe, [2000, 5000, 7000]) salary['high'] = fuzz.trimf(salary.universe, [6000, 10000, 10000]) salary.view() rules = [] rules.append(ctrl.Rule(age['young'] | jobs['few'], salary['low'])) rules.append(ctrl.Rule(age['middle aged'] & jobs['many'], salary['high'])) rules.append(ctrl.Rule(age['middle aged'] | jobs['medium'], salary['medium'])) rules.append(ctrl.Rule(age['middle aged'] & jobs['few'], salary['low'])) rules.append(ctrl.Rule(age['old'] | jobs['many'], salary['high'])) navigation_ctrl = ctrl.ControlSystem(rules) fis = ctrl.ControlSystemSimulation(navigation_ctrl)
sys.argv.append("--cp_ids")
sys.argv.append("1")
import RaicerSocket
from Utils import S_WAIT, S_COUNTDOWN, S_RUNNING, S_FINISHED, S_CRASHED, S_CANCELED, print_debug
import numpy as np
import skfuzzy as fuzz
from skfuzzy import control as ctrl
from Features import FeatureCalculator
import time
# build fuzzy logic
# Antecedents
s_h = ctrl.Antecedent(np.arange(-30, 31, .1), 'speed h')
s_h['fast left'] = fuzz.trimf(s_h.universe, [-30, -30, -10])
s_h['medium left'] = fuzz.trimf(s_h.universe, [-15, -10, -5])
s_h['slow left'] = fuzz.trimf(s_h.universe, [-10, -1, 0])
s_h['slow'] = fuzz.trimf(s_h.universe, [-1, 0, 1])
s_h['slow right'] = fuzz.trimf(s_h.universe, [0, 1, 10])
s_h['medium right'] = fuzz.trimf(s_h.universe, [5, 10, 15])
s_h['fast right'] = fuzz.trimf(s_h.universe, [10, 30, 30])
s_v = ctrl.Antecedent(np.arange(-30, 31, .1), 'speed v')
s_v['fast up'] = fuzz.trimf(s_h.universe, [-30, -30, -10])
s_v['medium up'] = fuzz.trimf(s_h.universe, [-15, -10, -5])
s_v['slow up'] = fuzz.trimf(s_h.universe, [-10, -1, 0])
s_v['slow'] = fuzz.trimf(s_v.universe, [-1, 0, 1])
s_v['slow down'] = fuzz.trimf(s_h.universe, [0, 1, 10])
s_v['medium down'] = fuzz.trimf(s_h.universe, [5, 10, 15])
s_v['fast down'] = fuzz.trimf(s_h.universe, [10, 30, 30])
mx = np.max(Im)
mx = mx.astype(int)
print mx
fcm_gr = ctrl.Antecedent(np.arange(-1, np.max(Im) + 2), 'grupos')
fcm_sl = ctrl.Consequent(np.arange(0,256,1), 'salida')
for i in range(ncenters):
if i == 0:
abc = [0, 0, cntr[ord[i+1]]]
#print(abc)
elif i == ncenters-1:
abc = [cntr[ord[i-1]], cntr[ord[i]], np.max(Im)+2]
else:
abc = [cntr[ord[i-1]], cntr[ord[i]], cntr[ord[i+1]]]
fu = fuzz.trimf(np.arange(-1, np.max(Im) + 2), abc)
fu2 = fuzz.gaussmf(np.arange(0,256,1), lev*i, w[ord[i]] / 2)
str1 = "ce" + str(i)
str2 = "sl" + str(i)
fcm_gr[str1] = fu
fcm_sl[str2] = fu2
rl = []
for i in range(ncenters):
s1 = "ce" + str(i)
s2 = "sl" + str(i)
rule = ctrl.Rule(fcm_gr[s1],fcm_sl[s2])
# A continuación se define manualmente el soporte de cada un de las etiquetas, en este caso trapezoidales. Muy_Arriba = fuzz.trapmf(Rango_Equipos, [1, 1, 3, 5]) Arriba = fuzz.trapmf(Rango_Equipos, [3, 5, 7, 9]) Mitad = fuzz.trapmf(Rango_Equipos, [7, 9, 11, 13]) Abajo = fuzz.trapmf(Rango_Equipos, [11, 13, 15, 17]) Muy_Abajo = fuzz.trapmf(Rango_Equipos, [15, 17, 20, 20]) Minimo_Presupuesto = fuzz.trapmf(Rango_Presupuestos, [1, 1, 10, 20]) Bajo_Presupuesto = fuzz.trapmf(Rango_Presupuestos, [10, 30, 50, 70]) Normal_Presupuesto = fuzz.trapmf(Rango_Presupuestos, [50, 80, 110, 140]) Alto_Presupuesto = fuzz.trapmf(Rango_Presupuestos, [100, 200, 400, 500]) Enorme_Presupuesto = fuzz.trapmf(Rango_Presupuestos, [400, 700, 950, 950]) No_Racha_Ganando = fuzz.trimf(Rango_Rachas, [0, 0, 3]) Corta_Racha_Ganando = fuzz.trapmf(Rango_Rachas, [2, 3, 4, 5]) Normal_Racha_Ganando = fuzz.trapmf(Rango_Rachas, [3, 4, 5, 6]) Buena_Racha_Ganando = fuzz.trapmf(Rango_Rachas, [5, 7, 9, 11]) Enorme_Racha_Ganando = fuzz.trapmf(Rango_Rachas, [7, 18, 38, 38]) Buena_Racha_Perdiendo = fuzz.trimf(Rango_Rachas, [0, 0, 3]) Normal_Racha_Perdiendo = fuzz.trapmf(Rango_Rachas, [2, 4, 6, 8]) Mala_Racha_Perdiendo = fuzz.trapmf(Rango_Rachas, [6, 8, 10, 12]) Pesima_Racha_Perdiendo = fuzz.trapmf(Rango_Rachas, [8, 15, 38, 38]) Buena_Racha_Empatando = fuzz.trimf(Rango_Rachas, [0, 0, 4]) Normal_Racha_Empatando = fuzz.trapmf(Rango_Rachas, [3, 5, 7, 9]) Mala_Racha_Empatando = fuzz.trapmf(Rango_Rachas, [6, 8, 10, 12]) Pesima_Racha_Empatando = fuzz.trapmf(Rango_Rachas, [8, 15, 38, 38])
import numpy as np import skfuzzy as fuzz from skfuzzy import control as ctrl import matplotlib.pyplot as plt temperatura = ctrl.Antecedent(np.arange(100, 500, 1), 'temperatura') pressao = ctrl.Antecedent(np.arange(4, 12, 1), 'pressao') acelerador = ctrl.Consequent(np.arange(0, 100, 1), 'acelerador') temperatura['baixa'] = fuzz.trapmf(temperatura.universe, [100, 100, 200, 300]) temperatura['media'] = fuzz.trimf(temperatura.universe, [200, 300, 400]) temperatura['alta'] = fuzz.trapmf(temperatura.universe, [300, 400, 500, 500]) temperatura.view() pressao['baixa'] = fuzz.trapmf(pressao.universe, [4, 4, 6, 8]) pressao['media'] = fuzz.trimf(pressao.universe, [6, 8, 10]) pressao['alta'] = fuzz.trapmf(pressao.universe, [8, 10, 12, 12]) pressao.view() acelerador['min'] = fuzz.trapmf(acelerador.universe, [0, 0, 25, 50]) acelerador['0'] = fuzz.trimf(acelerador.universe, [25, 50, 75]) acelerador['max'] = fuzz.trapmf(acelerador.universe, [50, 75, 100, 100]) acelerador.view() rule1 = ctrl.Rule(temperatura['media'] & pressao['media'], acelerador['0']) rule2 = ctrl.Rule(temperatura['alta'] & pressao['alta'], acelerador['max']) rule3 = ctrl.Rule(temperatura['media'] & pressao['media'], acelerador['0']) rule4 = ctrl.Rule(temperatura['alta'] & pressao['alta'], acelerador['max']) acelerador_ctrl = ctrl.ControlSystem([rule1, rule2, rule3, rule4]) acel = ctrl.ControlSystemSimulation(acelerador_ctrl)
def fis_opt(e_teta, error, params=[], grafica=False):
a, b, c, d, e, f, g, h = list(map(abs, params))
#print(params,e_teta, error)
factor_apertura = 1.3
x_e_teta = np.arange(-5, 5, 0.5)
x_error = np.arange(-5, 5, 0.5)
x_omega = np.arange(-5, 5, 0.5)
# Generate fuzzy membership functions trapezoidal y triangular
e_teta_hi_neg = fuzz.trapmf(x_e_teta, [-100, -100, -5 + (a * 2), -1])
e_teta_med_neg = fuzz.trimf(
x_e_teta, [-1 - b * factor_apertura, -1, -1 + b * factor_apertura])
e_teta_lo = fuzz.trimf(
x_e_teta, [0 - c * factor_apertura, 0, 0 + c * factor_apertura])
e_teta_med_pos = fuzz.trimf(
x_e_teta, [1 - d * factor_apertura, 1, 1 + d * factor_apertura])
e_teta_hi_pos = fuzz.trapmf(x_e_teta, [1, 5 - (a * 2), 100, 100])
error_hi_neg = fuzz.trapmf(x_error, [-100, -100, -5 + (e * 2), -1])
error_med_neg = fuzz.trimf(
x_e_teta, [-1 * factor_apertura - f, -1, -1 + f * factor_apertura])
error_lo = fuzz.trimf(
x_error, [0 - g * factor_apertura, 0, 0 + g * factor_apertura])
error_med_pos = fuzz.trimf(
x_e_teta, [1 - h * factor_apertura, 1, 1 + h * factor_apertura])
error_hi_pos = fuzz.trapmf(x_error, [1, 5 - (e * 2), 100, 100])
omega_hi_neg = fuzz.trapmf(x_omega, [-5, -5, -2, -1])
omega_med_neg = fuzz.trimf(x_omega, [-2, -1, -0])
omega_lo = fuzz.trimf(x_omega, [-1, 0, 1])
omega_med_pos = fuzz.trimf(x_omega, [0, 1, 2])
omega_hi_pos = fuzz.trapmf(x_omega, [1, 2, 5, 8])
# We need the activation of our fuzzy membership functions at these values.
# This is what fuzz.interp_membership exists for!
e_teta_level_hi_neg = fuzz.interp_membership(x_e_teta, e_teta_hi_neg,
e_teta)
e_teta_level_med_neg = fuzz.interp_membership(x_e_teta, e_teta_med_neg,
e_teta)
e_teta_level_lo = fuzz.interp_membership(x_e_teta, e_teta_lo, e_teta)
e_teta_level_med_pos = fuzz.interp_membership(x_e_teta, e_teta_med_pos,
e_teta)
e_teta_level_hi_pos = fuzz.interp_membership(x_e_teta, e_teta_hi_pos,
e_teta)
error_level_hi_neg = fuzz.interp_membership(x_error, error_hi_neg, error)
error_level_med_neg = fuzz.interp_membership(x_error, error_med_neg, error)
error_level_lo = fuzz.interp_membership(x_error, error_lo, error)
error_level_med_pos = fuzz.interp_membership(x_error, error_med_pos, error)
error_level_hi_pos = fuzz.interp_membership(x_error, error_hi_pos, error)
if grafica:
# Visualize these universes and membership functions
fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, figsize=(8, 9))
ax0.plot(x_e_teta,
e_teta_hi_neg,
'b',
linewidth=1.5,
label='Alto negativo')
ax0.plot(x_e_teta,
e_teta_med_neg,
'm',
linewidth=1.5,
label='Medio negativo')
ax0.plot(x_e_teta, e_teta_lo, 'g', linewidth=1.5, label='Bajo')
ax0.plot(x_e_teta,
e_teta_med_pos,
'k',
linewidth=1.5,
label='Medio positivo')
ax0.plot(x_e_teta,
e_teta_hi_pos,
'r',
linewidth=1.5,
label='Alto positivo')
ax0.set_title('Error Theta')
ax0.legend()
ax1.plot(x_error,
error_hi_neg,
'b',
linewidth=1.5,
label='Alto negativo')
ax1.plot(x_error,
error_med_neg,
'm',
linewidth=1.5,
label='Medio negativo')
ax1.plot(x_error, error_lo, 'g', linewidth=1.5, label='Bajo')
ax1.plot(x_error,
error_med_pos,
'k',
linewidth=1.5,
label='Medio positivo')
ax1.plot(x_error,
error_hi_pos,
'r',
linewidth=1.5,
label='Alto positivo')
ax1.set_title('Error')
ax1.legend()
ax2.plot(x_omega,
omega_hi_neg,
'b',
linewidth=1.5,
label='Alto negativo')
ax2.plot(x_omega,
omega_med_neg,
'm',
linewidth=1.5,
label='Medio negativo')
ax2.plot(x_omega, omega_lo, 'g', linewidth=1.5, label='Bajo')
ax2.plot(x_omega,
omega_med_pos,
'k',
linewidth=1.5,
label='Medio positivo')
ax2.plot(x_omega,
omega_hi_pos,
'r',
linewidth=1.5,
label='Alto positivo')
ax2.set_title('Omega')
ax2.legend()
# Turn off top/right axes
for ax in (ax0, ax1, ax2):
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
plt.tight_layout()
plt.show()
# Now we take our rules and apply them.
# fmin regresa el minimo de los dos arreglos o valores
# fmax regresa el maximo de los dos arreglos o valores
# The OR operator means we take the maximum of these two.
# The AND operator means we take el minimun of these two
# # Rule 1 si e_teta es hi_neg y error es low entonces omega es hi pos
active_rule1 = np.fmin(e_teta_level_hi_neg, error_level_lo)
# tip_activation_1 = np.fmin(active_rule1, omega_hi_pos)
#
# # Rule 2 si e_teta es hi_pos y error es low entonces omega es hi neg
active_rule2 = np.fmin(e_teta_level_hi_pos, error_level_lo)
# tip_activation_2 = np.fmin(active_rule2, omega_hi_neg)
#
# # Rule 3 si e_teta es low y error es low entonces omega es low
active_rule3 = np.fmin(e_teta_level_lo, error_level_lo)
# tip_activation_3 = np.fmin(active_rule3, omega_lo)
#
# # Rule 4 si e_teta es hi_neg y error es hi_neg entonces omega es hi pos
active_rule4 = np.fmin(e_teta_level_hi_neg, error_level_hi_neg)
# tip_activation_4 = np.fmin(active_rule4, omega_hi_pos)
#
# # Rule 5 si e_teta es hi_pos y error es hi_pos entonces omega es hi neg
active_rule5 = np.fmin(e_teta_level_hi_pos, error_level_hi_pos)
# tip_activation_5 = np.fmin(active_rule5, omega_hi_neg)
#
# # Rule 6 si e_teta es hi_pos y error es hi_neg entonces omega es bajo
active_rule6 = np.fmin(e_teta_level_hi_pos, error_level_hi_neg)
# tip_activation_6 = np.fmin(active_rule6, omega_lo)
#
# # Rule 7 si e_teta es hi_neg y error es hi_pos entonces omega es bajo
active_rule7 = np.fmin(e_teta_level_hi_neg, error_level_hi_pos)
# tip_activation_7 = np.fmin(active_rule7, omega_lo)
#
# # Rule 8 si e_teta es low y error es hi_pos entonces omega es hi_neg
active_rule8 = np.fmin(e_teta_level_lo, error_level_hi_pos)
# tip_activation_8 = np.fmin(active_rule8, omega_hi_neg)
#
# # Rule 9 si e_teta es low y error es hi_neg entonces omega es hi_pos
active_rule9 = np.fmin(e_teta_level_lo, error_level_hi_neg)
# tip_activation_9 = np.fmin(active_rule9, omega_hi_pos)
#
# # Rule 10 si e_teta es med_neg y error es med_neg entonces omega es med_neg
active_rule10 = np.fmin(e_teta_level_med_neg, error_level_med_neg)
# tip_activation_10 = np.fmin(active_rule10, omega_med_neg)
#
# # Rule 11 si e_teta es med_pos y error es med_pos entonces omega es med_neg
active_rule11 = np.fmin(e_teta_level_med_pos, error_level_med_pos)
# tip_activation_11 = np.fmin(active_rule11, omega_med_neg)
#
# # Rule 12 si e_teta es med_neg y error es med_pos entonces omega es med_neg
active_rule12 = np.fmin(e_teta_level_med_neg, error_level_med_pos)
# tip_activation_12 = np.fmin(active_rule12, omega_med_neg)
#
# # Rule 13 si e_teta es med_pos y error es med_neg entonces omega es med_pos
active_rule13 = np.fmin(e_teta_level_med_pos, error_level_med_neg)
# tip_activation_13 = np.fmin(active_rule13, omega_med_pos)
#
# # Rule 14 si e_teta es med_neg y error es hi_neg entonces omega es med_pos
active_rule14 = np.fmin(e_teta_level_med_neg, error_level_hi_neg)
# tip_activation_14 = np.fmin(active_rule14, omega_med_pos)
#
# # Rule 15 si e_teta es hi_neg y error es med_neg entonces omega es hi_pos
active_rule15 = np.fmin(e_teta_level_hi_neg, error_level_med_neg)
# tip_activation_15 = np.fmin(active_rule14, omega_hi_pos)
#
# # Rule 16 si e_teta es hi_neg y error es med_pos entonces omega es hi_neg
active_rule16 = np.fmin(e_teta_level_hi_neg, error_level_med_pos)
# tip_activation_16 = np.fmin(active_rule14, omega_hi_neg)
#
# # Rule 17 si e_teta es hi_pos y error es med_neg entonces omega es low
active_rule17 = np.fmin(e_teta_level_hi_pos, error_level_med_neg)
# tip_activation_17 = np.fmin(active_rule14, omega_lo)
#
# # Rule 18 si e_teta es hi_pos y error es med_pos entonces omega es lo
active_rule18 = np.fmin(e_teta_level_hi_pos, error_level_med_pos)
# tip_activation_18 = np.fmin(active_rule14, omega_lo)
#
# # Rule 19 si e_teta es med_neg y error es hi_pos entonces omega es med_pos
active_rule19 = np.fmin(e_teta_level_med_neg, error_level_hi_pos)
# tip_activation_19 = np.fmin(active_rule14, omega_med_pos)
#
# # Rule 20 si e_teta es med_pos y error es low entonces omega es med_neg
active_rule20 = np.fmin(e_teta_level_med_pos, error_level_lo)
# tip_activation_20 = np.fmin(active_rule14, omega_med_neg)
#
# # Rule 21 si e_teta es med_neg y error es low entonces omega es med_pos
active_rule21 = np.fmin(e_teta_level_med_neg, error_level_lo)
# tip_activation_21 = np.fmin(active_rule14, omega_med_pos)
#
# # Rule 22 si e_teta es low y error es med_neg entonces omega es med_pos
active_rule22 = np.fmin(e_teta_level_lo, error_level_med_neg)
# tip_activation_22 = np.fmin(active_rule14, omega_med_pos)
#
# # Rule 23 si e_teta es low y error es med_pos entonces omega es med_neg
active_rule23 = np.fmin(e_teta_level_lo, error_level_med_pos)
# tip_activation_23 = np.fmin(active_rule14, omega_med_neg)
# # Rule 24 si e_teta es med_pos y error es hi_pos entonces omega es med_neg
active_rule24 = np.fmin(e_teta_level_med_pos, error_level_hi_pos)
# tip_activation_24 = np.fmin(active_rule24, omega_med_neg)
# # Rule 25 si e_teta es med_pos y error es hi_neg entonces omega es med_pos
active_rule25 = np.fmin(e_teta_level_med_pos, error_level_hi_neg)
# tip_activation_25 = np.fmin(active_rule25, omega_med_neg)
active_rule_Hi_neg = np.fmin(
active_rule2,
np.fmin(active_rule5, np.fmin(active_rule8, active_rule16)))
tip_activation_Hi_neg = np.fmax(active_rule_Hi_neg, omega_hi_neg)
active_rule_Hi_pos = np.fmin(
active_rule1,
np.fmin(active_rule4, np.fmin(active_rule9, active_rule15)))
tip_activation_Hi_pos = np.fmax(active_rule_Hi_pos, omega_hi_pos)
active_rule_med_neg = np.fmin(
active_rule10,
np.fmin(
active_rule11,
np.fmin(
active_rule12,
np.fmin(active_rule20, np.fmin(active_rule23,
active_rule24)))))
tip_activation_med_neg = np.fmax(active_rule_med_neg, omega_med_neg)
active_rule_med_pos = np.fmin(
active_rule13,
np.fmin(
active_rule14,
np.fmin(
active_rule19,
np.fmin(active_rule21, np.fmin(active_rule22,
active_rule25)))))
tip_activation_med_pos = np.fmax(active_rule_med_pos, omega_med_pos)
active_rule_low = np.fmin(
active_rule3,
np.fmin(active_rule6,
np.fmin(active_rule7, np.fmin(active_rule17, active_rule18))))
tip_activation_low = np.fmax(active_rule_low, omega_lo)
#aggregated = np.fmax(tip_activation_23, np.fmax(tip_activation_22,np.fmax(tip_activation_21, np.fmax(tip_activation_20,np.fmax(tip_activation_19, np.fmax(tip_activation_18,np.fmax(tip_activation_17, np.fmax(tip_activation_16, np.fmax(tip_activation_15, np.fmax(tip_activation_14,np.fmax(tip_activation_13, np.fmax(tip_activation_12,np.fmax(tip_activation_11, np.fmax(tip_activation_10,np.fmax(tip_activation_9, np.fmax(tip_activation_8,np.fmax(tip_activation_7, np.fmax(tip_activation_6, np.fmax(tip_activation_5, np.fmax(tip_activation_4, np.fmax(tip_activation_1, np.fmax(tip_activation_2, tip_activation_3))))))))))))))))))))))
aggregated = np.fmax(
tip_activation_Hi_neg,
np.fmax(
tip_activation_Hi_pos,
np.fmax(tip_activation_med_neg,
np.fmax(tip_activation_med_pos, tip_activation_low))))
# Calculate defuzzified result
omega = fuzz.defuzz(x_omega, aggregated, 'centroid')
#tip_activation = fuzz.interp_membership(x_omega, aggregated, omega) # for plot
# Visualize this
# if grafica:
# fig, ax0 = plt.subplots(figsize=(8, 3))
#
# ax0.fill_between(x_omega, tip_activation_1, facecolor='b', alpha=0.7)
# ax0.plot(x_omega, omega_hi_neg, 'b', linewidth=0.5, linestyle='--', )
# ax0.fill_between(x_omega, tip_activation_2, facecolor='g', alpha=0.7)
# ax0.plot(x_omega, omega_lo, 'g', linewidth=0.5, linestyle='--')
# ax0.fill_between(x_omega, tip_activation_3, facecolor='r', alpha=0.7)
# ax0.plot(x_omega, omega_hi_pos, 'r', linewidth=0.5, linestyle='--')
# ax0.set_title('Output membership activity')
#
#
# # Visualize this
# if grafica:
# fig, ax0 = plt.subplots(figsize=(8, 3))
#
# ax0.plot(x_omega, omega_hi_neg, 'b', linewidth=0.5, linestyle='--', )
# ax0.plot(x_omega, omega_lo, 'g', linewidth=0.5, linestyle='--')
# ax0.plot(x_omega, omega_hi_pos, 'r', linewidth=0.5, linestyle='--')
# ax0.fill_between(x_omega, aggregated, facecolor='Orange', alpha=0.7)
# ax0.plot([omega, omega], [0, tip_activation], 'k', linewidth=1.5, alpha=0.9)
# ax0.set_title('Aggregated membership and result (line)')
# plt.tight_layout()
# plt.show()
return omega
def FuzzControl(error):
x_pos = np.arange(-480, 480, 1)
x_speed = np.arange(0, 255, 1)
# Generate fuzzy membership functions
pos_NB = fuzz.trimf(x_pos, [-480, -360, -240])
pos_NM = fuzz.trimf(x_pos, [-360, -240, -120])
pos_NS = fuzz.trimf(x_pos, [-240, -120, 0])
pos_0 = fuzz.trimf(x_pos, [-120, 0, 120])
pos_S = fuzz.trimf(x_pos, [0, 120, 240])
pos_M = fuzz.trimf(x_pos, [120, 240, 360])
pos_B = fuzz.trimf(x_pos, [240, 360, 480])
speed_NB = fuzz.trimf(x_speed, [0, 0, 43])
speed_NM = fuzz.trimf(x_speed, [0, 43, 85])
speed_NS = fuzz.trimf(x_speed, [43, 85, 128])
speed_0 = fuzz.trimf(x_speed, [85, 128, 171])
speed_S = fuzz.trimf(x_speed, [128, 171, 213])
speed_M = fuzz.trimf(x_speed, [171, 213, 255])
speed_B = fuzz.trimf(x_speed, [213, 255, 255])
"""
Fuzzy rules
-----------
1. Si Error en pos es NB, entonces la Velocidad es B
2. Si Error en pos es NM, entonces la Velocidad es M
3. Si Error en pos es NS, entonces la Velocidad es S
4. Si Error en pos es 0, entonces la Velocidad es 0
5. Si Error en pos es S, entonces la Velocidad es NS
6. Si Error en pos es M, entonces la Velocidad es NM
7. Si Error en pos es B, entonces la Velocidad es NB.
"""
level_pos_NB = fuzz.interp_membership(x_pos, pos_NB, error)
level_pos_NM = fuzz.interp_membership(x_pos, pos_NM, error)
level_pos_NS = fuzz.interp_membership(x_pos, pos_NS, error)
level_pos_0 = fuzz.interp_membership(x_pos, pos_0, error)
level_pos_S = fuzz.interp_membership(x_pos, pos_S, error)
level_pos_M = fuzz.interp_membership(x_pos, pos_M, error)
level_pos_B = fuzz.interp_membership(x_pos, pos_B, error)
#Regla 1
SNB = np.fmin(level_pos_NB, speed_B)
#Regla 2
SNM = np.fmin(level_pos_NM, speed_M)
#Regla 3
SNS = np.fmin(level_pos_NS, speed_S)
#Regla 4
S0 = np.fmin(level_pos_0, speed_0)
#Regla 5
SS = np.fmin(level_pos_S, speed_NS)
#Regla 6
SM = np.fmin(level_pos_M, speed_NM)
#Regla 7
SB = np.fmin(level_pos_B, speed_NB)
# Aggregate all three output membership functions together
aggregated = np.fmax(
SNB,
np.fmax(SNM, np.fmax(SNS, np.fmax(S0, np.fmax(SS, np.fmax(SM, SB))))))
# Calculate defuzzified result
Speed = fuzz.defuzz(x_speed, aggregated, 'centroid')
Speedf = int(np.around(Speed, decimals=0))
return Speedf
"""
if temperature is hot then ice cream parlor is crowded.
if temperature is moderate then ice cream parlor is busy.
if temperature is cool then ice cream parlor is quiet.
"""
import numpy as np
import skfuzzy as fuzz
import matplotlib.pyplot as plt
#Univerese functions
temp = np.arange(30, 101, 1)
customers = np.arange(0,36, 1)
#Membership function for heat
t_hot = fuzz.trimf(temp, [65, 100, 100])
t_mod = fuzz.trimf(temp, [30, 65, 100])
t_cool = fuzz.trapmf(temp, [20, 20, 30, 65])
#Membership function for customers
c_crowded = fuzz.trimf(customers, [24, 35, 35])
c_busy = fuzz.trimf(customers, [0, 24, 35])
c_quiet = fuzz.trimf(customers, [0, 0, 24])
"""Visualise system"""
# Visualize membership functions for temperature
'''fig, ax = plt.subplots()
ax.plot(temp, t_hot, 'r', temp, t_mod, 'm', temp, t_cool, 'b')
import matplotlib.pyplot as plt
import numpy as np, skfuzzy as fuzz
from skfuzzy import control as ctrl
# define domain interval and resolution of fuzzy sets inputs-output
x_err = np.arange(-4,4, 0.1)
x_errRate = np.arange(-10,10, 0.1)
x_outPower = np.arange(-100,100, 0.1)
# define fuzzy set shapes or membership functions
err_N = fuzz.trapmf(x_err,[-4,-4,-2,0])
err_Z = fuzz.trimf(x_err,[-2,0,2])
err_P = fuzz.trapmf(x_err,[0,2,4,4])
errRate_N = fuzz.trapmf(x_errRate,[-10,-10,-5,0])
errRate_Z = fuzz.trimf(x_errRate,[-5,0,5])
errRate_P = fuzz.trapmf(x_errRate,[0,5,10,10])
outPower_C = fuzz.trapmf(x_outPower, [-100,-100,-50,0])
outPower_NC = fuzz.trimf(x_outPower, [-50,0,50])
outPower_H = fuzz.trapmf(x_outPower, [0,50,100,100])
# Visualize membership functions ----------------------------
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(11, 7))
ax1.plot(x_err, err_N, 'b', lw=2, label='err_N')
ax1.plot(x_err, err_P, 'r', lw=2, label='err_P')
ax1.plot(x_err, err_Z, 'orange', lw=2, label='err_Z')
ax1.set_xlabel('err', fontsize=10)
ax1.set_ylabel('Membership', fontsize=10)
ax1.set_title('Temp Different Fuzzy Sets')
import numpy as np import skfuzzy as fuzz from skfuzzy import control as ctrl fused_estimation = ctrl.Antecedent(np.arange(0, 11, 1), 'fused_estimation') kde_result = ctrl.Antecedent(np.arange(0, 11, 1), 'kde_result') current = ctrl.Antecedent(np.arange(0, 11, 1), 'current') pov = ctrl.Consequent(np.arange(0, 11, 1), 'pov') fused_estimation['low'] = fuzz.trimf(fused_estimation.universe, [0, 0, 5]) fused_estimation['medium'] = fuzz.trimf(fused_estimation.universe, [2, 5, 8]) fused_estimation['high'] = fuzz.trimf(fused_estimation.universe, [5, 8, 10]) kde_result['low'] = fuzz.trimf(kde_result.universe, [0, 0, 5]) kde_result['medium'] = fuzz.trimf(kde_result.universe, [2, 5, 8]) kde_result['high'] = fuzz.trimf(kde_result.universe, [5, 10, 10]) current['low'] = fuzz.trimf(current.universe, [0, 0, 5]) current['medium'] = fuzz.trimf(current.universe, [2, 5, 8]) current['high'] = fuzz.trimf(current.universe, [5, 10, 10]) pov['low'] = fuzz.trimf(pov.universe, [0, 0, 5]) pov['medium'] = fuzz.trimf(pov.universe, [2, 5, 8]) pov['high'] = fuzz.trimf(pov.universe, [5, 10, 10]) proportional=1 if proportional==1:
def vegFIS(model_run):
arcpy.env.overwriteOutput = True
# get list of all fields in the flowline network
fields = [f.name for f in arcpy.ListFields(in_network)]
# set the carrying capacity and vegetation field depending on whether potential or existing run
if model_run == 'pt':
out_field = "oVC_PT"
riparian_field = "iVeg_100PT"
streamside_field = "iVeg_30PT"
else:
out_field = "oVC_EX"
riparian_field = "iVeg_100EX"
streamside_field = "iVeg_30EX"
# check for oVC_* field in the network attribute table and delete if exists
if out_field in fields:
arcpy.DeleteField_management(in_network, out_field)
# get arrays for fields of interest
segid_np = arcpy.da.FeatureClassToNumPyArray(in_network, "ReachID")
riparian_np = arcpy.da.FeatureClassToNumPyArray(in_network, riparian_field)
streamside_np = arcpy.da.FeatureClassToNumPyArray(in_network, streamside_field)
segid_array = np.asarray(segid_np, np.int64)
riparian_array = np.asarray(riparian_np, np.float64)
streamside_array = np.asarray(streamside_np, np.float64)
# check that inputs are within range of fis
# if not, re-assign the value to just within range
riparian_array[riparian_array < 0] = 0
riparian_array[riparian_array > 4] = 4
streamside_array[streamside_array < 0] = 0
streamside_array[streamside_array > 4] = 4
# delete temp arrays
items = [segid_np, riparian_np, streamside_np]
for item in items:
del item
# create antecedent (input) and consequent (output) objects to hold universe variables and membership functions
riparian = ctrl.Antecedent(np.arange(0, 4, 0.01), 'input1')
streamside = ctrl.Antecedent(np.arange(0, 4, 0.01), 'input2')
density = ctrl.Consequent(np.arange(0, 45, 0.01), 'result')
# build membership functions for each antecedent and consequent object
riparian['unsuitable'] = fuzz.trapmf(riparian.universe, [0, 0, 0.1, 1])
riparian['barely'] = fuzz.trimf(riparian.universe, [0.1, 1, 2])
riparian['moderately'] = fuzz.trimf(riparian.universe, [1, 2, 3])
riparian['suitable'] = fuzz.trimf(riparian.universe, [2, 3, 4])
riparian['preferred'] = fuzz.trimf(riparian.universe, [3, 4, 4])
streamside['unsuitable'] = fuzz.trapmf(streamside.universe, [0, 0, 0.1, 1])
streamside['barely'] = fuzz.trimf(streamside.universe, [0.1, 1, 2])
streamside['moderately'] = fuzz.trimf(streamside.universe, [1, 2, 3])
streamside['suitable'] = fuzz.trimf(streamside.universe, [2, 3, 4])
streamside['preferred'] = fuzz.trimf(streamside.universe, [3, 4, 4])
density['none'] = fuzz.trimf(density.universe, [0, 0, 0.1])
density['rare'] = fuzz.trapmf(density.universe, [0, 0.1, 0.5, 1.5])
density['occasional'] = fuzz.trapmf(density.universe, [0.5, 1.5, 4, 8])
density['frequent'] = fuzz.trapmf(density.universe, [4, 8, 12, 25])
density['pervasive'] = fuzz.trapmf(density.universe, [12, 25, 45, 45])
# build fis rule table
rule1 = ctrl.Rule(riparian['unsuitable'] & streamside['unsuitable'], density['none'])
rule2 = ctrl.Rule(riparian['barely'] & streamside['unsuitable'], density['rare'])
rule3 = ctrl.Rule(riparian['moderately'] & streamside['unsuitable'], density['rare'])
rule4 = ctrl.Rule(riparian['suitable'] & streamside['unsuitable'], density['occasional'])
rule5 = ctrl.Rule(riparian['preferred'] & streamside['unsuitable'], density['occasional'])
rule6 = ctrl.Rule(riparian['unsuitable'] & streamside['barely'], density['rare'])
rule7 = ctrl.Rule(riparian['barely'] & streamside['barely'], density['rare']) # matBRAT has consequnt as 'occasional'
rule8 = ctrl.Rule(riparian['moderately'] & streamside['barely'], density['occasional'])
rule9 = ctrl.Rule(riparian['suitable'] & streamside['barely'], density['occasional'])
rule10 = ctrl.Rule(riparian['preferred'] & streamside['barely'], density['occasional'])
rule11 = ctrl.Rule(riparian['unsuitable'] & streamside['moderately'], density['rare'])
rule12 = ctrl.Rule(riparian['barely'] & streamside['moderately'], density['occasional'])
rule13 = ctrl.Rule(riparian['moderately'] & streamside['moderately'], density['occasional'])
rule14 = ctrl.Rule(riparian['suitable'] & streamside['moderately'], density['frequent'])
rule15 = ctrl.Rule(riparian['preferred'] & streamside['moderately'], density['frequent'])
rule16 = ctrl.Rule(riparian['unsuitable'] & streamside['suitable'], density['occasional'])
rule17 = ctrl.Rule(riparian['barely'] & streamside['suitable'], density['occasional'])
rule18 = ctrl.Rule(riparian['moderately'] & streamside['suitable'], density['frequent'])
rule19 = ctrl.Rule(riparian['suitable'] & streamside['suitable'], density['frequent'])
rule20 = ctrl.Rule(riparian['preferred'] & streamside['suitable'], density['pervasive'])
rule21 = ctrl.Rule(riparian['unsuitable'] & streamside['preferred'], density['occasional'])
rule22 = ctrl.Rule(riparian['barely'] & streamside['preferred'], density['frequent'])
rule23 = ctrl.Rule(riparian['moderately'] & streamside['preferred'], density['pervasive'])
rule24 = ctrl.Rule(riparian['suitable'] & streamside['preferred'], density['pervasive'])
rule25 = ctrl.Rule(riparian['preferred'] & streamside['preferred'], density['pervasive'])
# FIS
veg_ctrl = ctrl.ControlSystem([rule1, rule2, rule3, rule4, rule5, rule6, rule7, rule8, rule9, rule10, rule11,
rule12, rule13, rule14, rule15, rule16, rule17, rule18, rule19, rule20, rule21, rule22, rule23, rule24, rule25])
veg_fis = ctrl.ControlSystemSimulation(veg_ctrl)
# run fuzzy inference system on inputs and defuzzify output
out = np.zeros(len(riparian_array))
for i in range(len(out)):
veg_fis.input['input1'] = riparian_array[i]
veg_fis.input['input2'] = streamside_array[i]
veg_fis.compute()
out[i] = veg_fis.output['result']
# save fuzzy inference system output as table
columns = np.column_stack((segid_array, out))
out_table = os.path.dirname(in_network) + "/" + out_field + "_Table.txt" # todo: see if possible to skip this step
np.savetxt(out_table, columns, delimiter = ",", header = "ReachID, " + out_field, comments = "")
ovc_table = scratch + "/" + out_field + "Tbl"
arcpy.CopyRows_management(out_table, ovc_table)
# join the fuzzy inference system output to the flowline network
# create empty dictionary to hold input table field values
tblDict = {}
# add values to dictionary
with arcpy.da.SearchCursor(ovc_table, ['ReachID', out_field]) as cursor:
for row in cursor:
tblDict[row[0]] = row[1]
# populate flowline network out field
arcpy.AddField_management(in_network, out_field, 'DOUBLE')
with arcpy.da.UpdateCursor(in_network, ['ReachID', out_field]) as cursor:
for row in cursor:
try:
aKey = row[0]
row[1] = tblDict[aKey]
cursor.updateRow(row)
except:
pass
tblDict.clear()
# calculate defuzzified centroid value for density 'none' MF group
# this will be used to re-classify output values that fall in this group
# important: will need to update the array (x) and MF values (mfx) if the
# density 'none' values are changed in the model
x = np.arange(0, 45, 0.01)
mfx = fuzz.trimf(x, [0, 0, 0.1])
defuzz_centroid = round(fuzz.defuzz(x, mfx, 'centroid'), 6)
# update vegetation capacity (ovc_*) values in stream network
# set ovc_* to 0 if output falls fully in 'none' category
with arcpy.da.UpdateCursor(in_network, [out_field]) as cursor:
for row in cursor:
if round(row[0], 6) == defuzz_centroid:
row[0] = 0.0
cursor.updateRow(row)
# delete temporary tables and arrays
arcpy.Delete_management(out_table)
arcpy.Delete_management(ovc_table)
items = [columns, out, x, mfx, defuzz_centroid]
for item in items:
del item
import numpy as np import skfuzzy as fuzz # New Antecedent/Consequent objects hold universe variables and membership # functions quality = fuzz.Antecedent(np.arange(0, 11, 1), 'quality') service = fuzz.Antecedent(np.arange(0, 11, 1), 'service') tip = fuzz.Consequent(np.arange(0, 26, 1), 'tip') # Auto-membership function population is possible with .automf(3, 5, or 7) quality.automf(3) service.automf(3) # Custom membership functions can be built interactively with a familiar, # Pythonic API tip['low'] = fuzz.trimf(tip.universe, [0, 0, 13]) tip['medium'] = fuzz.trimf(tip.universe, [0, 13, 25]) tip['high'] = fuzz.trimf(tip.universe, [13, 25, 25]) """ To help understand what the membership looks like, use the ``view`` methods. """ # You can see how these look with .view() quality['average'].view() """ .. image:: PLOT2RST.current_figure """ service.view() """ .. image:: PLOT2RST.current_figure
def fuzzy_system(pv, ppv, ld):
prev_req = ctrl.Antecedent(np.arange(0, 1.001, 0.001), 'prev_requests')
prevprev_req = ctrl.Antecedent(np.arange(0, 1.001, 0.001),
'prevprev_requests')
load = ctrl.Antecedent(np.arange(0, 1.001, 0.001), 'load')
crash = ctrl.Consequent(np.arange(0, 1.001, 0.001), 'crash')
prev_req['poor'] = fuzz.trimf(prev_req.universe, [0, 0, 0.4])
prev_req['average'] = fuzz.trimf(prev_req.universe, [0, 0.4, 0.6])
prev_req['good'] = fuzz.trimf(prev_req.universe, [0.6, 1, 1])
prevprev_req['poor'] = fuzz.trimf(prevprev_req.universe, [0, 0, 0.4])
prevprev_req['average'] = fuzz.trimf(prevprev_req.universe, [0, 0.4, 0.6])
prevprev_req['good'] = fuzz.trimf(prevprev_req.universe, [0.6, 1, 1])
load['poor'] = fuzz.trimf(load.universe, [0, 0, 0.4])
load['average'] = fuzz.trimf(load.universe, [0, 0.4, 0.6])
load['good'] = fuzz.trimf(load.universe, [0.6, 1, 1])
##########################################################
# Membership Definition #
##########################################################
prev_req.automf(3)
prevprev_req.automf(3)
load.automf(3)
crash['NO'] = fuzz.trimf(crash.universe, [0, 0, 0.4])
crash['Crash'] = fuzz.trimf(crash.universe, [0.4, 1, 1])
#prev_req.view()
#prevprev_req.view()
#load.view()
#crash.view()
#plt.show()
##########################################################
# Rules Definition #
##########################################################
rule1 = ctrl.Rule(prev_req['poor'] & prevprev_req['poor'] & load['poor'],
crash['NO'])
rule2 = ctrl.Rule(
prev_req['poor'] & prevprev_req['average'] & load['poor'], crash['NO'])
rule3 = ctrl.Rule(prev_req['poor'] & prevprev_req['good'] & load['poor'],
crash['NO'])
rule4 = ctrl.Rule(
prev_req['average'] & prevprev_req['poor'] & load['poor'], crash['NO'])
rule5 = ctrl.Rule(
prev_req['average'] & prevprev_req['average'] & load['poor'],
crash['NO'])
rule6 = ctrl.Rule(
prev_req['average'] & prevprev_req['good'] & load['poor'], crash['NO'])
rule7 = ctrl.Rule(prev_req['good'] & prevprev_req['poor'] & load['poor'],
crash['NO'])
rule8 = ctrl.Rule(
prev_req['good'] & prevprev_req['average'] & load['poor'], crash['NO'])
rule9 = ctrl.Rule(prev_req['good'] & prevprev_req['good'] & load['poor'],
crash['NO'])
rule10 = ctrl.Rule(
prev_req['poor'] & prevprev_req['poor'] & load['average'], crash['NO'])
rule11 = ctrl.Rule(
prev_req['poor'] & prevprev_req['average'] & load['average'],
crash['NO'])
rule12 = ctrl.Rule(
prev_req['poor'] & prevprev_req['good'] & load['average'], crash['NO'])
rule13 = ctrl.Rule(
prev_req['average'] & prevprev_req['poor'] & load['average'],
crash['NO'])
rule14 = ctrl.Rule(
prev_req['average'] & prevprev_req['average'] & load['average'],
crash['NO'])
rule15 = ctrl.Rule(
prev_req['average'] & prevprev_req['good'] & load['average'],
crash['NO'])
rule16 = ctrl.Rule(
prev_req['good'] & prevprev_req['poor'] & load['average'], crash['NO'])
rule17 = ctrl.Rule(
prev_req['good'] & prevprev_req['average'] & load['average'],
crash['NO'])
rule18 = ctrl.Rule(
prev_req['good'] & prevprev_req['good'] & load['average'], crash['NO'])
rule19 = ctrl.Rule(prev_req['poor'] & prevprev_req['poor'] & load['good'],
crash['NO'])
rule20 = ctrl.Rule(
prev_req['poor'] & prevprev_req['average'] & load['good'], crash['NO'])
rule21 = ctrl.Rule(prev_req['poor'] & prevprev_req['good'] & load['good'],
crash['NO']) #maybe
rule22 = ctrl.Rule(
prev_req['average'] & prevprev_req['poor'] & load['good'], crash['NO'])
rule23 = ctrl.Rule(
prev_req['average'] & prevprev_req['average'] & load['good'],
crash['Crash'])
rule24 = ctrl.Rule(
prev_req['average'] & prevprev_req['good'] & load['good'],
crash['Crash'])
rule25 = ctrl.Rule(prev_req['good'] & prevprev_req['poor'] & load['good'],
crash['Crash'])
rule26 = ctrl.Rule(
prev_req['good'] & prevprev_req['average'] & load['good'],
crash['Crash'])
rule27 = ctrl.Rule(prev_req['good'] & prevprev_req['good'] & load['good'],
crash['Crash'])
input_control = ctrl.ControlSystem([
rule1, rule2, rule3, rule4, rule5, rule6, rule7, rule8, rule9, rule10,
rule11, rule12, rule13, rule14, rule15, rule16, rule17, rule18, rule19,
rule20, rule21, rule22, rule23, rule24, rule25, rule26, rule27
])
crsh = ctrl.ControlSystemSimulation(input_control)
##########################################################
# Inputs Definition #
##########################################################
crsh.input['prev_requests'] = pv
crsh.input['prevprev_requests'] = ppv
crsh.input['load'] = ld
print("inputs", pv, ppv, ld)
crsh.compute()
result = crsh.output['crash']
print(result)
#crash.view(sim=crsh)
#plt.show()
return result
