def tri_pi_tri(var, term: str, values):
var[term] = fuzzy_or(
var.universe, trimf(var.universe,
[values[0], values[1], values[1]]),
var.universe,
pimf(var.universe, values[1], values[1], values[2], values[2]))[1]
var[term] = fuzzy_or(
var.universe, var[term].mf, var.universe,
trimf(var.universe, [values[2], values[2], values[3]]))[1]
return var
def init_fls_common_part(
): #TO DO: put this parameter as class or in another module
global object_distance, object_direction
# New Antecedent objects
object_distance = ctrl.Antecedent(range_meter, 'distance')
object_direction = ctrl.Antecedent(range_degree, 'direction')
# Membership functions
# 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])
# 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")
def init_risk_assessment():
range_type = np.arange(0, 2 + 1, 1)
# New Antecedent/Consequent objects
object_type = ctrl.Antecedent(range_type, 'type')
object_speed = ctrl.Antecedent(range_meter_per_second, 'speed')
object_orientation = ctrl.Antecedent(range_degree, 'orientation')
object_risk = ctrl.Consequent(range_risk, 'risk')
# 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])
# 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])
# 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])
# 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])
fls_name = "/rules/ra_full.data"
fls_data_path = package_path + fls_name
print(fls_data_path)
#if os.path.exists(fls_name):
if os.path.exists(fls_data_path):
print("FLS exists!")
#f = open(fls_name,'rb')
f = open(fls_data_path, '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')
f = open(fls_data_path, 'wb')
pickle.dump(ra_fls, f)
f.close
global risk_assessment_instance
risk_assessment_instance = ctrl.ControlSystemSimulation(ra_fls)
def aggregate(self, MFs):
o1 = MFs.pop()
if self.aggregator == 'MAX':
while len(MFs) > 0:
o2 = MFs.pop()
o1[0], o1[1] = fuzz.fuzzy_or(o1[0], o1[1], o2[0], o2[1])
return o1
def aggregate(self, MFs):
o1 = MFs.pop()
if self.aggregator == 'MAX':
while len(MFs) > 0:
o2 = MFs.pop()
o1[0],o1[1] = fuzz.fuzzy_or( o1[0],o1[1],o2[0],o2[1] )
return o1
def get_consequent_membership_function(consequent: ctrl.Consequent,
controller):
terms = list(consequent.terms.keys())
universe = consequent.universe
_, result, _ = CrispValueCalculator(consequent[terms[0]], controller)
for i in range(1, len(terms)):
term = terms[i]
_, temp, _ = CrispValueCalculator(consequent[term], controller)
_, result = fuzzy_or(universe, result, universe, temp)
return MfMapping(universe,
result), CrispValueCalculator(consequent, controller)
def init_RA():
object_distance = ctrl.Antecedent(range_meter, 'distance') # 0- 3 meter
object_direction = ctrl.Antecedent(range_degree,
'direction') # -180~180 degree
object_risk = ctrl.Antecedent(range_risk, 'risk')
#global left_speed,right_speed # When Consequent need visualization
left_speed = ctrl.Consequent(range_meter_per_second, 'left')
right_speed = ctrl.Consequent(range_meter_per_second, 'right')
# Custom membership functions can be built interactively with a familiar Pythonic API
distance_p1 = fuzz.gaussmf(range_meter, IZW, 0.1)
distance_p2 = fuzz.gaussmf(range_meter, IZW, 0.1)
# Distance
object_distance['Near'] = fuzz.gaussmf(range_meter, 0.5 * IZW, 0.1) #0.2
object_distance['Medium'] = fuzz.gaussmf(range_meter, IZW, 0.1) #0.4
object_distance['Far'] = fuzz.gaussmf(range_meter, 2.0 * IZW, 0.2) #0.8
# Direction
object_direction['Front'] = fuzz.gaussmf(range_degree, 0, 15)
object_direction['FrontLeft'] = fuzz.gaussmf(range_degree, 45, 15)
object_direction['Left'] = fuzz.gaussmf(range_degree, 90, 15)
object_direction['FrontRight'] = fuzz.gaussmf(range_degree, -45, 15)
object_direction['Right'] = fuzz.gaussmf(range_degree, -90, 15)
rear_d_p1 = fuzz.gaussmf(range_degree, 180, 60)
rear_d_p2 = fuzz.gaussmf(range_degree, -180, 60)
null, object_direction['BigRear'] = fuzz.fuzzy_or(range_degree, rear_d_p1,
range_degree, rear_d_p2)
# Risk
object_risk['VeryLow'] = fuzz.gaussmf(range_risk, 0, 0.3)
object_risk['Low'] = fuzz.gaussmf(range_risk, 1, 0.3)
object_risk['Medium'] = fuzz.gaussmf(range_risk, 2, 0.3)
object_risk['High'] = fuzz.gaussmf(range_risk, 3, 0.3)
object_risk['VeryHigh'] = fuzz.gaussmf(range_risk, 4, 0.3)
# Left Speed
left_speed['Stop'] = fuzz.gaussmf(range_meter_per_second, 0.0, 0.1)
left_speed['Slow'] = fuzz.gaussmf(range_meter_per_second, 0.2, 0.2)
left_speed['Medium'] = fuzz.gaussmf(range_meter_per_second, 0.8, 0.2)
left_speed['Fast'] = fuzz.gaussmf(range_meter_per_second, 1.2, 0.2)
# Right Speed
right_speed['Stop'] = fuzz.gaussmf(range_meter_per_second, 0.0, 0.1)
right_speed['Slow'] = fuzz.gaussmf(range_meter_per_second, 0.2, 0.2)
right_speed['Medium'] = fuzz.gaussmf(range_meter_per_second, 0.8, 0.2)
right_speed['Fast'] = fuzz.gaussmf(range_meter_per_second, 1.2, 0.2)
from mitigation_rules import rule_list_generator
rule_list = rule_list_generator(object_distance, object_direction,
object_risk, left_speed, right_speed)
global risk_mitigation_instance # We don't need to change the FLS
risk_mitigation_fls = ctrl.ControlSystem(rule_list)
risk_mitigation_instance = ctrl.ControlSystemSimulation(
risk_mitigation_fls)
def aggregate(self, MFs):
"""
Peform aggregation on the given outputs of the rules and returns aggregated MF
------INPUTS------
MFs : list
list of MF functions
------OUTPUTS------
o1 : list
aggregation of all MFs
"""
o1 = MFs.pop()
if self.aggregator == 'MAX':
while len(MFs) > 0:
o2 = MFs.pop()
o1[0],o1[1] = fuzz.fuzzy_or( o1[0],o1[1],o2[0],o2[1] )
else:
raise StandardError('Havent coded this yet!')
return o1
step = 0.5 x = np.arange(start, stop + delta, step) # Triangular membership function x1 = np.arange(0, 5 + delta, step) trimf = fuzz.trimf(x1, [0, 2.5, 5]) # Trapezoidal membership function x2 = np.arange(4, 10 + delta, step) trapmf = fuzz.trapmf(x2, [4, 6, 8, 10]) # fuzzy logic tri_not = fuzz.fuzzy_not(trimf) trap_not = fuzz.fuzzy_not(trapmf) x3, tri_trap_and = fuzz.fuzzy_and(x1, trimf, x2, trapmf) x3, tri_trap_or = fuzz.fuzzy_or(x1, trimf, x2, trapmf) # Defuzzify centroid_x = fuzz.defuzz(x3, tri_trap_or, "centroid") centroid_y = fuzz.interp_membership(x3, tri_trap_or, centroid_x) bisector_x = fuzz.defuzz(x3, tri_trap_or, "bisector") bisector_y = fuzz.interp_membership(x3, tri_trap_or, bisector_x) mom_x = fuzz.defuzz(x3, tri_trap_or, "mom") mom_y = fuzz.interp_membership(x3, tri_trap_or, mom_x) som_x = fuzz.defuzz(x3, tri_trap_or, "som") som_y = fuzz.interp_membership(x3, tri_trap_or, som_x) lom_x = fuzz.defuzz(x3, tri_trap_or, "lom") lom_y = fuzz.interp_membership(x3, tri_trap_or, lom_x) # Whole config fig_scale = 1.5
# Direction -180~180
rear_d_p2 = fuzz.trapmf(
range_degree, [-180, -180, -135, -90]) #fuzz.gaussmf(range_degree,-180,60)
object_direction['Right'] = fuzz.trimf(
range_degree, [-135, -90, -45]) #fuzz.gaussmf(range_degree,-90,15)
object_direction['FrontRight'] = fuzz.trimf(
range_degree, [-90, -45, 0]) #fuzz.gaussmf(range_degree,-45,15)
object_direction['Front'] = fuzz.trimf(
range_degree, [-45, 0, 45]) #fuzz.gaussmf(range_degree,0,15)
object_direction['FrontLeft'] = fuzz.trimf(
range_degree, [0, 45, 90]) #fuzz.gaussmf(range_degree,45,15)
object_direction['Left'] = fuzz.trimf(
range_degree, [45, 90, 135]) #fuzz.gaussmf(range_degree,90,15)
rear_d_p1 = fuzz.trapmf(
range_degree, [90, 135, 180, 180]) #fuzz.gaussmf(range_degree,180,60)
null, object_direction['BigRear'] = fuzz.fuzzy_or(
range_degree, rear_d_p1, range_degree, rear_d_p2) # Combine the two parts
#object_direction.view()
# Risk
object_risk['VeryLow'] = fuzz.trimf(range_risk,
[0, 0, 1]) #fuzz.gaussmf(range_risk,0,0.3)
object_risk['Low'] = fuzz.trimf(range_risk,
[0, 1, 2]) #fuzz.gaussmf(range_risk,1,0.3)
object_risk['Medium'] = fuzz.trimf(range_risk,
[1, 2, 3]) #fuzz.gaussmf(range_risk,2,0.3)
object_risk['High'] = fuzz.trimf(range_risk,
[2, 3, 4]) #fuzz.gaussmf(range_risk,3,0.3)
object_risk['VeryHigh'] = fuzz.trimf(
range_risk, [3, 4, 4]) #fuzz.gaussmf(range_risk,4,0.3)
#object_risk.view()
from curve_mf import kinked_curve_mf
from skfuzzy import trapmf
from skfuzzy import smf as s_shape_mf
from skfuzzy import zmf as z_shape_mf
font = {'family': 'DejaVu Sans', 'weight': 'normal', 'size': 7}
plt.rc('font', **font)
universe = np.arange(0, 13)
mfA = kinked_curve_mf(universe, [(2, 0), (8, 0.5), (9, 0.25), (14, 0)])
mfB = kinked_curve_mf(universe, [(0, 1), (5, 0)])
mfC = kinked_curve_mf(universe, [(0, 0), (3, 1), (5, 0)])
B_and_C, mf_B_and_C = fuzz.fuzzy_or(universe, mfA, universe, mfB)
A_or_B_and_C, mf_A_or_B_and_C = fuzz.fuzzy_and(B_and_C, mf_B_and_C, universe,
mfC)
fig = plt.figure(figsize=(10, 8))
grid = gridspec.GridSpec(nrows=2, ncols=6)
axA = fig.add_subplot(grid[0, :2],
xlim=(0, max(universe)),
ylim=(0, 1),
title='A')
axB = fig.add_subplot(grid[0, 2:4], sharex=axA, sharey=axA, title='B')
axC = fig.add_subplot(grid[0, 4:], sharex=axA, sharey=axA, title='C')
axBC = fig.add_subplot(grid[1, :3], sharex=axA, sharey=axA, title='B^C')
axD = fig.add_subplot(grid[1, 3:], sharex=axA, sharey=axA, title='AvB^C')
plt.figure()
plt.plot(x, bajo, 'b', linewidth=1.5, label='Bajo')
plt.plot(x, medio, 'r', linewidth=1.5, label='Medio')
# Ajustes gráfico
plt.title('Función Unión (máximo)')
plt.ylabel('Membresía')
plt.xlabel('Velocidad (Kilometros por hora)')
plt.legend(loc='best', fancybox=True, shadow=True)
for i in range(0, 11):
plt.axvline(x=i, ymin=0, ymax=10, color='g', linestyle='-.')
plt.plot(0, 1, marker='o', markersize=10, color='g')
plt.plot(1, 0.8, marker='o', markersize=10, color='g')
plt.plot(2, 0.6, marker='o', markersize=10, color='g')
plt.plot(3, 0.6, marker='o', markersize=10, color='g')
plt.plot(4, 0.8, marker='o', markersize=10, color='g')
plt.plot(5, 1, marker='o', markersize=10, color='g')
plt.plot(6, 0.8, marker='o', markersize=10, color='g')
plt.plot(7, 0.6, marker='o', markersize=10, color='g')
plt.plot(8, 0.4, marker='o', markersize=10, color='g')
plt.plot(9, 0.2, marker='o', markersize=10, color='g')
plt.plot(10, 0, marker='o', markersize=10, color='g')
plt.show()
# Encontrando el máximo (Fuzzy OR)
print(sk.fuzzy_or(x, bajo, x, medio))
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)
plt.axvline(x)
i = 0
while i <= range(10):
plt.axvline(i, ymin=0, ymax=10, color='g', licestyle='-.')
plt.plot(0, 1, marker='o', markersize=10, color='g')
plt.plot(1, 0.8, marker='o', markersize=10, color='g')
plt.plot(2, 0.6, marker='o', markersize=10, color='g')
plt.plot(3, 0.6, marker='o', markersize=10, color='g')
plt.plot(4, 0.8, marker='o', markersize=10, color='g')
plt.plot(5, 1, marker='o', markersize=10, color='g')
plt.plot(6, 0.8, marker='o', markersize=10, color='g')
plt.plot(7, 0.6, marker='o', markersize=10, color='g')
plt.plot(8, 0.4, marker='o', markersize=10, color='g')
plt.plot(9, 0.2, marker='o', markersize=10, color='g')
plt.plot(10, 0, marker='o', markersize=10, color='g')
plt.show()
sk.fuzzy_or(x, bajo, x, medio)
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 feedforward(self, inputs):
"""
------INPUTS------
inputs : dict
the set of outputs from previous nodes in form
{nodeName: value, nodeName: value, ...} (or inputs for input
nodes)
"""
#INPUT FEED FORWARD (fuzzify inputs)
for inp in self.layer1:
try:
if inp in inputs: #check if input is given
if not isinstance(inputs[inp], list):
MF = fuzzOps.paramsToMF([inputs[inp]]) #get fuzzy MF for singleton
self.layer1[inp] = MF
else:
self.layer1[inp] = inputs[inp]
else:
print "Not all inputs given!!!"
self.layer3[self.layer3.keys()[0]] = None #set system output to None
except:
raise StandardError("NEFPROX input error!")
#RULE FEED FORWARD (gets min (t-norm) firing strength of weights/MFterms and inputs)
for rule in self.layer2: #for each rule
for inp in self.connect1to2[rule]: #for each input in antecedent
fs = max(fuzz.fuzzy_and(self.inputMFs[inp][0], self.inputMFs[inp][1],
self.layer1[inp[0]][0], self.layer1[inp[0]][1])[1])
self.connect1to2[rule][inp] = fs
self.layer2[rule] = min([self.connect1to2[rule][inp] for inp in self.connect1to2[rule]])
#OUTPUT FEED FORWARD (apply minimum of firing strength and output MF (reduce output MF), then aggregate)
outMFs = []
for rule in self.connect2to3: #for each rule
cons = self.connect2to3[rule].keys()[0] #get consequent for single output
if self.layer2[rule] > 0.0: #only for active rules (save time)
outMF = copy.deepcopy(self.outputMFs[cons][:2])
outMF[1] = np.asarray([ min(self.layer2[rule], outMF[1][i]) for i in range(len(outMF[1])) ])
#apply minimum of firing strength and output MF (reduce output MF)
self.connect2to3[rule][cons] = outMF
outMFs.append(outMF)
else: #for inactive rules, applied MF is 0.0 for all
self.connect2to3[rule][cons] = [np.asarray([0.0, 0.0]), np.asarray([0.0,0.0])]
#once all rules are reduced with MFs aggregate
if len(outMFs) > 0: #check for no rules fired
while len(outMFs) > 1: #get maximum (union) of all MFs (aggregation)
outMFs0 = outMFs.pop(0)
outMFs[0][0], outMFs[0][1] = fuzz.fuzzy_or(outMFs0[0], outMFs0[1],
outMFs[0][0], outMFs[0][1])
if self.defuzz == None: pass
elif self.defuzz == 'centroid': outMFs[0] = fuzz.defuzz(outMFs[0][0],outMFs[0][1],'centroid')
elif self.defuzz == 'bisector': outMFs[0] = fuzz.defuzz(outMFs[0][0],outMFs[0][1],'bisector')
elif self.defuzz == 'mom': outMFs[0] = fuzz.defuzz(outMFs[0][0],outMFs[0][1],'mom') #mean of maximum
elif self.defuzz == 'som': outMFs[0] = fuzz.defuzz(outMFs[0][0],outMFs[0][1],'som') #min of maximum
elif self.defuzz == 'lom': outMFs[0] = fuzz.defuzz(outMFs[0][0],outMFs[0][1],'lom') #max of maximum
self.layer3[cons[0]] = outMFs[0]
else:#if no rules fire, then output is None
if self.defuzz == None:
self.layer3[cons[0]] = [[0.0,0.0],[0.0,0.0]] #result 0.0 in fuzzy MF form
else:
self.layer3[cons[0]] = 0.0 #result 0.0 as crisp if some defuzz method specified
return True
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
X = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
abc1 = [0, 25, 50]
abc2 = [25, 50, 75]
young = fuzz.membership.trimf(X, abc1)
middle_aged = fuzz.membership.trimf(X, abc2)
# Compute the different operations using inbuilt functions.
one = np.ones(75)
zero = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
union = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
intersection = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
complement_a = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
difference = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
alg_sum = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
alg_product = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
bdd_sum = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
bdd_difference = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# Speed
object_speed['Slow'] = fuzz.gaussmf(range_meter_per_second, 0.5, 0.2)
object_speed['Medium'] = fuzz.gaussmf(range_meter_per_second, 1.0, 0.2)
object_speed['Fast'] = fuzz.gaussmf(range_meter_per_second, 1.5, 0.2)
#object_speed.view()
# Direction
object_direction['Front'] = fuzz.gaussmf(range_degree, 0, 15)
object_direction['FrontLeft'] = fuzz.gaussmf(range_degree, 45, 15)
object_direction['Left'] = fuzz.gaussmf(range_degree, 90, 15)
object_direction['FrontRight'] = fuzz.gaussmf(range_degree, -45, 15)
object_direction['Right'] = fuzz.gaussmf(range_degree, -90, 15)
rear_d_p1 = fuzz.gaussmf(range_degree, 180, 60)
rear_d_p2 = fuzz.gaussmf(range_degree, -180, 60)
null, object_direction['BigRear'] = fuzz.fuzzy_or(range_degree, rear_d_p1,
range_degree, rear_d_p2)
#object_direction.view()
# Orientation
object_orientation['Front'] = fuzz.gaussmf(range_degree, 0, 15)
object_orientation['FrontLeft'] = fuzz.gaussmf(range_degree, 45, 15)
object_orientation['Left'] = fuzz.gaussmf(range_degree, 90, 15)
object_orientation['RearLeft'] = fuzz.gaussmf(range_degree, 135, 15)
rear_p1 = fuzz.gaussmf(range_degree, 180, 15)
rear_p2 = fuzz.gaussmf(range_degree, -180, 15)
null, object_orientation['Rear'] = fuzz.fuzzy_or(range_degree, rear_p1,
range_degree, rear_p2)
object_orientation['RearRight'] = fuzz.gaussmf(range_degree, -135, 15)
object_orientation['Right'] = fuzz.gaussmf(range_degree, -90, 15)
object_orientation['FrontRight'] = fuzz.gaussmf(range_degree, -45, 15)
#object_orientation.view()
