def main():
# -----------------------------------------
input_funcs, output_funcs, rules = get_data()
fuzzy = frbs.FRBS(input_funcs, output_funcs, 0.001)
lb = 0.0
rb = 10.0
step_size = 0.1
X = []
Y_fuzzy = []
Y_origin = []
for i in np.arange(lb, rb, step_size):
print("============ ", i)
input_x = {
"input_1": i,
"input_2": 10,
}
fuzz = fuzzy.fuzzification(input_x, fuzzy.input_func_set)
evaled_rules = fuzzy.rules_eval(fuzz, rules, "max_prod_comp")
crisp = fuzzy.defuzzification(evaled_rules, "output_1")
X.append(i)
Y_fuzzy.append(crisp)
plt.plot(X, Y_fuzzy)
plt.show()
def debug():
# -----------------------------------------
# this is an alternative to segment the domain in order to get data from all coners evenly
#input_1, input_2 = get_input_data_segmented(input_1_size, input_2_size, segment_num_1, segment_num_2)
# this is suit for supercomputer to getnerate huge data to cover all coners
#input_1, input_2 = get_input_data(input_1_size, input_2_size)
input_funcs, output_funcs, rules = get_mf_rule()
fuzzy = frbs.FRBS(input_funcs, output_funcs, 0.05)
water_fr = 250
water_lvl = 150
power = []
x = {
"water_fr": water_fr,
"water_lvl": water_lvl,
}
fuzz = fuzzy.fuzzification(x, fuzzy.input_func_set)
evaled_rules = fuzzy.rules_eval(fuzz, rules)
print(evaled_rules)
crisp = fuzzy.defuzzification(evaled_rules, "power")
print(x)
print(crisp)
def main():
#######################################################
###################### pre-setup ######################
#######################################################
# data storing setup
water_fr = []
water_lvl = []
power = []
# input generating setup
num_pts_1, num_pts_2 = 5, 5
segment_num_1, segment_num_2 = 10, 10
counter = 1
total = num_pts_1 * num_pts_2 * segment_num_1 * segment_num_2
#######################################################
# get input data
input_1, input_2 = get_input_data_segmented(
0, 4000,
0, 160,
num_pts_1, num_pts_2,
segment_num_1, segment_num_2)
# get membership function & rules
input_funcs, output_funcs, rules = get_mf_rule()
# get FRBS initialized
_frbs = frbs.FRBS(input_funcs, output_funcs, 0.05)
# iterate 2-D input data
for m in input_1:
for n in input_2:
# pack input data into dictionary datatype
x = {
"water_fr": m,
"water_lvl": n,
}
# fuzzification
fuzzied = _frbs.fuzzification(x, _frbs.input_func_set)
# get mu
evaled_rules = _frbs.rules_eval(fuzzied, rules)
# defuzzification
crisp = _frbs.defuzzification(evaled_rules, "power")
# store input & output data
water_fr.append(m)
water_lvl.append(n)
power.append(crisp)
# terminal output
print("Total run: ", total, " -----------> Current @ ", counter)
# store data into csv for backup
updateReport("/report.csv", [str(m), str(n), str(crisp)])
counter += 1
updateReport("/output_dim.csv", [str(len(input_1)), str(len(input_2))])
plot_3d(water_fr, water_lvl, power, len(input_1), len(input_2))
def debug(inp, out):
# -----------------------------------------
input_funcs, output_funcs, rules = get_data()
fuzzy = frbs.FRBS(input_funcs, output_funcs, 0.001)
input_1 = 0.99
fuzz = fuzzy.fuzzification(input_1, fuzzy.input_func_set[inp])
evaled_rules = fuzzy.rules_eval(fuzz, rules)
yH = fuzzy.defuzzification(fuzz, evaled_rules, out)
yT = math.pow(input_1, 0.45)
print(input_1)
print(yH)
print(yT)
print("------------")
print(evaled_rules)
def debug():
# -----------------------------------------
input_funcs, output_funcs, rules = get_data()
fuzzy = frbs.FRBS(input_funcs, output_funcs, 0.001)
input_x = {
"input_1": 6,
"input_2": 10,
}
fuzz = fuzzy.fuzzification(input_x, fuzzy.input_func_set)
evaled_rules = fuzzy.rules_eval(fuzz, rules, "max_prod_comp")
yH = fuzzy.defuzzification(evaled_rules, "output_1")
print("input_1 = ", input_x["input_1"], ", input_2 = ", input_x["input_2"])
print("output = ", yH)
def debug():
# -----------------------------------------
input_funcs, output_funcs, rules = get_hint_data()
fuzzy = frbs.FRBS(input_funcs, output_funcs, 0.001)
x = -0.1366
input_x = {
"input_1" : x
}
fuzz = fuzzy.fuzzification(input_x, fuzzy.input_func_set)
evaled_rules = fuzzy.rules_eval(fuzz, rules)
crisp = fuzzy.defuzzification(evaled_rules, "output_1")
print(x)
print(fuzz)
print(evaled_rules)
print(crisp)
def main():
# -----------------------------------------
"""
get_hint_data() using hw given MF and rules
get_data() using updated MF and rules
"""
input_funcs, output_funcs, rules = get_data()
#input_funcs, output_funcs, rules = get_hint_data()
# -----------------------------------------
lb = 0.0
rb = 1.0
step_size = 0.1
X = []
Y_fuzzy = []
Y_origin = []
fuzzy = frbs.FRBS(input_funcs, output_funcs, 0.001)
for i in np.arange(lb, rb + 0.2, step_size):
if i > rb:
break
print("-------------------- ", i)
input_x = {"input_1": i}
fuzz = fuzzy.fuzzification(input_x, fuzzy.input_func_set)
evaled_rules = fuzzy.rules_eval(fuzz, rules)
crisp = fuzzy.defuzzification(evaled_rules, "output_1")
X.append(i)
Y_fuzzy.append(crisp)
Y_origin.append(math.pow(i, 0.45))
plt.plot(X, Y_fuzzy, X, Y_origin)
plt.show()
avg_performance = performance(X, Y_fuzzy, Y_origin)
print(avg_performance)