def test_abs():
x = phuzzy.Trapezoid(alpha0=[-3, 5],
alpha1=[1, 2],
name="x",
number_of_alpha_levels=3)
y = abs(x)
print(y.df.values.tolist())
assert np.allclose(y.df.values.tolist(),
[[0.0, 0.0, 5.0], [0.5, 0.0, 3.5], [1.0, 1.0, 2.0]])
x = phuzzy.Trapezoid(alpha0=[1, 5],
alpha1=[2, 3],
name="x",
number_of_alpha_levels=3)
y = abs(x)
print(y.df.values.tolist())
assert np.allclose(y.df.values.tolist(),
[[0.0, 1.0, 5.0], [0.5, 1.5, 4.0], [1.0, 2.0, 3.0]])
x = phuzzy.Trapezoid(alpha0=[-5, -1],
alpha1=[-3, -2],
name="x",
number_of_alpha_levels=3)
y = abs(x)
print(y.df.values.tolist())
assert np.allclose(y.df.values.tolist(),
[[0.0, 1.0, 5.0], [0.5, 1.5, 4.0], [1.0, 2.0, 3.0]])
y = x.abs()
print(y.df.values.tolist())
assert np.allclose(y.df.values.tolist(),
[[0.0, 1.0, 5.0], [0.5, 1.5, 4.0], [1.0, 2.0, 3.0]])
def test_mul():
t = phuzzy.TruncNorm(alpha0=[1, 3],
alpha1=[2],
number_of_alpha_levels=3,
name="t")
print(t)
print(t.df.values.tolist())
assert len(t.df) == 3
p = phuzzy.Trapezoid(alpha0=[1, 4],
alpha1=[2, 3],
number_of_alpha_levels=3,
name="p")
print(p)
assert len(p.df) == 3
a = t * p
a.name = "t*p"
print(a)
print(a.df.values.tolist())
assert np.allclose(
a.df.values.tolist(),
[[0.0, 1.0, 12.0], [0.5, 2.4112937437280677, 8.373647931301177],
[1.0, 4.0, 6.0]])
b = t * 1.
print(b)
print(b.df)
print(b.df.values.tolist())
assert np.allclose(
b.df.values.tolist(),
[[0.0, 1.0, 3.0], [0.5, 1.6075291624853785, 2.392470837514622],
[1.0, 2.0, 2.0]])
b = t * 2.
print(b)
print(b.df)
print(b.df.values.tolist())
assert np.allclose(
b.df.values.tolist(),
[[0.0, 2.0, 6.0], [0.5, 3.215058324970757, 4.784941675029244],
[1.0, 4.0, 4.0]])
b = 2. * t
print(b)
print(b.df)
print(b.df.values.tolist())
assert np.allclose(
b.df.values.tolist(),
[[0.0, 2.0, 6.0], [0.5, 3.215058324970757, 4.784941675029244],
[1.0, 4.0, 4.0]])
p = phuzzy.Trapezoid(alpha0=[-1, 4],
alpha1=[2, 3],
number_of_alpha_levels=3,
name="p")
z = p * p
print(z.df)
def test_poor_mens_alpha_optimization_pow_fuzzy():
p = phuzzy.Trapezoid(alpha0=[-2, 2], alpha1=[-1, 1])
e = phuzzy.Trapezoid(alpha0=[2, 2], alpha1=[2, 2])
y = p**e
print(y.df)
# mix_mpl(y)
# y.plot(show=True)
assert np.isclose(y.min(), 0)
assert np.isclose(y.max(), 4)
assert np.isclose(y.df.iloc[-1][["l", "r"]].max(), 1)
def test_gt():
x = phuzzy.Trapezoid(alpha0=[1, 5],
alpha1=[2, 3],
name="x",
number_of_alpha_levels=3)
assert x > -1
assert not x > 2
y = phuzzy.Trapezoid(alpha0=[-5, -1],
alpha1=[-3, -2],
name="x",
number_of_alpha_levels=3)
print(y.max(), x.min())
assert x > y
def test_max():
x = phuzzy.Trapezoid(alpha0=[-3, 5],
alpha1=[1, 2],
name="x",
number_of_alpha_levels=3)
print(x.max())
assert np.isclose(x.max(), 5)
y = phuzzy.Trapezoid(alpha0=[-5, -1],
alpha1=[-3, -2],
name="x",
number_of_alpha_levels=3)
print(y.df)
print(y.max())
assert np.isclose(y.max(), -1)
def atest_div():
t = phuzzy.TruncNorm(alpha0=[2, 3], alpha1=[], number_of_alpha_levels=15, name="t")
print(t)
assert len(t.df) == 15
p = phuzzy.Trapezoid(alpha0=[0, 4], alpha1=[2, 3], number_of_alpha_levels=5, name="p")
print(p)
assert len(p.df) == 5
a = t ** p
a = p ** t
a.name = "t**p"
print(a)
print(a.df)
mix_mpl(t)
mix_mpl(p)
mix_mpl(a)
t.plot()
p.plot()
a.plot()
print(a.df)
# a.df.iloc[10, a.df.columns.get_loc("max")] = 8.5
# a.make_convex()
a.name += "!"
print(a.df)
a.plot(show=True)
def test_alo():
v1 = phuzzy.Triangle(alpha0=[0, 4], alpha1=[1], number_of_alpha_levels=5)
v2 = phuzzy.Superellipse(alpha0=[-1, 2.],
alpha1=None,
m=1.0,
n=.5,
number_of_alpha_levels=6)
v3 = phuzzy.TruncGenNorm(alpha0=[1, 4],
alpha1=[2, 3],
number_of_alpha_levels=5,
beta=3.)
v4 = phuzzy.Trapezoid(alpha0=[0, 4],
alpha1=[2, 3],
number_of_alpha_levels=5)
v5 = phuzzy.TruncNorm(alpha0=[1, 3], number_of_alpha_levels=5, name="y")
v6 = phuzzy.Triangle(alpha0=[1, 4], alpha1=[3], number_of_alpha_levels=5)
obj_function = '-1*((x[0] - 1) ** 2 + (x[1] + .1) ** 2 + .1 - (x[2] + 2) ** 2 - (x[3] - 0.1) ** 2 - (x[4] * x[5]) ** 2)'
name = 'Opti_Test'
kwargs = {
'var1': v1,
'var2': v2,
'var3': v3,
'var4': v4,
'var5': v5,
'var6': v6,
'obj_function': obj_function,
'name': name
}
# alo = phuzzy.analysis.alo.Alpha_Level_Optimization(name="test", obj_function=obj_function, vars=[v1, v2, v3, v4, v5, v6])
alo = phuzzy.analysis.alo.Alpha_Level_Optimization(**kwargs)
alo.calculation()
print(alo)
def test_min():
x = phuzzy.Trapezoid(alpha0=[-3, 5],
alpha1=[1, 2],
name="x",
number_of_alpha_levels=3)
print(x.min())
assert np.isclose(x.min(), -3)
def test_poor_mens_alpha_optimization_pow_fuzzy2():
t = phuzzy.TruncNorm(alpha0=[2, 3],
alpha1=[],
number_of_alpha_levels=5,
name="t")
p = phuzzy.Trapezoid(alpha0=[0, 4],
alpha1=[2, 3],
number_of_alpha_levels=5,
name="p")
# a = t ** p
a = p**t
a.name = "t**p"
print(a.df.values.tolist())
print(a)
print(a.df)
assert np.allclose(
a.df.values.tolist(),
[[0.0, 0.0, 64.0], [0.25, 0.125, 52.734375], [0.5, 1.0, 42.875],
[0.75, 2.25, 34.328125], [1.0, 4.0, 27.0]])
# mix_mpl(a)
# a.plot(show=True)
print(a.df.values.tolist())
a = t**p
print(a.df.values.tolist())
# mix_mpl(a)
# a.plot(show=True)
assert np.allclose(a.df.values.tolist(),
[[0.0, 1.0, 81.0], [0.25, 1.0, 59.51536637372663],
[0.5, 1.0, 52.84832630002708],
[0.75, 1.0, 47.58354751779905], [1.0, 1.0, 39.0625]])
def plot_sub():
H = 100. # mm
B = 300. # mm
fig, axs = plt.subplots(1,
3,
dpi=90,
facecolor='w',
edgecolor='k',
figsize=(B / 25.4, H / 25.4))
x = phuzzy.Trapezoid(alpha0=[0, 4],
alpha1=[2, 3],
number_of_alpha_levels=5)
mix_mpl(x)
x.plot(ax=axs[0])
y = phuzzy.TruncNorm(alpha0=[1, 3], number_of_alpha_levels=15, name="y")
mix_mpl(y)
y.plot(ax=axs[1])
z = x - y
z.name = "x-y"
mix_mpl(z)
z.plot(ax=axs[2])
fig.tight_layout()
fig.savefig("x-y.png")
plt.show()
def atest_sub():
t = phuzzy.TruncNorm(alpha0=[1, 3], alpha1=[2], number_of_alpha_levels=15, name="t")
print(t)
assert len(t.df) == 15
p = phuzzy.Trapezoid(alpha0=[1, 4], alpha1=[2, 3], number_of_alpha_levels=5, name="p")
print(p)
assert len(p.df) == 5
a = t - p
a.name = "t-p"
print(a)
print(a.df)
mix_mpl(t)
mix_mpl(p)
mix_mpl(a)
t.plot()
p.plot()
a.plot()
print(a.df)
a.df.iloc[10, a.df.columns.get_loc("r")] = 1.5
a.make_convex()
a.name += "!"
print(a.df)
a.plot(show=True)
def test_defuzzification():
p = phuzzy.Trapezoid(alpha0=[-1, 6],
alpha1=[1, 5],
number_of_alpha_levels=5)**2
print(p)
x = p.defuzzification(method='alpha_one')
print(x)
def test_mean():
x = phuzzy.Trapezoid(alpha0=[-2, 4],
alpha1=[-1, 3],
name="x",
number_of_alpha_levels=3)
print(x.mean())
assert np.isclose(x.mean(), 1)
def test_poor_mens_alpha_optimization_neg_pow_fuzzy():
p = phuzzy.Trapezoid(alpha0=[-2, 2], alpha1=[-1, 1])
e = phuzzy.Trapezoid(alpha0=[0, 0], alpha1=[0, 0])
y = p**e
print(y.df)
# mix_mpl(y)
# y.plot(show=True)
assert np.isclose(y.min(), 1)
assert np.isclose(y.max(), 1)
p = phuzzy.Trapezoid(alpha0=[0, 4],
alpha1=[2, 3],
number_of_alpha_levels=2,
name="p")
print(p)
assert len(p.df) == 2
def test_unify():
x = phuzzy.Uniform(alpha0=[1, 4], number_of_alpha_levels=5)
y = phuzzy.Trapezoid(alpha0=[1, 4],
alpha1=[2, 3],
number_of_alpha_levels=7)
a, b = x._unify(y)
assert len(a.df) == 7
assert len(b.df) == 7
def test_contains():
x = phuzzy.Trapezoid(alpha0=[1, 5],
alpha1=[2, 3],
name="x",
number_of_alpha_levels=3)
assert 2 in x
y = phuzzy.Trapezoid(alpha0=[2, 4],
alpha1=[2, 3],
name="y",
number_of_alpha_levels=3)
z = phuzzy.Trapezoid(alpha0=[2, 7],
alpha1=[2, 3],
name="z",
number_of_alpha_levels=3)
assert y in x
assert not x in y
assert not z in x
def test_trapz():
x = np.linspace(-2, 5, 11)
p = phuzzy.Trapezoid(alpha0=[1, 4],
alpha1=[2, 3],
number_of_alpha_levels=5)
P = p.cdf(x)
print(P)
def atest_add():
t = phuzzy.TruncNorm(alpha0=[1, 3], alpha1=[2], number_of_alpha_levels=15)
print(t)
assert len(t.df) == 15
p = phuzzy.Trapezoid(alpha0=[1, 4], alpha1=[2, 3], number_of_alpha_levels=5)
print(p)
assert len(p.df) == 5
a = t + p
print(a)
print(a.df)
def test_neg():
p = phuzzy.Trapezoid(alpha0=[0, 4],
alpha1=[2, 3],
number_of_alpha_levels=3,
name="p")
print(p)
print(p.df.values.tolist())
assert len(p.df) == 3
b = -p
print(b.df.values.tolist())
assert np.allclose(
b.df.values.tolist(),
[[0.0, -4.0, -1e-10], [0.5, -3.5, -1.00000000005], [1.0, -3.0, -2.0]])
def test_pow_zero():
# p = phuzzy.Trapezoid(alpha0=[-1, 4], alpha1=[2, 3], number_of_alpha_levels=5, name="p")
# y = p ** 0
x = phuzzy.Trapezoid(alpha0=[-1, 4],
alpha1=[.5, .8],
number_of_alpha_levels=5,
name="p")
y = x**0
mix_mpl(y)
y.plot()
print(y.df.values.tolist())
assert np.isclose(y.min(), 1)
assert np.isclose(y.max(), 1)
def test_div():
t = phuzzy.TruncNorm(alpha0=[2, 3],
alpha1=[],
number_of_alpha_levels=3,
name="t")
print(t.df.values.tolist())
print(t)
assert len(t.df) == 3
p = phuzzy.Trapezoid(alpha0=[0, 4],
alpha1=[2, 3],
number_of_alpha_levels=3,
name="p")
print(p)
assert len(p.df) == 3
# a = t / p
a = p / t
a.name = "t/p"
print(a.df.values.tolist())
assert np.allclose(
a.df.values.tolist(),
[[0.0, 3.3333333333333335e-11, 2.0],
[0.5, 0.37088749487272066, 1.519252456825272], [1.0, 0.8, 1.2]])
print(a)
print(a.df)
print("_" * 80)
b = t / 1.
print(b)
print(b.df)
print(b.df.values.tolist())
assert np.allclose(
b.df.values.tolist(),
[[0.0, 2.0, 3.0], [0.5, 2.3037645812426892, 2.6962354187573108],
[1.0, 2.5, 2.5]])
print(a)
print(a.df)
print("_" * 80)
b = t / 2.
print(b)
print(b.df)
print(b.df.values.tolist())
assert np.allclose(
b.df.values.tolist(),
[[0.0, 1.0, 1.5], [0.5, 1.1518822906213446, 1.3481177093786554],
[1.0, 1.25, 1.25]])
def test_import_export():
y = phuzzy.Trapezoid(alpha0=[1, 4],
alpha1=[2, 3],
number_of_alpha_levels=7)
e = y.export_csv()
print(e)
if is_py2:
e = e.decode()
fh = StringIO(e)
fh.seek(0)
z = phuzzy.FuzzyNumber(alpha0=[1, 4],
alpha1=[2, 3],
number_of_alpha_levels=2)
z.import_csv(fh)
assert len(z.df) == 7
def test_trapezoid():
p = phuzzy.Trapezoid(alpha0=[1, 4],
alpha1=[2, 3],
number_of_alpha_levels=5)
print(p)
print("number_of_alpha_levels", p.number_of_alpha_levels)
print(p.df)
print(len(p.df))
assert len(p.df) == 5
print(p.get_01)
p.discretize(alpha0=[1, 4], alpha1=[2, 3], alpha_levels=5)
s = p.to_str()
print(s)
pdf = p.pdf([2])
print(pdf)
cdf = p.cdf([2])
print(cdf)
p.discretize(alpha0=[1, 3], alpha1=[2, 3], alpha_levels=5)
def test_add():
t = phuzzy.TruncNorm(alpha0=[1, 3], alpha1=[2], number_of_alpha_levels=3)
print(t)
assert len(t.df) == 3
p = phuzzy.Trapezoid(alpha0=[1, 4],
alpha1=[2, 3],
number_of_alpha_levels=5)
print(p)
assert len(p.df) == 5
a = t + p
print(a)
print(a.df)
print(a.df.values.tolist())
assert np.allclose(
a.df.values.tolist(),
[[0.0, 2.0, 7.0], [0.25, 2.5537645812426892, 6.446235418757311],
[0.5, 3.1075291624853785, 5.892470837514622],
[0.75, 3.5537645812426892, 5.446235418757311], [1.0, 4.0, 5.0]])
# mix_mpl(t)
# mix_mpl(p)
# mix_mpl(a)
# t.plot()
# p.plot()
# a.plot(show=True)
b = t + 4.
print(b)
print(b.df)
print(b.df.values.tolist())
assert np.allclose(
b.df.values.tolist(),
[[0.0, 5.0, 7.0], [0.5, 5.6075291624853785, 6.392470837514622],
[1.0, 6.0, 6.0]])
b = 4 - t
print(b)
print(b.df)
print(b.df.values.tolist())
assert np.allclose(
b.df.values.tolist(),
[[0.0, -3.0, -1.0], [0.5, -2.3924708375146215, -1.607529162485378],
[1.0, -2.0, -2.0]])
def test_sub():
t = phuzzy.TruncNorm(alpha0=[1, 3],
alpha1=[2],
number_of_alpha_levels=3,
name="t")
print(t)
assert len(t.df) == 3
p = phuzzy.Trapezoid(alpha0=[1, 4],
alpha1=[2, 3],
number_of_alpha_levels=3,
name="p")
print(p)
assert len(p.df) == 3
a = t - p
a.name = "t-p"
print(a.df)
print(a.df.values.tolist())
assert np.allclose(
a.df.values.tolist(),
[[0.0, -3.0, 2.0], [0.5, -1.8924708375146215, 0.892470837514622],
[1.0, -1.0, 0.0]])
b = t - 4.
print(b)
print(b.df)
print(b.df.values.tolist())
assert np.allclose(
b.df.values.tolist(),
[[0.0, -3.0, -1.0], [0.5, -2.3924708375146215, -1.607529162485378],
[1.0, -2.0, -2.0]])
b = 4. - t
print(b)
print(b.df)
print(b.df.values.tolist())
assert np.allclose(
b.df.values.tolist(),
[[0.0, -3.0, -1.0], [0.5, -2.3924708375146215, -1.607529162485378],
[1.0, -2.0, -2.0]])
def test_operation_class():
tra = phuzzy.Trapezoid(alpha0=[1, 4],
alpha1=[2, 3],
number_of_alpha_levels=5)
tri = phuzzy.Triangle(alpha0=[1, 4],
alpha1=[2, 3],
number_of_alpha_levels=5)
uni = phuzzy.Uniform(alpha0=[1, 4],
alpha1=[2, 3],
number_of_alpha_levels=5)
x = tra + tra
assert isinstance(x, phuzzy.Trapezoid)
x = uni + uni
assert isinstance(x, phuzzy.Uniform)
x = tri + uni
assert isinstance(x, phuzzy.Triangle)
x = uni + tri
print(x.__class__)
assert isinstance(x, phuzzy.Triangle)
x = tra + uni
assert isinstance(x, phuzzy.Trapezoid)
x = uni + tra
assert isinstance(x, phuzzy.Trapezoid)
x = tra + tri
assert isinstance(x, phuzzy.Trapezoid)
x = tri + tra
assert isinstance(x, phuzzy.Trapezoid)
x = uni + tri + tra
assert isinstance(x, phuzzy.Trapezoid)
x = uni + 2.
assert isinstance(x, phuzzy.Uniform)
def test_fuzzy():
n = phuzzy.FuzzyNumber()
print(n)
print(n.__class__.__name__)
assert hasattr(n, "name")
assert hasattr(n, "df")
assert hasattr(n, "number_of_alpha_levels")
t = phuzzy.FuzzyNumber.from_data(data=[1, 3, 5], number_of_alpha_levels=3)
print(t.df)
assert len(t.df) == 3
print(t.__class__.__name__)
s = t.to_str()
print(s)
pdf = t.pdf([2])
print(pdf)
cdf = t.cdf([2])
print(cdf)
n = phuzzy.FuzzyNumber()
with pytest.raises(NotImplementedError) as exp:
s = n.to_str()
print(s)
with pytest.raises(NotImplementedError) as exp:
s = phuzzy.FuzzyNumber.from_str("!")
print(s)
t = phuzzy.Triangle(alpha0=[1, 3], alpha1=[2], number_of_alpha_levels=15)
p = phuzzy.Trapezoid(alpha0=[1, 4],
alpha1=[2, 3],
number_of_alpha_levels=5)
n = t + p
pdf = n.pdf([2])
print(pdf)
cdf = n.cdf([2])
print(cdf)
def plot_fuzzynumber_cdf():
import matplotlib.pyplot as plt
H = 100. # mm
B = 300. # mm
fig, axs = plt.subplots(1,
2,
dpi=90,
facecolor='w',
edgecolor='k',
figsize=(B / 25.4, H / 25.4))
x = np.linspace(0, 5, 200)
t = phuzzy.Trapezoid(alpha0=[1, 5],
alpha1=[2, 3],
number_of_alpha_levels=5)
p = t.pdf(x)
P = t.cdf(x)
axs[0].plot(x, p, label="pdf", lw=1)
axs[1].plot(x, P, label="cdf", lw=1)
f = phuzzy.FuzzyNumber(alpha0=[1, 2],
alpha1=[1.1, 1.4],
number_of_alpha_levels=5)
f.df = t.df.copy()
print(f.df)
pf = f.pdf(x)
Pf = f.cdf(x)
mix_mpl(f)
f.plot(show=False)
axs[0].plot(x, pf, label="pdf", lw=3, c="r", alpha=.5)
axs[1].plot(x, Pf, label="cdf", lw=3, c="r", alpha=.5)
axs[0].legend(loc="best")
axs[1].legend(loc="best")
plt.show()
def plot_traz_cdf():
import matplotlib.pyplot as plt
H = 100. # mm
B = 300. # mm
fig, axs = plt.subplots(1,
2,
dpi=90,
facecolor='w',
edgecolor='k',
figsize=(B / 25.4, H / 25.4))
x = np.linspace(0, 6, 200)
f = phuzzy.Trapezoid(alpha0=[1, 5],
alpha1=[2, 3],
number_of_alpha_levels=5)
p = f.pdf(x)
P = f.cdf(x)
axs[0].plot(x, p, label="pdf", lw=2)
axs[1].plot(x, P, label="cdf", lw=2)
axs[0].legend(loc="best")
axs[1].legend(loc="best")
plt.show()
import streamlit as st
import phuzzy
from phuzzy.mpl import mix_mpl
import matplotlib.pyplot as plt
add_selectbox = st.sidebar.selectbox("phuzzy numbers",
("Triangle", "Uniform", "Trapezoid"))
st.text('phuzzy')
p1_slot = st.empty()
uniform = phuzzy.Uniform(alpha0=[1, 2])
mix_mpl(uniform)
trapezoid = phuzzy.Trapezoid(alpha0=[1, 2], alpha1=[1.2, 1.5])
mix_mpl(trapezoid)
triangle = phuzzy.Triangle(alpha0=[1, 2], alpha1=[1.8])
mix_mpl(triangle)
p_dict = {"Triangle": triangle, "Uniform": uniform, "Trapezoid": trapezoid}
print(add_selectbox)
p = p_dict.get(add_selectbox, triangle)
def plot_phuzzy_number(p):
H = 170. #mm
B = 270. #mm
fig, ax = plt.subplots(dpi=90,
