def plot_truncnorm_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.TruncNorm(alpha0=[1, 5], number_of_alpha_levels=25)
p = t.pdf(x)
P = t.cdf(x)
axs[0].plot(x, p, label="pdf ana", lw=1)
axs[1].plot(x, P, label="cdf ana", 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, ppf=[0, 0.01, .5, .99, 1])
axs[0].plot(x, pf, label="pdf num", lw=3, c="r", alpha=.5)
axs[1].plot(x, Pf, label="cdf num", lw=3, c="r", alpha=.5)
axs[0].legend(loc="best")
axs[1].legend(loc="best")
plt.show()
def plot_xy(x, y, height=100, width=200):
"""plot two fuzzy numbers
:param x: first fuzzy number
:param y: second fuzzy number
:param height: figure height
:param width: figure width
:return: fig, axs (1x2)
"""
if not hasattr(x, "plot"):
x = x.copy()
mix_mpl(x)
if not hasattr(y, "plot"):
y = y.copy()
mix_mpl(y)
fig, axs = plt.subplots(1,
2,
dpi=90,
facecolor='w',
edgecolor='k',
figsize=(width / 25.4, height / 25.4))
x.plot(ax=axs[0])
y.plot(ax=axs[1])
fig.tight_layout()
return fig, axs
def normal_estimator():
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))
alpha0 = [-3, 8]
alpha1 = [-1]
size = 5000
data = np.random.triangular(alpha0[0], alpha1[0], alpha0[1], size)
p0 = phuzzy.Triangle(name="org", alpha0=alpha0, alpha1=alpha1)
p = phuzzy.Triangle.from_data(data=data, name="estimate")
print(p.df)
mix_mpl(p)
mix_mpl(p0)
p0.plot(ax=axs[1])
p.plot(ax=axs[1], labels=False)
print(data.mean())
print(data.shape)
p.plot(ax=axs[0])
h = axs[0].hist(data, bins=50, normed=True, alpha=.5)
fig.tight_layout()
plt.show()
def plot_xy_3d(x, y, height=200, width=200):
"""plot two fuzzy numbers
:param x: first fuzzy number
:param y: second fuzzy number
:return: fig, axs (2x2)
"""
if not hasattr(x, "plot"):
x = x.copy()
mix_mpl(x)
if not hasattr(y, "plot"):
y = y.copy()
mix_mpl(y)
fig, axs = plt.subplots(2,
2,
dpi=90,
facecolor='w',
edgecolor='k',
figsize=(width / 25.4, height / 25.4))
axx = axs[0, 0]
axy = axs[1, 1]
axxy = axs[1, 0]
ax3d_ = axs[0, 1]
axxy.set_xlabel("x")
axxy.set_ylabel("y")
axx.get_shared_x_axes().join(axx, axxy)
axy.get_shared_y_axes().join(axy, axxy)
x.plot(ax=axx)
y.vplot(ax=axy)
for i, xi in x.df.iterrows():
yi = y.df.loc[i]
axxy.plot([xi.l, xi.r, xi.r, xi.l, xi.l],
[yi.l, yi.l, yi.r, yi.r, yi.l],
c="k",
alpha=.5,
lw=.5,
dashes=[2, 2])
fig.tight_layout()
bbox = ax3d_.get_position()
ax3d = fig.gca(projection='3d')
ax3d.set_position(bbox)
ax3d_.axis('off')
ax3d = plot_3d(x, y, ax=ax3d)
axs[0, 1] = ax3d
return fig, axs
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 plot_xy(x, y, show=False, ftype=True):
H = 100. # mm
B = 300. # mm
for i in [x,y]:
phm.mix_mpl(i)
fig, axs = plt.subplots(1, 2, dpi=90, facecolor='w', edgecolor='k', figsize=(B / 25.4, H / 25.4))
if ftype is True:
axs[0].set_title(x.__class__.__name__)
axs[1].set_title(y.__class__.__name__)
x.plot(ax=axs[0])
y.plot(ax=axs[1])
fig.tight_layout()
# fig.savefig("A.png")
if show==True:
plt.show()
def plot_mul(x, y, z):
H = 100. # mm
B = 300. # mm
for i in [x, y, z]:
phm.mix_mpl(i)
fig, axs = plt.subplots(1,
3,
dpi=90,
facecolor='w',
edgecolor='k',
figsize=(B / 25.4, H / 25.4))
x.plot(ax=axs[0])
y.plot(ax=axs[1])
z.plot(ax=axs[2])
fig.tight_layout()
# fig.savefig("A.png")
plt.show()
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_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 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 plot_op_x(x, show=False, ftype=True):
H = 250. # mm
B = 300. # mm
fig, axs = plt.subplots(3, 3, dpi=90, facecolor='w', edgecolor='k', figsize=(B / 25.4, H / 25.4))
ys = [x, -x, abs(x),
x+2, 2+x, x-2,
x*2, x/2, x**2]
ys[4].name = "2+x"
for i, y in enumerate(ys):
phm.mix_mpl(y)
ax = axs.ravel()[i]
y.plot(ax=ax)
if ftype is True:
ax.set_title(y.__class__.__name__)
fig.tight_layout()
fig.savefig("operations_%s.png" % x.__class__.__name__)
if show==True:
plt.show()
def plot_gennorm_mix():
n = 50
p = phuzzy.TruncGenNorm(alpha0=[1, 4],
alpha1=[2, 3],
number_of_alpha_levels=n,
beta=1)
p2 = phuzzy.TruncGenNorm(alpha0=[1, 4],
alpha1=[2, 3],
number_of_alpha_levels=n,
beta=2)
p10 = phuzzy.TruncGenNorm(alpha0=[1, 4],
alpha1=[2, 3],
number_of_alpha_levels=n,
beta=5)
print(p)
# print(p.df)
# # p.convert_df(5)
# print(p.df)
mix_mpl(p)
mix_mpl(p2)
mix_mpl(p10)
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))
p.plot(ax=axs[0])
p2.plot(ax=axs[1])
p10.plot(ax=axs[2])
ps = [p, p2, p10]
for p, ax in zip(ps, axs):
ax.set_title(r"$\beta=%.1f$" % p.beta)
fig.tight_layout()
fig.savefig("truncgennorm.png")
plt.show()
def plot_pow2():
H = 300. # mm
B = 300. # mm
fig, axs = plt.subplots(3,
3,
dpi=90,
facecolor='w',
edgecolor='k',
figsize=(B / 25.4, H / 25.4))
for i in range(3):
axs[0, i].get_shared_x_axes().join(axs[0, i], axs[1, i], axs[2, i])
for i in range(1, 3):
axs[i, 0].set_xlabel("x")
axs[i, 1].set_xlabel("y")
axs[i, 2].set_xlabel("x^y")
i = 2
axs[i, 0].get_shared_y_axes().join(axs[i, 0], axs[i, 1], axs[i, 2])
x = phuzzy.Trapezoid(alpha0=[0, 4],
alpha1=[2, 3],
number_of_alpha_levels=5)
mix_mpl(x)
x.plot(ax=axs[0, 0])
y = phuzzy.TruncNorm(alpha0=[1, 3], number_of_alpha_levels=15, name="y")
mix_mpl(y)
y.plot(ax=axs[0, 1])
z = x**y
z.name = "x^y"
mix_mpl(z)
z.plot(ax=axs[0, 2])
b = np.linspace(-1, 5, 200)
px = x.pdf(b)
Px = x.cdf(b)
axs[1, 0].fill_between(b, 0, px, alpha=.2)
axs[1, 0].plot(b, px, label="pdf", lw=1)
axs[2, 0].fill_between(b, 0, Px, alpha=.2)
axs[2, 0].plot(b, Px, label="cdf", lw=1)
py = y.pdf(b)
Py = y.cdf(b)
axs[1, 1].fill_between(b, 0, py, alpha=.2)
axs[1, 1].plot(b, py, label="pdf", lw=1)
axs[2, 1].fill_between(b, 0, Py, alpha=.2)
axs[2, 1].plot(b, Py, label="cdf", lw=1)
b = np.linspace(z.alpha0["low"], z.alpha0["high"], 200)
pz = z.pdf(b)
Pz = z.cdf(b)
axs[1, 2].fill_between(b, 0, pz, alpha=.2)
axs[1, 2].plot(b, pz, label="pdf", lw=1)
axs[2, 2].fill_between(b, 0, Pz, alpha=.2)
axs[2, 2].plot(b, Pz, label="cdf", lw=1)
for i in range(3):
axs[2, i].axhline(1, alpha=.4, c="k", lw=.5)
# axs[1,i].set_ylabel("pdf")
# axs[2,i].set_ylabel("cdf")
axs[1, i].annotate('pdf', (0.01, 0.99),
xycoords='axes fraction',
size=8,
ha='left',
va='top',
textcoords='axes fraction')
axs[2, i].annotate('cdf', (0.01, 0.99),
xycoords='axes fraction',
size=8,
ha='left',
va='top',
textcoords='axes fraction')
# axs[1,1].sharex = axs[0,1]
# axs[2,1].sharex = axs[0,1]
# axs[1,1].share_x_axes(axs[0,1])
for ax in axs.ravel():
ax.set_ylim(0, None)
fig.tight_layout()
fig.savefig("x**y_pdf_cdf.png")
plt.show()
# exact reference solution
z = f(x, y)
z.name = "z"
# create Expression object
expr = phuzzy.approx.doe.Expression(designvars=[x, y],
function=f,
name="f(x,y)")
# expr.generate_training_doe(name="train", n=10, method="cc")
expr.generate_training_doe(name="train", n=10, method="lhs")
expr.eval()
z_a = expr.get_fuzzynumber_from_results()
expr.fit_model()
print(expr.model)
# expr.generate_prediction_doe(name="prediction", n=10000, method="lhs")
expr.generate_prediction_doe(name="prediction", n=10000, method="meshgrid")
z_b = expr.predict(name="z_b")
print(z_b)
mix_mpl(z_b)
fig, axs = phuzzy.mpl.plots.plot_xy(z, z_b)
mix_mpl(z)
z.plot(axs[1], labels=False)
samples = expr.results_training
axs[1].scatter(samples.res, samples.alpha)
plt.show()
# z.plot(show=True)
print("w = {} mm".format(w))
# w = FuzzyNumber(P*L^3/E*48*W*H^3/12.0:[[5.594629603627992, 8.024370049019725], [6.666666666666667, 6.666666666666667]]) mm
w_mean = w.mean()
dw_l = w_mean - w.min()
dw_r = w.max() - w_mean
print("w = {:.4g} mm (- {:.4g}|+ {:.4g})".format(w_mean, dw_l, dw_r))
# w = 6.703 mm (- 1.109|+ 1.321)
print("w = {:.4g} mm [{:.4g},{:.4g}]".format(w_mean, w.min(), w.max()))
# w = 6.703 mm [5.595,8.024]
from phuzzy.mpl import mix_mpl
import matplotlib.pyplot as plt
mix_mpl(I)
mix_mpl(w)
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))
axs[0].axvline(I0, lw=2, alpha=.4, c="r", label="$I_0$")
axs[1].axvline(w0,
lw=2,
axl.add_collection(pl)
pr = PatchCollection([polygon], alpha=1, color=sm.to_rgba(zi.r))
axr.add_collection(pr)
axl.plot([xi.l, xi.r, xi.r, xi.l, xi.l], [yi.l, yi.l, yi.r, yi.r, yi.l],
c="k",
alpha=.5,
lw=.5,
dashes=[2, 2])
axr.plot([xi.l, xi.r, xi.r, xi.l, xi.l], [yi.l, yi.l, yi.r, yi.r, yi.l],
c="k",
alpha=.5,
lw=.5,
dashes=[2, 2])
# # csr = axr.contourf(Xs, Ys, Zs, alpha=.2, cmap="hot")
# csr2 = axr.contour(Xs, Ys, Zs, color="k", cmap=cmap)
# axr.clabel(csr2, fontsize=9, inline=1, alpha=1)
axl.scatter([1], [-.4])
axr.scatter([1], [-.4])
axc.scatter([1], [-.4])
mix_mpl(z)
z.plot(ax=axz, show=False)
axz.set_title(z)
axc.set_title("f(x,y)")
fig.tight_layout()
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,
import numpy as np
import pandas as pd
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import phuzzy.mpl.plots
import matplotlib.pyplot as plt
from sklearn import neighbors
from sklearn.svm import SVR
from sklearn.model_selection import GridSearchCV
x = phuzzy.TruncNorm(alpha0=[1, 2], name="x")
y = phuzzy.TruncNorm(alpha0=[1, 2], name="y")
# y = phuzzy.Triangle(alpha0=[3, 6], alpha1=[4], name="y")
mix_mpl(x)
mix_mpl(y)
def f(x1, x2):
return (x1-1)**2+(x2+.4)**2 + .1
z = f(x,y)
doe = phuzzy.approx.doe.DOE(designvars = [x,y], name="xy")
print(doe)
# #############################################################################
# Generate sample data
import numpy as np
samples = doe.sample_doe(n=20, method="cc").copy()
X = samples[["x", "y"]].values
