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demon_curve.py
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demon_curve.py
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# -*- coding: utf-8 -*-
"""
Created on 2019
@author: Tangmei [email protected]
Hypothesis transfer learning based on fuzzy residual
"""
import numpy as np
import matplotlib
matplotlib.rcParams['backend'] = 'SVG'
import matplotlib.pyplot as plt
import ResTL
from sklearn.model_selection import GridSearchCV
from sklearn.kernel_ridge import KernelRidge
'''
CURVE : which demon curve 1, 2, 3
RULES : visual the rules or not, only available when the basemodel is TSK
nc : the number of rules
C : regularization
width ：the width of rules
'''
CURVE = 3
RULES = 1
nc = 6
C = 1e-8
width = 1.5
Title = 'Parameters: '+'nc='+str(nc)+' C='+str(C)+' width='+str(width)
# basemodel should be 'TSK' if you want to visual the basic rules
basemodel = GridSearchCV(KernelRidge(kernel='rbf', gamma=0.1), cv=5,
param_grid={"alpha": [1e0, 0.1, 1e-2, 1e-3],
"gamma": np.logspace(-2, 2, 5)})
'''Demon curve 1
Xs, Ys: source data
Xt, Yt: target data
X_test：test data
'''
if CURVE == 1:
n_source1, n_target_train = 100, 5
Xs_array = np.linspace(-10, 10, n_source1)
Xs = Xs_array.reshape(len(Xs_array),1)
Ys = np.cos(Xs[:,0])*Xs[:,0] + np.random.normal(0, 0.15, Xs.shape[0])
Ys = Ys[:, np.newaxis]
Xt = np.linspace(-10, -2, n_target_train)
Xt = Xt.reshape(len(Xt),1)
Yt = np.cos(Xt[:,0])*Xt[:,0] + Xt[:,0] + np.random.normal(0, 0.15, Xt.shape[0])
Yt = Yt[:, np.newaxis]
X_test = np.linspace(-10, 10, 100)[:, np.newaxis]
'''Demon curve 2'''
if CURVE == 2:
n_source1, n_target_train = 100, 8
Xs_array = np.linspace(-1, 1, n_source1)
Xs = Xs_array.reshape(len(Xs_array),1)
Ys = np.cos(10*Xs[:,0])*Xs[:,0] + np.random.normal(0, 0.0085, Xs.shape[0])
Ys = Ys[:, np.newaxis]
Xt = np.linspace(-0.4, 1, n_target_train)
Xt = Xt.reshape(len(Xt),1)
Yt = np.cos(10*Xt[:,0])*Xt[:,0] + Xt[:,0] + np.random.normal(0, 0.0085, Xt.shape[0]) +1
Yt = Yt[:, np.newaxis]
X_test = np.linspace(-1, 1, 100)[:, np.newaxis]
'''Demon curve 3'''
if CURVE == 3:
Xs = np.linspace(0, 1, 30)
Xs = Xs.reshape(len(Xs),1)
Ys = np.sin(7*Xs) + 1
# Ys = Ys[:, np.newaxis]
Xt = np.linspace(0.5, 0.8, 3)
Xt = Xt.reshape(len(Xt),1)
Yt = Xt*np.sin(7*Xt) + 0.1
# Yt = Yt[:, np.newaxis]
X_test = np.linspace(0, 1, 100)
X_test = X_test.reshape(len(X_test),1)
'''main'''
Y_RD_KRR, x_each, y_each = ResTL.model(Xs, Xt, Ys, Yt, X_test,
n_cluster=nc,
C=C,
width = width,
residual = 'RD' ,
basemodel = basemodel,
fit_with_target=False)
Y_LS_TSK, x_each, y_each = ResTL.model(Xs, Xt, Ys, Yt, X_test,
n_cluster=nc,
C=C,
width = width,
residual = 'LS' ,
basemodel = 'TSK',
fit_with_target=False)
'''fig'''
fig, ax = plt.subplots()
plt.plot(X_test, Y_LS_TSK, 'purple', lw=2, zorder=9, label='ResTL$_{LS}$')
plt.plot(X_test, Y_RD_KRR, 'g', lw=2, zorder=9, label='ResTL$_{RD}$')
'''visual rules'''
if RULES:
for i in range(nc):
plt.plot(x_each[:,i], y_each[:,i], 'p-', lw=2)
plt.plot(Xs, Ys, 'r-', lw=3, label='Source model')
plt.scatter(Xt, Yt, c='b', s=100, label='Target Data')
fontfamily = 'NSimSun'
font = {'family':fontfamily,
'size':12,
'weight':23}
ax.set_xlabel('X',fontproperties = fontfamily, size = 12)
ax.set_ylabel('Y',fontproperties = fontfamily, size = 12)
plt.yticks(fontproperties = fontfamily, size = 12)
plt.xticks(fontproperties = fontfamily, size = 12)
ax.set_title(Title, fontproperties = fontfamily, size = 12)
plt.legend(prop=font)
plt.tight_layout()
plt.ylim([-2,3])
plt.legend(prop=font)
plt.show()