# x3 = x[:, 3]
#
# t = expr01
# func0 = sympy.utilities.lambdify(terminals, t)
# re = func0(*x.T)
# p = BasePlot(font=None)
# p.scatter(y, re, strx='Experimental $E_{gap}$', stry='Calculated $E_{gap}$')
# import matplotlib.pyplot as plt
#
# plt.show()
if __name__ == '__main__':
store = Store(r'C:\Users\Administrator\Desktop\band_gap_exp\4.symbol')
data = Call(r'C:\Users\Administrator\Desktop\c', index_col=None)
data_import = data.xlsx().sr
X = data_import["delt_x"].values
input_x = data_import[["delt_x", "G"]].values
Pexp = data_import["Pexp"].values
Pmix = data_import["Pmix"].values
G = data_import["G"].values
y = data_import["PG_y"].values
y = y * G
testfunc = input_x[:, 0] * input_x[:, 1]
t = np.corrcoef(y, input_x[:, 0] * input_x[:, 1])
dim1 = Dim([0, 0, 0, 0, 0, 0, 0])
target_dim = [Dim([0, 0, 0, 0, 0, 0, 0])]
exps1 = exps1.subs(subbb1) exps1 = exps1.subs(subbb2) # exps1 = sympy.simplify(exps1) exps2 = exps2.subs(subbb1) exps2 = exps2.subs(subbb2) # exps2 = sympy.simplify(exps2) exps3 = exps3.subs(subbb1) exps3 = exps3.subs(subbb2) # exps3 = sympy.simplify(exps3) from mgetool.imports import Call data = Call(r'C:\Users\Administrator\Desktop\cl', index_col=None) values_data = data.xlsx().values_data E_values = values_data["E"].values Rct_values = values_data["Rct"].values Rp_values = values_data["Rp"] taup_values = (values_data["Rp"] * values_data["Cp"]).values R0_values = -(Rct_values**2 + Rct_values * Rp_values) / Rp_values F_values = 96485 T_values = 298 R_values = 8.314 q_values = 8 * 10e-5 beta_values = 0.5 std_Rct = np.std(Rct_values) std_taup = np.std(taup_values) std_R0 = np.std(R0_values)
import matplotlib.pyplot as plt
import numpy as np
import sklearn
from bgp.selection.backforward import BackForward
from mgetool.exports import Store
from mgetool.imports import Call
from sklearn import svm
from sklearn.model_selection import GridSearchCV, LeaveOneOut
warnings.filterwarnings("ignore")
# 数据导入
store = Store(r'/data/home/wangchangxin/data/zlj/')
data = Call(r'/data/home/wangchangxin/data/zlj/', index_col=None)
all_import = data.xlsx().data
x_name = all_import.index.values
y = all_import["y"].values
x_frame = all_import.drop("y", axis=1)
x = x_frame.values
# # 预处理
# minmax = MinMaxScaler()
# x = minmax.fit_transform(x)
# 数据划分
xtrain, xtest = x[3:], x[:3]
ytrain, ytest = y[3:], y[:3]
xtrain, ytrain = sklearn.utils.shuffle(xtrain, ytrain, random_state=3)
# x = minmax.inverse_transform(x_new)
mutpb=mutpb,
ngen=ngen,
stats=stats,
halloffame=hof,
pset=pset,
store=store)
return hof
if __name__ == '__main__':
# 输入
store = Store(r'D:\sy')
data = Call(r'D:\sy')
data_import = data.xlsx().featuredata
name_abbr = data_import.columns.values
x_name = name_abbr[:-1]
# data_import = data_import.iloc[np.where(data_import['f1'] <= 1)[0]]
X_frame = data_import[x_name]
y_frame = data_import['y']
X = X_frame.values
y = y_frame.values
# 处理
# scal = preprocessing.MinMaxScaler()
# X = scal.fit_transform(X)
X, y = shuffle(X, y, random_state=0)