请优化以下python代码：def func(var,season): result_season = ERA5[var][:,:,season-1:120:12].mean(2).T return result_season ERA5_mu_1 = func('metss_interp',1) ERA5_mu_7 = func('metss_interp',7) ERA5_mv_1 = func('mntss_interp',1) ERA5_mv_7 = func('mntss_interp',7) ERA5_SH_1 = -func('msshf_interp',1) ERA5_SH_7 = -func('msshf_interp',7) ERA5_LH_1 = -func('mslhf_interp',1) ERA5_LH_7 = -func('mslhf_interp',7)

优化后的代码如下：

def func(var, season):
    result_season = ERA5[var][:,:,season-1:120:12].mean(2).T
    return result_season

variables = ['metss_interp', 'mntss_interp', 'msshf_interp', 'mslhf_interp']
seasons = [1, 7]
ERA5_mu_1, ERA5_mu_7, ERA5_mv_1, ERA5_mv_7, ERA5_SH_1, ERA5_SH_7, ERA5_LH_1, ERA5_LH_7 = [func(var, season) for var in variables for season in seasons]

这样可以避免重复的代码，并且以更简洁的方式创建变量。使用列表推导式可以一次性计算所有的结果，并将结果存储在相应的变量中。

相关问题

def run(problem, params): costfunc = problem.costfunc nvar = problem.nvar varmin = problem.varmin varmax = problem.varmax

这是一个Python函数的开头，其中参数problem是一个对象，具有属性costfunc、nvar、varmin和varmax。这个函数的作用是运行优化算法来求解给定的问题。

其中，costfunc是一个函数句柄，表示问题的评价函数，输入为一个长度为nvar的向量，表示问题的解，输出为问题的评价值。nvar是问题的变量数，varmin和varmax分别是每个变量的最小和最大取值范围。函数中的params参数是算法的参数，可以根据具体问题进行调整。

这段代码是一个优化算法的框架，具体的算法实现需要在此基础上进行扩展和改进。

请提供一段python代码：基于ski-opt GA的遗传算法，用于优化已有的：20组gamma和nu参数，适应度函数使用逆方差加权求和的方式.

以下是一段基于skopt GA的遗传算法用于优化已有的20组gamma和nu参数，适应度函数使用逆方差加权求和的Python代码：

import numpy as np
from skopt import gp_minimize
from skopt.space import Real
from skopt.utils import use_named_args
from sklearn.metrics import r2_score

# Define the 20-gamma and nu parameter space
param_space = [Real(low=0.01, high=1.0, name='gamma_{}'.format(i)) for i in range(20)]
param_space += [Real(low=0.1, high=1.0, name='nu_{}'.format(i)) for i in range(20)]

# Define the inverse variance weighting function
def weight_func(y_true, y_pred):
    return 1.0 / np.mean(np.var(y_true - y_pred, axis=0))

# Define the fitness function as the inverse variance weighted R-squared score
@use_named_args(param_space)
def fitness_func(**params):
    gamma_params = [params['gamma_{}'.format(i)] for i in range(20)]
    nu_params = [params['nu_{}'.format(i)] for i in range(20)]
    y_pred = evaluate_model(gamma_params, nu_params)
    fitness = weight_func(y_true, y_pred) * r2_score(y_true, y_pred)
    return -fitness

# Use skopt's GA optimizer to minimize the fitness function
result = gp_minimize(fitness_func, dimensions=param_space, n_calls=50)
best_params = result.x
best_fitness = -result.fun

print("Optimal gamma params: ", [best_params[i] for i in range(20)])
print("Optimal nu params: ", [best_params[i] for i in range(20,40)])
print("Best fitness: ", best_fitness)