random.uniform(a,b)for i in x

This is not a valid statement in Python.

If you want to generate a list of random numbers between a and b using the uniform distribution, you can use the following code:

import random

x = [random.uniform(a,b) for i in range(n)]

Where `n` is the number of random numbers you want to generate.

相关问题

帮我在下面的代码中添加高斯优化，原代码如下：import numpy as np from sklearn.svm import OneClassSVM from scipy.optimize import minimize def fitness_function(x): """ 定义适应度函数，即使用当前参数下的模型进行计算得到的损失值 """ gamma, nu = x clf = OneClassSVM(kernel='rbf', gamma=gamma, nu=nu) clf.fit(train_data) y_pred = clf.predict(test_data) # 计算错误的预测数量 error_count = len([i for i in y_pred if i != 1]) # 将错误数量作为损失值进行优化 return error_count def genetic_algorithm(x0, bounds): """ 定义遗传算法优化函数 """ population_size = 20 # 种群大小 mutation_rate = 0.1 # 变异率 num_generations = 50 # 迭代次数 num_parents = 2 # 选择的父代数量 num_elites = 1 # 精英数量 num_genes = x0.shape[0] # 参数数量 # 随机初始化种群 population = np.random.uniform(bounds[:, 0], bounds[:, 1], size=(population_size, num_genes)) for gen in range(num_generations): # 选择父代 fitness = np.array([fitness_function(x) for x in population]) parents_idx = np.argsort(fitness)[:num_parents] parents = population[parents_idx] # 交叉 children = np.zeros_like(parents) for i in range(num_parents): j = (i + 1) % num_parents mask = np.random.uniform(size=num_genes) < 0.5 children[i, mask] = parents[i, mask] children[i, ~mask] = parents[j, ~mask] # 变异 mask = np.random.uniform(size=children.shape) < mutation_rate children[mask] = np.random.uniform(bounds[:, 0], bounds[:, 1], size=np.sum(mask)) # 合并种群 population = np.vstack([parents, children]) # 选择新种群 fitness = np.array([fitness_function(x) for x in population]) elites_idx = np.argsort(fitness)[:num_elites] elites = population[elites_idx] # 输出结果 best_fitness = fitness[elites_idx[0]] print(f"Gen {gen+1}, best fitness: {best_fitness}") return elites[0] # 初始化参数 gamma0, nu0 = 0.1, 0.5 x0 = np.array([gamma0, nu0]) bounds = np.array([[0.01, 1], [0.01, 1]]) # 调用遗传算法优化 best_param = genetic_algorithm(x0, bounds) # 在最佳参数下训练模型，并在测试集上进行测试 clf = OneClassSVM(kernel='rbf', gamma=best_param[0], nu=best_param[1]) clf.fit(train_data) y_pred = clf.predict(test_data) # 计算错误的预测数量 error_count = len([i for i in y_pred if i != 1]) print(f"Best fitness: {error_count}, best parameters: gamma={best_param[0]}, nu={best_param[1]}")

您好，为了在下面的代码中添加高斯优化，您需要使用scipy模块中的optimize包，并使用minimize函数进行优化。具体修改如下：

import numpy as np
from sklearn.svm import OneClassSVM
from scipy.optimize import minimize

def fitness_function(x):
"""
定义适应度函数，即使用当前参数下的模型进行计算得到的损失值
"""
gamma, nu = x
clf = OneClassSVM(kernel='rbf', gamma=gamma, nu=nu)
clf.fit(train_data)
y_pred = clf.predict(test_data)

# 计算损失值
loss = np.sum(y_pred != test_label) / len(test_label)
return loss

# 定义初始参数值
gamma_init = 0.1
nu_init = 0.01
x_init = np.array([gamma_init, nu_init])

# 进行高斯优化
res = minimize(fitness_function, x_init, method='L-BFGS-B', bounds=((0, None), (0, 1)))
gamma_opt, nu_opt = res.x

# 使用优化后的参数值构建模型
clf_opt = OneClassSVM(kernel='rbf', gamma=gamma_opt, nu=nu_opt)
clf_opt.fit(train_data)
y_pred_opt = clf_opt.predict(test_data)

# 输出优化后的模型损失
loss_opt = np.sum(y_pred_opt != test_label) / len(test_label)
print('优化后模型的损失值：', loss_opt)

class Givens(): def __init__(self,Tm,Tn,X): self.Tm=Tm self.Tn=Tn self.X1=X[0:10] self.X2=X[10:16] def hbf_T(self): Tm = self.Tm Tn = self.Tn a_b = np.random.uniform(0, 1, (Tm, Tn, 4)) c = a_b[:, :, 0]**2 + a_b[:, :, 1]**2 mask = c < 1 TT = np.zeros((Tm, Tn), dtype=complex) # 初始化 TT det_TT = 1 while det_TT != 0: for i in range(Tn): X1 = np.zeros(Tm, dtype=complex) X1[mask[:, i]] = a_b[:, i, 0][mask[:, i]] + 1j*a_b[:, i, 1][mask[:, i]] TT[:, i] = X1 det_TT = np.linalg.det(np.dot(np.transpose(TT), TT)) return TT

这段代码实现了一个 Givens 变换（Givens rotation）。Givens 变换是一种矩阵旋转，可以将一个矩阵的某两行或某两列通过正交变换旋转到一个新的位置，从而得到一个更简单的矩阵。这段代码中，Givens 变换作用于一个复矩阵，其中 X1 和 X2 是矩阵 X 的前 10 行和后 6 行，Tm 和 Tn 是 Givens 变换的参数。具体地，代码中通过随机生成一个 a_b 矩阵，然后选择其中符合条件的部分进行 Givens 变换，直到得到满足条件的 TT 矩阵。最后返回 TT 矩阵。