'>=' 在 numpy 的 'list' 和 'float' 实例之间不支持，其中

Question

我是编码新手，我正在研究我的大学项目，我需要在该项目上制作一个基于生物启发的算法教师学习算法，但是这里出现了一些错误这是我的 tlbo 代码

class Student:
def __init__(self, fitness, dim, minx, maxx, seed):
    self.rnd = random.Random(seed)

    # a list of size dim
    # with 0.0 as value of all the elements
    self.position = [0.0 for i in range(dim)]

    # loop dim times and randomly select value of decision var
    # value should be in between minx and maxx
    for i in range(dim):
        self.position[i] = ((maxx - minx) *
                            self.rnd.random() + minx)

    # compute the fitness of student
    self.fitness = fitness(self.position)
def tlbo(fitness, max_iter, n, dim, minx, maxx):
rnd = random.Random(0)

# create n random students
classroom = [Student(fitness, dim, minx, maxx, i) for i in range(n)]

# compute the value of best_position and best_fitness in the classroom
Xbest = [0.0 for i in range(dim)]
Fbest = sys.float_info.max

for i in range(n):  # check each Student
    if classroom[i].fitness < Fbest:
        Fbest = classroom[i].fitness
        Xbest = copy.copy(classroom[i].position)
# convergence graph
convergence1 = []
timerStart = time.time()
# main loop of tlbo
Iter = 0
while Iter < max_iter:

    # after every 10 iterations
    # print iteration number and best fitness value so far
    if Iter % 10 == 0 and Iter > 1:
        print("Iter = " + str(Iter) + " best fitness = %.3f" % Fbest)
    if Iter % 1 ==0 :
        convergence1.append(Fbest)

    # for each student of classroom
    for i in range(n):

        ### Teaching phase of ith student

        # compute the mean of all the students in the class
        Xmean = [0.0 for i in range(dim)]
        for k in range(n):
            for j in range(dim):
                Xmean[j] += classroom[k].position[j]

        for j in range(dim):
            Xmean[j] /= n;

        # initialize new solution
        Xnew = [0.0 for i in range(dim)]

        # teaching factor (TF)
        # either 1 or 2 ( randomly chosen)
        TF = random.randint(1, 3)

        # best student of the class is teacher
        Xteacher = Xbest

        # compute new solution
        for j in range(dim):
            Xnew[j] = classroom[i].position[j] + rnd.random() * (Xteacher[j] - TF * Xmean[j])

        # if Xnew < minx OR Xnew > maxx
        # then clip it
        for j in range(dim):
            Xnew[j] = max(Xnew[j], minx)
            Xnew[j] = min(Xnew[j], maxx)

        # compute fitness of new solution
        fnew = fitness(Xnew)

        # if new solution is better than old
        # replace old with new solution
        if (fnew < classroom[i].fitness):
            classroom[i].position = Xnew
            classroom[i].fitness = fnew

        # update best student
        if (fnew < Fbest):
            Fbest = fnew
            Xbest = Xnew

        ### learning phase of ith student

        # randomly choose a solution from classroom
        # chosen solution should not be ith student
        p = random.randint(0, n - 1)
        while (p == i):
            p = random.randint(0, n - 1)

        # partner solution
        Xpartner = classroom[p]

        Xnew = [0.0 for i in range(dim)]
        if (classroom[i].fitness < Xpartner.fitness):
            for j in range(dim):
                Xnew[j] = classroom[i].position[j] + rnd.random() * (
                            classroom[i].position[j] - Xpartner.position[j])
        else:
            for j in range(dim):
                Xnew[j] = classroom[i].position[j] - rnd.random() * (
                            classroom[i].position[j] - Xpartner.position[j])

        # if Xnew < minx OR Xnew > maxx
        # then clip it
        for j in range(dim):
            Xnew[j] = max(Xnew[j], minx)
            Xnew[j] = min(Xnew[j], maxx)

        # compute fitness of new solution
        fnew = fitness(Xnew)

        # if new solution is better than old
        # replace old with new solution
        if (fnew < classroom[i].fitness):
            classroom[i].position = Xnew
            classroom[i].fitness = fnew

        # update best student
        if (fnew < Fbest):
            Fbest = fnew
            Xbest = Xnew



    Iter += 1
# end-while
timerEnd = time.time()
print(timerEnd-timerStart)
y = np.array(convergence1, dtype=np.longdouble)
x = np.arange(0, max_iter, dtype=int) + 1
print(x)
print(y)
timerEnd = time.time()
print('Completed in', (timerEnd - timerStart))
fire = round((timerEnd - timerStart), 2)
plt.plot(x, y, 'o-')
plt.xlabel("Iterations")
plt.ylabel("Fitness")
plt.title(
    f"Convergence_curve for CSO for parameter including population "
    f"{n}, \niteration {max_iter},and  max fitness is:{round(min(convergence1), 3)}")
plt.show()

opts = {"p": Xbest, 'c': round(min(convergence1), 3), "ti": fire}
return opts

这是我的适应度函数

def fitness_function(positions):
print(positions)
features = np.where(positions >= 0.4999)[0]
# print('selected_features:', features)

# print(train_df.head())

train_xf = train_x.iloc[:, features]
test_xf = test_x.iloc[:, features]

knn_classifier = Pipeline([('s', StandardScaler()), ('t', MinMaxScaler()),
                           ('m', RandomForestClassifier(n_estimators=100, n_jobs=14))])
knn_classifier.fit(train_xf, train_y)

accuracy = knn_classifier.score(test_xf, test_y)

# print('Accuracy:', accuracy)

w = 0.9

return -(w * accuracy + (1 - w) * 1 / (len(features)))

这里的主要问题是，

Traceback (most recent call last):
self.fitness = fitness(self.position)
features = np.where(positions >= 0.4999)[0]
TypeError: '>' not supported between instances of 'list' and 'float'

主要问题是我的 tlbo 代码正在为最合适的位置 [0.456621, -0.616164564] 生成值，我需要将其转换为 [1,2] 以便我可以运行 knn 和得到准确率结果和RUC曲线，那么我现在应该做什么？

Answer 1

将行替换为 features = np.where(np.array(positions) >= 0.4999)[0]

'>=' 在 numpy 的 'list' 和 'float' 实例之间不支持，其中

'>=' not supported between instances of 'list' and 'float' for numpy where

numpy

machine-learning

python-3.x

scikit-learn