crossover21
时间: 2023-05-02 12:07:33 浏览: 88
Crossover21 是一款专门为篮球爱好者设计的品牌,其定位是致力于打造高品质的篮球装备和服装。Crossover21 的目标消费者群体是年轻一代的篮球运动员和爱好者,他们对篮球品牌的要求除了功能性之外,还更加强调时尚和个性化。
作为新兴品牌,Crossover21 在设计上非常注重细节,力求将功能性、时尚性和个性化完美结合起来。产品线包括篮球鞋、篮球服和篮球配件,款式多样,颜色鲜艳,以满足年轻人的需求。
此外,Crossover21 也非常注重品牌营销,通过与明星球员、篮球比赛和活动的合作,提高品牌知名度和影响力。在社交媒体方面,Crossover21 也在不断扩大其影响力,通过打造独特的品牌形象和内容,吸引更多的年轻人关注和喜欢。
总之,Crossover21 是一家注重细节的篮球品牌,它的成功不仅在于产品的质量和设计,还在于其独特的品牌形象和营销策略。未来,Crossover21 在篮球品牌市场上有着广阔的发展前景。
相关问题
crossover21下载
Crossover21是一款非常实用的软件,它可以将Windows应用程序直接在macOS上运行,无需安装Windows系统,这样便能够在Mac机器上使用各种需要Windows环境的软件。
如果您需要下载Crossover21,可以前往官方网站进行下载。Crossover21支持MacOS Catalina,Big Sur以及更高版本的操作系统,用户可以根据自己的系统版本进行相应下载。此外,Crossover21官网还提供了免费试用的机会,让用户在下载之前先尝试一下软件,以便更全面地了解其功能和使用方式。
在使用Crossover21时,用户需要先安装该软件,并在Mac中打开它。接着,用户可以在Crossover21应用库中选择需要运行的Windows应用程序,并进行相应的安装和设置,最后便能够愉快地使用Windows应用程序了。
总之,Crossover21是一款十分方便实用的软件,使用户在Mac上也能够使用Windows软件,加强了Mac的兼容性和可用性。
请在不影响结果的条件下改变代码的样子:import numpy as np import matplotlib.pyplot as plt x1len = 21 x2len = 18 LEN = x1len + x2len POPULATION_SIZE = 100 GENERATIONS = 251 CROSSOVER_RATE = 0.7 MUTATION_RATE = 0.3 pop = np.random.randint(0,2,size=(POPULATION_SIZE,LEN)) def BinToX(pop): x1 = pop[:,0:x1len] x2 = pop[:,x1len:] x1 = x1.dot(2**np.arange(x1len)[::-1]) x2 = x2.dot(2**np.arange(x2len)[::-1]) x1 = -2.9 + x1*(12 + 2.9)/(np.power(2,x1len)-1) x2 = 4.2 + x2*(5.7 - 4.2)/(np.power(2,x2len)-1) return x1,x2 def func(pop): x1,x2 = BinToX(pop) return 21.5 + x1*np.sin(4*np.pi*x1) + x2*np.sin(20*np.pi*x2) def fn(pop): return func(pop); def selection(pop, fitness): idx = np.random.choice(np.arange(pop.shape[0]), size=POPULATION_SIZE, replace=True, p=fitness/fitness.sum()) return pop[idx] def crossover(IdxP1,pop): if np.random.rand() < CROSSOVER_RATE: C = np.zeros((1,LEN)) IdxP2 = np.random.randint(0, POPULATION_SIZE) pt = np.random.randint(0, LEN) C[0,:pt] = pop[IdxP1,:pt] C[0,pt:] = pop[IdxP2, pt:] np.append(pop, C, axis=0) return def mutation(idx,pop): if np.random.rand() < MUTATION_RATE: mut_index = np.random.randint(0, LEN) pop[idx,mut_index] = 1- pop[idx,mut_index] return best_chrom = np.zeros(LEN) best_score = 0 fig = plt.figure() for generation in range(GENERATIONS): fitness = fn(pop) pop = selection(pop, fitness) if generation%50 == 0: ax = fig.add_subplot(2,3,generation//50 +1, projection='3d', title = "generation:"+str(generation)+" best="+str(np.max(fitness))) x1,x2 = BinToX(pop) z = func(pop) ax.scatter(x1,x2,z) for idx in range(POPULATION_SIZE): crossover(idx,pop) mutation(idx,pop) idx = np.argmax(fitness) if best_score < fitness[idx]: best_score = fitness[idx] best_chrom = pop[idx, :] plt.show() print('最优解:', best_chrom, '| best score: %.2f' % best_score)
import numpy as np
import matplotlib.pyplot as plt
x1len = 21
x2len = 18
LEN = x1len + x2len
POPULATION_SIZE = 100
GENERATIONS = 251
CROSSOVER_RATE = 0.7
MUTATION_RATE = 0.3
pop = np.random.randint(0,2,size=(POPULATION_SIZE,LEN))
def BinToX(pop):
x1 = pop[:,0:x1len]
x2 = pop[:,x1len:]
x1 = -2.9 + x1*(12 + 2.9)/(np.power(2,x1len)-1)
x2 = 4.2 + x2*(5.7 - 4.2)/(np.power(2,x2len)-1)
return x1,x2
def func(x1,x2):
return 21.5 + x1*np.sin(4*np.pi*x1) + x2*np.sin(20*np.pi*x2)
def fn(pop):
x1,x2 = BinToX(pop)
return func(x1,x2);
def selection(pop, fitness):
idx = np.random.choice(np.arange(pop.shape[0]),size=POPULATION_SIZE, replace=True, p=fitness/fitness.sum())
return pop[idx]
def crossover(IdxP1,pop):
if np.random.rand() < CROSSOVER_RATE:
C = np.zeros((1,LEN))
IdxP2 = np.random.randint(0, POPULATION_SIZE)
pt = np.random.randint(0, LEN)
C[0,:pt] = pop[IdxP1,:pt]
C[0,pt:] = pop[IdxP2, pt:]
np.append(pop, C, axis=0)
return
def mutation(idx,pop):
if np.random.rand() < MUTATION_RATE:
mut_index = np.random.randint(0, LEN)
pop[idx,mut_index] = 1- pop[idx,mut_index]
return
best_chrom = np.zeros(LEN)
best_score = 0
fig = plt.figure()
for generation in range(GENERATIONS):
fitness = fn(pop)
pop = selection(pop, fitness)
if generation%50 == 0:
ax = fig.add_subplot(2,3,generation//50 +1, projection='3d',title = "generation:"+str(generation)+" best="+str(np.max(fitness)))
x1,x2 = BinToX(pop)
z = func(x1,x2)
ax.scatter(x1,x2,z)
for idx in range(POPULATION_SIZE):
crossover(idx,pop)
mutation(idx,pop)
idx = np.argmax(fitness)
if best_score < fitness[idx]:
best_score = fitness[idx]
best_chrom = pop[idx, :]
plt.show()
print('最优解:', best_chrom, '| best score: %.2f' % best_score)