random_number = random.random() print("固定种子下的随机浮点数:", random_number) 8. random.getrandbits(k) random.getrandbits(k)函数生成k比特长的随机整数。适用于需要生成指定位数的随机整数的情况。 import random random_bits = random.getrandbits(4) # 生成4比特长的随机整数 print("随机整数(4...
python中定义一个 Gaussian random variable python中定义一个数,Python3基本数据类型Python中的变量不需要声明。每个变量在使用前都必须赋值,变量赋值以后该变量才会被创建。在Python中,变量没有类型,"类型"是变量所指的内存中对象的类型。(简单的认为:变量存储的是内
Weight = (unsigned int)(expf(-(j*j)/sigma22) / sigma_sqrt2PI * 65536.0f); pGaussianCD[n] = Weight + pGaussianCD[n-1]; n++; } } /* Return a Gaussian random number between [0, 2*r], mean is r. */ unsigned int GaussianRandom(int radius) { static int r = 0, mn, m;...
importrandomrandom.seed(42) # 设置随机数生成器的种子为42random_number =random.random()print("固...
# generate random Gaussian valuesfromrandomimportseedfromrandomimportgauss# seed random number generatorseed(1)# generate some Gaussian valuesfor_inrange(10):value=gauss(0,1)print(value) 运行示例生成并打印10个高斯随机值。 1.28818475315546291.4494456086997710.06633580893826191-0.7645436509716318-1.09217321510414140...
random.gauss(mu,sigma) Gaussian distribution.muis the mean, andsigmais the standard deviation. This is slightly faster than thenormalvariate()function defined below. random.lognormvariate(mu,sigma) Log normal distribution. If you take the natural logarithm of this distribution, you’ll get a norm...
generate random permutation distributions on the real line: --- uniform triangular normal (Gaussian) lognormal negative exponential gamma beta pareto Weibull distributions on the circle (angles 0 to 2pi) --- circular uniform von Mises General notes...
(alpha) * beta ** alphaNo. 6 :Help on method gauss in module random:gauss(mu, sigma) method of random.Random instanceGaussian distribution.mu is the mean, and sigma is the standard deviation. This isslightly faster than the normalvariate() function.Not thread-safe without a lock around ...
gmm = GaussianMixture(n_components=n_components, random_state=42) gmm_labels = gmm.fit_predict(tsne_results) # Visualize the t-SNE results with GMM clustering plt.figure(figsize=(10, 6)) sns.scatterplot(x=tsne_results[:, 0], y=tsne_results[:, 1], hue=gmm_labels, palette='viridis'...
(13)random.gauss(mu, sigma)高斯分布 Gaussian distribution. mu is the mean, and sigma is the standard deviation. This is slightly faster than the normalvariate() function defined below. (14)random.lognormvariate(mu, sigma)对数正态分布