points_list.append(np.random.randn(n_point_per_cate,2) + np.array(point))returnnp.concatenate(points_list, axis=0) # - generate random datadata = generate_data(100, [[3,4], [10,-4], [-5,0]]) data.shape (300,2) # - visulize dataplt.scatter(data[:,0], data[:,1]) # ...
importnumpyasnp# Generate some random datadata=np.random.randn(2,3)dataarray([[-0.2047,0.4789,-0.5194],[-0.5557,1.9658,1.3934]])data*10data+dataarray([[-0.4094,0.9579,-1.0389],[-1.1115,3.9316,2.7868]])#shape(一个表示各维度大小的元组)data.shape(2,3)#dtype(一个用于说明数组数据类型的对象...
1.numpy.random.rand()- 生成均匀分布的在[0, 1)内的随机数 参数:numpy.random.rand(d0, d1, ..., dn)接受多个整数参数,每个参数代表生成随机数的维度。可以使用逗号分隔的整数来指定多维数组的形状。 import numpy as np # 生成一个[0, 1)范围内的随机浮点数 rand_num = np.random.rand() print(r...
As you’ll now see, it’s possible to generate a range of random sample data that follows a Poisson distribution. To achieve this, you call the Generator object’s .poisson() method. The poisson() method takes two paramters: lam and size. The lam parameter takes the known lambda value...
In [12]: import numpy as np # Generate some random data In [13]: data = np.random.randn(2, 3) In [14]: data Out[14]: array([[-0.2047, 0.4789, -0.5194], [-0.5557, 1.9658, 1.3934]]) 1. 2. 3. 4. 5. 6. 7. 8. 9.然后进行数学运算: ...
# Generate some random data data = np.random.randn(2, 3) data data * 10 data + data data.shape data.dtype 1. 2. 3. 4. 5. 6. 7. 8. 2.3 生成ndarray (1)生成数组最简单的方式就是使用array函数,接受任意序列型对象,举例: import numpy as np ...
NumPy 提供了ones()、zeros()和random.Generator类来生成随机数,你只需传递你想要生成的元素数量即可: 代码语言:javascript 复制 >>> np.ones(3) array([1., 1., 1.]) >>> np.zeros(3) array([0., 0., 0.]) >>> rng = np.random.default_rng() # the simplest way to generate random ...
In [4]: import numpy as np # Generate some random data data = np.random.randn(2, 3) data Out[4]: array([[-0.2047, 0.4789, -0.5194], [-0.5557, 1.9658, 1.3934]])In [5]: data * 10 data + data Out[5]: array([[-0.4094, 0.9579, -1.0389], [-1.1115, 3.9316, 2.7868]])...
NumPy 提供类似ones()and的函数zeros(),以及 random.Generator用于生成随机数的类。您需要做的就是传递您希望它生成的元素数量: >>> np.ones(3)array([1., 1., 1.])>>> np.zeros(3)array([0., 0., 0.])# the simplest way to generate random numbers>>> rng = np.random.default_rng(0)>>...
numpy.random.random(size=None)和rand函数功能⼀样，参数不同⽽已 c、在区间[low, high)中均匀分布：numpy.random.uniform(low=0.0, high=1.0, size=None)d、随机整数：在区间[low, high)中离散均匀抽样：numpy.random.randint(low, high=None, size=None, dtype='l')⼆、⽣成随机数-均匀分布 ...