importnumpyasnp# 创建一个数组arr=np.arange(10)print("Original array from numpyarray.com:",arr)# 随机排列数组np.random.shuffle(arr)print("Shuffled array from numpyarray.com:",arr)# 从数组中随机选择元素random_choice=np.random.cho
numpy.random.hypergeometric(ngood, nbad, nsample, size=None)Draw samples from a Hypergeometric distribution. 表示对一个超几何分布进行采样,size表示采样的次数,ngood表示总体中具有成功标志的元素个数,nbad表示总体中不具有成功标志的元素个数,ngood+nbad表示总体样本容量,nsample表示抽取元素的次数(小于或等于...
#Randomly permute a sequence, or return a permuted range. #If x is a multi-dimensional array, it is only shuffled along its first index. #permutation()函数的作用与shuffle()函数相同,可以打乱第0轴的数据,但是它不会改变原来的数组。 1. 2. 3. 4. import numpy as np np.random.seed(20200614...
shuffle会改变原来数组,但是numpy.random.permutation(x) Randomly permute a sequence, or return a permuted range不会改变原来数组 补充: sample函数 随机抽取特别有用,可以当作随机打折的样式 import random #举例子 data = [1,2,3,4,5,6,7] print(random.sample(data,2)) [6, 4] 泊松分布 random 正...
Sometimes I need to randomly sample from a specific set of values. Therandom.choice()function is perfect for this: # Creating an array of US states states = np.array(['California', 'Texas', 'Florida', 'New York', 'Illinois'])
from sklearn import datasets%matplotlib inlineimport matplotlib.pyplot as plt## Boston House Prices datasetboston = datasets.load_boston()x = boston.datay = boston.targetboston.feature_namesarray(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD','TAX', 'PTRATIO'...
from sklearn import datasets %matplotlib inline import matplotlib.pyplot as plt ## Boston House Prices dataset boston = datasets.load_boston() x = boston.data y = boston.target boston.feature_names array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX'...
fromsklearnimportdatasets %matplotlib inlineimportmatplotlib.pyplotasplt## Boston House Prices datasetboston = datasets.load_boston() x = boston.data y = boston.target boston.feature_names array(['CRIM','ZN','INDUS','CHAS','NOX','RM','AGE','DIS','RAD','TAX','PTRATIO','B','LSTAT'...
["mean", "variance", "EV", "sample"]) # create randomly-weighted edges print("Building graph") E = [] G = random_DAG(n_vertices, p) V = G.vertices for e in G.edges: mean, var = np.random.uniform(0, 1), np.random.uniform(0, 1) w = lambda: np.random.normal(mean, ...
shuffle Randomly permute(转换) a sequence in-place (随机洗牌) rand Draw samples from a uniform distribution U~[0, 1], rand(shape)(均匀分布, 每个值出现的可能性是一样的) uniform U~[0, 1], uniform(low=0, high=1, size) randint Draw random integers from a given low-to-high range. (...