all,any,apply_along_axis,argmax,argmin,argsort,average,bincount,ceil,clip,conj,corrcoef,cov,cross,cumprod,cumsum,diff,dot,floor,inner,invert,lexsort,max,maximum,mean,median,min,minimum,nonzero,outer,prod,re,round,sort,std,sum,trace,transpose,var,vdot,vectorize,where 索引、切片和迭代 一维数组可...
cumsum, diff, dot, floor, inner, inv, lexsort, max, maximum, mean, median, min, minimum, nonzero, outer, prod, re, round, sometrue, sort, std, sum, trace, transpose, var, vdot, vectorize, where 参见:NumPy示例
cumsum, diff, dot, floor, inner, inv, lexsort, max, maximum, mean, median, min, minimum, nonzero, outer, prod, re, round, sometrue, sort, std, sum, trace, transpose, var, vdot, vectorize, where 参见:NumPy示例
5.2 高斯分布 vs 泊松分布 importnumpyasnp# 生成高斯分布和泊松分布的样本gaussian_samples=np.random.normal(loc=5,scale=1,size=1000)poisson_samples=np.random.poisson(lam=5,size=1000)print("numpyarray.com - Gaussian samples variance:",np.var(gaussian_samples))print("numpyarray.com - Poisson sampl...
>>> import numpy as np >>> rg = np.random.default_rng(1) >>> import matplotlib.pyplot as plt >>> # Build a vector of 10000 normal deviates with variance 0.5² and mean 2 >>> mu, sigma = 2, 0.5 >>> v = rg.normal(mu, sigma, 10000) >>> # Plot a normalized histogram...
程序说明 这些NumPy的数据有计算平均值mean和标准差std的方法。 要对矩阵进行标准化,我们需要减去均值得到一个零均值,以通过零均值并除以矩阵的标准差得到一个单位方差矩阵。
import numpy import pylab # Build a vector of 10000 normal deviates with variance 0.5^2 and mean 2 mu, sigma = 2, 0.5 v = numpy.random.normal(mu,sigma,10000) # Plot a normalized histogram with 50 bins pylab.hist(v, bins=50, normed=1) # matplotlib version (plot) pylab.show() # ...
# Build a vector of 10000 normal deviates with variance 0.5^2 and mean 2 mu, sigma = 2, 0.5 v = numpy.random.normal(mu,sigma,10000) # Plot a normalized histogram with 50 bins pylab.hist(v, bins=50, normed=1) # matplotlib version (plot) ...
numpy.ndarray.std ndarrays 的对应函数 numpy.std 等效函数 numpy.ma.sum 原文:numpy.org/doc/1.26/reference/generated/numpy.ma.sum.html ma.sum(self, axis=None, dtype=None, out=None, keepdims=<no value>) = <numpy.ma.core._frommethodobject> ...
import n mpy import pylab # B ild a vector of 10000 normal deviates with variance 0.5^2 and mean 2 m , sigma = 2, 0.5 v = n mpy.random.normal(m ,sigma,10000) # Plot a normalized histogram with 50 bins pylab.hist(v, bins=50, normed=1) # matplotlib version (plot) pyl ...