python-numpy模块[自学笔记]

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import numpy as np

np.random.rand(n,2)
np.random.randn(n,2)        #normal

[[[[1]]]]]                np.random:
(1)
#rand(d0, d1, ..., dn)        随机(parm:(d1,d2...)shape纬度)
#randn(d0, d1, ..., dn)        正态(parm:(d1,d2...)shape纬度)
#randint(low[, high, size])        Return random integers from low (inclusive) to high (exclusive).
#random_integers(low[, high, size])        Return random integers between low and high, inclusive.
random([size])        Return random floats in the half-open interval [0.0, 1.0).
random_sample([size])        Return random floats in the half-open interval [0.0, 1.0).
ranf([size])        Return random floats in the half-open interval [0.0, 1.0).
sample([size])        Return random floats in the half-open interval [0.0, 1.0).
choice(a[, size, replace, p])        Generates a random sample from a given 1-D array ..
bytes(length)        Return random bytes.
(2)                # 打乱排序
#permutation(x)        有返回,不作用于本身,返回一个打乱的别的
#shuffle(x)        无返回,作用于本身
(3)                # 分布
#exponential([scale, size])        Draw samples from an exponential distribution.
#chisquare(df[, size])        Draw samples from a chi-square distribution.
#f(dfnum, dfden[, size])        Draw samples from an F distribution.
#gamma(shape[, scale, size])        Draw samples from a Gamma distribution.
#normal([loc, scale, size])        Draw random samples from a normal (Gaussian) distribution.
#poisson([lam, size])        Draw samples from a Poisson distribution.
#uniform([low, high, size])        Draw samples from a uniform distribution.
#standard_t(df[, size])        Draw samples from a standard Student’s t distribution with df degrees of freedom.
#binomial(n, p[, size])        Draw samples from a binomial distribution.
beta(a, b[, size])        Draw samples from a Beta distribution.
dirichlet(alpha[, size])        Draw samples from the Dirichlet distribution.
geometric(p[, size])        Draw samples from the geometric distribution.
gumbel([loc, scale, size])        Draw samples from a Gumbel distribution.
hypergeometric(ngood, nbad, nsample[, size])        Draw samples from a Hypergeometric distribution.
laplace([loc, scale, size])        Draw samples from the Laplace or double exponential distribution with specified location (or mean) and scale (decay).
logistic([loc, scale, size])        Draw samples from a logistic distribution.
lognormal([mean, sigma, size])        Draw samples from a log-normal distribution.
logseries(p[, size])        Draw samples from a logarithmic series distribution.
multinomial(n, pvals[, size])        Draw samples from a multinomial distribution.
multivariate_normal(mean, cov[, size])        Draw random samples from a multivariate normal distribution.
negative_binomial(n, p[, size])        Draw samples from a negative binomial distribution.
noncentral_chisquare(df, nonc[, size])        Draw samples from a noncentral chi-square distribution.
noncentral_f(dfnum, dfden, nonc[, size])        Draw samples from the noncentral F distribution.
pareto(a[, size])        Draw samples from a Pareto II or Lomax distribution with specified shape.
power(a[, size])        Draws samples in [0, 1] from a power distribution with positive exponent a - 1.
rayleigh([scale, size])        Draw samples from a Rayleigh distribution.
standard_cauchy([size])        Draw samples from a standard Cauchy distribution with mode = 0.
standard_exponential([size])        Draw samples from the standard exponential distribution.
standard_gamma(shape[, size])        Draw samples from a standard Gamma distribution.
standard_normal([size])        Draw samples from a standard Normal distribution (mean=0, stdev=1).
triangular(left, mode, right[, size])        Draw samples from the triangular distribution.
vonmises(mu, kappa[, size])        Draw samples from a von Mises distribution.
wald(mean, scale[, size])        Draw samples from a Wald, or inverse Gaussian, distribution.
weibull(a[, size])        Draw samples from a Weibull distribution.
zipf(a[, size])        Draw samples from a Zipf distribution.
(4)
Random generator
RandomState        Container for the Mersenne Twister pseudo-random number generator.
seed([seed])        Seed the generator.
get_state()        Return a tuple representing the internal state of the generator.
set_state(state)        Set the internal state of the generator from a tuple.

**** issubclass() ****
[[[[2]]]]
np.abs()
np.mean()
np.sum()
np.diag()                [np.diag(,k = -1)]:对角左移1个
np.dot(a,b)  =   a.dot(b)                # 矩阵乘法
.T                         # 转置
.inv(a)                # 求逆

numpy.ravel                                [Return a contiguous flattened array]
order : {‘C’,’F’, ‘A’, ‘K’}, optional
The elements of a are read using this index order. ‘C’ means to index the elements in row-major, C-style order, with the last axis index changing fastest, back to the first axis index changing slowest. ‘F’ means to index the elements in column-major, Fortran-style order, with the first index changing fastest, and the last index changing slowest. Note that the ‘C’ and ‘F’ options take no account of the memory layout of the underlying array, and only refer to the order of axis indexing. ‘A’ means to read the elements in Fortran-like index order if a is Fortran contiguous in memory, C-like order otherwise. ‘K’ means to read the elements in the order they occur in memory, except for reversing the data when strides are negative. By default, ‘C’ index order is used.

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关键词：python Numpy NUM distribution Multivariate

shape 应该是维度