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1.np.arange 创立指定步长
函数定义：
def arange(start=None, *args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__
"""
arange([start,] stop[, step,], dtype=None)
Return evenly spaced values within a given interval.
Values are generated within the half-open interval ``[start, stop)``
(in other words, the interval including `start` but excluding `stop`).
For integer arguments the function is equivalent to the Python built-in
`range` function, but returns an ndarray rather than a list.
When using a non-integer step, such as 0.1, the results will often not
be consistent. It is better to use `numpy.linspace` for these cases.
Parameters
----------
start : number, optional
Start of interval. The interval includes this value. The default
start value is 0.
stop : number
End of interval. The interval does not include this value, except
in some cases where `step` is not an integer and floating point
round-off affects the length of `out`.
step : number, optional
Spacing between values. For any output `out`, this is the distance
between two adjacent values, ``out[i+1] - out[i]``. The default
step size is 1. If `step` is specified as a position argument,
`start` must also be given.
dtype : dtype
The type of the output array. If `dtype` is not given, infer the data
type from the other input arguments.
Returns
-------
arange : ndarray
Array of evenly spaced values.
For floating point arguments, the length of the result is
``ceil((stop - start)/step)``. Because of floating point overflow,
this rule may result in the last element of `out` being greater
than `stop`.
See Also
--------
numpy.linspace : Evenly spaced numbers with careful handling of endpoints.
numpy.ogrid: Arrays of evenly spaced numbers in N-dimensions.
numpy.mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions.
"""
pass阐明：numpy.arange函数和常常应用的 range 函数十分的相似，只是多减少了一个 dtype 参数,dtype 参数的作用和 numpy.array 外面介绍的作用是统一的。
range()和 arange()只所以这么灵便，一方面是 python 的灵便的参数机制；另一方面是对接管的参数数目进行判断，依据参数数目的不同执行不同的操作。
示例代码：
# 指定起点
a = np.arange(10)
print(a)
print('--' * 20)
# 指定终点、起点
b = np.arange(1, 10)
print(b)
print('--' * 20)
# 指定终点、起点、步长
c = np.arange(1, 10, 2)
print(c)
print('--' * 20)
# 指定终点、起点、步长、dtype 类型
d = np.arange(1, 10, 2, float)
print(d)
print('--' * 20)
# 小数的状况也能应用 numpy，理论状况这样应用的比拟少
e = np.arange(0.1, 1.0, 0.1, float)
print(e)运行后果：
[0 1 2 3 4 5 6 7 8 9]
----------------------------------------
[1 2 3 4 5 6 7 8 9]
----------------------------------------
[1 3 5 7 9]
----------------------------------------
[1. 3. 5. 7. 9.]
----------------------------------------
[0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9]2.np.random.random 创立随机数
用于创立值范畴在 [0.0, 1.0) 区间的随机数组
函数定义：
def random(size=None): # real signature unknown; restored from __doc__
"""
random(size=None)
Return random floats in the half-open interval [0.0, 1.0). Alias for
`random_sample` to ease forward-porting to the new random API.
"""
pass通过介绍能够晓得，random 是 random_sample 的别名。咱们再来看一下 random_sample 函数。
def random_sample(size=None): # real signature unknown; restored from __doc__
"""
random_sample(size=None)
Return random floats in the half-open interval [0.0, 1.0).
Results are from the "continuous uniform" distribution over the
stated interval. To sample :math:`Unif[a, b), b > a` multiply
the output of `random_sample` by `(b-a)` and add `a`::
(b - a) * random_sample() + a
.. note::
New code should use the ``random`` method of a ``default_rng()``
instance instead; see `random-quick-start`.
Parameters
----------
size : int or tuple of ints, optional
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn. Default is None, in which case a
single value is returned.
Returns
-------
out : float or ndarray of floats
Array of random floats of shape `size` (unless ``size=None``, in which
case a single float is returned).
"""
pass示例代码：
import numpy as np
a1 = np.random.random(size=1)
a2 = np.random.random(size=(1,))
a3 = np.random.random_sample(size=(1,))
print(a1)
print("~~" * 10)
print(a2)
print("~~" * 10)
print(a3)
print('--' * 20)
b1 = np.random.random(size=(2, 3))
b2 = np.random.random_sample(size=(2, 3))
print(b1)
print("~~" * 10)
print(b2)
print("--" * 20)运行后果：
[0.12406671][0.51463238]
[0.89463238]
----------------------------------------
[[0.10907993 0.16789092 0.43668195]
[0.79106801 0.22137333 0.01017769]][[0.65803265 0.11789976 0.56492191]
[0.74975911 0.09096749 0.05589122]]
程序阐明：通过运行后果咱们能够看到 a1、a2、a3 这三个构造统一，阐明传递参数最终是以元组的模式进行解析的，另外一个就是 random 和 random_sample 成果统一。> 为了程序规规范性，倡议创立 ndarray 数组过程指定参数 size 以元组的模式传递。### 3.np.random.randint 创立随机整数
次要用于创立指定区间范畴的整数数据类型数组
函数定义：def randint(low, high=None, size=None, dtype=None): # real signature unknown; restored from doc
"""
randint(low, high=None, size=None, dtype=int)
Return random integers from `low` (inclusive) to `high` (exclusive).
Return random integers from the "discrete uniform" distribution of
the specified dtype in the "half-open" interval [`low`, `high`). If
`high` is None (the default), then results are from [0, `low`).
.. note::
New code should use the ``integers`` method of a ``default_rng()``
instance instead; see `random-quick-start`.
Parameters
----------
low : int or array-like of ints
Lowest (signed) integers to be drawn from the distribution (unless
``high=None``, in which case this parameter is one above the
*highest* such integer).
high : int or array-like of ints, optional
If provided, one above the largest (signed) integer to be drawn
from the distribution (see above for behavior if ``high=None``).
If array-like, must contain integer values
size : int or tuple of ints, optional
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn. Default is None, in which case a
single value is returned.
dtype : dtype, optional
Desired dtype of the result. Byteorder must be native.
The default value is int.
.. versionadded:: 1.11.0
Returns
-------
out : int or ndarray of ints
`size`-shaped array of random integers from the appropriate
distribution, or a single such random int if `size` not provided.
"""
pass
阐明：1. 参数 `low` 和参数 `high` 应用相似于 random 函数的应用办法
2. size 用法和下面 random 函数介绍的一样，倡议应用元组
3. dtype 函数用于指定数据类型，留神：** 因为 randint 自身曾经指定整数类型的范畴，所以不能指定非整形数据类型。**
示例代码：import numpy as np
指定起点
a1 = np.random.randint(10)
print(a1)
print(‘–‘ * 20)
指定终点、起点
b1 = np.random.randint(1, 10)
print(b1)
print(‘–‘ * 20)
指定终点、起点、大小
c1 = np.random.randint(1, 10, size=(2, 3))
print(c1)
print(‘–‘ * 20)
指定终点、起点、大小、数据类型
d1 = np.random.randint(1, 10, size=(2, 3), dtype=np.uint8)
print(d1)
print(‘–‘ * 20)
运行后果：9
9
[[9 8 6]
[1 1 5]]
[[6 3 8]
[9 9 5]]
### 4. 创立正态分布数组
#### 4.1 np.random.randn 创立规范正太散布
用于创立符合标准正态分布(冀望为 0，方差为 1)
函数定义
def randn(*dn): # known case of numpy.random.mtrand.randn
"""
randn(d0, d1, ..., dn)
Return a sample (or samples) from the "standard normal" distribution.
.. note::
This is a convenience function for users porting code from Matlab,
and wraps `standard_normal`. That function takes a
tuple to specify the size of the output, which is consistent with
other NumPy functions like `numpy.zeros` and `numpy.ones`.
.. note::
New code should use the ``standard_normal`` method of a ``default_rng()``
instance instead; see `random-quick-start`.
If positive int_like arguments are provided, `randn` generates an array
of shape ``(d0, d1, ..., dn)``, filled
with random floats sampled from a univariate "normal" (Gaussian)
distribution of mean 0 and variance 1. A single float randomly sampled
from the distribution is returned if no argument is provided.
Parameters
----------
d0, d1, ..., dn : int, optional
The dimensions of the returned array, must be non-negative.
If no argument is given a single Python float is returned.
Returns
-------
Z : ndarray or float
A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from
the standard normal distribution, or a single such float if
no parameters were supplied.
"""
pass
#### 4.2 np.random.common 指定方差和冀望
用于创立指定冀望和方差正态分布数据的数组
函数定义
def normal(loc=0.0, scale=1.0, size=None): # real signature unknown; restored from doc
"""
normal(loc=0.0, scale=1.0, size=None)
Draw random samples from a normal (Gaussian) distribution.
The probability density function of the normal distribution, first
derived by De Moivre and 200 years later by both Gauss and Laplace
independently [2]_, is often called the bell curve because of
its characteristic shape (see the example below).
The normal distributions occurs often in nature. For example, it
describes the commonly occurring distribution of samples influenced
by a large number of tiny, random disturbances, each with its own
unique distribution [2]_.
.. note::
New code should use the ``normal`` method of a ``default_rng()``
instance instead; see `random-quick-start`.
Parameters
----------
loc : float or array_like of floats
Mean ("centre") of the distribution.
scale : float or array_like of floats
Standard deviation (spread or "width") of the distribution. Must be
non-negative.
size : int or tuple of ints, optional
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn. If size is ``None`` (default),
a single value is returned if ``loc`` and ``scale`` are both scalars.
Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.
Returns
-------
out : ndarray or scalar
Drawn samples from the parameterized normal distribution.
See Also
--------
scipy.stats.norm : probability density function, distribution or
cumulative density function, etc.
Generator.normal: which should be used for new code.
Notes
-----
The probability density for the Gaussian distribution is
.. math:: p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2}}
e^{- \frac{ (x - \mu)^2 } {2 \sigma^2} },
where :math:`\mu` is the mean and :math:`\sigma` the standard
deviation. The square of the standard deviation, :math:`\sigma^2`,
is called the variance.
The function has its peak at the mean, and its "spread" increases with
the standard deviation (the function reaches 0.607 times its maximum at
:math:`x + \sigma` and :math:`x - \sigma` [2]_). This implies that
normal is more likely to return samples lying close to the mean, rather
than those far away.
References
----------
.. [1] Wikipedia, "Normal distribution",
https://en.wikipedia.org/wiki/Normal_distribution
.. [2] P. R. Peebles Jr., "Central Limit Theorem" in "Probability,
Random Variables and Random Signal Principles", 4th ed., 2001,
pp. 51, 51, 125.
"""
pass
示例代码：import numpy as np
a1 = np.random.randn(2)
a2 = np.random.normal(0, 1, 2)
print(a1)
print(‘~~’ * 10)
print(a2)
print(‘–‘ * 20)
b1 = np.random.randn(2, 3)
b2 = np.random.normal(0, 1, (2, 3))
print(b1)
print(‘~~’ * 10)
print(b2)
运行后果：[-0.08968467 0.19935229]
[-2.70345057 0.31810813]
----------------------------------------
[[0.26098236 0.59379753 -0.70686308]
[-0.78541554 -0.27910239 -0.15193886]][[-0.92466689 0.580677 0.80772163]
[2.17103711 -0.11340317 -0.06021829]]
---
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