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import matplotlib as mpl
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
plt.ion()
%matplotlib inline


    

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x = np.random.ranf(20) * 2
w = 0.5
b = 1
y = w * x + b + np.random.normal(loc=0, size=20, scale=0.2) * x
lx = np.linspace(0,2,20)
ly = lx * w + b


    

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ccs = plt.scatter(x, y)
plt.setp(ccs, "color", "orange")
ccs.set_label("样本")
plt.setp(ccs, "lw", 0.0001)
lines = plt.plot(lx, ly)
for line in lines:
    line.set_label("目标函数")
    
plt.legend()
plt.show()


    

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plt.setp(ccs)


    

    




  agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array 
  alpha: float or None 
  animated: bool 
  antialiased or antialiaseds: Boolean or sequence of booleans 
  array: ndarray
  clim: a length 2 sequence of floats 
  clip_box: a `~.Bbox` instance 
  clip_on: bool 
  clip_path: [(`~matplotlib.path.Path`, `~.Transform`) | `~.Patch` | None] 
  cmap: a colormap or registered colormap name 
  color: matplotlib color arg or sequence of rgba tuples
  contains: a callable function 
  edgecolor or edgecolors: matplotlib color spec or sequence of specs 
  facecolor or facecolors: matplotlib color spec or sequence of specs 
  figure: a `~.Figure` instance 
  gid: an id string 
  hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] 
  label: object 
  linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
  linewidth or linewidths or lw: float or sequence of floats 
  norm: `~.Normalize`
  offset_position: [ 'screen' | 'data' ] 
  offsets: float or sequence of floats 
  path_effects: `~.AbstractPathEffect` 
  paths: unknown
  picker: [None | bool | float | callable] 
  pickradius: float distance in points
  rasterized: bool or None 
  sizes: unknown
  sketch_params: (scale: float, length: float, randomness: float) 
  snap: bool or None 
  transform: `~.Transform` 
  url: a url string 
  urls: List[str] or None 
  visible: bool 
  zorder: float 

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ccs.set_label("aaa")


    

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help(np.random.normal)


    

    




Help on built-in function normal:

normal(...) method of mtrand.RandomState instance
    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]_.
    
    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.
    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.
    
    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
    `numpy.random.normal` is more likely to return samples lying close to
    the mean, rather than those far away.
    
    References
    ----------
    .. [1] Wikipedia, "Normal distribution",
           http://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.
    
    Examples
    --------
    Draw samples from the distribution:
    
    >>> mu, sigma = 0, 0.1 # mean and standard deviation
    >>> s = np.random.normal(mu, sigma, 1000)
    
    Verify the mean and the variance:
    
    >>> abs(mu - np.mean(s)) < 0.01
    True
    
    >>> abs(sigma - np.std(s, ddof=1)) < 0.01
    True
    
    Display the histogram of the samples, along with
    the probability density function:
    
    >>> import matplotlib.pyplot as plt
    >>> count, bins, ignored = plt.hist(s, 30, normed=True)
    >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
    ...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),
    ...          linewidth=2, color='r')
    >>> plt.show()

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