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This article introduces the relevant knowledge of "how to use the leastsq function in python". In the operation of actual cases, many people will encounter such a dilemma, so let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Leastsq action: minimizes the sum of squares of a set of equations.

Parameter settings:

Func error function

Parameters initialized by X0

Other additional parameters of args

For example:

First create a sample point

Import numpy as npimport scipy as spfrom scipy.optimize import leastsqimport matplotlib.pyplot as pltplt.rcParams ['font.sans-serif'] = [' SimHei'] plt.rcParams ['axes.unicode_minus'] = Falsex= [1meme 2meme 3meme 4] y = [2pjong 3meme 4meme 5]

Fitting line

Def y_pre (pmaine x): f=np.poly1d (p) return f (x)

Among them, np.polyld

F=np.poly1d ([1Jing 2jue 3]) # x ^ 2 + 2x+3f (1) "" 6 "

Error function

Def error (pmaine x ~ (y)): return y-y_pre (p ~ ~ () x)

And then it's easy.

P = [1Magne2] # write the value casually # y=w1*x+w2res=leastsq (error,p,args= (XPenagy)) w1gramres [0] # res [0] is the parameter list of wi "at this point w1 and w2 have been found. Here is a drawing to take a look at the" x_=np.linspace (1Min 10100) # isometric sequence. The fitting curve obtained by y_p=w1*x_+w2 # plt.scatter (XMagne y) # sample point plt.plot (Xerogramme) # draw the fitting curve

Can be directly encapsulated into a function

X=np.linspace (0jing2jing10) y=np.sin (np.pi*x) # original sample yearly = [y + np.random.normal (0Power0.1) for y in y] # np.random.normal (loc,scale,size): the mean of the normal distribution, the standard deviation of the normal distribution, the shape # np.random.randn () # the standard normal distribution is the normal distribution with 0 as the mean and 1 as the standard deviation, marked as N (0) 1) def fit (multiple 1): p=np.random.rand (multiple 1) # returns a random sample value or a set of random sample values that obey the uniform distribution of "zero one". The range of random sample values is [0Magne1) res=leastsq (error,p,args= (XMagany)) # wi value x_point=np.linspace (0Magne2100) # increase the amount of data to smooth y_point=np.sin (np.pi*x_point) # increase the amount of data in order to smooth plt.plot (x_point) Y_pre (res [0], x_point), 'baked mahogany labelings' fit') plt.scatter (XMagne yy) plt.legend () fit (3)

You can also output the middle result:

X=np.linspace (0jing2jing10) y=np.sin (np.pi*x) # original sample yearly = [y + np.random.normal (0Power0.1) for y in y] # np.random.normal (loc,scale,size): the mean of the normal distribution, the standard deviation of the normal distribution, the shape # np.random.randn () # the standard normal distribution is the normal distribution with 0 as the mean and 1 as the standard deviation, marked as N (0) 1) def fit (multiple 1): p=np.random.rand (multiple 1) # returns a random sample value or a set of random sample values that obey the uniform distribution of "zero one". The range of random sample values is [0Power1) res=leastsq (error,p,args= (xpeny)) # wi's value x_point=np.linspace (0meme2100) y_point=np.sin (np.pi*x_point) plt.plot (xpenalty pointline) plt.plot (res [0], x_point),'b' Label=' fitting') print (res [0]) plt.scatter (xQuery y _) plt.legend () fit (3)

The fitting straight line is:

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