Python:为数据列表找到最适合的函数(Python: Find a best fit function for a list of data)
我知道许多概率函数在python中构建, random模块。
我想知道,如果给出浮标列表,是否有可能找到最适合列表的分布方程式?
我不知道numpy是否会这样做,但是这个函数可以与Excel的“趋势”函数进行比较(不相等,但相似)。
我会怎么做?
I am aware of many probabilistic functions builted-in python, with the random module.
I'd like to know if, given a list of floats, would be possible to find the distribution equation that best fits the list?
I don't know if numpy does it, but this function could be compared (not equal, but similar) with the Excel's "Trend" function.
How would I do that?
最满意答案
看看numpy.polyfit
numpy.polyfit(x, y, deg, rcond=None, full=False)¶ Least squares polynomial fit. Fit a polynomial p(x) = p[0] * x**deg + ... + p[deg] of degree deg to points (x, y). Returns a vector of coefficients p that minimises the squared error.Look at numpy.polyfit
numpy.polyfit(x, y, deg, rcond=None, full=False)¶ Least squares polynomial fit. Fit a polynomial p(x) = p[0] * x**deg + ... + p[deg] of degree deg to points (x, y). Returns a vector of coefficients p that minimises the squared error.更多推荐
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