举个例子:
import pylab as py
import numpy as np
from scipy import optimize
# Generate a
y = np.random.standard_normal(10000)
data = py.hist(y, bins = 100)
# Equation for Gaussian
def f(x, a, b, c):
return a * py.exp(-(x - b)**2.0 / (2 * c**2))
# Generate data from bins as a set of points
x = [0.5 * (data[1][i] + data[1][i+1]) for i in xrange(len(data[1])-1)]
y = data[0]
popt, pcov = optimize.curve_fit(f, x, y)
x_fit = py.linspace(x[0], x[-1], 100)
y_fit = f(x_fit, *popt)
plot(x_fit, y_fit, lw=4, color="r")
这将使高斯图适合于分布,您应该使用pcov
来给出定量的拟合程度。
确定数据是高斯分布还是任何分布的更好方法是Pearson卡方检验。需要进行一些练习才能理解,但这是一个非常强大的工具。