一元拟合算法

整理临时用到的一元拟合算法

1.指数拟合

指数函数定义：

def exponential_func(x, a, b):
    return a**x + b


def func_fit(func, xdata, ydata):
    from scipy.optimize import curve_fit
    popt, pcov = curve_fit(func, xdata, ydata)
    return popt


if __name__=='__main__':
    import numpy as np
    xdata = np.linspace(0, 4, 50)
    y = exponential_func(xdata, 2.5, 1.3)
    popt= func_fit(exponential_func, xdata, y)
    print(popt)
# [2.5  1.3] 参数a,b的值

2.幂型拟合

def power_func(x, a, b):
    return x**a + b

if __name__=='__main__':
    import numpy as np
    xdata = np.linspace(0, 4, 50)
    y = power_func(xdata, 2.5, 1.3)
    popt= func_fit(power_func, xdata, y)
    print(popt)
# [2.5  1.3] 参数a,b的值

3.线性拟合

# 线性拟合
def linear_func(x, a, b):
    return a*x + b
xdata = np.linspace(0, 4, 50)
y = linear_func(xdata, 2.5, 1.3)
popt= func_fit(linear_func, xdata, y)

4.对数拟合

（后续补充）

5.多项式拟合

引用自https://www.cnblogs.com/volcao/p/9273612.html

# 多项式拟合，需确认是否由用户输入degree的值，还是自动寻优
def ploy_func(xdata, ydata, degree):
    """
    degree:用于控制多项式的次方
    """
    from sklearn.preprocessing import PolynomialFeatures
    from sklearn.linear_model import  LinearRegression
    ploy = PolynomialFeatures(degree=degree)
    xdata = xdata.reshape(-1,1)
    x_feature = ploy.fit_transform(xdata)  # 生成对应的多项式特征
    result = LinearRegression().fit(x_feature,ydata)
    return result.coef_,result.intercept_