I'm trying to fit a logistic growth curve to my data using curve_fit using the following function as the input.
我正在尝试用curve_fit将逻辑斯蒂增长曲线拟合到我的数据中，使用下面的函数作为输入。
def logistic(x, y0, k, d, a, b):
if b > 0 and a > 0:
y = (k * pow(1 + np.exp(d - (a * b * x) ), (-1/b) )) + y0
elif b >= -1 or b < 0 or a < 0:
y = (k * pow(1 - np.exp(d - (a * b * x) ), (-1/b) )) + y0
return y
As you can see the function i am using has some restrictions on the values it can accept for parameter a and b. Any guess on how to handle the incorrect values? Should the input function raise an exception or return a dummy value? Thanks in advance.
正如您所看到的，我正在使用的函数对参数a和b可以接受的值有一些限制。输入函数应该引发异常还是返回一个虚拟值?提前谢谢。
1 个解决方案
#1
7
When the parameters fall out of the admissible range, return a wildly huge number (far from the data to be fitted). This will (hopefully) penalize this choice of parameters so much that curve_fit will settle on some other admissible set of parameters as optimal:
当参数超出允许范围时，返回一个非常大的数字(远离要拟合的数据)。这将(希望)对参数选择的惩罚如此之大，以至于curve_fit将把一些其他可接受的参数集确定为最优:
def logistic(x, y0, k, d, a, b):
if b > 0 and a > 0:
y = (k * pow(1 + np.exp(d - (a * b * x) ), (-1/b) )) + y0
elif b >= -1 or b < 0 or a < 0:
y = (k * pow(1 - np.exp(d - (a * b * x) ), (-1/b) )) + y0
else:
y = 1e10
return y
#1
7
When the parameters fall out of the admissible range, return a wildly huge number (far from the data to be fitted). This will (hopefully) penalize this choice of parameters so much that curve_fit will settle on some other admissible set of parameters as optimal:
当参数超出允许范围时，返回一个非常大的数字(远离要拟合的数据)。这将(希望)对参数选择的惩罚如此之大，以至于curve_fit将把一些其他可接受的参数集确定为最优:
def logistic(x, y0, k, d, a, b):
if b > 0 and a > 0:
y = (k * pow(1 + np.exp(d - (a * b * x) ), (-1/b) )) + y0
elif b >= -1 or b < 0 or a < 0:
y = (k * pow(1 - np.exp(d - (a * b * x) ), (-1/b) )) + y0
else:
y = 1e10
return y