Question

我正在尝试优化我构建的模型的参数。它是一个非常简单的模型预测山的水径流。大学课程的一部分：

def model(params, snowProportion,temperature):
    '''
    Calculates predicted runoff.
    '''
    K = params[0]
    p = params[1]
    tempThresh = params[2]
    meltDays = np.where(temperature > tempThresh)[0]
    accum = snowProportion*0.
    for d in meltDays:
        water = K * snowProportion[d]
        n = np.arange(len(snowProportion)) - d
        m = p ** n
        m[np.where(n<0)]=0
        accum += m * water
    np.savetxt('2005predicted.dat', accum)

params = [2000, 0.96, 9]

我被告知要使用scipy.optimize.fmin_cg;

所以我认为我做了一些事情：

x = scipy.optimize.fmin_cg(model, params, args=[snowProportion, temperature])

我不断遇到错误：

TypeError: 'numpy.ndarray' object is not callable

所以我假设我需要他们在列表中 - 但我遇到了同样的问题：

TypeError: 'list' object is not callable

我想更好地估计这些参数。 SnowProportion和温度的形状（365，）

RMSE：

将numpy导入为np import scipy.optimize

def RMSE(params,temperature, snowProportion):
    '''
    Calculates the RMSE of a model from measured and predicted.
    '''
    measured = np.loadtxt('/home/david/Documents/HydroM/runoff2005.dat')
    K = params[0]
    p = params[1]
    tempThresh = params[2]
    meltDays = np.where(temperature > tempThresh)[0]
    predicted = snowProportion*0.
    for d in meltDays:
        water = K * snowProportion[d]
        n = np.arange(len(snowProportion)) - d
        m = p ** n
        m[np.where(n<0)]=0
        predicted += m * water
    err = np.sqrt((measured - predicted) ** 2).mean()
    return err

Answer 1

scipy.optimize.fmin_cg的第一个参数 - 在您的代码中命名为model - 应该代表您想要最小化的函数（可能是您的模型估计的错误与给定的集合参数与某些参考值相比 ...）具体来说，它应该是返回标量值的函数算法需要知道模型在给定的一组参数下的“良好”程度。

Scipy.Optimize Python

1 个答案: