fmin=optimize.fmin_powell报错

import numpy as np
import matplotlib.pyplot as plt
# Intialize random number generator
np.random.seed(123)
# True parameter values
alpha, sigma = 1, 1
beta = [1, 2.5]
# Size of dataset
size = 100
# Predictor variable
X1 = np.linspace(0, 1, size)
X2 = np.linspace(0,0.2, size)
# Simulate outcome variable
Y = alpha + beta[0]*X1 + beta[1]*X2 + np.random.randn(size)*sigma
from pymc3 import Model, Normal, HalfNormal
basic_model = Model()
basic_model = Model()
with basic_model:
    # Priors for unknown model parameters
    alpha = Normal('alpha', mu=0, sd=10)
    beta = Normal('beta', mu=0, sd=10, shape=2)
    sigma = HalfNormal('sigma', sd=1)
    # Expected value of outcome
    mu = alpha + beta[0]*X1 + beta[1]*X2
    # Likelihood (sampling distribution) of observations
    Y_obs = Normal('Y_obs', mu=mu, sd=sigma, observed=Y)
，
from scipy import optimize
map_estimate = find_MAP(model=basic_model, fmin=optimize.fmin_powell)
print(map_estimate)

错误提示为：

TypeError                                 Traceback (most recent call last)
<ipython-input-37-91a861868506> in <module>
      3     # obtain starting values via MAP
      4     #start = find_MAP()
----> 5     start = find_MAP(fmin=optimize.fmin_powell)
      6     # instantiate sampler
      7     step = NUTS(scaling=start)

~\anaconda3\lib\site-packages\pymc3\tuning\starting.py in find_MAP(start, vars, method, return_raw, include_transformed, progressbar, maxeval, model, *args, **kwargs)
    133 
    134     try:
--> 135         opt_result = minimize(cost_func, x0, method=method, jac=compute_gradient, *args, **kwargs)
    136         mx0 = opt_result["x"]  # r -> opt_result
    137     except (KeyboardInterrupt, StopIteration) as e:

TypeError: minimize() got an unexpected keyword argument 'fmin'

等待回答。

fmin参数现在已经换成了method参数，但是带进去还是有bug，其实可以这个去掉这个优化器指定，就可以运行了