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<p>我尝试在我已经编写的函数中使用一个函数,它可以用于<code>list comprehension</code>和部分函数,但不能用于<code>lambda</code>函数。你知道吗</p>
<p>所以我的功能是:</p>
<pre><code>import numpy as np
from scipy.optimize import minimize
from _functools import partial
from sklearn.metrics import mean_squared_error
arpdau = np.random.randint(0,100,15)
def fitARPDAU(arpdau, max_cohortday, method, par=None):
valid = {'log', 'power', 'all'}
if method not in valid:
raise ValueError("results: method must be one of %r." % valid)
values = par
if method == 'log':
if values == None:
a = 1
b = 0
c = 1
values = [a, b, c]
bounds = [(1e-10, None), (1e-10, None), (None, None)]
def getArpdauFunction(x, values):
return values[0] * np.log(x + values[1]) + values[2]
elif method == 'power':
if values == None:
a = 1
b = 0
c = .5
d = 0
values = [a, b, c, d]
bounds = [(1e-10, None), (None, None), (1e-10, 1), (None, None)]
def getArpdauFunction(x, values):
return values[0] * (x + values[1]) ** values[2]+ values[3]
elif method == 'all':
log_loss = fitARPDAU(arpdau, max_cohortday, method='log', par=par)
power_loss = fitARPDAU(arpdau, max_cohortday, method='power', par=par)
combined_models = [log_loss, power_loss]
losses = map(lambda x: x[0].fun, combined_models)
return combined_models[np.argmin(losses)]
def getLossOptim(values):
# import ipdb; ipdb.set_trace()
# arpdau_pred = [getArpdauFunction(x, values) for x in range(max_cohortday)]
arpdau_pred_1 = map(lambda x: getArpdauFunction(x, values), range(max_cohortday))
# arpdau_pred_2 = partial(getArpdauFunction, values=values)(range(271))
return mean_squared_error(arpdau, arpdau_pred_1[:len(arpdau)])
result = minimize(getLossOptim, values, method='L-BFGS-B', bounds=bounds)
return result, [getArpdauFunction(x, result.x) for x in range(max_cohortday)], result.x, method, getArpdauFunction
print fitARPDAU(arpdau, 100, method='all', par=None)
</code></pre>
<p>在getLossOptim中,部分和列表理解可以工作,但是lambda函数不能工作,这有什么原因吗?你知道吗</p>
<p><code>lambda</code>函数返回</p>
<pre><code>NameError: global name 'getArpdauFunction' is not defined
</code></pre>
<p>谢谢!你知道吗</p>
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