如何修复“ _gauss_basis（）在Python中为参数'axis'“ TypeError获得多个值？

Question

我正在用Jupyter Notebook写一本书的简单代码。它是Gaussian基本函数的代码，但我收到了 TypeError消息 TypeError: _gauss_basis() got multiple values for argument 'axis'。

下面是我的代码和结果：

    from sklearn.base import BaseEstimator, TransformerMixin
    import numpy as np
    import matplotlib.pyplot as plt

    class GaussianFeatures(BaseEstimator, TransformerMixin):
      def __init__(self, N, width_factor= 2):
        self.N= N
        self.width_factor= width_factor

      def _gauss_basis(x, y, width, axis= None):
        arg= (x- y)/ width
        return np.exp(-.5 * np.sum(arg ** 2, axis))

      def fit(self, X, y= None):
        self.centers_=np.linspace(X.min(), X.max(), self.N)
        self.width_=self.width_factor * (self.centers_[1] - self.centers_[0])
        return self

      def transform(self, X):
        return self._gauss_basis(X[:,:, np.newaxis], self.centers_, self.width_, axis= 1)

    gauss_model= make_pipeline(GaussianFeatures(20),LinearRegression())
    gauss_model.fit(x[:, np.newaxis], y)

    yfit= gauss_model.predict(xfit[:, np.newaxis])
    plt.scatter(x, y)
    plt.plot(xfit, yfit)
    plt.xlim(0, 10)

TypeError                                 Traceback (most recent call last)
<ipython-input-105-ce19f63fb83d> in <module>
     20 
     21 gauss_model= make_pipeline(GaussianFeatures(20),LinearRegression())
---> 22 gauss_model.fit(x[:, np.newaxis], y)
     23 yfit= gauss_model.predict(xfit[:, np.newaxis])
     24 

~/.local/lib/python3.5/site-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params)
    263             This estimator
    264         
--> 265         Xt, fit_params = self._fit(X, y, **fit_params)
    266         if self._final_estimator is not None:
    267             self._final_estimator.fit(Xt, y, **fit_params)
~/.local/lib/python3.5/site-packages/sklearn/pipeline.py in _fit(self, X, y, **fit_params)
    228                 Xt, fitted_transformer = fit_transform_one_cached(
    229                     cloned_transformer, Xt, y, None,
--> 230                     **fit_params_steps[name])
    231                 # Replace the transformer of the step with the fitted
    232                 # transformer. This is necessary when loading the transformer

~/.local/lib/python3.5/site-packages/sklearn/externals/joblib/memory.py in __call__(self, *args, **kwargs)
    340 
    341     def __call__(self, *args, **kwargs):
--> 342         return self.func(*args, **kwargs)
    343 
    344     def call_and_shelve(self, *args, **kwargs):

~/.local/lib/python3.5/site-packages/sklearn/pipeline.py in _fit_transform_one(transformer, X, y, weight, **fit_params)
    612 def _fit_transform_one(transformer, X, y, weight, **fit_params):
    613     if hasattr(transformer, 'fit_transform'):
--> 614         res = transformer.fit_transform(X, y, **fit_params)
    615     else:
    616         res = transformer.fit(X, y, **fit_params).transform(X)

~/.local/lib/python3.5/site-packages/sklearn/base.py in fit_transform(self, X, y, **fit_params)
    465         else:
    466             # fit method of arity 2 (supervised transformation)
--> 467             return self.fit(X, y, **fit_params).transform(X)
    468 
    469 

<ipython-input-105-ce19f63fb83d> in transform(self, X)
     17 
     18     def transform(self, X):
---> 19         return self._gauss_basis(X[:,:, np.newaxis], self.centers_, self.width_, axis= 1)
     20 
     21 gauss_model= make_pipeline(GaussianFeatures(20),LinearRegression())

TypeError: _gauss_basis() got multiple values for argument 'axis'