FWIW, there are quite a few more files with non ASCII character:
[vagrant@localhost scikit-learn]$ find sklearn/ -name "*.py" -exec grep
--color='auto' -H -P -n "[\x80-\xFF]" '{}' \;
sklearn/naive_bayes.py:428: C.D. Manning, P. Raghavan and H. Sch��tze
(2008). Introduction to
sklearn/naive_bayes.py:429: Information Retrieval. Cambridge University
Press, pp. 234���265.
sklearn/naive_bayes.py:518: C.D. Manning, P. Raghavan and H. Sch��tze
(2008). Introduction to
sklearn/naive_bayes.py:519: Information Retrieval. Cambridge University
Press, pp. 234���265.
sklearn/naive_bayes.py:524: Text Categorization, pp. 41���48.
sklearn/naive_bayes.py:569: # Compute neg_prob �� (1 - X).T as
���neg_prob - X �� neg_prob
sklearn/kernel_approximation.py:32: Parameter of RBF kernel: exp(-��
�� x��)
sklearn/kernel_approximation.py:193: """Approximate feature map for
additive chi�� kernel.
sklearn/kernel_approximation.py:200: space is transformed into
2��sample_steps+1 features, where sample_steps is
sklearn/kernel_approximation.py:269: shape = (n_samples,
n_features �� (2��sample_steps + 1))
sklearn/kernel_approximation.py:360: Gamma parameter for the RBF,
polynomial, exponential chi�� and
sklearn/kernel_approximation.py:397: "Using the Nystr��m method to
speed up kernel machines",
sklearn/metrics/pairwise.py:386: K(x, y) = exp(-�� ||x-y||��)
sklearn/metrics/pairwise.py:459: k(x, y) = -������ [(x��� - y���)��
/ (x��� + y���)]
sklearn/metrics/pairwise.py:518: k(x, y) = exp(-�� ������ [(x��� -
y���)�� / (x��� + y���)])
sklearn/metrics/metrics.py:15:# Jochen Wersd��rfer <
joc...@wersdoerfer.de>
sklearn/metrics/metrics.py:2141: """R�� (coefficient of determination)
regression score function.
sklearn/metrics/metrics.py:2156: The R�� score.
sklearn/metrics/metrics.py:2162: Unlike most other scores, R�� score may
be negative (it need not actually
sklearn/feature_extraction/text.py:6:# Jochen Wersd��rfer <
joc...@wersdoerfer.de>
sklearn/feature_extraction/text.py:882: """Transform a count matrix to a
normalized tf or tf���idf representation
sklearn/feature_extraction/text.py:884: Tf means term-frequency while
tf���idf means term-frequency times inverse
sklearn/feature_extraction/text.py:888: The goal of using tf���idf
instead of the raw frequencies of occurrence of a
sklearn/feature_extraction/text.py:894: In the SMART notation used in
IR, this class implements several tf���idf
sklearn/feature_extraction/text.py:921: Information
Retrieval. Addison Wesley, pp. 68���74.`
sklearn/feature_extraction/text.py:923: .. [MSR2008] `C.D. Manning, H.
Sch��tze and P. Raghavan (2008). Introduction
sklearn/feature_extraction/text.py:925: pp. 121���125.`
sklearn/feature_extraction/text.py:961: """Transform a count matrix
to a tf or tf���idf representation
sklearn/feature_extraction/text.py:1228: """Transform raw text
documents to tf���idf vectors
sklearn/random_projection.py:56: distance between two points by a factor
(1 �� eps) in an euclidean space
sklearn/neighbors/nearest_centroid.py:59: When used for text
classification with tf���idf vectors, this classifier is
sklearn/feature_selection/univariate_selection.py:153: # Reuse f_obs for
���� statistics
sklearn/feature_selection/univariate_selection.py:163: """Compute ����
(chi-squared) statistic for each class/feature combination.
sklearn/feature_selection/univariate_selection.py:166: highest values
for the ���� (chi-square) statistic from X, which must
sklearn/feature_selection/univariate_selection.py:170: Recall that the
���� statistic measures dependence between stochastic
sklearn/semi_supervised/label_propagation.py:365: Bernhard Sch��lkopf.
Learning with local and global consistency (2004)
sklearn/cluster/dbscan_.py:77: Ester, M., H. P. Kriegel, J. Sander, and
X. Xu, ���A Density-Based
sklearn/cluster/dbscan_.py:78: Algorithm for Discovering Clusters in
Large Spatial Databases with Noise���.
sklearn/cluster/dbscan_.py:80: and Data Mining, Portland, OR, AAAI
Press, pp. 226���231. 1996
sklearn/cluster/dbscan_.py:221: Ester, M., H. P. Kriegel, J. Sander, and
X. Xu, ���A Density-Based
sklearn/cluster/dbscan_.py:222: Algorithm for Discovering Clusters in
Large Spatial Databases with Noise���.
sklearn/cluster/dbscan_.py:224: and Data Mining, Portland, OR, AAAI
Press, pp. 226���231. 1996
sklearn/cluster/spectral.py:304: Scaling factor of RBF, polynomial,
exponential chi�� and
David
On Mon, Aug 26, 2013 at 1:52 PM, Gael Varoquaux <
gael.varoqu...@normalesup.org> wrote:
> Hi Scikit-learn developers,
>
> I just removed a non-ASCII character from truncated_svd.py, because it
> was crashing a certain version of IPython when displaying the help.
>
> UTF-8 characters are not very pleasant to work with, as only a fraction
> of the world knows how to type them (that fraction depends on the
> corresponding character).
>
> I notice that they have creeped in a bunch of our files. I'll try to
> remove them (if I find time). I believe that we should really never set
> the encoding of our source files, and stick to plain ASCII.
>
> Cheers,
>
> Gaël
>
>
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