8000 convert to floating point instead of using future · scikit-learn/scikit-learn@1d620d4 · GitHub
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SebastinSantySebastinSanty
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convert to floating point instead of using future
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4 files changed

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‎build_tools/travis/flake8_diff.sh‎

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@@ -138,7 +138,7 @@ if [[ "$MODIFIED_FILES" == "no_match" ]]; then
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echo "No file outside sklearn/externals and doc/sphinxext/sphinx_gallery has been modified"
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else
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# Default ignore PEP8 violations are from flake8 3.3.0
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DEFAULT_IGNORED_PEP8=E121,E123,E126,E226,E24,E704,F404,W503,W504
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DEFAULT_IGNORED_PEP8=E121,E123,E126,E226,E24,E704,W503,W504
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check_files "$(echo "$MODIFIED_FILES" | grep -v ^examples)" \
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--ignore $DEFAULT_IGNORED_PEP8
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# Examples are allowed to not have imports at top of file

‎examples/applications/plot_tomography_l1_reconstruction.py‎

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@@ -39,7 +39,6 @@ class :class:`sklearn.linear_model.Lasso`, that uses the coordinate descent
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# Author: Emmanuelle Gouillart <emmanuelle.gouillart@nsup.org>
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# License: BSD 3 clause
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from __future__ import division
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import numpy as np
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from scipy import sparse
@@ -102,7 +101,7 @@ def generate_synthetic_data():
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rs = np.random.RandomState(0)
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n_pts = 36
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x, y = np.ogrid[0:l, 0:l]
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mask_outer = (x - l / 2) ** 2 + (y - l / 2) ** 2 < (l / 2) ** 2
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mask_outer = (x - (l*1.0) / 2) ** 2 + (y - (l*1.0) / 2) ** 2 < ((l*1.0) / 2) ** 2
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mask = np.zeros((l, l))
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points = l * rs.rand(2, n_pts)
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mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1

‎examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py‎

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@@ -18,7 +18,6 @@
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input features would be to use univariate feature selection followed by a
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traditional (l2-penalised) logistic regression model.
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"""
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from __future__ import division
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import time
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import matplotlib.pyplot as plt

‎examples/linear_model/plot_sparse_logistic_regression_mnist.py‎

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@@ -52,7 +52,7 @@
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X_test = scaler.transform(X_test)
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# Turn up tolerance for faster convergence
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clf = LogisticRegression(C=50 / train_samples,
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clf = LogisticRegression(C=50. / train_samples,
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multi_class='multinomial',
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penalty='l1', solver='saga', tol=0.1)
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clf.fit(X_train, y_train)

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