diff --git a/examples/applications/plot_species_distribution_modeling.py b/examples/applications/plot_species_distribution_modeling.py
index bb726ad644996a96b955feb1cb3bd1d5827d88b8..fedfd2c1e98c63cfd8406303436533a658c134f9 100644
--- a/examples/applications/plot_species_distribution_modeling.py
+++ b/examples/applications/plot_species_distribution_modeling.py
@@ -157,8 +157,8 @@ def plot_species_distribution(species=["bradypus_variegatus_0",
else:
print(" - plot coastlines from coverage")
plt.contour(X, Y, land_reference,
- levels=[-9999], colors="k",
- linestyles="solid")
+ levels=[-9999], colors="k",
+ linestyles="solid")
plt.xticks([])
plt.yticks([])
@@ -184,11 +184,11 @@ def plot_species_distribution(species=["bradypus_variegatus_0",
# scatter training/testing points
plt.scatter(species.pts_train['dd long'], species.pts_train['dd lat'],
- s=2 ** 2, c='black',
- marker='^', label='train')
+ s=2 ** 2, c='black',
+ marker='^', label='train')
plt.scatter(species.pts_test['dd long'], species.pts_test['dd lat'],
- s=2 ** 2, c='black',
- marker='x', label='test')
+ s=2 ** 2, c='black',
+ marker='x', label='test')
plt.legend()
plt.title(species.name)
plt.axis('equal')
diff --git a/examples/cluster/plot_adjusted_for_chance_measures.py b/examples/cluster/plot_adjusted_for_chance_measures.py
index 64ab40537291e7cc18dc233d67bb7703a49bd2fb..66298d3c5ba96dbd2bdaa3abffeba0e8e4b50bca 100644
--- a/examples/cluster/plot_adjusted_for_chance_measures.py
+++ b/examples/cluster/plot_adjusted_for_chance_measures.py
@@ -84,7 +84,7 @@ for score_func in score_funcs:
names.append(score_func.__name__)
plt.title("Clustering measures for 2 random uniform labelings\n"
- "with equal number of clusters")
+ "with equal number of clusters")
plt.xlabel('Number of clusters (Number of samples is fixed to %d)' % n_samples)
plt.ylabel('Score value')
plt.legend(plots, names)
@@ -115,7 +115,7 @@ for score_func in score_funcs:
names.append(score_func.__name__)
plt.title("Clustering measures for random uniform labeling\n"
- "against reference assignment with %d classes" % n_classes)
+ "against reference assignment with %d classes" % n_classes)
plt.xlabel('Number of clusters (Number of samples is fixed to %d)' % n_samples)
plt.ylabel('Score value')
plt.ylim(ymin=-0.05, ymax=1.05)
diff --git a/examples/cluster/plot_affinity_propagation.py b/examples/cluster/plot_affinity_propagation.py
index 4c62f72c3d31a9ea025e504ade3e8f904ddf37fa..0d6c395a4e4bfd07a835f042688e717c24fbf428 100644
--- a/examples/cluster/plot_affinity_propagation.py
+++ b/examples/cluster/plot_affinity_propagation.py
@@ -54,7 +54,7 @@ for k, col in zip(range(n_clusters_), colors):
cluster_center = X[cluster_centers_indices[k]]
plt.plot(X[class_members, 0], X[class_members, 1], col + '.')
plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,
- markeredgecolor='k', markersize=14)
+ markeredgecolor='k', markersize=14)
for x in X[class_members]:
plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col)
diff --git a/examples/cluster/plot_cluster_comparison.py b/examples/cluster/plot_cluster_comparison.py
index 59aa5060ad037e405414eae781565eb7fec6697a..b15c28aef2f63fd882b199392adb33564ded6313 100644
--- a/examples/cluster/plot_cluster_comparison.py
+++ b/examples/cluster/plot_cluster_comparison.py
@@ -48,7 +48,7 @@ colors = np.hstack([colors] * 20)
plt.figure(figsize=(17, 9.5))
plt.subplots_adjust(left=.001, right=.999, bottom=.001, top=.96, wspace=.05,
- hspace=.01)
+ hspace=.01)
plot_num = 1
for i_dataset, dataset in enumerate([noisy_circles, noisy_moons, blobs,
@@ -118,8 +118,8 @@ for i_dataset, dataset in enumerate([noisy_circles, noisy_moons, blobs,
plt.xticks(())
plt.yticks(())
plt.text(.99, .01, ('%.2fs' % (t1 - t0)).lstrip('0'),
- transform=plt.gca().transAxes, size=15,
- horizontalalignment='right')
+ transform=plt.gca().transAxes, size=15,
+ horizontalalignment='right')
plot_num += 1
plt.show()
diff --git a/examples/cluster/plot_dbscan.py b/examples/cluster/plot_dbscan.py
index cdec595afed5438166ebb853279776b6a6988034..46e315835a0483bc339e1443811f0204dfc2edca 100644
--- a/examples/cluster/plot_dbscan.py
+++ b/examples/cluster/plot_dbscan.py
@@ -62,11 +62,11 @@ for k, col in zip(unique_labels, colors):
xy = X[class_member_mask & core_samples_mask]
plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
- markeredgecolor='k', markersize=14)
+ markeredgecolor='k', markersize=14)
xy = X[class_member_mask & ~core_samples_mask]
plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
- markeredgecolor='k', markersize=6)
+ markeredgecolor='k', markersize=6)
plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.show()
diff --git a/examples/cluster/plot_dict_face_patches.py b/examples/cluster/plot_dict_face_patches.py
index 05db8c95b2007e037f199d372b99e0f0b878625b..18c4fcf43c342dcf441e6e1c2882cd7e0e656af0 100644
--- a/examples/cluster/plot_dict_face_patches.py
+++ b/examples/cluster/plot_dict_face_patches.py
@@ -72,13 +72,13 @@ plt.figure(figsize=(4.2, 4))
for i, patch in enumerate(kmeans.cluster_centers_):
plt.subplot(9, 9, i + 1)
plt.imshow(patch.reshape(patch_size), cmap=plt.cm.gray,
- interpolation='nearest')
+ interpolation='nearest')
plt.xticks(())
plt.yticks(())
plt.suptitle('Patches of faces\nTrain time %.1fs on %d patches' %
- (dt, 8 * len(faces.images)), fontsize=16)
+ (dt, 8 * len(faces.images)), fontsize=16)
plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
plt.show()
diff --git a/examples/cluster/plot_digits_agglomeration.py b/examples/cluster/plot_digits_agglomeration.py
index f058f3aca376b6c287acb2f476146cc04f3805b5..31d8094e1cf2d17b2a1affac4b9046077bc23197 100644
--- a/examples/cluster/plot_digits_agglomeration.py
+++ b/examples/cluster/plot_digits_agglomeration.py
@@ -46,7 +46,7 @@ for i in range(4):
plt.title('Original data')
plt.subplot(3, 4, 4 + i + 1)
plt.imshow(images_restored[i], cmap=plt.cm.gray, vmax=16,
- interpolation='nearest')
+ interpolation='nearest')
if i == 1:
plt.title('Agglomerated data')
plt.xticks(())
@@ -54,7 +54,7 @@ for i in range(4):
plt.subplot(3, 4, 10)
plt.imshow(np.reshape(agglo.labels_, images[0].shape),
- interpolation='nearest', cmap=plt.cm.spectral)
+ interpolation='nearest', cmap=plt.cm.spectral)
plt.xticks(())
plt.yticks(())
plt.title('Labels')
diff --git a/examples/cluster/plot_kmeans_digits.py b/examples/cluster/plot_kmeans_digits.py
index 10e468b64deb9000de0d99a0075317197c4e9a58..e56634d7ee202a75a8bf8759c732b680684e9714 100644
--- a/examples/cluster/plot_kmeans_digits.py
+++ b/examples/cluster/plot_kmeans_digits.py
@@ -108,18 +108,18 @@ Z = Z.reshape(xx.shape)
plt.figure(1)
plt.clf()
plt.imshow(Z, interpolation='nearest',
- extent=(xx.min(), xx.max(), yy.min(), yy.max()),
- cmap=plt.cm.Paired,
- aspect='auto', origin='lower')
+ extent=(xx.min(), xx.max(), yy.min(), yy.max()),
+ cmap=plt.cm.Paired,
+ aspect='auto', origin='lower')
plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)
# Plot the centroids as a white X
centroids = kmeans.cluster_centers_
plt.scatter(centroids[:, 0], centroids[:, 1],
- marker='x', s=169, linewidths=3,
- color='w', zorder=10)
+ marker='x', s=169, linewidths=3,
+ color='w', zorder=10)
plt.title('K-means clustering on the digits dataset (PCA-reduced data)\n'
- 'Centroids are marked with white cross')
+ 'Centroids are marked with white cross')
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
diff --git a/examples/cluster/plot_kmeans_stability_low_dim_dense.py b/examples/cluster/plot_kmeans_stability_low_dim_dense.py
index 8fc8599f65955952668716267e7b05ff7251c7e5..2783ce01a35c21b9919d408a7ac3d057b85353cb 100644
--- a/examples/cluster/plot_kmeans_stability_low_dim_dense.py
+++ b/examples/cluster/plot_kmeans_stability_low_dim_dense.py
@@ -112,8 +112,8 @@ for k in range(n_clusters):
plt.plot(X[my_members, 0], X[my_members, 1], 'o', marker='.', c=color)
cluster_center = km.cluster_centers_[k]
plt.plot(cluster_center[0], cluster_center[1], 'o',
- markerfacecolor=color, markeredgecolor='k', markersize=6)
+ markerfacecolor=color, markeredgecolor='k', markersize=6)
plt.title("Example cluster allocation with a single random init\n"
- "with MiniBatchKMeans")
+ "with MiniBatchKMeans")
plt.show()
diff --git a/examples/cluster/plot_lena_segmentation.py b/examples/cluster/plot_lena_segmentation.py
index 11ec1e49c1d3269e5c1872aefcce2db01c83ce83..0a7ffe09e9ad622b6274c6982990839e351c5034 100644
--- a/examples/cluster/plot_lena_segmentation.py
+++ b/examples/cluster/plot_lena_segmentation.py
@@ -66,7 +66,7 @@ for assign_labels in ('kmeans', 'discretize'):
plt.imshow(lena, cmap=plt.cm.gray)
for l in range(N_REGIONS):
plt.contour(labels == l, contours=1,
- colors=[plt.cm.spectral(l / float(N_REGIONS)), ])
+ colors=[plt.cm.spectral(l / float(N_REGIONS)), ])
plt.xticks(())
plt.yticks(())
plt.title('Spectral clustering: %s, %.2fs' % (assign_labels, (t1 - t0)))
diff --git a/examples/cluster/plot_lena_ward_segmentation.py b/examples/cluster/plot_lena_ward_segmentation.py
index 060104f14705aefac2a4d4362878285e2ff74067..2d313255888956e7d69c7d96df9b32a9445f8132 100644
--- a/examples/cluster/plot_lena_ward_segmentation.py
+++ b/examples/cluster/plot_lena_ward_segmentation.py
@@ -50,7 +50,7 @@ plt.figure(figsize=(5, 5))
plt.imshow(lena, cmap=plt.cm.gray)
for l in range(n_clusters):
plt.contour(label == l, contours=1,
- colors=[plt.cm.spectral(l / float(n_clusters)), ])
+ colors=[plt.cm.spectral(l / float(n_clusters)), ])
plt.xticks(())
plt.yticks(())
plt.show()
diff --git a/examples/cluster/plot_mean_shift.py b/examples/cluster/plot_mean_shift.py
index a4cae0753de9bfad884de042c8b770eec30f175f..775cd98e595279ba45465ce53bf166075f4e2c9d 100644
--- a/examples/cluster/plot_mean_shift.py
+++ b/examples/cluster/plot_mean_shift.py
@@ -51,6 +51,6 @@ for k, col in zip(range(n_clusters_), colors):
cluster_center = cluster_centers[k]
plt.plot(X[my_members, 0], X[my_members, 1], col + '.')
plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,
- markeredgecolor='k', markersize=14)
+ markeredgecolor='k', markersize=14)
plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.show()
diff --git a/examples/cluster/plot_mini_batch_kmeans.py b/examples/cluster/plot_mini_batch_kmeans.py
index abf2afcb7a440c411f6b90333d4a86a8465a53a4..ed4afcf9957944d281ff329c5c3c6d9a5934473d 100644
--- a/examples/cluster/plot_mini_batch_kmeans.py
+++ b/examples/cluster/plot_mini_batch_kmeans.py
@@ -97,7 +97,7 @@ ax.set_title('MiniBatchKMeans')
ax.set_xticks(())
ax.set_yticks(())
plt.text(-3.5, 1.8, 'train time: %.2fs\ninertia: %f' %
- (t_mini_batch, mbk.inertia_))
+ (t_mini_batch, mbk.inertia_))
# Initialise the different array to all False
different = (mbk_means_labels == 4)
diff --git a/examples/covariance/plot_covariance_estimation.py b/examples/covariance/plot_covariance_estimation.py
index b90b07a7e7a81112b8a2e1b801134e493f78d602..85e26705b03e955e7c43f0fbdb20c4d016c65a93 100644
--- a/examples/covariance/plot_covariance_estimation.py
+++ b/examples/covariance/plot_covariance_estimation.py
@@ -104,7 +104,7 @@ plt.ylabel('Error: negative log-likelihood on test data')
plt.loglog(shrinkages, negative_logliks, label="Negative log-likelihood")
plt.plot(plt.xlim(), 2 * [loglik_real], '--r',
- label="Real covariance likelihood")
+ label="Real covariance likelihood")
# adjust view
lik_max = np.amax(negative_logliks)
@@ -115,14 +115,14 @@ xmin = shrinkages[0]
xmax = shrinkages[-1]
# LW likelihood
plt.vlines(lw.shrinkage_, ymin, -loglik_lw, color='magenta',
- linewidth=3, label='Ledoit-Wolf estimate')
+ linewidth=3, label='Ledoit-Wolf estimate')
# OAS likelihood
plt.vlines(oa.shrinkage_, ymin, -loglik_oa, color='purple',
- linewidth=3, label='OAS estimate')
+ linewidth=3, label='OAS estimate')
# best CV estimator likelihood
plt.vlines(cv.best_estimator_.shrinkage, ymin,
- -cv.best_estimator_.score(X_test), color='cyan',
- linewidth=3, label='Cross-validation best estimate')
+ -cv.best_estimator_.score(X_test), color='cyan',
+ linewidth=3, label='Cross-validation best estimate')
plt.ylim(ymin, ymax)
plt.xlim(xmin, xmax)
diff --git a/examples/covariance/plot_lw_vs_oas.py b/examples/covariance/plot_lw_vs_oas.py
index 59267bf9c253945fe93a5fa56b5fd3e67618e2f8..d7b21f2bbe68d48c9e818c58a17761f42743f3c0 100644
--- a/examples/covariance/plot_lw_vs_oas.py
+++ b/examples/covariance/plot_lw_vs_oas.py
@@ -60,9 +60,9 @@ for i, n_samples in enumerate(n_samples_range):
# plot MSE
plt.subplot(2, 1, 1)
plt.errorbar(n_samples_range, lw_mse.mean(1), yerr=lw_mse.std(1),
- label='Ledoit-Wolf', color='g')
+ label='Ledoit-Wolf', color='g')
plt.errorbar(n_samples_range, oa_mse.mean(1), yerr=oa_mse.std(1),
- label='OAS', color='r')
+ label='OAS', color='r')
plt.ylabel("Squared error")
plt.legend(loc="upper right")
plt.title("Comparison of covariance estimators")
@@ -71,9 +71,9 @@ plt.xlim(5, 31)
# plot shrinkage coefficient
plt.subplot(2, 1, 2)
plt.errorbar(n_samples_range, lw_shrinkage.mean(1), yerr=lw_shrinkage.std(1),
- label='Ledoit-Wolf', color='g')
+ label='Ledoit-Wolf', color='g')
plt.errorbar(n_samples_range, oa_shrinkage.mean(1), yerr=oa_shrinkage.std(1),
- label='OAS', color='r')
+ label='OAS', color='r')
plt.xlabel("n_samples")
plt.ylabel("Shrinkage")
plt.legend(loc="lower right")
diff --git a/examples/covariance/plot_robust_vs_empirical_covariance.py b/examples/covariance/plot_robust_vs_empirical_covariance.py
index 6ac597912445a4de2f0edca2b3dbe97fc15f9a3f..234396e50f0909d0bf55398470e61ef854cab920 100644
--- a/examples/covariance/plot_robust_vs_empirical_covariance.py
+++ b/examples/covariance/plot_robust_vs_empirical_covariance.py
@@ -113,14 +113,14 @@ for i, n_outliers in enumerate(range_n_outliers):
font_prop = matplotlib.font_manager.FontProperties(size=11)
plt.subplot(2, 1, 1)
plt.errorbar(range_n_outliers, err_loc_mcd.mean(1),
- yerr=err_loc_mcd.std(1) / np.sqrt(repeat),
- label="Robust location", color='m')
+ yerr=err_loc_mcd.std(1) / np.sqrt(repeat),
+ label="Robust location", color='m')
plt.errorbar(range_n_outliers, err_loc_emp_full.mean(1),
- yerr=err_loc_emp_full.std(1) / np.sqrt(repeat),
- label="Full data set mean", color='green')
+ yerr=err_loc_emp_full.std(1) / np.sqrt(repeat),
+ label="Full data set mean", color='green')
plt.errorbar(range_n_outliers, err_loc_emp_pure.mean(1),
- yerr=err_loc_emp_pure.std(1) / np.sqrt(repeat),
- label="Pure data set mean", color='black')
+ yerr=err_loc_emp_pure.std(1) / np.sqrt(repeat),
+ label="Pure data set mean", color='black')
plt.title("Influence of outliers on the location estimation")
plt.ylabel(r"Error ($||\mu - \hat{\mu}||_2^2$)")
plt.legend(loc="upper left", prop=font_prop)
@@ -128,18 +128,18 @@ plt.legend(loc="upper left", prop=font_prop)
plt.subplot(2, 1, 2)
x_size = range_n_outliers.size
plt.errorbar(range_n_outliers, err_cov_mcd.mean(1),
- yerr=err_cov_mcd.std(1),
- label="Robust covariance (mcd)", color='m')
+ yerr=err_cov_mcd.std(1),
+ label="Robust covariance (mcd)", color='m')
plt.errorbar(range_n_outliers[:(x_size / 5 + 1)],
- err_cov_emp_full.mean(1)[:(x_size / 5 + 1)],
- yerr=err_cov_emp_full.std(1)[:(x_size / 5 + 1)],
- label="Full data set empirical covariance", color='green')
+ err_cov_emp_full.mean(1)[:(x_size / 5 + 1)],
+ yerr=err_cov_emp_full.std(1)[:(x_size / 5 + 1)],
+ label="Full data set empirical covariance", color='green')
plt.plot(range_n_outliers[(x_size / 5):(x_size / 2 - 1)],
- err_cov_emp_full.mean(1)[(x_size / 5):(x_size / 2 - 1)], color='green',
- ls='--')
+ err_cov_emp_full.mean(1)[(x_size / 5):(x_size / 2 - 1)], color='green',
+ ls='--')
plt.errorbar(range_n_outliers, err_cov_emp_pure.mean(1),
- yerr=err_cov_emp_pure.std(1),
- label="Pure data set empirical covariance", color='black')
+ yerr=err_cov_emp_pure.std(1),
+ label="Pure data set empirical covariance", color='black')
plt.title("Influence of outliers on the covariance estimation")
plt.xlabel("Amount of contamination (%)")
plt.ylabel("RMSE")
diff --git a/examples/covariance/plot_sparse_cov.py b/examples/covariance/plot_sparse_cov.py
index ee7bfe4dd3e8cb5c8c2067959cbfa3057133b000..237b33a9ba1b954ad433dc5a57e7980f528029f2 100644
--- a/examples/covariance/plot_sparse_cov.py
+++ b/examples/covariance/plot_sparse_cov.py
@@ -103,7 +103,7 @@ vmax = cov_.max()
for i, (name, this_cov) in enumerate(covs):
plt.subplot(2, 4, i + 1)
plt.imshow(this_cov, interpolation='nearest', vmin=-vmax, vmax=vmax,
- cmap=plt.cm.RdBu_r)
+ cmap=plt.cm.RdBu_r)
plt.xticks(())
plt.yticks(())
plt.title('%s covariance' % name)
@@ -116,8 +116,8 @@ vmax = .9 * prec_.max()
for i, (name, this_prec) in enumerate(precs):
ax = plt.subplot(2, 4, i + 5)
plt.imshow(np.ma.masked_equal(this_prec, 0),
- interpolation='nearest', vmin=-vmax, vmax=vmax,
- cmap=plt.cm.RdBu_r)
+ interpolation='nearest', vmin=-vmax, vmax=vmax,
+ cmap=plt.cm.RdBu_r)
plt.xticks(())
plt.yticks(())
plt.title('%s precision' % name)
diff --git a/examples/cross_decomposition/plot_compare_cross_decomposition.py b/examples/cross_decomposition/plot_compare_cross_decomposition.py
index b9284a95fcb0c74a2d0440732cc2f4e3d6ff8304..f582be9e000489e1c22ea862039662b100f787d5 100644
--- a/examples/cross_decomposition/plot_compare_cross_decomposition.py
+++ b/examples/cross_decomposition/plot_compare_cross_decomposition.py
@@ -66,7 +66,7 @@ plt.plot(X_test_r[:, 0], Y_test_r[:, 0], "or", label="test")
plt.xlabel("x scores")
plt.ylabel("y scores")
plt.title('Comp. 1: X vs Y (test corr = %.2f)' %
- np.corrcoef(X_test_r[:, 0], Y_test_r[:, 0])[0, 1])
+ np.corrcoef(X_test_r[:, 0], Y_test_r[:, 0])[0, 1])
plt.xticks(())
plt.yticks(())
plt.legend(loc="best")
@@ -77,7 +77,7 @@ plt.plot(X_test_r[:, 1], Y_test_r[:, 1], "or", label="test")
plt.xlabel("x scores")
plt.ylabel("y scores")
plt.title('Comp. 2: X vs Y (test corr = %.2f)' %
- np.corrcoef(X_test_r[:, 1], Y_test_r[:, 1])[0, 1])
+ np.corrcoef(X_test_r[:, 1], Y_test_r[:, 1])[0, 1])
plt.xticks(())
plt.yticks(())
plt.legend(loc="best")
@@ -89,7 +89,7 @@ plt.plot(X_test_r[:, 0], X_test_r[:, 1], "*r", label="test")
plt.xlabel("X comp. 1")
plt.ylabel("X comp. 2")
plt.title('X comp. 1 vs X comp. 2 (test corr = %.2f)'
- % np.corrcoef(X_test_r[:, 0], X_test_r[:, 1])[0, 1])
+ % np.corrcoef(X_test_r[:, 0], X_test_r[:, 1])[0, 1])
plt.legend(loc="best")
plt.xticks(())
plt.yticks(())
@@ -100,7 +100,7 @@ plt.plot(Y_test_r[:, 0], Y_test_r[:, 1], "*r", label="test")
plt.xlabel("Y comp. 1")
plt.ylabel("Y comp. 2")
plt.title('Y comp. 1 vs Y comp. 2 , (test corr = %.2f)'
- % np.corrcoef(Y_test_r[:, 0], Y_test_r[:, 1])[0, 1])
+ % np.corrcoef(Y_test_r[:, 0], Y_test_r[:, 1])[0, 1])
plt.legend(loc="best")
plt.xticks(())
plt.yticks(())
diff --git a/examples/datasets/plot_random_dataset.py b/examples/datasets/plot_random_dataset.py
index 7a033a3128f83e9b3774b10de29a06dd6e78d302..be5108754c145fe1537581856dfa7e0cd4d619a4 100644
--- a/examples/datasets/plot_random_dataset.py
+++ b/examples/datasets/plot_random_dataset.py
@@ -41,7 +41,7 @@ plt.scatter(X2[:, 0], X2[:, 1], marker='o', c=Y2)
plt.subplot(224)
plt.title("Multi-class, two informative features, one cluster",
- fontsize='small')
+ fontsize='small')
X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=2,
n_clusters_per_class=1, n_classes=3)
plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)
diff --git a/examples/decomposition/plot_faces_decomposition.py b/examples/decomposition/plot_faces_decomposition.py
index 1ec454eba64eb05f6a811f09ef0ebe87464ef31a..e85da1b952253754453a3d849190e60123b7266e 100644
--- a/examples/decomposition/plot_faces_decomposition.py
+++ b/examples/decomposition/plot_faces_decomposition.py
@@ -56,8 +56,8 @@ def plot_gallery(title, images, n_col=n_col, n_row=n_row):
plt.subplot(n_row, n_col, i + 1)
vmax = max(comp.max(), -comp.min())
plt.imshow(comp.reshape(image_shape), cmap=plt.cm.gray,
- interpolation='nearest',
- vmin=-vmax, vmax=vmax)
+ interpolation='nearest',
+ vmin=-vmax, vmax=vmax)
plt.xticks(())
plt.yticks(())
plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)
diff --git a/examples/decomposition/plot_image_denoising.py b/examples/decomposition/plot_image_denoising.py
index ca545005780c39ab6656507411686dc35aea24c4..01cfdfbe1f0a0198e274eb3ad6c016bab889fba6 100644
--- a/examples/decomposition/plot_image_denoising.py
+++ b/examples/decomposition/plot_image_denoising.py
@@ -83,12 +83,12 @@ plt.figure(figsize=(4.2, 4))
for i, comp in enumerate(V[:100]):
plt.subplot(10, 10, i + 1)
plt.imshow(comp.reshape(patch_size), cmap=plt.cm.gray_r,
- interpolation='nearest')
+ interpolation='nearest')
plt.xticks(())
plt.yticks(())
plt.suptitle('Dictionary learned from Lena patches\n' +
- 'Train time %.1fs on %d patches' % (dt, len(data)),
- fontsize=16)
+ 'Train time %.1fs on %d patches' % (dt, len(data)),
+ fontsize=16)
plt.subplots_adjust(0.08, 0.02, 0.92, 0.85, 0.08, 0.23)
@@ -108,7 +108,7 @@ def show_with_diff(image, reference, title):
plt.title('Difference (norm: %.2f)' % np.sqrt(np.sum(difference ** 2)))
plt.imshow(difference, vmin=-0.5, vmax=0.5, cmap=plt.cm.PuOr,
- interpolation='nearest')
+ interpolation='nearest')
plt.xticks(())
plt.yticks(())
plt.suptitle(title, size=16)
diff --git a/examples/decomposition/plot_pca_vs_fa_model_selection.py b/examples/decomposition/plot_pca_vs_fa_model_selection.py
index aaa21c1fd1aa3267cef7f1b1c8bc3a33363cfaa9..ec01d91cb140759f9b7aff66756abacfc30752ca 100755
--- a/examples/decomposition/plot_pca_vs_fa_model_selection.py
+++ b/examples/decomposition/plot_pca_vs_fa_model_selection.py
@@ -106,17 +106,17 @@ for X, title in [(X_homo, 'Homoscedastic Noise'),
plt.plot(n_components, fa_scores, 'r', label='FA scores')
plt.axvline(rank, color='g', label='TRUTH: %d' % rank, linestyle='-')
plt.axvline(n_components_pca, color='b',
- label='PCA CV: %d' % n_components_pca, linestyle='--')
+ label='PCA CV: %d' % n_components_pca, linestyle='--')
plt.axvline(n_components_fa, color='r',
- label='FactorAnalysis CV: %d' % n_components_fa, linestyle='--')
+ label='FactorAnalysis CV: %d' % n_components_fa, linestyle='--')
plt.axvline(n_components_pca_mle, color='k',
- label='PCA MLE: %d' % n_components_pca_mle, linestyle='--')
+ label='PCA MLE: %d' % n_components_pca_mle, linestyle='--')
# compare with other covariance estimators
plt.axhline(shrunk_cov_score(X), color='violet',
- label='Shrunk Covariance MLE', linestyle='-.')
+ label='Shrunk Covariance MLE', linestyle='-.')
plt.axhline(lw_score(X), color='orange',
- label='LedoitWolf MLE' % n_components_pca_mle, linestyle='-.')
+ label='LedoitWolf MLE' % n_components_pca_mle, linestyle='-.')
plt.xlabel('nb of components')
plt.ylabel('CV scores')
diff --git a/examples/exercises/plot_cv_diabetes.py b/examples/exercises/plot_cv_diabetes.py
index edccbc33d37fecb2c705b11d0ca5c7d8b2e7ef40..5e07a4f2dc99648180a6cfcd7dc52bc042190d5e 100644
--- a/examples/exercises/plot_cv_diabetes.py
+++ b/examples/exercises/plot_cv_diabetes.py
@@ -38,9 +38,9 @@ plt.figure(figsize=(4, 3))
plt.semilogx(alphas, scores)
# plot error lines showing +/- std. errors of the scores
plt.semilogx(alphas, np.array(scores) + np.array(scores_std) / np.sqrt(len(X)),
- 'b--')
+ 'b--')
plt.semilogx(alphas, np.array(scores) - np.array(scores_std) / np.sqrt(len(X)),
- 'b--')
+ 'b--')
plt.ylabel('CV score')
plt.xlabel('alpha')
plt.axhline(np.max(scores), linestyle='--', color='.5')
diff --git a/examples/exercises/plot_iris_exercise.py b/examples/exercises/plot_iris_exercise.py
index 1c73c0272a05d5bbef2e53978302cbfeb739e21b..cf1da9109aaaf0b210ca481c8440275e087eeaf1 100644
--- a/examples/exercises/plot_iris_exercise.py
+++ b/examples/exercises/plot_iris_exercise.py
@@ -59,7 +59,7 @@ for fig_num, kernel in enumerate(('linear', 'rbf', 'poly')):
Z = Z.reshape(XX.shape)
plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired)
plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'],
- levels=[-.5, 0, .5])
+ levels=[-.5, 0, .5])
plt.title(kernel)
plt.show()
diff --git a/examples/linear_model/plot_ard.py b/examples/linear_model/plot_ard.py
index 8927022070a12ef2a9ac188babd16f6865e8ae1c..89a8fa07f48bed1b32b321880f5ee36d7db1e3c4 100644
--- a/examples/linear_model/plot_ard.py
+++ b/examples/linear_model/plot_ard.py
@@ -69,7 +69,7 @@ plt.figure(figsize=(6, 5))
plt.title("Histogram of the weights")
plt.hist(clf.coef_, bins=n_features, log=True)
plt.plot(clf.coef_[relevant_features], 5 * np.ones(len(relevant_features)),
- 'ro', label="Relevant features")
+ 'ro', label="Relevant features")
plt.ylabel("Features")
plt.xlabel("Values of the weights")
plt.legend(loc=1)
diff --git a/examples/linear_model/plot_bayesian_ridge.py b/examples/linear_model/plot_bayesian_ridge.py
index 0d2bdb81c3c8976925bbaec63397624f4f3fd3e8..c268ab95f119fe53cde7e2ef8ba2b8986aedf178 100644
--- a/examples/linear_model/plot_bayesian_ridge.py
+++ b/examples/linear_model/plot_bayesian_ridge.py
@@ -65,7 +65,7 @@ plt.figure(figsize=(6, 5))
plt.title("Histogram of the weights")
plt.hist(clf.coef_, bins=n_features, log=True)
plt.plot(clf.coef_[relevant_features], 5 * np.ones(len(relevant_features)),
- 'ro', label="Relevant features")
+ 'ro', label="Relevant features")
plt.ylabel("Features")
plt.xlabel("Values of the weights")
plt.legend(loc="lower left")
diff --git a/examples/linear_model/plot_lasso_and_elasticnet.py b/examples/linear_model/plot_lasso_and_elasticnet.py
index cd032d83ecbc0024e494a9748c4026a629643904..c9a1fbb2c3196cf0ca83fa485c51dda18a7a0685 100644
--- a/examples/linear_model/plot_lasso_and_elasticnet.py
+++ b/examples/linear_model/plot_lasso_and_elasticnet.py
@@ -63,5 +63,5 @@ plt.plot(lasso.coef_, label='Lasso coefficients')
plt.plot(coef, '--', label='original coefficients')
plt.legend(loc='best')
plt.title("Lasso R^2: %f, Elastic Net R^2: %f"
- % (r2_score_lasso, r2_score_enet))
+ % (r2_score_lasso, r2_score_enet))
plt.show()
diff --git a/examples/linear_model/plot_lasso_coordinate_descent_path.py b/examples/linear_model/plot_lasso_coordinate_descent_path.py
index d8afd3c479516e99436a7bdfcf5f742020f72677..d4c1bd79f7f8a84887ac98f92055756c5cbccfd1 100644
--- a/examples/linear_model/plot_lasso_coordinate_descent_path.py
+++ b/examples/linear_model/plot_lasso_coordinate_descent_path.py
@@ -83,6 +83,6 @@ plt.xlabel('-Log(alpha)')
plt.ylabel('coefficients')
plt.title('Elastic-Net and positive Elastic-Net')
plt.legend((l1[-1], l2[-1]), ('Elastic-Net', 'positive Elastic-Net'),
- loc='lower left')
+ loc='lower left')
plt.axis('tight')
plt.show()
diff --git a/examples/linear_model/plot_lasso_model_selection.py b/examples/linear_model/plot_lasso_model_selection.py
index 8fb6b32d13a406d0cf16e060f7e41db9117a65f1..10bdc9b5f1d406a93bb09ef64d7129d6190caaf8 100644
--- a/examples/linear_model/plot_lasso_model_selection.py
+++ b/examples/linear_model/plot_lasso_model_selection.py
@@ -83,9 +83,9 @@ def plot_ic_criterion(model, name, color):
alphas_ = model.alphas_
criterion_ = model.criterion_
plt.plot(-np.log10(alphas_), criterion_, '--', color=color,
- linewidth=3, label='%s criterion' % name)
+ linewidth=3, label='%s criterion' % name)
plt.axvline(-np.log10(alpha_), color=color, linewidth=3,
- label='alpha: %s estimate' % name)
+ label='alpha: %s estimate' % name)
plt.xlabel('-log(alpha)')
plt.ylabel('criterion')
@@ -94,7 +94,7 @@ plot_ic_criterion(model_aic, 'AIC', 'b')
plot_ic_criterion(model_bic, 'BIC', 'r')
plt.legend()
plt.title('Information-criterion for model selection (training time %.3fs)'
- % t_bic)
+ % t_bic)
##############################################################################
# LassoCV: coordinate descent
@@ -112,16 +112,16 @@ plt.figure()
ymin, ymax = 2300, 3800
plt.plot(m_log_alphas, model.mse_path_, ':')
plt.plot(m_log_alphas, model.mse_path_.mean(axis=-1), 'k',
- label='Average across the folds', linewidth=2)
+ label='Average across the folds', linewidth=2)
plt.axvline(-np.log10(model.alpha_), linestyle='--', color='k',
- label='alpha: CV estimate')
+ label='alpha: CV estimate')
plt.legend()
plt.xlabel('-log(alpha)')
plt.ylabel('Mean square error')
plt.title('Mean square error on each fold: coordinate descent '
- '(train time: %.2fs)' % t_lasso_cv)
+ '(train time: %.2fs)' % t_lasso_cv)
plt.axis('tight')
plt.ylim(ymin, ymax)
@@ -140,15 +140,15 @@ m_log_alphas = -np.log10(model.cv_alphas_)
plt.figure()
plt.plot(m_log_alphas, model.cv_mse_path_, ':')
plt.plot(m_log_alphas, model.cv_mse_path_.mean(axis=-1), 'k',
- label='Average across the folds', linewidth=2)
+ label='Average across the folds', linewidth=2)
plt.axvline(-np.log10(model.alpha_), linestyle='--', color='k',
- label='alpha CV')
+ label='alpha CV')
plt.legend()
plt.xlabel('-log(alpha)')
plt.ylabel('Mean square error')
plt.title('Mean square error on each fold: Lars (train time: %.2fs)'
- % t_lasso_lars_cv)
+ % t_lasso_lars_cv)
plt.axis('tight')
plt.ylim(ymin, ymax)
diff --git a/examples/linear_model/plot_multi_task_lasso_support.py b/examples/linear_model/plot_multi_task_lasso_support.py
index 0f6a1b7c5a6239473a173fbceb790a54391249db..940282d678d5ba9abb77b01141a3ea61b45c0367 100644
--- a/examples/linear_model/plot_multi_task_lasso_support.py
+++ b/examples/linear_model/plot_multi_task_lasso_support.py
@@ -59,7 +59,7 @@ plt.figure()
plt.plot(coef[:, feature_to_plot], 'k', label='Ground truth')
plt.plot(coef_lasso_[:, feature_to_plot], 'g', label='Lasso')
plt.plot(coef_multi_task_lasso_[:, feature_to_plot],
- 'r', label='MultiTaskLasso')
+ 'r', label='MultiTaskLasso')
plt.legend(loc='upper center')
plt.axis('tight')
plt.ylim([-1.1, 1.1])
diff --git a/examples/linear_model/plot_ols.py b/examples/linear_model/plot_ols.py
index f107da1adb023a741320888a27c32ae67803a86b..39a03d1fa0ad4e992d005689cdffc96dc510803f 100644
--- a/examples/linear_model/plot_ols.py
+++ b/examples/linear_model/plot_ols.py
@@ -60,7 +60,7 @@ print('Variance score: %.2f' % regr.score(diabetes_X_test, diabetes_y_test))
# Plot outputs
plt.scatter(diabetes_X_test, diabetes_y_test, color='black')
plt.plot(diabetes_X_test, regr.predict(diabetes_X_test), color='blue',
- linewidth=3)
+ linewidth=3)
plt.xticks(())
plt.yticks(())
diff --git a/examples/linear_model/plot_omp.py b/examples/linear_model/plot_omp.py
index 1ae2b6db7eea8e29163515be03493c51de2ac6c8..f07b7d723340a3f59693a5723e590ba1b5963cc5 100644
--- a/examples/linear_model/plot_omp.py
+++ b/examples/linear_model/plot_omp.py
@@ -78,5 +78,5 @@ plt.stem(idx_r, coef[idx_r])
plt.subplots_adjust(0.06, 0.04, 0.94, 0.90, 0.20, 0.38)
plt.suptitle('Sparse signal recovery with Orthogonal Matching Pursuit',
- fontsize=16)
+ fontsize=16)
plt.show()
diff --git a/examples/linear_model/plot_sgd_iris.py b/examples/linear_model/plot_sgd_iris.py
index d48dea9cc2c32fbb32394be763ad42f2e7262642..cd2c8dd6b03f57418babbf1f76e3ed44a570693c 100644
--- a/examples/linear_model/plot_sgd_iris.py
+++ b/examples/linear_model/plot_sgd_iris.py
@@ -56,7 +56,7 @@ plt.axis('tight')
for i, color in zip(clf.classes_, colors):
idx = np.where(y == i)
plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],
- cmap=plt.cm.Paired)
+ cmap=plt.cm.Paired)
plt.title("Decision surface of multi-class SGD")
plt.axis('tight')
@@ -72,7 +72,7 @@ def plot_hyperplane(c, color):
return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]
plt.plot([xmin, xmax], [line(xmin), line(xmax)],
- ls="--", color=color)
+ ls="--", color=color)
for i, color in zip(clf.classes_, colors):
plot_hyperplane(i, color)
diff --git a/examples/linear_model/plot_sgd_loss_functions.py b/examples/linear_model/plot_sgd_loss_functions.py
index aa7d1bfa015a789f4582b9caeeba63e0deea5a91..c2668dd6d760b9f3dc43a3a9e4fc8c746d0a510b 100644
--- a/examples/linear_model/plot_sgd_loss_functions.py
+++ b/examples/linear_model/plot_sgd_loss_functions.py
@@ -23,17 +23,17 @@ def modified_huber_loss(y_true, y_pred):
xmin, xmax = -4, 4
xx = np.linspace(xmin, xmax, 100)
plt.plot([xmin, 0, 0, xmax], [1, 1, 0, 0], 'k-',
- label="Zero-one loss")
+ label="Zero-one loss")
plt.plot(xx, np.where(xx < 1, 1 - xx, 0), 'g-',
- label="Hinge loss")
+ label="Hinge loss")
plt.plot(xx, -np.minimum(xx, 0), 'm-',
- label="Perceptron loss")
+ label="Perceptron loss")
plt.plot(xx, np.log2(1 + np.exp(-xx)), 'r-',
- label="Log loss")
+ label="Log loss")
plt.plot(xx, np.where(xx < 1, 1 - xx, 0) ** 2, 'b-',
- label="Squared hinge loss")
+ label="Squared hinge loss")
plt.plot(xx, modified_huber_loss(xx, 1), 'y--',
- label="Modified Huber loss")
+ label="Modified Huber loss")
plt.ylim((0, 8))
plt.legend(loc="upper right")
plt.xlabel(r"Decision function $f(x)$")
diff --git a/examples/linear_model/plot_sgd_weighted_samples.py b/examples/linear_model/plot_sgd_weighted_samples.py
index 6e4e5f2664d520637067560186b766d2a71801fe..15dd72866f4dfb909b2038362b934a02726f0b6c 100644
--- a/examples/linear_model/plot_sgd_weighted_samples.py
+++ b/examples/linear_model/plot_sgd_weighted_samples.py
@@ -24,7 +24,7 @@ sample_weight[:10] *= 10
xx, yy = np.meshgrid(np.linspace(-4, 5, 500), np.linspace(-4, 5, 500))
plt.figure()
plt.scatter(X[:, 0], X[:, 1], c=y, s=sample_weight, alpha=0.9,
- cmap=plt.cm.bone)
+ cmap=plt.cm.bone)
## fit the unweighted model
clf = linear_model.SGDClassifier(alpha=0.01, n_iter=100)
@@ -41,7 +41,7 @@ Z = Z.reshape(xx.shape)
samples_weights = plt.contour(xx, yy, Z, levels=[0], linestyles=['dashed'])
plt.legend([no_weights.collections[0], samples_weights.collections[0]],
- ["no weights", "with weights"], loc="lower left")
+ ["no weights", "with weights"], loc="lower left")
plt.xticks(())
plt.yticks(())
diff --git a/examples/manifold/plot_compare_methods.py b/examples/manifold/plot_compare_methods.py
index e321c812b5b325c380c6d54924f1f97b68eb040d..62a8f318d35948de3bddfb3d299033f7747e3b44 100644
--- a/examples/manifold/plot_compare_methods.py
+++ b/examples/manifold/plot_compare_methods.py
@@ -41,7 +41,7 @@ n_components = 2
fig = plt.figure(figsize=(15, 8))
plt.suptitle("Manifold Learning with %i points, %i neighbors"
- % (1000, n_neighbors), fontsize=14)
+ % (1000, n_neighbors), fontsize=14)
try:
# compatibility matplotlib < 1.0
diff --git a/examples/manifold/plot_lle_digits.py b/examples/manifold/plot_lle_digits.py
index 928a175ae73fcf298905dfdb9bc1aa406f1af024..1e1800918de1071e33384e7fdee79f59f4eb91de 100644
--- a/examples/manifold/plot_lle_digits.py
+++ b/examples/manifold/plot_lle_digits.py
@@ -44,8 +44,8 @@ def plot_embedding(X, title=None):
ax = plt.subplot(111)
for i in range(X.shape[0]):
plt.text(X[i, 0], X[i, 1], str(digits.target[i]),
- color=plt.cm.Set1(y[i] / 10.),
- fontdict={'weight': 'bold', 'size': 9})
+ color=plt.cm.Set1(y[i] / 10.),
+ fontdict={'weight': 'bold', 'size': 9})
if hasattr(offsetbox, 'AnnotationBbox'):
# only print thumbnails with matplotlib > 1.0
diff --git a/examples/manifold/plot_manifold_sphere.py b/examples/manifold/plot_manifold_sphere.py
index 83d91e59c2f0270f9bf539dfc299dac9f0212171..b6720849fb911bfa8109cd281c1c29fa0d44ccf9 100644
--- a/examples/manifold/plot_manifold_sphere.py
+++ b/examples/manifold/plot_manifold_sphere.py
@@ -64,7 +64,7 @@ x, y, z = np.sin(t[indices]) * np.cos(p[indices]), \
# Plot our dataset.
fig = plt.figure(figsize=(15, 8))
plt.suptitle("Manifold Learning with %i points, %i neighbors"
- % (1000, n_neighbors), fontsize=14)
+ % (1000, n_neighbors), fontsize=14)
ax = fig.add_subplot(241, projection='3d')
ax.scatter(x, y, z, c=p[indices], cmap=plt.cm.rainbow)
diff --git a/examples/mixture/plot_gmm_classifier.py b/examples/mixture/plot_gmm_classifier.py
index 0aa6c3c5fce2bca0500ffd85c85e48850f08d548..cebbe36cada69abab7d8510413be87f4c94bb09e 100644
--- a/examples/mixture/plot_gmm_classifier.py
+++ b/examples/mixture/plot_gmm_classifier.py
@@ -76,7 +76,7 @@ n_classifiers = len(classifiers)
plt.figure(figsize=(3 * n_classifiers / 2, 6))
plt.subplots_adjust(bottom=.01, top=0.95, hspace=.15, wspace=.05,
- left=.01, right=.99)
+ left=.01, right=.99)
for index, (name, classifier) in enumerate(classifiers.items()):
@@ -94,7 +94,7 @@ for index, (name, classifier) in enumerate(classifiers.items()):
for n, color in enumerate('rgb'):
data = iris.data[iris.target == n]
plt.scatter(data[:, 0], data[:, 1], 0.8, color=color,
- label=iris.target_names[n])
+ label=iris.target_names[n])
# Plot the test data with crosses
for n, color in enumerate('rgb'):
data = X_test[y_test == n]
@@ -103,12 +103,12 @@ for index, (name, classifier) in enumerate(classifiers.items()):
y_train_pred = classifier.predict(X_train)
train_accuracy = np.mean(y_train_pred.ravel() == y_train.ravel()) * 100
plt.text(0.05, 0.9, 'Train accuracy: %.1f' % train_accuracy,
- transform=h.transAxes)
+ transform=h.transAxes)
y_test_pred = classifier.predict(X_test)
test_accuracy = np.mean(y_test_pred.ravel() == y_test.ravel()) * 100
plt.text(0.05, 0.8, 'Test accuracy: %.1f' % test_accuracy,
- transform=h.transAxes)
+ transform=h.transAxes)
plt.xticks(())
plt.yticks(())
diff --git a/examples/mixture/plot_gmm_selection.py b/examples/mixture/plot_gmm_selection.py
index e74b52ecb94d3a167766f7b447da4474deaf0500..ad175cf17187b90d702065228af9edbf1f4b5dc8 100644
--- a/examples/mixture/plot_gmm_selection.py
+++ b/examples/mixture/plot_gmm_selection.py
@@ -58,8 +58,8 @@ spl = plt.subplot(2, 1, 1)
for i, (cv_type, color) in enumerate(zip(cv_types, color_iter)):
xpos = np.array(n_components_range) + .2 * (i - 2)
bars.append(plt.bar(xpos, bic[i * len(n_components_range):
- (i + 1) * len(n_components_range)],
- width=.2, color=color))
+ (i + 1) * len(n_components_range)],
+ width=.2, color=color))
plt.xticks(n_components_range)
plt.ylim([bic.min() * 1.01 - .01 * bic.max(), bic.max()])
plt.title('BIC score per model')
diff --git a/examples/neighbors/plot_classification.py b/examples/neighbors/plot_classification.py
index 1f479c3ec9394c8a47754843f0770d056fe57ce9..9bd7c92f51117b3c0635b5c96e04bfb6805035c8 100644
--- a/examples/neighbors/plot_classification.py
+++ b/examples/neighbors/plot_classification.py
@@ -50,6 +50,6 @@ for weights in ['uniform', 'distance']:
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.title("3-Class classification (k = %i, weights = '%s')"
- % (n_neighbors, weights))
+ % (n_neighbors, weights))
plt.show()
diff --git a/examples/neighbors/plot_nearest_centroid.py b/examples/neighbors/plot_nearest_centroid.py
index dd0fd2d1eafb115ff504478d7d205255aedf7184..05d277db08f02a8dcb362593cfe882ea517a8f9f 100644
--- a/examples/neighbors/plot_nearest_centroid.py
+++ b/examples/neighbors/plot_nearest_centroid.py
@@ -50,7 +50,7 @@ for shrinkage in [None, 0.1]:
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
plt.title("3-Class classification (shrink_threshold=%r)"
- % shrinkage)
+ % shrinkage)
plt.axis('tight')
plt.show()
diff --git a/examples/neighbors/plot_regression.py b/examples/neighbors/plot_regression.py
index e7a0e86d84cf6d60fe390e8e985fc085ced41fc9..c664d7f173b0e34ac371f463065214df0629c6a0 100644
--- a/examples/neighbors/plot_regression.py
+++ b/examples/neighbors/plot_regression.py
@@ -44,6 +44,6 @@ for i, weights in enumerate(['uniform', 'distance']):
plt.axis('tight')
plt.legend()
plt.title("KNeighborsRegressor (k = %i, weights = '%s')" % (n_neighbors,
- weights))
+ weights))
plt.show()
diff --git a/examples/plot_classification_probability.py b/examples/plot_classification_probability.py
index 860d7754cdaa623b9c2aa991ea5acbbd8edd117a..e63bc1d376099b3d86ea24bbc7cefcfbd64c565a 100644
--- a/examples/plot_classification_probability.py
+++ b/examples/plot_classification_probability.py
@@ -62,7 +62,7 @@ for index, (name, classifier) in enumerate(classifiers.items()):
if k == 0:
plt.ylabel(name)
imshow_handle = plt.imshow(probas[:, k].reshape((100, 100)),
- extent=(3, 9, 1, 5), origin='lower')
+ extent=(3, 9, 1, 5), origin='lower')
plt.xticks(())
plt.yticks(())
idx = (y_pred == k)
diff --git a/examples/plot_digits_pipe.py b/examples/plot_digits_pipe.py
index 81f0d3f63eb10d4fbbee6fea1714cc3f94fa6582..139ade15ba7c2512faab3277fb87503446f93a92 100644
--- a/examples/plot_digits_pipe.py
+++ b/examples/plot_digits_pipe.py
@@ -62,6 +62,6 @@ estimator = GridSearchCV(pipe,
estimator.fit(X_digits, y_digits)
plt.axvline(estimator.best_estimator_.named_steps['pca'].n_components,
- linestyle=':', label='n_components chosen')
+ linestyle=':', label='n_components chosen')
plt.legend(prop=dict(size=12))
plt.show()
diff --git a/examples/plot_feature_selection.py b/examples/plot_feature_selection.py
index 46968c6186fe64983464f6110975cd5d4e3edd7f..9361791ce81d13c2954f9a4760e33eb72f65b780 100644
--- a/examples/plot_feature_selection.py
+++ b/examples/plot_feature_selection.py
@@ -54,7 +54,7 @@ selector.fit(X, y)
scores = -np.log10(selector.pvalues_)
scores /= scores.max()
plt.bar(X_indices - .45, scores, width=.2,
- label=r'Univariate score ($-Log(p_{value})$)', color='g')
+ label=r'Univariate score ($-Log(p_{value})$)', color='g')
###############################################################################
# Compare to the weights of an SVM
@@ -72,8 +72,8 @@ clf_selected.fit(selector.transform(X), y)
svm_weights_selected = (clf_selected.coef_ ** 2).sum(axis=0)
svm_weights_selected /= svm_weights_selected.max()
-plt.bar(X_indices[selector.get_support()] - .05, svm_weights_selected, width=.2,
- label='SVM weights after selection', color='b')
+plt.bar(X_indices[selector.get_support()] - .05, svm_weights_selected,
+ width=.2, label='SVM weights after selection', color='b')
plt.title("Comparing feature selection")
diff --git a/examples/plot_johnson_lindenstrauss_bound.py b/examples/plot_johnson_lindenstrauss_bound.py
index 658d3ebef23af3d12c60cf6fe937087a4cdefabf..9849991598817afe2333916d49bba771ce475328 100644
--- a/examples/plot_johnson_lindenstrauss_bound.py
+++ b/examples/plot_johnson_lindenstrauss_bound.py
@@ -178,7 +178,7 @@ for n_components in n_components_range:
plt.xlabel("Pairwise squared distances in original space")
plt.ylabel("Pairwise squared distances in projected space")
plt.title("Pairwise distances distribution for n_components=%d" %
- n_components)
+ n_components)
cb = plt.colorbar()
cb.set_label('Sample pairs counts')
@@ -191,7 +191,7 @@ for n_components in n_components_range:
plt.xlabel("Squared distances rate: projected / original")
plt.ylabel("Distribution of samples pairs")
plt.title("Histogram of pairwise distance rates for n_components=%d" %
- n_components)
+ n_components)
plt.show()
# TODO: compute the expected value of eps and add them to the previous plot
diff --git a/examples/plot_lda_qda.py b/examples/plot_lda_qda.py
index 3700d77489bbbe85dda002185d0d05e649e87274..bad9f3da7840712d4f451bcd989b7bdb49db6062 100644
--- a/examples/plot_lda_qda.py
+++ b/examples/plot_lda_qda.py
@@ -87,14 +87,14 @@ def plot_data(lda, X, y, y_pred, fig_index):
Z = lda.predict_proba(np.c_[xx.ravel(), yy.ravel()])
Z = Z[:, 1].reshape(xx.shape)
plt.pcolormesh(xx, yy, Z, cmap='red_blue_classes',
- norm=colors.Normalize(0., 1.))
+ norm=colors.Normalize(0., 1.))
plt.contour(xx, yy, Z, [0.5], linewidths=2., colors='k')
# means
plt.plot(lda.means_[0][0], lda.means_[0][1],
- 'o', color='black', markersize=10)
+ 'o', color='black', markersize=10)
plt.plot(lda.means_[1][0], lda.means_[1][1],
- 'o', color='black', markersize=10)
+ 'o', color='black', markersize=10)
return splot
diff --git a/examples/plot_permutation_test_for_classification.py b/examples/plot_permutation_test_for_classification.py
index 833fd1cc38d4691fef424f3d3171771a03e51c99..4df102578c9da4cce8c89d25f4485117c295024f 100644
--- a/examples/plot_permutation_test_for_classification.py
+++ b/examples/plot_permutation_test_for_classification.py
@@ -57,8 +57,8 @@ ylim = plt.ylim()
#plt.vlines(1.0 / n_classes, ylim[0], ylim[1], linestyle='--',
# color='k', linewidth=3, label='Luck')
plt.plot(2 * [score], ylim, '--g', linewidth=3,
- label='Classification Score'
- ' (pvalue %s)' % pvalue)
+ label='Classification Score'
+ ' (pvalue %s)' % pvalue)
plt.plot(2 * [1. / n_classes], ylim, '--k', linewidth=3, label='Luck')
plt.ylim(ylim)
diff --git a/examples/plot_precision_recall.py b/examples/plot_precision_recall.py
index c1a47cae46b782aa572c5e2cfdfae7826e378d00..6617a6b5d88fae352e4c2b1357146ba4e10a742f 100644
--- a/examples/plot_precision_recall.py
+++ b/examples/plot_precision_recall.py
@@ -134,12 +134,12 @@ plt.show()
# Plot Precision-Recall curve for each class
plt.clf()
plt.plot(recall["micro"], precision["micro"],
- label='micro-average Precision-recall curve (area = {0:0.2f})'
- ''.format(average_precision["micro"]))
+ label='micro-average Precision-recall curve (area = {0:0.2f})'
+ ''.format(average_precision["micro"]))
for i in range(n_classes):
plt.plot(recall[i], precision[i],
- label='Precision-recall curve of class {0} (area = {1:0.2f})'
- ''.format(i, average_precision[i]))
+ label='Precision-recall curve of class {0} (area = {1:0.2f})'
+ ''.format(i, average_precision[i]))
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
diff --git a/examples/plot_roc.py b/examples/plot_roc.py
index 22702527187435542066b761f8b7d70b3b920e56..de8ad92f6ad94208ffc3d6e9fc52a2d46f0b3123 100644
--- a/examples/plot_roc.py
+++ b/examples/plot_roc.py
@@ -88,11 +88,11 @@ plt.show()
# Plot ROC curve
plt.clf()
plt.plot(fpr["micro"], tpr["micro"],
- label='micro-average ROC curve (area = {0:0.2f})'
- ''.format(roc_auc["micro"]))
+ label='micro-average ROC curve (area = {0:0.2f})'
+ ''.format(roc_auc["micro"]))
for i in range(n_classes):
plt.plot(fpr[i], tpr[i], label='ROC curve of class {0} (area = {1:0.2f})'
- ''.format(i, roc_auc[i]))
+ ''.format(i, roc_auc[i]))
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
diff --git a/examples/plot_roc_crossval.py b/examples/plot_roc_crossval.py
index 2396d30cca7527bb99a4067017dd4baaf9fe516c..9557334ccd9431f061417a0569e579b839d7a922 100644
--- a/examples/plot_roc_crossval.py
+++ b/examples/plot_roc_crossval.py
@@ -80,7 +80,7 @@ mean_tpr /= len(cv)
mean_tpr[-1] = 1.0
mean_auc = auc(mean_fpr, mean_tpr)
plt.plot(mean_fpr, mean_tpr, 'k--',
- label='Mean ROC (area = %0.2f)' % mean_auc, lw=2)
+ label='Mean ROC (area = %0.2f)' % mean_auc, lw=2)
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
diff --git a/examples/plot_train_error_vs_test_error.py b/examples/plot_train_error_vs_test_error.py
index b423efcbf02e2843cc6cacdc9fd5d29c172d97cc..9002a0a3a5f30cfa15f7e3878be8b28fe08eeb25 100644
--- a/examples/plot_train_error_vs_test_error.py
+++ b/examples/plot_train_error_vs_test_error.py
@@ -60,7 +60,7 @@ plt.subplot(2, 1, 1)
plt.semilogx(alphas, train_errors, label='Train')
plt.semilogx(alphas, test_errors, label='Test')
plt.vlines(alpha_optim, plt.ylim()[0], np.max(test_errors), color='k',
- linewidth=3, label='Optimum on test')
+ linewidth=3, label='Optimum on test')
plt.legend(loc='lower left')
plt.ylim([0, 1.2])
plt.xlabel('Regularization parameter')
diff --git a/examples/semi_supervised/plot_label_propagation_structure.py b/examples/semi_supervised/plot_label_propagation_structure.py
index 2935edd643079b73a2e3a180ca8f6863a9cc1653..24ab31ba33d6df27920a89229d4a8b8d11dc9ecb 100644
--- a/examples/semi_supervised/plot_label_propagation_structure.py
+++ b/examples/semi_supervised/plot_label_propagation_structure.py
@@ -39,13 +39,13 @@ output_labels = label_spread.transduction_
plt.figure(figsize=(8.5, 4))
plt.subplot(1, 2, 1)
plot_outer_labeled, = plt.plot(X[labels == outer, 0],
- X[labels == outer, 1], 'rs')
+ X[labels == outer, 1], 'rs')
plot_unlabeled, = plt.plot(X[labels == -1, 0], X[labels == -1, 1], 'g.')
plot_inner_labeled, = plt.plot(X[labels == inner, 0],
- X[labels == inner, 1], 'bs')
+ X[labels == inner, 1], 'bs')
plt.legend((plot_outer_labeled, plot_inner_labeled, plot_unlabeled),
- ('Outer Labeled', 'Inner Labeled', 'Unlabeled'), 'upper left',
- numpoints=1, shadow=False)
+ ('Outer Labeled', 'Inner Labeled', 'Unlabeled'), 'upper left',
+ numpoints=1, shadow=False)
plt.title("Raw data (2 classes=red and blue)")
plt.subplot(1, 2, 2)
@@ -55,7 +55,7 @@ inner_numbers = np.where(output_label_array == inner)[0]
plot_outer, = plt.plot(X[outer_numbers, 0], X[outer_numbers, 1], 'rs')
plot_inner, = plt.plot(X[inner_numbers, 0], X[inner_numbers, 1], 'bs')
plt.legend((plot_outer, plot_inner), ('Outer Learned', 'Inner Learned'),
- 'upper left', numpoints=1, shadow=False)
+ 'upper left', numpoints=1, shadow=False)
plt.title("Labels learned with Label Spreading (KNN)")
plt.subplots_adjust(left=0.07, bottom=0.07, right=0.93, top=0.92)
diff --git a/examples/svm/plot_custom_kernel.py b/examples/svm/plot_custom_kernel.py
index 4a80a55a23592480a87d7b72d886ec504af43713..d36a48f561abf6912304ef1027b404ac2e32400d 100644
--- a/examples/svm/plot_custom_kernel.py
+++ b/examples/svm/plot_custom_kernel.py
@@ -52,6 +52,6 @@ plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)
plt.title('3-Class classification using Support Vector Machine with custom'
- ' kernel')
+ ' kernel')
plt.axis('tight')
plt.show()
diff --git a/examples/svm/plot_oneclass.py b/examples/svm/plot_oneclass.py
index ac785bcea75f0014a84bd02d9785a56b830c67e2..8c765673a63cd67229b3a00fafd62b00ee60f231 100644
--- a/examples/svm/plot_oneclass.py
+++ b/examples/svm/plot_oneclass.py
@@ -52,10 +52,10 @@ plt.axis('tight')
plt.xlim((-5, 5))
plt.ylim((-5, 5))
plt.legend([a.collections[0], b1, b2, c],
- ["learned frontier", "training observations",
- "new regular observations", "new abnormal observations"],
- loc="upper left",
- prop=matplotlib.font_manager.FontProperties(size=11))
+ ["learned frontier", "training observations",
+ "new regular observations", "new abnormal observations"],
+ loc="upper left",
+ prop=matplotlib.font_manager.FontProperties(size=11))
plt.xlabel(
"error train: %d/200 ; errors novel regular: %d/40 ; "
"errors novel abnormal: %d/40"
diff --git a/examples/svm/plot_rbf_parameters.py b/examples/svm/plot_rbf_parameters.py
index 8bdecffc8b3717a0300f835c1db3bee38c7ae7e0..072f4559d56f3df2d3a225e34791f1ecb44b979c 100644
--- a/examples/svm/plot_rbf_parameters.py
+++ b/examples/svm/plot_rbf_parameters.py
@@ -97,7 +97,7 @@ for (k, (C, gamma, clf)) in enumerate(classifiers):
# visualize decision function for these parameters
plt.subplot(len(C_2d_range), len(gamma_2d_range), k + 1)
plt.title("gamma 10^%d, C 10^%d" % (np.log10(gamma), np.log10(C)),
- size='medium')
+ size='medium')
# visualize parameter's effect on decision function
plt.pcolormesh(xx, yy, -Z, cmap=plt.cm.jet)
diff --git a/examples/svm/plot_separating_hyperplane.py b/examples/svm/plot_separating_hyperplane.py
index ca3a0f69a7e5f46ff72c2baec68fdfa014112f25..254368fb421d45beeb106c19d8e272bcded50b69 100644
--- a/examples/svm/plot_separating_hyperplane.py
+++ b/examples/svm/plot_separating_hyperplane.py
@@ -41,7 +41,7 @@ plt.plot(xx, yy_down, 'k--')
plt.plot(xx, yy_up, 'k--')
plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],
- s=80, facecolors='none')
+ s=80, facecolors='none')
plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)
plt.axis('tight')
diff --git a/examples/svm/plot_svm_kernels.py b/examples/svm/plot_svm_kernels.py
index 62b987a791910b7188d8e82cb3d28141ef779a86..f0d06dd7968a6b27613e7ddf2f08d2a6e5ea27bd 100644
--- a/examples/svm/plot_svm_kernels.py
+++ b/examples/svm/plot_svm_kernels.py
@@ -56,7 +56,7 @@ for kernel in ('linear', 'poly', 'rbf'):
plt.clf()
plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=80,
- facecolors='none', zorder=10)
+ facecolors='none', zorder=10)
plt.scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=plt.cm.Paired)
plt.axis('tight')
@@ -73,7 +73,7 @@ for kernel in ('linear', 'poly', 'rbf'):
plt.figure(fignum, figsize=(4, 3))
plt.pcolormesh(XX, YY, Z > 0, cmap=plt.cm.Paired)
plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'],
- levels=[-.5, 0, .5])
+ levels=[-.5, 0, .5])
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
diff --git a/examples/svm/plot_svm_margin.py b/examples/svm/plot_svm_margin.py
index 8dec13ad65bd252c4b2ba9148b393b0db82842eb..a999ea34d33e299de5b8eebea145b8fe69e06d35 100644
--- a/examples/svm/plot_svm_margin.py
+++ b/examples/svm/plot_svm_margin.py
@@ -60,7 +60,7 @@ for name, penalty in (('unreg', 1), ('reg', 0.05)):
plt.plot(xx, yy_up, 'k--')
plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=80,
- facecolors='none', zorder=10)
+ facecolors='none', zorder=10)
plt.scatter(X[:, 0], X[:, 1], c=Y, zorder=10, cmap=plt.cm.Paired)
plt.axis('tight')
diff --git a/examples/svm/plot_svm_nonlinear.py b/examples/svm/plot_svm_nonlinear.py
index 74c38451149c4540638bfc4f57ef32dc89ba8fa5..672a19d30705b5e0490fd3f9d69dd1acb38d1c99 100644
--- a/examples/svm/plot_svm_nonlinear.py
+++ b/examples/svm/plot_svm_nonlinear.py
@@ -30,10 +30,10 @@ Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.imshow(Z, interpolation='nearest',
- extent=(xx.min(), xx.max(), yy.min(), yy.max()), aspect='auto',
- origin='lower', cmap=plt.cm.PuOr_r)
+ extent=(xx.min(), xx.max(), yy.min(), yy.max()), aspect='auto',
+ origin='lower', cmap=plt.cm.PuOr_r)
contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2,
- linetypes='--')
+ linetypes='--')
plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired)
plt.xticks(())
plt.yticks(())
diff --git a/examples/svm/plot_svm_scale_c.py b/examples/svm/plot_svm_scale_c.py
index e1ab5cb5c32bd125d9b7206e7c7347fda0ad5b9c..bd3fd69deb41e5068fbfa0a7749a0b5a187a1b08 100644
--- a/examples/svm/plot_svm_scale_c.py
+++ b/examples/svm/plot_svm_scale_c.py
@@ -143,9 +143,9 @@ for fignum, (clf, cs, X, y) in enumerate(clf_sets):
plt.ylabel('CV Score')
grid_cs = cs * float(scaler) # scale the C's
plt.semilogx(grid_cs, scores, label="fraction %.2f" %
- train_size)
+ train_size)
plt.title('scaling=%s, penalty=%s, loss=%s' %
- (name, clf.penalty, clf.loss))
+ (name, clf.penalty, clf.loss))
plt.legend(loc="best")
plt.show()
diff --git a/examples/tree/plot_iris.py b/examples/tree/plot_iris.py
index 5f01c02cf26dbf4d024d72f54052ad4771f9e19b..b649f0ecc0194c28d009e0a1079c92a5b9a12a8f 100644
--- a/examples/tree/plot_iris.py
+++ b/examples/tree/plot_iris.py
@@ -69,7 +69,7 @@ for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3],
for i, color in zip(range(n_classes), plot_colors):
idx = np.where(y == i)
plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],
- cmap=plt.cm.Paired)
+ cmap=plt.cm.Paired)
plt.axis("tight")