Sphinx-doc absolute path for auto_example images

Author: Titan-C

doc/modules/linear_model.rst

@@ -1081,7 +1081,7 @@ and :class:`RANSACRegressor` because it does not ignore the effect of the outlie
 but gives a lesser weight to them.
 
 .. figure:: /auto_examples/linear_model/images/sphx_glr_plot_huber_vs_ridge_001.png
-   :target: /auto_examples/linear_model/plot_huber_vs_ridge.html
+   :target: ../auto_examples/linear_model/plot_huber_vs_ridge.html
    :align: center
    :scale: 50%
 

doc/tutorial/basic/tutorial.rst

@@ -190,7 +190,7 @@ which we have not used to train the classifier::
 
 The corresponding image is the following:
 
-.. image:: ../../auto_examples/datasets/images/sphx_glr_plot_digits_last_image_001.png
+.. image:: /auto_examples/datasets/images/sphx_glr_plot_digits_last_image_001.png
     :target: ../../auto_examples/datasets/plot_digits_last_image.html
     :align: center
     :scale: 50

doc/tutorial/statistical_inference/model_selection.rst

@@ -183,7 +183,7 @@ scoring method.
 .. topic:: **Exercise**
    :class: green
 
-   .. image:: ../../auto_examples/exercises/images/sphx_glr_plot_cv_digits_001.png
+   .. image:: /auto_examples/exercises/images/sphx_glr_plot_cv_digits_001.png
         :target: ../../auto_examples/exercises/plot_cv_digits.html
         :align: right
         :scale: 90
@@ -290,5 +290,3 @@ appended to their name.
        :lines: 17-24
 
    **Solution:** :ref:`sphx_glr_auto_examples_exercises_plot_cv_diabetes.py`
-
-

doc/tutorial/statistical_inference/putting_together.rst

@@ -11,7 +11,7 @@ Pipelining
 We have seen that some estimators can transform data and that some estimators
 can predict variables. We can also create combined estimators:
 
-.. image:: ../../auto_examples/images/sphx_glr_plot_digits_pipe_001.png
+.. image:: /auto_examples/images/sphx_glr_plot_digits_pipe_001.png
    :target: ../../auto_examples/plot_digits_pipe.html
    :scale: 65
    :align: right

doc/tutorial/statistical_inference/settings.rst

@@ -31,7 +31,7 @@ needs to be preprocessed in order to be used by scikit-learn.
 
 .. topic:: An example of reshaping data would be the digits dataset
 
-    .. image:: ../../auto_examples/datasets/images/sphx_glr_plot_digits_last_image_001.png
+    .. image:: /auto_examples/datasets/images/sphx_glr_plot_digits_last_image_001.png
         :target: ../../auto_examples/datasets/plot_digits_last_image.html
         :align: right
         :scale: 60

doc/tutorial/statistical_inference/supervised_learning.rst

@@ -38,7 +38,7 @@ Nearest neighbor and the curse of dimensionality
 
 .. topic:: Classifying irises:
 
-    .. image:: ../../auto_examples/datasets/images/sphx_glr_plot_iris_dataset_001.png
+    .. image:: /auto_examples/datasets/images/sphx_glr_plot_iris_dataset_001.png
         :target: ../../auto_examples/datasets/plot_iris_dataset.html
         :align: right
 	:scale: 65
@@ -75,7 +75,7 @@ Scikit-learn documentation for more information about this type of classifier.)
 
 **KNN (k nearest neighbors) classification example**:
 
-.. image:: ../../auto_examples/neighbors/images/sphx_glr_plot_classification_001.png
+.. image:: /auto_examples/neighbors/images/sphx_glr_plot_classification_001.png
    :target: ../../auto_examples/neighbors/plot_classification.html
    :align: center
    :scale: 70
@@ -159,7 +159,7 @@ in its simplest form, fits a linear model to the data set by adjusting
 a set of parameters in order to make the sum of the squared residuals
 of the model as small as possible.
 
-.. image:: ../../auto_examples/linear_model/images/sphx_glr_plot_ols_001.png
+.. image:: /auto_examples/linear_model/images/sphx_glr_plot_ols_001.png
    :target: ../../auto_examples/linear_model/plot_ols.html
    :scale: 40
    :align: right
@@ -200,7 +200,7 @@ Shrinkage
 If there are few data points per dimension, noise in the observations
 induces high variance:
 
-.. image:: ../../auto_examples/linear_model/images/sphx_glr_plot_ols_ridge_variance_001.png
+.. image:: /auto_examples/linear_model/images/sphx_glr_plot_ols_ridge_variance_001.png
    :target: ../../auto_examples/linear_model/plot_ols_ridge_variance.html
    :scale: 70
    :align: right
@@ -229,7 +229,7 @@ regression coefficients to zero: any two randomly chosen set of
 observations are likely to be uncorrelated. This is called :class:`Ridge`
 regression:
 
-.. image:: ../../auto_examples/linear_model/images/sphx_glr_plot_ols_ridge_variance_002.png
+.. image:: /auto_examples/linear_model/images/sphx_glr_plot_ols_ridge_variance_002.png
    :target: ../../auto_examples/linear_model/plot_ols_ridge_variance.html
    :scale: 70
    :align: right
@@ -275,15 +275,15 @@ Sparsity
 ----------
 
 
-.. |diabetes_ols_1| image:: ../../auto_examples/linear_model/images/sphx_glr_plot_ols_3d_001.png
+.. |diabetes_ols_1| image:: /auto_examples/linear_model/images/sphx_glr_plot_ols_3d_001.png
    :target: ../../auto_examples/linear_model/plot_ols_3d.html
    :scale: 65
 
-.. |diabetes_ols_3| image:: ../../auto_examples/linear_model/images/sphx_glr_plot_ols_3d_003.png
+.. |diabetes_ols_3| image:: /auto_examples/linear_model/images/sphx_glr_plot_ols_3d_003.png
    :target: ../../auto_examples/linear_model/plot_ols_3d.html
    :scale: 65
 
-.. |diabetes_ols_2| image:: ../../auto_examples/linear_model/images/sphx_glr_plot_ols_3d_002.png
+.. |diabetes_ols_2| image:: /auto_examples/linear_model/images/sphx_glr_plot_ols_3d_002.png
    :target: ../../auto_examples/linear_model/plot_ols_3d.html
    :scale: 65
 
@@ -350,7 +350,7 @@ application of Occam's razor: *prefer simpler models*.
 Classification
 ---------------
 
-.. image:: ../../auto_examples/linear_model/images/sphx_glr_plot_logistic_001.png
+.. image:: /auto_examples/linear_model/images/sphx_glr_plot_logistic_001.png
    :target: ../../auto_examples/linear_model/plot_logistic.html
    :scale: 65
    :align: right
@@ -377,7 +377,7 @@ function or **logistic** function:
 
 This is known as :class:`LogisticRegression`.
 
-.. image:: ../../auto_examples/linear_model/images/sphx_glr_plot_iris_logistic_001.png
+.. image:: /auto_examples/linear_model/images/sphx_glr_plot_iris_logistic_001.png
    :target: ../../auto_examples/linear_model/plot_iris_logistic.html
    :scale: 83
 
@@ -425,11 +425,11 @@ the separating line (less regularization).
 
 .. currentmodule :: sklearn.svm
 
-.. |svm_margin_unreg| image:: ../../auto_examples/svm/images/sphx_glr_plot_svm_margin_001.png
+.. |svm_margin_unreg| image:: /auto_examples/svm/images/sphx_glr_plot_svm_margin_001.png
    :target: ../../auto_examples/svm/plot_svm_margin.html
    :scale: 70
 
-.. |svm_margin_reg| image:: ../../auto_examples/svm/images/sphx_glr_plot_svm_margin_002.png
+.. |svm_margin_reg| image:: /auto_examples/svm/images/sphx_glr_plot_svm_margin_002.png
    :target: ../../auto_examples/svm/plot_svm_margin.html
    :scale: 70
 
@@ -476,11 +476,11 @@ build a decision function that is not linear but may be polynomial instead.
 This is done using the *kernel trick* that can be seen as
 creating a decision energy by positioning *kernels* on observations:
 
-.. |svm_kernel_linear| image:: ../../auto_examples/svm/images/sphx_glr_plot_svm_kernels_001.png
+.. |svm_kernel_linear| image:: /auto_examples/svm/images/sphx_glr_plot_svm_kernels_001.png
    :target: ../../auto_examples/svm/plot_svm_kernels.html
    :scale: 65
 
-.. |svm_kernel_poly| image:: ../../auto_examples/svm/images/sphx_glr_plot_svm_kernels_002.png
+.. |svm_kernel_poly| image:: /auto_examples/svm/images/sphx_glr_plot_svm_kernels_002.png
    :target: ../../auto_examples/svm/plot_svm_kernels.html
    :scale: 65
 
@@ -518,7 +518,7 @@ creating a decision energy by positioning *kernels* on observations:
 
 
 
-.. |svm_kernel_rbf| image:: ../../auto_examples/svm/images/sphx_glr_plot_svm_kernels_003.png
+.. |svm_kernel_rbf| image:: /auto_examples/svm/images/sphx_glr_plot_svm_kernels_003.png
    :target: ../../auto_examples/svm/plot_svm_kernels.html
    :scale: 65
 
@@ -551,7 +551,7 @@ creating a decision energy by positioning *kernels* on observations:
    ``svm_gui.py``; add data points of both classes with right and left button,
    fit the model and change parameters and data.
 
-.. image:: ../../auto_examples/datasets/images/sphx_glr_plot_iris_dataset_001.png
+.. image:: /auto_examples/datasets/images/sphx_glr_plot_iris_dataset_001.png
     :target: ../../auto_examples/datasets/plot_iris_dataset.html
     :align: right
     :scale: 70

doc/tutorial/statistical_inference/unsupervised_learning.rst

@@ -24,7 +24,7 @@ Note that there exist a lot of different clustering criteria and associated
 algorithms. The simplest clustering algorithm is
 :ref:`k_means`.
 
-.. image:: ../../auto_examples/cluster/images/sphx_glr_plot_cluster_iris_002.png
+.. image:: /auto_examples/cluster/images/sphx_glr_plot_cluster_iris_002.png
     :target: ../../auto_examples/cluster/plot_cluster_iris.html
     :scale: 70
     :align: right
@@ -45,15 +45,15 @@ algorithms. The simplest clustering algorithm is
     >>> print(y_iris[::10])
     [0 0 0 0 0 1 1 1 1 1 2 2 2 2 2]
 
-.. |k_means_iris_bad_init| image:: ../../auto_examples/cluster/images/sphx_glr_plot_cluster_iris_003.png
+.. |k_means_iris_bad_init| image:: /auto_examples/cluster/images/sphx_glr_plot_cluster_iris_003.png
    :target: ../../auto_examples/cluster/plot_cluster_iris.html
    :scale: 63
 
-.. |k_means_iris_8| image:: ../../auto_examples/cluster/images/sphx_glr_plot_cluster_iris_001.png
+.. |k_means_iris_8| image:: /auto_examples/cluster/images/sphx_glr_plot_cluster_iris_001.png
    :target: ../../auto_examples/cluster/plot_cluster_iris.html
    :scale: 63
 
-.. |cluster_iris_truth| image:: ../../auto_examples/cluster/images/sphx_glr_plot_cluster_iris_004.png
+.. |cluster_iris_truth| image:: /auto_examples/cluster/images/sphx_glr_plot_cluster_iris_004.png
    :target: ../../auto_examples/cluster/plot_cluster_iris.html
    :scale: 63
 
@@ -85,27 +85,27 @@ algorithms. The simplest clustering algorithm is
 
     **Don't over-interpret clustering results**
 
-.. |face| image:: ../../auto_examples/cluster/images/sphx_glr_plot_face_compress_001.png
+.. |face| image:: /auto_examples/cluster/images/sphx_glr_plot_face_compress_001.png
    :target: ../../auto_examples/cluster/plot_face_compress.html
    :scale: 60
 
-.. |face_regular| image:: ../../auto_examples/cluster/images/sphx_glr_plot_face_compress_002.png
+.. |face_regular| image:: /auto_examples/cluster/images/sphx_glr_plot_face_compress_002.png
    :target: ../../auto_examples/cluster/plot_face_compress.html
    :scale: 60
 
-.. |face_compressed| image:: ../../auto_examples/cluster/images/sphx_glr_plot_face_compress_003.png
+.. |face_compressed| image:: /auto_examples/cluster/images/sphx_glr_plot_face_compress_003.png
    :target: ../../auto_examples/cluster/plot_face_compress.html
    :scale: 60
 
-.. |face_histogram| image:: ../../auto_examples/cluster/images/sphx_glr_plot_face_compress_004.png
+.. |face_histogram| image:: /auto_examples/cluster/images/sphx_glr_plot_face_compress_004.png
    :target: ../../auto_examples/cluster/plot_face_compress.html
    :scale: 60
 
 .. topic:: **Application example: vector quantization**
 
     Clustering in general and KMeans, in particular, can be seen as a way
     of choosing a small number of exemplars to compress the information.
-    The problem is sometimes known as 
+    The problem is sometimes known as
     `vector quantization <https://en.wikipedia.org/wiki/Vector_quantization>`_.
     For instance, this can be used to posterize an image::
 
@@ -177,7 +177,7 @@ This can be useful, for instance, to retrieve connected regions (sometimes
 also referred to as connected components) when
 clustering an image:
 
-.. image:: ../../auto_examples/cluster/images/sphx_glr_plot_face_ward_segmentation_001.png
+.. image:: /auto_examples/cluster/images/sphx_glr_plot_face_ward_segmentation_001.png
     :target: ../../auto_examples/cluster/plot_face_ward_segmentation.html
     :scale: 40
     :align: right
@@ -200,7 +200,7 @@ features: **feature agglomeration**. This approach can be implemented by
 clustering in the feature direction, in other words clustering the
 transposed data.
 
-.. image:: ../../auto_examples/cluster/images/sphx_glr_plot_digits_agglomeration_001.png
+.. image:: /auto_examples/cluster/images/sphx_glr_plot_digits_agglomeration_001.png
     :target: ../../auto_examples/cluster/plot_digits_agglomeration.html
     :align: right
     :scale: 57
@@ -242,11 +242,11 @@ Principal component analysis: PCA
 :ref:`PCA` selects the successive components that
 explain the maximum variance in the signal.
 
-.. |pca_3d_axis| image:: ../../auto_examples/decomposition/images/sphx_glr_plot_pca_3d_001.png
+.. |pca_3d_axis| image:: /auto_examples/decomposition/images/sphx_glr_plot_pca_3d_001.png
    :target: ../../auto_examples/decomposition/plot_pca_3d.html
    :scale: 70
 
-.. |pca_3d_aligned| image:: ../../auto_examples/decomposition/images/sphx_glr_plot_pca_3d_002.png
+.. |pca_3d_aligned| image:: /auto_examples/decomposition/images/sphx_glr_plot_pca_3d_002.png
    :target: ../../auto_examples/decomposition/plot_pca_3d.html
    :scale: 70
 
@@ -295,7 +295,7 @@ Independent Component Analysis: ICA
 a maximum amount of independent information. It is able to recover
 **non-Gaussian** independent signals:
 
-.. image:: ../../auto_examples/decomposition/images/sphx_glr_plot_ica_blind_source_separation_001.png
+.. image:: /auto_examples/decomposition/images/sphx_glr_plot_ica_blind_source_separation_001.png
    :target: ../../auto_examples/decomposition/plot_ica_blind_source_separation.html
    :scale: 70
    :align: center