Pushing the docs to dev/ for branch: master, commit 74b56dbc57d9295df8fb653adccb265da356b670

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Species distribution modeling\n\n\nModeling species' geographic distributions is an important\nproblem in conservation biology. In this example we\nmodel the geographic distribution of two south american\nmammals given past observations and 14 environmental\nvariables. Since we have only positive examples (there are\nno unsuccessful observations), we cast this problem as a\ndensity estimation problem and use the `OneClassSVM` provided\nby the package `sklearn.svm` as our modeling tool.\nThe dataset is provided by Phillips et. al. (2006).\nIf available, the example uses\n`basemap <http://matplotlib.org/basemap>`_\nto plot the coast lines and national boundaries of South America.\n\nThe two species are:\n\n - `\"Bradypus variegatus\"\n   <http://www.iucnredlist.org/details/3038/0>`_ ,\n   the Brown-throated Sloth.\n\n - `\"Microryzomys minutus\"\n   <http://www.iucnredlist.org/details/13408/0>`_ ,\n   also known as the Forest Small Rice Rat, a rodent that lives in Peru,\n   Colombia, Ecuador, Peru, and Venezuela.\n\nReferences\n----------\n\n * `\"Maximum entropy modeling of species geographic distributions\"\n   <http://rob.schapire.net/papers/ecolmod.pdf>`_\n   S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,\n   190:231-259, 2006.\n\n"
+        "\n# Species distribution modeling\n\n\nModeling species' geographic distributions is an important\nproblem in conservation biology. In this example we\nmodel the geographic distribution of two south american\nmammals given past observations and 14 environmental\nvariables. Since we have only positive examples (there are\nno unsuccessful observations), we cast this problem as a\ndensity estimation problem and use the `OneClassSVM` provided\nby the package `sklearn.svm` as our modeling tool.\nThe dataset is provided by Phillips et. al. (2006).\nIf available, the example uses\n`basemap <https://matplotlib.org/basemap/>`_\nto plot the coast lines and national boundaries of South America.\n\nThe two species are:\n\n - `\"Bradypus variegatus\"\n   <http://www.iucnredlist.org/details/3038/0>`_ ,\n   the Brown-throated Sloth.\n\n - `\"Microryzomys minutus\"\n   <http://www.iucnredlist.org/details/13408/0>`_ ,\n   also known as the Forest Small Rice Rat, a rodent that lives in Peru,\n   Colombia, Ecuador, Peru, and Venezuela.\n\nReferences\n----------\n\n * `\"Maximum entropy modeling of species geographic distributions\"\n   <http://rob.schapire.net/papers/ecolmod.pdf>`_\n   S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,\n   190:231-259, 2006.\n\n"
       ]
     },
     {

dev/_downloads/auto_examples_python.zip

@@ -13,7 +13,7 @@
 by the package `sklearn.svm` as our modeling tool.
 The dataset is provided by Phillips et. al. (2006).
 If available, the example uses
-`basemap <http://matplotlib.org/basemap>`_
+`basemap <https://matplotlib.org/basemap/>`_
 to plot the coast lines and national boundaries of South America.
 
 The two species are:

dev/_downloads/plot_species_distribution_modeling.ipynb

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Kernel Density Estimate of Species Distributions\n\nThis shows an example of a neighbors-based query (in particular a kernel\ndensity estimate) on geospatial data, using a Ball Tree built upon the\nHaversine distance metric -- i.e. distances over points in latitude/longitude.\nThe dataset is provided by Phillips et. al. (2006).\nIf available, the example uses\n`basemap <http://matplotlib.org/basemap>`_\nto plot the coast lines and national boundaries of South America.\n\nThis example does not perform any learning over the data\n(see `sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py` for\nan example of classification based on the attributes in this dataset).  It\nsimply shows the kernel density estimate of observed data points in\ngeospatial coordinates.\n\nThe two species are:\n\n - `\"Bradypus variegatus\"\n   <http://www.iucnredlist.org/apps/redlist/details/3038/0>`_ ,\n   the Brown-throated Sloth.\n\n - `\"Microryzomys minutus\"\n   <http://www.iucnredlist.org/details/13408/0>`_ ,\n   also known as the Forest Small Rice Rat, a rodent that lives in Peru,\n   Colombia, Ecuador, Peru, and Venezuela.\n\nReferences\n----------\n\n * `\"Maximum entropy modeling of species geographic distributions\"\n   <http://rob.schapire.net/papers/ecolmod.pdf>`_\n   S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,\n   190:231-259, 2006.\n\n"
+        "\n# Kernel Density Estimate of Species Distributions\n\nThis shows an example of a neighbors-based query (in particular a kernel\ndensity estimate) on geospatial data, using a Ball Tree built upon the\nHaversine distance metric -- i.e. distances over points in latitude/longitude.\nThe dataset is provided by Phillips et. al. (2006).\nIf available, the example uses\n`basemap <https://matplotlib.org/basemap/>`_\nto plot the coast lines and national boundaries of South America.\n\nThis example does not perform any learning over the data\n(see `sphx_glr_auto_examples_applications_plot_species_distribution_modeling.py` for\nan example of classification based on the attributes in this dataset).  It\nsimply shows the kernel density estimate of observed data points in\ngeospatial coordinates.\n\nThe two species are:\n\n - `\"Bradypus variegatus\"\n   <http://www.iucnredlist.org/apps/redlist/details/3038/0>`_ ,\n   the Brown-throated Sloth.\n\n - `\"Microryzomys minutus\"\n   <http://www.iucnredlist.org/details/13408/0>`_ ,\n   also known as the Forest Small Rice Rat, a rodent that lives in Peru,\n   Colombia, Ecuador, Peru, and Venezuela.\n\nReferences\n----------\n\n * `\"Maximum entropy modeling of species geographic distributions\"\n   <http://rob.schapire.net/papers/ecolmod.pdf>`_\n   S. J. Phillips, R. P. Anderson, R. E. Schapire - Ecological Modelling,\n   190:231-259, 2006.\n\n"
       ]
     },
     {

dev/_downloads/plot_species_distribution_modeling.py

@@ -7,7 +7,7 @@
 Haversine distance metric -- i.e. distances over points in latitude/longitude.
 The dataset is provided by Phillips et. al. (2006).
 If available, the example uses
-`basemap <http://matplotlib.org/basemap>`_
+`basemap <https://matplotlib.org/basemap/>`_
 to plot the coast lines and national boundaries of South America.
 
 This example does not perform any learning over the data