docs update

jumutc · Sep 28, 2015 · 8f5c0f6 · 8f5c0f6
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diff --git a/docs/source/examples.rst b/docs/source/examples.rst
@@ -4,7 +4,7 @@ Examples & notebooks
 Prerequisites
 ~~~~~~~~~~~~~~
 
-Please refer to `Julia downloads <http://julialang.org/downloads>`_ page for installing **Julia** language and all dependencies. The instructions for installing the **SALSA** package can be found :ref:`here <installation>`. Some additional plotting and data management packages might be required to run examples below (like ``Winston``, ``Gadfly`` or ``DataFrames``). If you prefer Python-style notebooks please refer to `Project Jupyter <http://jupyter.org>`_ and `IJulia <http://github.com/JuliaLang/IJulia.jl>`_ package for instructions. In this section we provide code snippets which can be easily copied into the **Julia** console or **Jupyter** notebook. 
+Please refer to `Julia downloads <http://julialang.org/downloads>`_ page for installing **Julia** language and all dependencies. The instructions for installing the **SALSA** package can be found :ref:`here <installation>`. Some additional plotting and data management packages might be required to run examples below (like ``Winston``, ``Gadfly`` or ``DataFrames``). If you prefer Python-style notebooks please refer to the `Project Jupyter <http://jupyter.org>`_ and `IJulia <http://github.com/JuliaLang/IJulia.jl>`_ package for instructions. In this section we provide code snippets which can be easily copied into the **Julia** console or **Jupyter** notebook. 
 
 
 Advanced Classification
@@ -14,39 +14,39 @@ This example provides a use-case for nonlinear classification using :doc:`Nystr
 
 .. code-block:: julia
 
-	using SALSA, MAT
-
-	ripley = matread(joinpath(Pkg.dir("SALSA"), "data", "ripley.mat")); srand(123);
-	model = SALSAModel(NONLINEAR, PEGASOS(), LOGISTIC, validation_criterion=AUC(100));
-	model = salsa(ripley["X"], ripley["Y"], model, ripley["Xt"]);
-	
-	range1 = linspace(-1.5,1.5,200);
-	range2 = linspace(-0.5,1.5,200);
-	grid = [[i j] for i in range1, j in range2];
-	diff1 = range1[2] - range1[1];
-	diff2 = range2[2] - range2[1];
-	
-	Xgrid = foldl(vcat, grid);
-	Xtest = ripley["Xt"];
-	
-	yhat = model.output.Ytest;
-	yplot = map_predict_latent(model,Xgrid);
-	yplot = yplot - minimum(yplot);
-	yplot = 2*(yplot ./ maximum(yplot)) - 1;
-
-	using DataFrames
-	df = DataFrame();
-	df[:Xmin] = Xgrid[:,1][:];
-	df[:Ymin] = Xgrid[:,2][:];
-	df[:Xmax] = Xgrid[:,1][:] + diff1;
-	df[:Ymax] = Xgrid[:,2][:] + diff2;
-	df[:class] = yplot[:];
-
-	using Gadfly
-	set_default_plot_size(20cm, 20cm);
-	plot(layer(x=Xtest[yhat.>0,1], y=Xtest[yhat.>0,2], Geom.point, Theme(default_color=colorant"orange")),
-    layer(x=Xtest[yhat.<0,1], y=Xtest[yhat.<0,2], Geom.point, Theme(default_color=colorant"black")),
-    layer(df, x_min="Xmin", x_max="Xmax", y_min="Ymin", y_max="Ymax", color="class", Geom.rectbin))
+using SALSA, MAT
+
+ripley = matread(joinpath(Pkg.dir("SALSA"), "data", "ripley.mat")); srand(123);
+model = SALSAModel(NONLINEAR, PEGASOS(), LOGISTIC, validation_criterion=AUC(100));
+model = salsa(ripley["X"], ripley["Y"], model, ripley["Xt"]);
+
+range1 = linspace(-1.5,1.5,200);
+range2 = linspace(-0.5,1.5,200);
+grid = [[i j] for i in range1, j in range2];
+diff1 = range1[2] - range1[1];
+diff2 = range2[2] - range2[1];
+
+Xgrid = foldl(vcat, grid);
+Xtest = ripley["Xt"];
+
+yhat = model.output.Ytest;
+yplot = map_predict_latent(model,Xgrid);
+yplot = yplot - minimum(yplot);
+yplot = 2*(yplot ./ maximum(yplot)) - 1;
+
+using DataFrames
+df = DataFrame();
+df[:Xmin] = Xgrid[:,1][:];
+df[:Ymin] = Xgrid[:,2][:];
+df[:Xmax] = Xgrid[:,1][:] + diff1;
+df[:Ymax] = Xgrid[:,2][:] + diff2;
+df[:class] = yplot[:];
+
+using Gadfly
+set_default_plot_size(20cm, 20cm);
+plot(layer(x=Xtest[yhat.>0,1], y=Xtest[yhat.>0,2], Geom.point, Theme(default_color=colorant"orange")),
+     layer(x=Xtest[yhat.<0,1], y=Xtest[yhat.<0,2], Geom.point, Theme(default_color=colorant"black")),
+     layer(df, x_min="Xmin", x_max="Xmax", y_min="Ymin", y_max="Ymax", color="class", Geom.rectbin))
 
 .. image:: ../ripley.png
 	:alt: Advanced Classification Example
@@ -60,23 +60,23 @@ This example provides a use-case for regression using :doc:`Nyström approximati
 
 .. code-block:: julia
 
-	using SALSA, MLBase
+using SALSA, MLBase
 
-	sinc(x) = sin(x)./x;
-    X = linspace(0.1,20,100)'';
-    Xtest = linspace(0.1,20,200)'';
-    Y = sinc(X);
-    srand(1234);
+sinc(x) = sin(x)./x;
+X = linspace(0.1,20,100)'';
+Xtest = linspace(0.1,20,200)'';
+Y = sinc(X);
+srand(1234);
 
-    model = SALSAModel(NONLINEAR, PEGASOS(), LEAST_SQUARES, 
+model = SALSAModel(NONLINEAR, PEGASOS(), LEAST_SQUARES, 
     	cv_gen=Nullable{CrossValGenerator}(LOOCV(100)),
     	validation_criterion=MSE(), process_labels=false, subset_size=5.0);
-    model = salsa(X, Y, model, Xtest);
+model = salsa(X, Y, model, Xtest);
 
-    using Gadfly
-	set_default_plot_size(20cm, 20cm);
-	plot(layer(x=Xtest[:], y=sinc(Xtest), Geom.point),
-     	 layer(x=Xtest[:], y=model.output.Ytest, Geom.line, Theme(default_color=colorant"orange")))
+using Gadfly
+set_default_plot_size(20cm, 20cm);
+plot(layer(x=Xtest[:], y=sinc(Xtest), Geom.point),
+     layer(x=Xtest[:], y=model.output.Ytest, Geom.line, Theme(default_color=colorant"orange")))
 
 
 .. image:: ../sinc.png