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MelJan · Oct 14, 2018 · 3668b82 · 3668b82
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diff --git a/docs/tutorials/GRBM_2D_example.rst b/docs/tutorials/GRBM_2D_example.rst
@@ -22,14 +22,14 @@ The code_ given below produces the following output.
 Visualization of the weight vectors learned by the GRBM with 4 hidden units together with the contour plot of the learned probability density function (PDF).
 
 .. figure:: images/GRBM_2D_PDF.png
-   :scale: 75 %
+   :scale: 50 %
    :align: center
    :alt: Visualization of the PDF learned by the GRBM
 
 For a better visualization also the log-PDF.
 
 .. figure:: images/GRBM_2D_logPDF.png
-   :scale: 75 %
+   :scale: 50 %
    :align: center
    :alt: Visualization of the log PDF learned by the GRBM
 
@@ -65,12 +65,12 @@ The exact log-likelihood, annealed importance sampling estimation, and reverse a
 For comparison here is the original mixing matrix an the corresponding ICA estimation.
 
 .. image:: images/GRBM_2D_MIX.png
-   :scale: 75 %
+   :scale: 50 %
    :align: center
    :alt: Examples of mixing matrix 2D
 
 .. image:: images/GRBM_2D_ICA.png
-   :scale: 75 %
+   :scale: 50 %
    :align: center
    :alt: Examples ICA estimation of the mixing matrix.
 

diff --git a/docs/tutorials/ICA_2D_example.rst b/docs/tutorials/ICA_2D_example.rst
@@ -32,7 +32,7 @@ The code_ given below produces the following output.
 Visualization of the data and true mixing matrix projected to the whitened space.
 
 .. figure:: images/ICA_2D_mixing_whitened.png
-   :scale: 75 %
+   :scale: 50 %
    :align: center
    :alt: Examples of mixing matrix 2D in whitened space
 
@@ -46,19 +46,19 @@ Amari distanca between true mixing matrix and estimated mixing matrix:
    0.00989836830489
 
 .. image:: images/ICA_2D_estimate_whitened.png
-   :scale: 75 %
+   :scale: 50 %
    :align: center
    :alt: Examples of ICA 2D in whitened space
 
 We can also project the ICA projection matrix back to the original space and compare the results in the original space.
 
 .. image:: images/ICA_2D_mixing.png
-   :scale: 75 %
+   :scale: 50 %
    :align: center
    :alt: Examples of mixing matrix 2D
 
 .. image:: images/ICA_2D_estimate.png
-   :scale: 75 %
+   :scale: 50 %
    :align: center
    :alt: Examples of ICA 2D
 

diff --git a/docs/tutorials/PCA_2D_example.rst b/docs/tutorials/PCA_2D_example.rst
@@ -24,21 +24,21 @@ The code_ given below produces the following output.
 The data is plotted with the extracted principal components.
 
 .. figure:: images/PCA_2D.png
-   :scale: 75 %
+   :scale: 50 %
    :align: center
    :alt: Examples of PCA 2D
 
 Data and extracted principal components can also be plotted in the projected space.
 
 .. image:: images/PCA_2D_projected.png
-   :scale: 75 %
+   :scale: 50 %
    :align: center
    :alt: Examples of PCA 2D in projected space
 
 The PCA-class can also perform whitening. Data and extracted principal components are plotted in the whitened space.
 
 .. image:: images/PCA_2D_whitened.png
-   :scale: 75 %
+   :scale: 50 %
    :align: center
    :alt: Examples of PCA 2D in whitened space
 

diff --git a/docs/tutorials/PCA_eigenfaces.rst b/docs/tutorials/PCA_eigenfaces.rst
@@ -30,7 +30,7 @@ The first 100 principal components extracted from the dataset. The components fo
 The cumulative sum of the Eigenvalues show how 'compressable' the dataset is.
 
 .. image:: images/eigenspectrum_faces.png
-   :scale: 75 %
+   :scale: 50 %
    :align: center
    :alt: Eigenspectrum of the face dataset