Merge pull request #1 from oreillymedia/beta

merged

Author: fchasen

README.md

@@ -85,7 +85,7 @@ for i in range(ncols * nrows):
     ax.matshow(digits.images[i,...])
     plt.xticks([]); plt.yticks([])
     plt.title(digits.target[i])
-plt.savefig('images/digits.png', dpi=150)
+plt.savefig('images/digits-generated.png', dpi=150)
 </pre>
 
 ![Digits](images/digits.png)
@@ -147,7 +147,7 @@ Here is the result.
      data-executable="true"
      data-type="programlisting">
 scatter(digits_proj, y)
-plt.savefig('images/digits_tsne.png', dpi=120)
+plt.savefig('images/digits_tsne-generated.png', dpi=120)
 </pre>
 
 ![Transformed digits with t-SNE](images/digits_tsne.png)
@@ -224,7 +224,7 @@ plt.subplot(133)
 plt.imshow(P_binary_s[::10, ::10], interpolation='none', cmap=pal)
 plt.axis('off')
 plt.title("$p_{j|i}$ (variable $\sigma$)", fontdict={'fontsize': 16})
-plt.savefig('images/similarity.png', dpi=120)
+plt.savefig('images/similarity-generated.png', dpi=120)
 </pre>
 
 We can already observe the 10 groups in the data, corresponding to the 10 numbers.
@@ -245,8 +245,8 @@ The final mapping is obtained when the equilibrium is reached.
 
 Here is an illustration of a dynamic graph layout based on a similar idea. Nodes are connected via springs and the system evolves according to law of physics (example by [Mike Bostock](http://bl.ocks.org/mbostock/4062045)).
 
-<iframe src="http://bl.ocks.org/rossant/raw/f06184034ba66a0bd06a/"
-        style="border: 0; width: 620px; height: 440px; margin: 0; padding: 0;" />
+<iframe src="https://d3ansictanv2wj.cloudfront.net/rossant-f06184034ba66a0bd06a-001.html"
+        style="border: 0; width: 620px; height: 440px; margin: 0; padding: 0;" sandbox="allow-scripts" />
 
 
 
@@ -422,7 +422,7 @@ for i, D in enumerate((2, 5, 10)):
     ax.hist(norm(points, axis=1),
             bins=np.linspace(0., 1., 50))
     ax.set_title('D=%d' % D, loc='left')
-plt.savefig('images/spheres.png', dpi=100, bbox_inches='tight')
+plt.savefig('images/spheres-generated.png', dpi=100, bbox_inches='tight')
 </pre>
 
 ![Spheres](images/spheres.png)
@@ -440,7 +440,7 @@ cauchy = 1/(1+z**2)
 plt.plot(z, gauss, label='Gaussian distribution')
 plt.plot(z, cauchy, label='Cauchy distribution')
 plt.legend()
-plt.savefig('images/distributions.png', dpi=100)
+plt.savefig('images/distributions-generated.png', dpi=100)
 </pre>
 
 ![Gaussian and Cauchy distributions](images/distributions.png)

atlas.json

@@ -1,7 +1,7 @@
 {
   "branch": "master",
   "files": [
-    "main.md"
+    "README.md"
   ],
   "formats": {
     "pdf": {
@@ -29,10 +29,15 @@
       "toc": false,
       "syntaxhighlighting": false,
       "show_comments": false,
-      "consolidated": false
+      "consolidated": false,
+      "javascripts": [
+        "http://rawgit.com/oreillymedia/thebe/master/static/main-built.js",
+        "page.js"
+      ]
     }
   },
   "theme": "oreillymedia/jupyter_theme",
   "title": "An illustrated introduction to the t-SNE algorithm",
-  "github_url": "https://github.com/oreillymedia/ipython-tutorial-content"
+  "github_url": "https://github.com/oreillymedia/ipython-tutorial-content",
+  "uuid": "b0c6f45f-cc4f-4c32-9a70-c3c4adc54c70"
 }

page.js

@@ -0,0 +1,3 @@
+$(function(){
+    new Thebe({url:"https://oreillyorchard.com:8000/spawn/"});
+});