Updated SDH docs

Author: stephenchouca

sdh-docs/benchmarking/index.html

@@ -66,6 +66,10 @@
 
     <a class="" href="../pycomputations/index.html">Computing on Tensors</a>
                 </li>
+                <li class="">
+                    
+    <a class="" href="../pyreference/index.html">Reference Manual</a>
+                </li>
     </ul>
 	    </li>
 
@@ -98,21 +102,6 @@
     <a class="current" href="index.html">Guide to Benchmarking</a>
     <ul class="subnav">
 
-    </ul>
-	    </li>
-          
-            <li class="toctree-l1">
-		
-    <span class="caption-text">C++ Library</span>
-    <ul class="subnav">
-                <li class="">
-                    
-    <a class="" href="../tensors/index.html">Defining Tensors</a>
-                </li>
-                <li class="">
-                    
-    <a class="" href="../computations/index.html">Computing on Tensors</a>
-                </li>
     </ul>
 	    </li>
 
@@ -153,15 +142,15 @@
 applications.  As an example, we can benchmark the performance of the
 scientific computing application shown <a href="../scientific_computing/index.html">here</a> as
 follows:</p>
-<pre><code class="python">import pytaco as pt
+<pre class="highlight"><code class="language-python">import pytaco as pt
 from pytaco import compressed, dense
 import numpy as np
 import time
 
 csr = pt.format([dense, compressed])
 dv  = pt.format([dense])
 
-A = pt.read(&quot;pwtk.mtx&quot;, csr)
+A = pt.read("pwtk.mtx", csr)
 x = pt.from_array(np.random.uniform(size=A.shape[1]))
 z = pt.from_array(np.random.uniform(size=A.shape[0]))
 y = pt.tensor([A.shape[0]], dv)
@@ -177,8 +166,7 @@
 y.compute()
 end = time.perf_counter()
 
-print(&quot;Execution time: {0} seconds&quot;.format(end - start))
-</code></pre>
+print("Execution time: {0} seconds".format(end - start))</code></pre>
 
 <p>In order to accurately measure TACO's computational performance, <strong>only the
 time it takes to actually perform a computation should be measured.  The time
@@ -196,7 +184,7 @@
 matrices to TACO tensors return fully constructed tensors.  If you add nonzero
 elements to an input tensor by calling <code>insert</code> though, then <code>pack</code> must also
 be explicitly invoked before any benchmarking is done:</p>
-<pre><code class="python">import pytaco as pt
+<pre class="highlight"><code class="language-python">import pytaco as pt
 from pytaco import compressed, dense
 import numpy as np
 import random
@@ -205,7 +193,7 @@
 csr = pt.format([dense, compressed])
 dv  = pt.format([dense])
 
-A = pt.read(&quot;pwtk.mtx&quot;, csr)
+A = pt.read("pwtk.mtx", csr)
 x = pt.tensor([A.shape[1]], dv)
 z = pt.tensor([A.shape[0]], dv)
 y = pt.tensor([A.shape[0]], dv)
@@ -227,8 +215,7 @@
 y.compute()
 end = time.perf_counter()
 
-print(&quot;Execution time: {0} seconds&quot;.format(end - start))
-</code></pre>
+print("Execution time: {0} seconds".format(end - start))</code></pre>
 
 <p>TACO avoids regenerating code for performing the same computation though as
 long as the computation is redefined with the same index variables and with the
@@ -238,15 +225,15 @@
 associated with generating code for performing the computation.  In such 
 scenarios, it is acceptable to include the initial code generation overhead 
 in the performance measurement:</p>
-<pre><code class="python">import pytaco as pt
+<pre class="highlight"><code class="language-python">import pytaco as pt
 from pytaco import compressed, dense
 import numpy as np
 import time
 
 csr = pt.format([dense, compressed])
 dv  = pt.format([dense])
 
-A = pt.read(&quot;pwtk.mtx&quot;, csr)
+A = pt.read("pwtk.mtx", csr)
 x = pt.tensor([A.shape[1]], dv)
 z = pt.tensor([A.shape[0]], dv)
 y = pt.tensor([A.shape[0]], dv)
@@ -270,8 +257,7 @@
   x.evaluate()
 end = time.perf_counter()
 
-print(&quot;Execution time: {0} seconds&quot;.format(end - start))
-</code></pre>
+print("Execution time: {0} seconds".format(end - start))</code></pre>
 
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -300,8 +286,6 @@
 
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
 
-        <a href="../tensors/index.html" class="btn btn-neutral float-right" title="Defining Tensors">Next <span class="icon icon-circle-arrow-right"></span></a>
-      
 
         <a href="../optimization/index.html" class="btn btn-neutral" title="Strategies for Optimization"><span class="icon icon-circle-arrow-left"></span> Previous</a>
 
@@ -340,8 +324,6 @@
         <span><a href="../optimization/index.html" style="color: #fcfcfc;">&laquo; Previous</a></span>
 
 
-        <span style="margin-left: 15px"><a href="../tensors/index.html" style="color: #fcfcfc">Next &raquo;</a></span>
-      
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 </div>
     <script>var base_url = '..';</script>

sdh-docs/data_analytics/index.html

@@ -66,6 +66,10 @@
 
     <a class="" href="../pycomputations/index.html">Computing on Tensors</a>
                 </li>
+                <li class="">
+                    
+    <a class="" href="../pyreference/index.html">Reference Manual</a>
+                </li>
     </ul>
 	    </li>
 
@@ -101,21 +105,6 @@
     <a class="" href="../benchmarking/index.html">Guide to Benchmarking</a>
 	    </li>
 
-            <li class="toctree-l1">
-		
-    <span class="caption-text">C++ Library</span>
-    <ul class="subnav">
-                <li class="">
-                    
-    <a class="" href="../tensors/index.html">Defining Tensors</a>
-                </li>
-                <li class="">
-                    
-    <a class="" href="../computations/index.html">Computing on Tensors</a>
-                </li>
-    </ul>
-	    </li>
-          
         </ul>
       </div>
       &nbsp;
@@ -162,14 +151,14 @@
 </p>
 <p>where <script type="math/tex">A</script>, <script type="math/tex">C</script>, and <script type="math/tex">D</script> are typically dense matrices, <script type="math/tex">B</script> is a
 three-dimensional tensor (matricizied along the first mode), and <script type="math/tex">\odot</script>
-denotes the Khatri-Rao product. This operation can also be expressed in <a href="../computations/index.html#specifying-tensor-algebra-computations">index
+denotes the Khatri-Rao product. This operation can also be expressed in <a href="computations.md#specifying-tensor-algebra-computations">index
 notation</a> as </p>
 <p>
 <script type="math/tex; mode=display">A_{ij} = B_{ikl} \cdot D_{lj} \cdot C_{kj}.</script>
 </p>
 <p>You can use the TACO Python library to easily and efficiently compute MTTKRP,
 as shown here:</p>
-<pre><code class="python">import pytaco as pt
+<pre class="highlight"><code class="language-python">import pytaco as pt
 import numpy as np
 from pytaco import compressed, dense
 
@@ -180,7 +169,7 @@
 # Load a sparse three-dimensional tensor from file (stored in the FROSTT
 # format) and store it as a compressed sparse fiber tensor. The tensor in this
 # example can be download from: http://frostt.io/tensors/nell-2/
-B = pt.read(&quot;nell-2.tns&quot;, csf);
+B = pt.read("nell-2.tns", csf);
 
 # Generate two random matrices using NumPy and pass them into TACO
 C = pt.from_array(np.random.uniform(size=(B.shape[1], 25)))
@@ -196,8 +185,7 @@
 A[i, j] = B[i, k, l] * D[l, j] * C[k, j]
 
 # Perform the MTTKRP computation and write the result to file
-pt.write(&quot;A.tns&quot;, A)
-</code></pre>
+pt.write("A.tns", A)</code></pre>
 
 <p>When you run the above Python program, TACO will generate code under the hood
 that efficiently performs the computation in one shot.  This lets TACO avoid

sdh-docs/index.html

@@ -57,9 +57,6 @@
     <li class="toctree-l2"><a href="#system-requirements">System Requirements</a></li>
 
 
-    <li class="toctree-l2"><a href="#getting-help">Getting Help</a></li>
-    
-
     </ul>
 	    </li>
 
@@ -75,6 +72,10 @@
 
     <a class="" href="pycomputations/index.html">Computing on Tensors</a>
                 </li>
+                <li class="">
+                    
+    <a class="" href="pyreference/index.html">Reference Manual</a>
+                </li>
     </ul>
 	    </li>
 
@@ -107,21 +108,6 @@
     <a class="" href="benchmarking/index.html">Guide to Benchmarking</a>
 	    </li>
 
-            <li class="toctree-l1">
-		
-    <span class="caption-text">C++ Library</span>
-    <ul class="subnav">
-                <li class="">
-                    
-    <a class="" href="tensors/index.html">Defining Tensors</a>
-                </li>
-                <li class="">
-                    
-    <a class="" href="computations/index.html">Computing on Tensors</a>
-                </li>
-    </ul>
-	    </li>
-          
         </ul>
       </div>
       &nbsp;
@@ -166,11 +152,15 @@
 applications.</p>
 <h1 id="system-requirements">System Requirements</h1>
 <ul>
-<li>A C compiler that supports C99 and OpenMP (if parallel execution is desired), such as GCC or Clang.</li>
-<li>Python 3 with NumPy and SciPy.</li>
+<li>A C compiler that supports C99, such as GCC or Clang<ul>
+<li>Support for OpenMP is also required if parallel execution is desired</li>
 </ul>
-<h1 id="getting-help">Getting Help</h1>
-<p>Questions and bug reports can be submitted <a href="https://github.com/tensor-compiler/taco/issues">here</a>.</p>
+</li>
+<li>Python 3 with NumPy and SciPy</li>
+</ul>
+<!--# Getting Help
+
+Questions and bug reports can be submitted [here](https://github.com/tensor-compiler/taco/issues).-->
 
             </div>
           </div>
@@ -229,5 +219,5 @@ <h1 id="getting-help">Getting Help</h1>
 
 <!--
 MkDocs version : 0.17.2
-Build Date UTC : 2019-05-30 03:24:49
+Build Date UTC : 2019-05-30 18:02:31
 -->

sdh-docs/machine_learning/index.html

@@ -66,6 +66,10 @@
 
     <a class="" href="../pycomputations/index.html">Computing on Tensors</a>
                 </li>
+                <li class="">
+                    
+    <a class="" href="../pyreference/index.html">Reference Manual</a>
+                </li>
     </ul>
 	    </li>
 
@@ -101,21 +105,6 @@
     <a class="" href="../benchmarking/index.html">Guide to Benchmarking</a>
 	    </li>
 
-            <li class="toctree-l1">
-		
-    <span class="caption-text">C++ Library</span>
-    <ul class="subnav">
-                <li class="">
-                    
-    <a class="" href="../tensors/index.html">Defining Tensors</a>
-                </li>
-                <li class="">
-                    
-    <a class="" href="../computations/index.html">Computing on Tensors</a>
-                </li>
-    </ul>
-	    </li>
-          
         </ul>
       </div>
       &nbsp;
@@ -161,14 +150,14 @@
 </p>
 <p>where <script type="math/tex">A</script> and <script type="math/tex">B</script> are sparse matrices, <script type="math/tex">C</script> and <script type="math/tex">D</script> are dense matrices,
 and <script type="math/tex">\circ</script> denotes component-wise multiplication. This operation can also be
-expressed in <a href="../computations/index.html#specifying-tensor-algebra-computations">index
+expressed in <a href="computations.md#specifying-tensor-algebra-computations">index
 notation</a> as </p>
 <p>
 <script type="math/tex; mode=display">A_{ij} = B_{ij} \cdot C_{ik} \cdot C_{kj}.</script>
 </p>
 <p>You can use the TACO Python library to easily and efficiently compute SDDMM, as
 shown here:</p>
-<pre><code class="python">import pytaco as pt
+<pre class="highlight"><code class="language-python">import pytaco as pt
 from pytaco import dense, compressed
 import numpy as np
 
@@ -183,7 +172,7 @@
 
 # The matrix in this example can be download from:
 # https://www.cise.ufl.edu/research/sparse/MM/Williams/webbase-1M.tar.gz
-B = pt.read(&quot;webbase-1M.mtx&quot;, dcsr)
+B = pt.read("webbase-1M.mtx", dcsr)
 
 # Generate two random matrices using NumPy and pass them into TACO
 x = pt.from_array(np.random.uniform(size=(B.shape[0], 1000)))
@@ -199,8 +188,7 @@
 A[i, j] = B[i, j] * C[i, k] * D[k, j]
 
 # Perform the SDDMM computation and write the result to file
-pt.write(&quot;A.mtx&quot;, A)
-</code></pre>
+pt.write("A.mtx", A)</code></pre>
 
 <p>When you run the above Python program, TACO will generate code under the hood
 that efficiently performs the computation in one shot.  This lets TACO only