Merge e5a53f7 into 5bb8abd

localgraphclustering/approximate_PageRank.py

@@ -137,15 +137,26 @@ def approximate_PageRank(G,
     elif method == "l1reg":
         #print("Uses the Fast Iterative Soft Thresholding Algorithm (FISTA).")
         # TODO fix the following warning
-        warnings.warn("The normalization of this routine hasn't been adjusted to the new system yet")
+        # warnings.warn("The normalization of this routine hasn't been adjusted to the new system yet")
         if cpp:
             if ys == None:
-                return proxl1PRaccel_cpp(G.ai, G.aj, G.adjacency_matrix.data, ref_nodes, G.d, G.d_sqrt, G.dn_sqrt, alpha = alpha,
+                p = proxl1PRaccel_cpp(G.ai, G.aj, G.adjacency_matrix.data, ref_nodes, G.d, G.d_sqrt, G.dn_sqrt, alpha = alpha,
                                      rho = rho, epsilon = epsilon, maxiter = iterations, max_time = timeout)[2]
             else:
-                return proxl1PRaccel_cpp(G.ai, G.aj, G.adjacency_matrix.data, ref_nodes, G.d, G.d_sqrt, G.dn_sqrt, y = ys, alpha = alpha,
+                p = proxl1PRaccel_cpp(G.ai, G.aj, G.adjacency_matrix.data, ref_nodes, G.d, G.d_sqrt, G.dn_sqrt, y = ys, alpha = alpha,
                                      rho = rho, epsilon = epsilon, maxiter = iterations, max_time = timeout)[2]
         else:
-            return fista_dinput_dense(ref_nodes, G, alpha = alpha, rho = rho, epsilon = epsilon, max_iter = iterations, max_time = timeout)
+            p = fista_dinput_dense(ref_nodes, G, alpha = alpha, rho = rho, epsilon = epsilon, max_iter = iterations, max_time = timeout)
+        # convert result to a sparse vector
+        nonzeros = np.count_nonzero(p)
+        idx = np.zeros(nonzeros,dtype=np.dtype(G.aj[0]))
+        vals = np.zeros(nonzeros,dtype=np.float64)
+        it = 0
+        for i in range(len(p)):
+            if p[i] != 0:
+                idx[it] = i
+                vals[it] = p[i]*1.0 * G.dn[i] if normalize else p[i]
+                it += 1
+        return (idx,vals)
     else:
         raise Exception("Unknown method, available methods are \"acl\" or \"acl_weighted\" or \"l1reg\".")

localgraphclustering/sweep_cut.py

@@ -8,7 +8,6 @@
 def sweep_cut(G: GraphLocal,
               p: Union[Sequence[float],Tuple[Sequence[int],Sequence[float]]],
               do_sort: bool = True,
-              normalized: bool = True,
               cpp: bool = True):
     """
     It implements a sweep cut rounding procedure for local graph clustering.

notebooks/Helper-Example.ipynb

@@ -3,10 +3,20 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
-    "from localgraphclustering import *\n",
+    "import sys,os\n",
+    "\n",
+    "try:\n",
+    "    from localgraphclustering import *\n",
+    "except:\n",
+    "    # when the package is not installed, import the local version instead. \n",
+    "    # the notebook must be placed in the original \"notebooks/\" folder\n",
+    "    sys.path.append(\"../\")\n",
+    "    from localgraphclustering import *\n",
     "import helper\n",
     "\n",
     "import numpy as np\n",
@@ -16,7 +26,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -77,63 +89,81 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   }