Merge branch 'model_training'

Author: solomonik

doc/CTF introductory demo.ipynb renamed to doc/CTF_introductory_demo.ipynb

@@ -15,10 +15,10 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 0.98560831,  0.11823295,  0.72342078,  0.95922546],\n",
-       "       [ 0.20515565,  0.37738114,  0.38818925,  0.33459847],\n",
-       "       [ 0.78185553,  0.95349057,  0.74539072,  0.34848531],\n",
-       "       [ 0.68354569,  0.66207449,  0.56496249,  0.43039886]])"
+       "array([[ 0.49922613,  0.07979019,  0.23518855,  0.1647902 ],\n",
+       "       [ 0.74323602,  0.04472799,  0.54030603,  0.37305325],\n",
+       "       [ 0.40874152,  0.51150855,  0.17782078,  0.6479568 ],\n",
+       "       [ 0.57472887,  0.67596946,  0.73410843,  0.97596626]])"
       ]
      },
      "execution_count": 1,
@@ -48,10 +48,10 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 1.37028366,  1.67135975,  1.38344438,  1.3174106 ],\n",
-       "       [ 0.69550475,  0.85317029,  0.51779038,  0.87487786],\n",
-       "       [ 1.66146624,  1.87982872,  1.3491488 ,  1.88574364],\n",
-       "       [ 1.33055926,  1.5305887 ,  1.10025433,  1.4429062 ]])"
+       "array([[ 0.75001965,  0.43859658,  0.73518856,  0.83285544],\n",
+       "       [ 1.27756494,  0.72708474,  1.27528118,  1.39893694],\n",
+       "       [ 1.16023982,  0.85408688,  1.17364888,  1.42447064],\n",
+       "       [ 1.90054979,  1.44349232,  1.91599113,  2.32447089]])"
       ]
      },
      "execution_count": 2,
@@ -79,10 +79,10 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 1.37028366,  1.67135975,  1.38344438,  1.3174106 ],\n",
-       "       [ 0.69550475,  0.85317029,  0.51779038,  0.87487786],\n",
-       "       [ 1.66146624,  1.87982872,  1.3491488 ,  1.88574364],\n",
-       "       [ 1.33055926,  1.5305887 ,  1.10025433,  1.4429062 ]])"
+       "array([[ 0.75001965,  0.43859658,  0.73518856,  0.83285544],\n",
+       "       [ 1.27756494,  0.72708474,  1.27528118,  1.39893694],\n",
+       "       [ 1.16023982,  0.85408688,  1.17364888,  1.42447064],\n",
+       "       [ 1.90054979,  1.44349232,  1.91599113,  2.32447089]])"
       ]
      },
      "execution_count": 3,
@@ -112,10 +112,10 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 1.37028366,  1.67135975,  1.38344438,  1.3174106 ],\n",
-       "       [ 0.69550475,  0.85317029,  0.51779038,  0.87487786],\n",
-       "       [ 1.66146624,  1.87982872,  1.3491488 ,  1.88574364],\n",
-       "       [ 1.33055926,  1.5305887 ,  1.10025433,  1.4429062 ]])"
+       "array([[ 0.75001965,  0.43859658,  0.73518856,  0.83285544],\n",
+       "       [ 1.27756494,  0.72708474,  1.27528118,  1.39893694],\n",
+       "       [ 1.16023982,  0.85408688,  1.17364888,  1.42447064],\n",
+       "       [ 1.90054979,  1.44349232,  1.91599113,  2.32447089]])"
       ]
      },
      "execution_count": 4,
@@ -144,10 +144,10 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 1.37028366,  1.67135975,  1.38344438,  1.3174106 ],\n",
-       "       [ 0.69550475,  0.85317029,  0.51779038,  0.87487786],\n",
-       "       [ 1.66146624,  1.87982872,  1.3491488 ,  1.88574364],\n",
-       "       [ 1.33055926,  1.5305887 ,  1.10025433,  1.4429062 ]])"
+       "array([[ 0.75001965,  0.43859658,  0.73518856,  0.83285544],\n",
+       "       [ 1.27756494,  0.72708474,  1.27528118,  1.39893694],\n",
+       "       [ 1.16023982,  0.85408688,  1.17364888,  1.42447064],\n",
+       "       [ 1.90054979,  1.44349232,  1.91599113,  2.32447089]])"
       ]
      },
      "execution_count": 5,
@@ -176,10 +176,10 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 1.37028366,  1.67135975,  1.38344438,  1.3174106 ],\n",
-       "       [ 0.69550475,  0.85317029,  0.51779038,  0.87487786],\n",
-       "       [ 1.66146624,  1.87982872,  1.3491488 ,  1.88574364],\n",
-       "       [ 1.33055926,  1.5305887 ,  1.10025433,  1.4429062 ]])"
+       "array([[ 0.75001965,  0.43859658,  0.73518856,  0.83285544],\n",
+       "       [ 1.27756494,  0.72708474,  1.27528118,  1.39893694],\n",
+       "       [ 1.16023982,  0.85408688,  1.17364888,  1.42447064],\n",
+       "       [ 1.90054979,  1.44349232,  1.91599113,  2.32447089]])"
       ]
      },
      "execution_count": 6,
@@ -199,10 +199,10 @@
     {
      "data": {
       "text/plain": [
-       "array([[ 1.37028366,  1.67135975,  1.38344438,  1.3174106 ],\n",
-       "       [ 0.69550475,  0.85317029,  0.51779038,  0.87487786],\n",
-       "       [ 1.66146624,  1.87982872,  1.3491488 ,  1.88574364],\n",
-       "       [ 1.33055926,  1.5305887 ,  1.10025433,  1.4429062 ]])"
+       "array([[ 0.75001965,  0.43859658,  0.73518856,  0.83285544],\n",
+       "       [ 1.27756494,  0.72708474,  1.27528118,  1.39893694],\n",
+       "       [ 1.16023982,  0.85408688,  1.17364888,  1.42447064],\n",
+       "       [ 1.90054979,  1.44349232,  1.91599113,  2.32447089]])"
       ]
      },
      "execution_count": 7,
@@ -223,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {

doc/Tensor Decompositions for 3D Poisson using Cyclops.ipynb renamed to doc/Tensor_Decompositions_for_3D_Poisson_using_Cyclops.ipynb

@@ -3,7 +3,9 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import ctf\n",
@@ -51,7 +53,9 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "I = ctf.eye(n,n,sp=True) # sparse identity matrix\n",
@@ -154,7 +158,9 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "[U1,S1,V1] = ctf.svd(T2.reshape((n*n, n*n*n*n)),2) # compute rank-2 SVD on unfolded tensor\n",
@@ -293,14 +299,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   }