Update stable version

tensorly · Dec 7, 2020 · 55bdab7 · 55bdab7
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diff --git a/home.html b/home.html
@@ -3,22 +3,22 @@
 <html lang="en">
     <head>
 
-        <meta http-equiv="refresh" content="1; url=https://tensorly.github.io/stable/home.html">
+        <meta http-equiv="refresh" content="1; url=https://tensorly.github.io/stable/index.html">
 
 		<meta name="keywords" content="tensor learning, tensor decomposition, tensor operations">
         <meta name="description" content="TensorLy: Tensor learning in Python -- Redirection to stable">
         <meta name="author" content="Jean Kossaifi">
         <title>TensorLy: Tensor learning in Python</title>
 
         <script type="text/javascript">
-            window.location.href = "https://tensorly.github.io/stable/home.html"
+            window.location.href = "https://tensorly.github.io/stable/index.html"
         </script>
 
     </head>
 
     <body>
 
-        If you are not redirected automatically, follow the link to the <a href='https://tensorly.github.io/stable/home.html'>stable version</a> or <a href='https://tensorly.github.io/dev/home.html'>dev version</a>.
+        If you are not redirected automatically, follow the link to the <a href='https://tensorly.github.io/stable/index.html'>stable version</a> or <a href='https://tensorly.github.io/dev/index.html'>dev version</a>.
 
     </body>
 

diff --git a/stable/.buildinfo b/stable/.buildinfo
diff --git a/stable/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip b/stable/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
diff --git a/...9353737bb641397d4c/parafac_line_search.py → ...b9ad2fd36324f584e8/plot_cp_line_search.py b/...9353737bb641397d4c/parafac_line_search.py → ...b9ad2fd36324f584e8/plot_cp_line_search.py
@@ -1,17 +1,16 @@
-
 """
 Using line search with PARAFAC
 ==============================
 
 Example on how to use :func:`tensorly.decomposition.parafac` with line search to accelerate convergence.
 """
+import matplotlib.pyplot as plt
 
 from time import time
 import numpy as np
 import tensorly as tl
 from tensorly.random import random_cp
-from tensorly.decomposition import parafac
-import matplotlib.pyplot as plt
+from tensorly.decomposition import CP, parafac
 
 tol = np.logspace(-1, -9)
 err = np.empty_like(tol)
@@ -27,20 +26,28 @@
 for ii, toll in enumerate(tol):
 	# Run PARAFAC decomposition without line search and time
     start = time()
-    fac = parafac(tensor, rank=3, n_iter_max=2000000, tol=toll)
+    cp = CP(rank=3, n_iter_max=2000000, tol=toll, linesearch=False)
+    fac = cp.fit_transform(tensor)
     tt[ii] = time() - start
-    # Run PARAFAC decomposition with line search and time
+    err[ii] = tl.norm(tl.cp_to_tensor(fac) - tensor)
+
+# Run PARAFAC decomposition with line search and time
+for ii, toll in enumerate(tol):
     start = time()
-    fac_ls = parafac(tensor, rank=3, n_iter_max=2000000, tol=toll, linesearch=True)
+    cp = CP(rank=3, n_iter_max=2000000, tol=toll, linesearch=True)
+    fac_ls = cp.fit_transform(tensor)
     tt_ls[ii] = time() - start
 
     # Calculate the error of both decompositions
-    err[ii] = tl.norm(tl.cp_to_tensor(fac) - tensor)
     err_ls[ii] = tl.norm(tl.cp_to_tensor(fac_ls) - tensor)
 
-plt.loglog(tt, err - err_min, '.', label="No line search")
-plt.loglog(tt_ls, err_ls - err_min, '.r', label="Line search")
-plt.ylabel("Time")
-plt.xlabel("Error")
-plt.legend()
+
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1)
+ax.loglog(tt, err - err_min, '.', label="No line search")
+ax.loglog(tt_ls, err_ls - err_min, '.r', label="Line search")
+ax.legend()
+ax.set_ylabel("Time")
+ax.set_xlabel("Error")
+
 plt.show()
diff --git a/stable/_downloads/4646f2ed882fdbd7317db8bc1eb3916e/plot_cp_regression.ipynb b/stable/_downloads/4646f2ed882fdbd7317db8bc1eb3916e/plot_cp_regression.ipynb
@@ -46,7 +46,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.3"
+      "version": "3.8.5"
     }
   },
   "nbformat": 4,

diff --git a/stable/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip b/stable/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
diff --git a/stable/_downloads/6f392aba7031ac3e4c8365850f77f099/plot_cp_line_search.ipynb b/stable/_downloads/6f392aba7031ac3e4c8365850f77f099/plot_cp_line_search.ipynb
@@ -0,0 +1,54 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Using line search with PARAFAC\n\nExample on how to use :func:`tensorly.decomposition.parafac` with line search to accelerate convergence.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n\nfrom time import time\nimport numpy as np\nimport tensorly as tl\nfrom tensorly.random import random_cp\nfrom tensorly.decomposition import CP, parafac\n\ntol = np.logspace(-1, -9)\nerr = np.empty_like(tol)\nerr_ls = np.empty_like(tol)\ntt = np.empty_like(tol)\ntt_ls = np.empty_like(tol)\ntensor = random_cp((10, 10, 10), 3, random_state=1234, full=True)\n\n# Get a high-accuracy decomposition for comparison\nfac = parafac(tensor, rank=3, n_iter_max=2000000, tol=1.0e-15, linesearch=True)\nerr_min = tl.norm(tl.cp_to_tensor(fac) - tensor)\n\nfor ii, toll in enumerate(tol):\n\t# Run PARAFAC decomposition without line search and time\n    start = time()\n    cp = CP(rank=3, n_iter_max=2000000, tol=toll, linesearch=False)\n    fac = cp.fit_transform(tensor)\n    tt[ii] = time() - start\n    err[ii] = tl.norm(tl.cp_to_tensor(fac) - tensor)\n\n# Run PARAFAC decomposition with line search and time\nfor ii, toll in enumerate(tol):\n    start = time()\n    cp = CP(rank=3, n_iter_max=2000000, tol=toll, linesearch=True)\n    fac_ls = cp.fit_transform(tensor)\n    tt_ls[ii] = time() - start\n\n    # Calculate the error of both decompositions\n    err_ls[ii] = tl.norm(tl.cp_to_tensor(fac_ls) - tensor)\n\n\nfig = plt.figure()\nax = fig.add_subplot(1, 1, 1)\nax.loglog(tt, err - err_min, '.', label=\"No line search\")\nax.loglog(tt_ls, err_ls - err_min, '.r', label=\"Line search\")\nax.legend()\nax.set_ylabel(\"Time\")\nax.set_xlabel(\"Error\")\n\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/stable/_downloads/7c4762fd973c92c13874f6405624a983/plot_tensor.ipynb b/stable/_downloads/7c4762fd973c92c13874f6405624a983/plot_tensor.ipynb
@@ -100,7 +100,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.3"
+      "version": "3.8.5"
     }
   },
   "nbformat": 4,

diff --git a/stable/_downloads/8c1716b060db83a5d0c4cf467673c021/plot_parafac2.ipynb b/stable/_downloads/8c1716b060db83a5d0c4cf467673c021/plot_parafac2.ipynb
@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.3"
+      "version": "3.8.5"
     }
   },
   "nbformat": 4,

diff --git a/stable/_downloads/8f729f463f008579b6ad75ad9ebd2491/plot_image_compression.py b/stable/_downloads/8f729f463f008579b6ad75ad9ebd2491/plot_image_compression.py
@@ -40,7 +40,7 @@ def to_image(tensor):
 cp_reconstruction = tl.cp_to_tensor((weights, factors))
 
 # Tucker decomposition
-core, tucker_factors = tucker(image, ranks=tucker_rank, init='random', tol=10e-5, random_state=random_state)
+core, tucker_factors = tucker(image, rank=tucker_rank, init='random', tol=10e-5, random_state=random_state)
 tucker_reconstruction = tl.tucker_to_tensor((core, tucker_factors))
 
 # Plotting the original and reconstruction from the decompositions

diff --git a/stable/_downloads/ba9ab31b668e2e402c959f74d1c232fd/plot_tucker_regression.ipynb b/stable/_downloads/ba9ab31b668e2e402c959f74d1c232fd/plot_tucker_regression.ipynb
@@ -46,7 +46,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.3"
+      "version": "3.8.5"
     }
   },
   "nbformat": 4,

diff --git a/stable/_downloads/cb64fc91dbcad9e7acccd75909fc6017/parafac_line_search.ipynb b/stable/_downloads/cb64fc91dbcad9e7acccd75909fc6017/parafac_line_search.ipynb
diff --git a/stable/_downloads/eb34a708a9848194f7d8356c806596de/plot_image_compression.ipynb b/stable/_downloads/eb34a708a9848194f7d8356c806596de/plot_image_compression.ipynb
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport tensorly as tl\nimport numpy as np\nfrom scipy.misc import face\nfrom scipy.ndimage import zoom\nfrom tensorly.decomposition import parafac\nfrom tensorly.decomposition import tucker\nfrom math import ceil\n\n\nrandom_state = 12345\n\nimage = face()\nimage = tl.tensor(zoom(face(), (0.3, 0.3, 1)), dtype='float64')\n\ndef to_image(tensor):\n    \"\"\"A convenience function to convert from a float dtype back to uint8\"\"\"\n    im = tl.to_numpy(tensor)\n    im -= im.min()\n    im /= im.max()\n    im *= 255\n    return im.astype(np.uint8)\n\n# Rank of the CP decomposition\ncp_rank = 25\n# Rank of the Tucker decomposition\ntucker_rank = [100, 100, 2]\n\n# Perform the CP decomposition\nweights, factors = parafac(image, rank=cp_rank, init='random', tol=10e-6)\n# Reconstruct the image from the factors\ncp_reconstruction = tl.cp_to_tensor((weights, factors))\n\n# Tucker decomposition\ncore, tucker_factors = tucker(image, ranks=tucker_rank, init='random', tol=10e-5, random_state=random_state)\ntucker_reconstruction = tl.tucker_to_tensor((core, tucker_factors))\n\n# Plotting the original and reconstruction from the decompositions\nfig = plt.figure()\nax = fig.add_subplot(1, 3, 1)\nax.set_axis_off()\nax.imshow(to_image(image))\nax.set_title('original')\n\nax = fig.add_subplot(1, 3, 2)\nax.set_axis_off()\nax.imshow(to_image(cp_reconstruction))\nax.set_title('CP')\n\nax = fig.add_subplot(1, 3, 3)\nax.set_axis_off()\nax.imshow(to_image(tucker_reconstruction))\nax.set_title('Tucker')\n\nplt.tight_layout()\nplt.show()"
+        "import matplotlib.pyplot as plt\nimport tensorly as tl\nimport numpy as np\nfrom scipy.misc import face\nfrom scipy.ndimage import zoom\nfrom tensorly.decomposition import parafac\nfrom tensorly.decomposition import tucker\nfrom math import ceil\n\n\nrandom_state = 12345\n\nimage = face()\nimage = tl.tensor(zoom(face(), (0.3, 0.3, 1)), dtype='float64')\n\ndef to_image(tensor):\n    \"\"\"A convenience function to convert from a float dtype back to uint8\"\"\"\n    im = tl.to_numpy(tensor)\n    im -= im.min()\n    im /= im.max()\n    im *= 255\n    return im.astype(np.uint8)\n\n# Rank of the CP decomposition\ncp_rank = 25\n# Rank of the Tucker decomposition\ntucker_rank = [100, 100, 2]\n\n# Perform the CP decomposition\nweights, factors = parafac(image, rank=cp_rank, init='random', tol=10e-6)\n# Reconstruct the image from the factors\ncp_reconstruction = tl.cp_to_tensor((weights, factors))\n\n# Tucker decomposition\ncore, tucker_factors = tucker(image, rank=tucker_rank, init='random', tol=10e-5, random_state=random_state)\ntucker_reconstruction = tl.tucker_to_tensor((core, tucker_factors))\n\n# Plotting the original and reconstruction from the decompositions\nfig = plt.figure()\nax = fig.add_subplot(1, 3, 1)\nax.set_axis_off()\nax.imshow(to_image(image))\nax.set_title('original')\n\nax = fig.add_subplot(1, 3, 2)\nax.set_axis_off()\nax.imshow(to_image(cp_reconstruction))\nax.set_title('CP')\n\nax = fig.add_subplot(1, 3, 3)\nax.set_axis_off()\nax.imshow(to_image(tucker_reconstruction))\nax.set_title('Tucker')\n\nplt.tight_layout()\nplt.show()"
       ]
     }
   ],
@@ -46,7 +46,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.8.3"
+      "version": "3.8.5"
     }
   },
   "nbformat": 4,

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