Running ruff format

Author: dmdunla

docs/source/tutorial/algorithm_cp_als.ipynb

@@ -53,7 +53,7 @@
     "np.random.seed(0)\n",
     "\n",
     "# Create a low-rank dense tensor from a Kruskal tensor\n",
-    "factor_matrices = [np.random.rand(s,R) for s in tensor_shape]\n",
+    "factor_matrices = [np.random.rand(s, R) for s in tensor_shape]\n",
     "M_true = ttb.ktensor(factor_matrices)\n",
     "X = M_true.to_tensor()"
    ]
@@ -103,7 +103,8 @@
    "outputs": [],
    "source": [
     "print(\"M1_info:\")\n",
-    "for k, v in M1_info.items(): print(f\"\\t{k}: {v}\")"
+    "for k, v in M1_info.items():\n",
+    "    print(f\"\\t{k}: {v}\")"
    ]
   },
   {
@@ -121,7 +122,9 @@
    "source": [
     "# Compute a solution with final ktensor stored in M2\n",
     "np.random.seed(2)  # Set seed for reproducibility\n",
-    "M2, M2_init, _ = ttb.cp_als(X, R, maxiters=few_iters)  # Will not use third output, so leaving unassigned"
+    "M2, M2_init, _ = ttb.cp_als(\n",
+    "    X, R, maxiters=few_iters\n",
+    ")  # Will not use third output, so leaving unassigned"
    ]
   },
   {
@@ -254,7 +257,7 @@
     "M3, _, _ = ttb.cp_als(X, R, maxiters=few_iters, init=\"nvecs\", printitn=0)\n",
     "score_M3 = M3.score(M_true)\n",
     "print(f\"Score of M2 and M_true: {score_M2[0]}\")\n",
-    "print(f\"Score of M3 and M_true: {score_M3[0]}\")\n"
+    "print(f\"Score of M3 and M_true: {score_M3[0]}\")"
    ]
   },
   {
@@ -271,10 +274,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "M4, _, M4_info = ttb.cp_als(X, R, maxiters=few_iters, init=\"nvecs\", printitn=0, dimorder=[2, 1, 0])\n",
+    "M4, _, M4_info = ttb.cp_als(\n",
+    "    X, R, maxiters=few_iters, init=\"nvecs\", printitn=0, dimorder=[2, 1, 0]\n",
+    ")\n",
     "score_M4 = M4.score(M_true)\n",
     "print(f\"Score of M3 and M_true: {score_M3[0]}\")\n",
-    "print(f\"Score of M4 and M_true: {score_M4[0]}\")\n"
+    "print(f\"Score of M4 and M_true: {score_M4[0]}\")"
    ]
   },
   {
@@ -295,10 +300,12 @@
     "print(f\"Score of M4 and M4_rerun: {score_M4_rerun[0]}\")\n",
     "\n",
     "print(\"M4_info:\")\n",
-    "for k, v in M4_info.items(): print(f\"\\t{k}: {v}\")\n",
+    "for k, v in M4_info.items():\n",
+    "    print(f\"\\t{k}: {v}\")\n",
     "\n",
     "print(\"M4_rerun_info:\")\n",
-    "for k, v in M4_rerun_info.items(): print(f\"\\t{k}: {v}\")"
+    "for k, v in M4_rerun_info.items():\n",
+    "    print(f\"\\t{k}: {v}\")"
    ]
   },
   {
@@ -332,21 +339,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Create rank-2 tensor \n",
+    "# Create rank-2 tensor\n",
     "X2 = ttb.ktensor(\n",
     "    factor_matrices=[\n",
     "        np.array([[1.0, 1.0], [-1.0, -10.0]]),\n",
     "        np.array([[1.0, 1.0], [-2.0, -10.0]]),\n",
     "    ],\n",
-    "    weights=np.array([1.0, 1.0])\n",
+    "    weights=np.array([1.0, 1.0]),\n",
     ")\n",
     "print(f\"X2=\\n{X2}\\n\")\n",
     "\n",
     "M_fixsigns, _, _ = ttb.cp_als(X2, 2, printitn=0, init=ttb.ktensor(X2.factor_matrices))\n",
     "print(f\"M_fixsigns=\\n{M_fixsigns}\\n\")  # default behavior, fixsigns called\n",
     "\n",
-    "M_no_fixsigns, _, _ = ttb.cp_als(X2, 2, printitn=0, init=ttb.ktensor(X2.factor_matrices), fixsigns=False)\n",
-    "print(f\"M_no_fixsigns=\\n{M_no_fixsigns}\\n\")  # fixsigns not called\n"
+    "M_no_fixsigns, _, _ = ttb.cp_als(\n",
+    "    X2, 2, printitn=0, init=ttb.ktensor(X2.factor_matrices), fixsigns=False\n",
+    ")\n",
+    "print(f\"M_no_fixsigns=\\n{M_no_fixsigns}\\n\")  # fixsigns not called"
    ]
   },
   {
@@ -359,7 +368,11 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 2
 }