updated weak implicit_terms

dynamicslab · Jun 7, 2022 · f43833a · f43833a
1 parent df0472a
commit f43833a
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diff --git a/examples/1_feature_overview.ipynb b/examples/1_feature_overview.ipynb
diff --git a/examples/9_sindypi_with_sympy.ipynb b/examples/9_sindypi_with_sympy.ipynb
@@ -76,10 +76,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/alankaptanoglu/pysindy/pysindy/feature_library/sindy_pi_library.py:161: UserWarning: This library is deprecated in PySINDy versions > 1.7. Please use the PDE or WeakPDE libraries instead. \n",
-      "  \"This library is deprecated in PySINDy versions > 1.7. Please \"\n",
-      "/Users/alankaptanoglu/pysindy/pysindy/feature_library/sindy_pi_library.py:161: UserWarning: This library is deprecated in PySINDy versions > 1.7. Please use the PDE or WeakPDE libraries instead. \n",
-      "  \"This library is deprecated in PySINDy versions > 1.7. Please \"\n"
+      "/Users/zack/pysindy/pysindy/feature_library/sindy_pi_library.py:160: UserWarning: This library is deprecated in PySINDy versions > 1.7. Please use the PDE or WeakPDE libraries instead. \n",
+      "  warnings.warn(\n",
+      "/Users/zack/pysindy/pysindy/feature_library/sindy_pi_library.py:160: UserWarning: This library is deprecated in PySINDy versions > 1.7. Please use the PDE or WeakPDE libraries instead. \n",
+      "  warnings.warn(\n"
      ]
     }
    ],
@@ -165,6 +165,13 @@
     "This reason this modified optimization is required is that linear terms in $\\dot{x}$ are no longer guaranteed, so every candidate library term is fit with a different model to find the best combination of implicit terms. Note $\\text{diag}(\\Xi) = 0$ is required because without this, the model would just fit every candidate library term with itself, $\\dot{x} = \\dot{x}$, $x\\dot{x} = x\\dot{x}$, $x^2\\dot{x} = x^2\\dot{x}$, and so on."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -174,16 +181,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/alankaptanoglu/pysindy/pysindy/feature_library/sindy_pi_library.py:161: UserWarning: This library is deprecated in PySINDy versions > 1.7. Please use the PDE or WeakPDE libraries instead. \n",
-      "  \"This library is deprecated in PySINDy versions > 1.7. Please \"\n"
+      "/Users/zack/pysindy/pysindy/feature_library/sindy_pi_library.py:160: UserWarning: This library is deprecated in PySINDy versions > 1.7. Please use the PDE or WeakPDE libraries instead. \n",
+      "  warnings.warn(\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(10, 1)\n",
-      "(1, 10)\n",
       "Model  0\n",
       "Model  1\n",
       "Model  2\n",
@@ -297,15 +302,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(10, 1)\n",
-      "(1, 10)\n",
       "Model  0\n",
       "Model  1\n",
       "Model  2\n",
@@ -315,17 +318,18 @@
       "Model  6\n",
       "Model  7\n",
       "Model  8\n",
+      "Solver failed on model  8 , setting coefs to zeros\n",
       "Model  9\n",
-      "1 = 5.000 x0 + 8.355 x0_t + 5.552 x0x0_t\n",
-      "x0 = 0.200 1 + -1.665 x0_t + -1.113 x0x0_t\n",
-      "x0x0 = 0.012 1 + 0.113 x0 + 0.676 x0x0x0 + -0.578 x0x0_t\n",
-      "x0x0x0 = 0.008 1 + -0.103 x0 + 0.494 x0x0 + 0.721 x0x0x0x0\n",
-      "x0x0x0x0 = 0.009 1 + -0.064 x0 + 0.665 x0x0x0\n",
-      "x0_t = 0.081 1 + -0.483 x0 + 0.376 x0x0\n",
-      "x0x0_t = 0.056 1 + -0.164 x0 + -0.580 x0x0\n",
-      "x0x0x0_t = 0.002 1 + 0.043 x0 + -0.133 x0x0 + -0.598 x0x0x0\n",
-      "x0x0x0x0_t = -0.007 1 + 0.052 x0 + -0.485 x0x0x0\n",
-      "x0x0x0x0x0_t = -0.006 1 + 0.040 x0 + -0.269 x0x0x0\n"
+      "1 = 5.000 x0 + 1.667 x0_t + 5.556 x0x0_t\n",
+      "x0 = 0.200 1 + 0.005 x0x0 + -0.334 x0_t + -1.104 x0x0_t\n",
+      "x0x0 = 0.019 1 + 0.079 x0 + 0.625 x0x0x0 + -0.672 x0x0_t\n",
+      "x0x0x0 = -0.008 1 + 0.371 x0x0 + 0.827 x0x0x0x0 + 0.033 x0_t\n",
+      "x0x0x0x0 = -0.005 1 + 0.858 x0x0x0 + -0.022 x0_t + 0.294 x0x0_t + -0.110 x0x0x0x0x0_t\n",
+      "x0_t = 0.599 1 + -2.996 x0 + 0.015 x0x0 + -3.307 x0x0_t\n",
+      "x0x0_t = 0.177 1 + -0.882 x0 + -0.014 x0x0 + -0.293 x0_t\n",
+      "x0x0x0_t = 0.013 1 + -0.246 x0x0 + -0.406 x0x0x0 + -0.043 x0_t\n",
+      "x0x0x0x0_t = 0.000\n",
+      "x0x0x0x0x0_t = 0.001 1 + -0.492 x0x0x0x0 + 0.007 x0_t + -0.068 x0x0_t\n"
      ]
     },
     {
@@ -343,12 +347,24 @@
        " 'x0x0x0x0x0_t']"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 145,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "library_functions = [\n",
+    "    lambda x: x,\n",
+    "    lambda x, y: x * y,\n",
+    "    lambda x: x ** 2,\n",
+    "    lambda x, y, z: x * y * z,\n",
+    "    lambda x, y: x * y ** 2,\n",
+    "    lambda x: x ** 3,\n",
+    "    lambda x, y, z, w: x * y * z * w,\n",
+    "    lambda x, y, z: x * y * z ** 2,\n",
+    "    lambda x, y: x * y ** 3,\n",
+    "    lambda x: x ** 4,\n",
+    "]\n",
     "library_function_names = [\n",
     "    lambda x: x,\n",
     "    lambda x, y: x + y,\n",
@@ -362,13 +378,20 @@
     "    lambda x: x + x + x + x,\n",
     "]\n",
     "\n",
+    "sindy_opt = ps.SINDyPI(\n",
+    "    threshold=1e-6,\n",
+    "    tol=1e-9,\n",
+    "    thresholder=\"l1\",\n",
+    "    max_iter=20000,\n",
+    ")\n",
+    "\n",
     "sindy_library = ps.WeakPDELibrary(\n",
     "    library_functions=library_functions,\n",
-    "    spatiotemporal_grid=t,\n",
+    "    spatiotemporal_grid=t[:,np.newaxis],\n",
     "    function_names=library_function_names,\n",
     "    include_bias=True,\n",
     "    implicit_terms=True,\n",
-    "    derivative_order=1\n",
+    "    derivative_order=1, K=2000,\n",
     ")\n",
     "\n",
     "model = ps.SINDy(\n",
@@ -383,15 +406,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(10, 1)\n",
-      "(1, 10)\n",
       "Model  0\n",
       "Model  1\n",
       "Model  2\n",
@@ -430,7 +451,7 @@
        " 'x0x0x0x0x0_t']"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -875,7 +896,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -889,7 +910,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,

diff --git a/pysindy/feature_library/weak_pde_library.py b/pysindy/feature_library/weak_pde_library.py
@@ -506,7 +506,10 @@ def _set_up_weights(self):
         for k in range(self.K):
             weights2 = []
             for j in range(self.num_derivatives):
-                deriv = np.concatenate([self.multiindices[j], [0]])
+                if not self.implicit_terms:
+                    deriv = np.concatenate([self.multiindices[j], [0]])
+                else:
+                    deriv = self.multiindices[j]
 
                 ret = np.ones(shapes_k[k])
                 for i in range(self.grid_ndim):