update examples

examples/computation/data.json

@@ -1,31 +1,8 @@
 {
-  "input_data": [
-    [
-      23.2, 92.8, 91.0, 37.2, 82.0, 15.5, 79.3, 46.6, 98.1, 75.5, 78.9, 77.6,
-      33.8, 75.7, 96.8, 12.3, 18.4, 13.4, 6.0, 8.2, 25.8, 41.3, 68.5, 15.2,
-      74.7, 72.7, 18.0, 42.2, 36.1, 76.7, 1.2, 96.4, 4.9, 92.0, 12.8, 28.2,
-      61.8, 56.9, 44.3, 50.4, 81.6, 72.5, 12.9, 40.3, 12.8, 28.8, 36.3, 16.1,
-      68.4, 35.3, 79.2, 48.4, 97.1, 93.7, 77.0, 48.7, 93.7, 54.1, 65.4, 30.8,
-      34.4, 31.4, 78.7, 12.7, 90.7, 39.4, 86.0, 55.9, 6.8, 22.2, 65.3, 18.8,
-      7.1, 55.9, 38.6, 15.6, 59.2, 77.3, 76.9, 11.9, 19.9, 19.4, 54.3, 39.4,
-      4.0, 61.1, 16.8, 81.9, 49.3, 76.9, 19.2, 68.2, 54.4, 70.2, 89.8, 23.4,
-      67.5, 18.7, 10.8, 80.7, 80.3, 96.2, 62.3, 17.2, 23.0, 98.0, 19.1, 8.1,
-      36.2, 7.5, 55.9, 1.2, 56.8, 85.1, 18.9, 23.0, 13.5, 64.3, 9.1, 14.1, 14.1,
-      23.1, 73.2, 86.6, 39.1, 45.5, 85.0, 79.0, 15.8, 5.2, 81.5, 34.3, 24.3,
-      14.2, 84.6, 33.7, 86.3, 83.3, 62.8, 72.7, 14.7, 36.8, 92.5, 4.7, 30.0,
-      59.4, 57.6, 37.4, 22.0, 20.9, 61.6, 26.8, 47.1, 63.6, 6.0, 96.6, 61.2,
-      80.2, 59.3, 23.1, 29.3, 46.3, 89.2, 77.6, 83.2, 87.2, 63.2, 81.8, 55.0,
-      59.7, 57.8, 43.4, 92.4, 66.9, 82.1, 51.0, 22.1, 29.9, 41.0, 85.2, 61.5,
-      14.6, 48.0, 52.7, 31.4, 83.9, 35.5, 77.3, 35.8, 32.6, 22.2, 19.3, 49.1,
-      70.9, 43.9, 88.8, 56.3, 41.8, 90.3, 20.4, 80.4, 36.4, 91.5, 69.6, 75.3,
-      92.4, 84.8, 17.7, 2.3, 41.3, 91.3, 68.6, 73.3, 62.5, 60.5, 73.5, 70.7,
-      77.5, 76.8, 98.1, 40.9, 66.3, 8.6, 48.9, 75.4, 14.7, 35.9, 89.6, 15.1,
-      45.0, 77.6, 30.5, 76.1, 46.9, 34.3, 65.1, 43.9, 91.6, 88.8, 8.9, 42.9,
-      11.8, 32.1, 20.1, 48.9, 79.7, 15.3, 45.4, 80.1, 73.1, 76.5, 52.4, 9.6,
-      41.9, 52.7, 55.1, 30.9, 83.7, 46.7, 39.3, 40.5, 52.4, 19.2, 25.8, 52.7,
-      81.0, 38.0, 54.5, 15.3, 64.3, 88.3, 49.8, 90.5, 90.4, 79.7, 87.3, 32.3,
-      11.9, 5.7, 33.6, 75.1, 65.9, 29.1, 39.4, 87.5, 3.3, 66.3, 79.0, 97.9,
-      69.6, 22.0, 62.8, 97.1, 90.4, 39.5, 11.7, 30.3, 18.9, 34.6, 6.6
-    ]
+  "x": [
+    0, 1, 2, 3, 4
+  ],
+  "y": [
+    2.0, 5.2, 47.4, 23.6, 24.8
   ]
 }

examples/correlation/data.json

@@ -1,13 +1,8 @@
 {
   "x": [
-    0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
-    21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
-    40, 41, 42, 43, 44, 45, 46, 47, 48, 49
+    0, 1, 2, 3, 4, 5
   ],
   "y": [
-    2.0, 5.2, 47.4, 23.6, 24.8, 27.0, 47.2, 50.4, 58.6, 57.8, 60.0, 27.2, 40.4,
-    63.6, 28.8, 19.0, 65.2, 50.4, 63.6, 24.8, 35.0, 41.2, 54.4, 61.6, 57.8,
-    78.0, 63.2, 55.4, 78.6, 72.8, 51.0, 62.2, 42.4, 47.6, 83.8, 62.0, 47.26,
-    90.4, 80.6, 87.8, 82.0, 50.2, 80.4, 86.6, 80.8, 66.0, 95.2, 58.4, 74.6, 95.8
+    2.0, 5.2, 47.4, 23.6, 24.8, 27.0
   ]
 }

examples/geomean/geomean.ipynb

@@ -138,6 +138,17 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scales = [1]\n",
+    "selected_columns = ['col_name']\n",
+    "commitment_maps = get_data_commitment_maps(data_path, scales)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -204,7 +215,7 @@
     "\n",
     "\n",
     "\n",
-    "prover_gen_settings(data_path,['col_name'], sel_data_path, prover_model,prover_model_path, 'default', \"resources\", settings_path)"
+    "prover_gen_settings(data_path, selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
    ]
   },
   {
@@ -297,7 +308,7 @@
    ],
    "source": [
     "# Verifier verifies\n",
-    "verifier_verify(proof_path, settings_path, vk_path)"
+    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
    ]
   },
   {
@@ -331,7 +342,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.12.1"
   },
   "orig_nbformat": 4
  },

examples/harmomean/harmomean.ipynb

@@ -131,6 +131,17 @@
     "theory_output = torch.div(1.0,torch.mean(torch.div(1.0,data_tensor)))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scales = [6]\n",
+    "selected_columns = ['col_name']\n",
+    "commitment_maps = get_data_commitment_maps(data_path, scales)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -181,7 +192,7 @@
     "    def forward(self,X):\n",
     "        return (torch.abs((self.w*torch.sum(torch.div(1.0,X)))-X.size()[1])<=torch.abs(0.1*X.size()[1]), self.w)\n",
     "\n",
-    "prover_gen_settings(data_path,['col_name'], sel_data_path, prover_model,prover_model_path, [6], \"resources\", settings_path)"
+    "prover_gen_settings(data_path, selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
    ]
   },
   {
@@ -245,7 +256,7 @@
     }
    ],
    "source": [
-    "# Here verifier & prover can concurrently call setup since all params are public to get pk. \n",
+    "# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
     "# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
     "verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
     "\n",
@@ -274,7 +285,7 @@
    ],
    "source": [
     "# Verifier verifies\n",
-    "verifier_verify(proof_path, settings_path, vk_path)"
+    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
    ]
   },
   {

examples/mean+median/mean+median.ipynb

@@ -182,7 +182,7 @@
     "    count_equal = torch.sum((torch.abs(X-median)<=torch.abs(0.01*median)).double())\n",
     "    len = X.size()[1]\n",
     "    half_len = torch.floor(torch.div(len, 2))\n",
-    "        \n",
+    "\n",
     "    # not support modulo yet\n",
     "    less_cons = count_less<half_len+2*(len/2 - torch.floor(len/2))\n",
     "    more_cons = count_less+count_equal>half_len\n",
@@ -198,7 +198,7 @@
     "\n",
     "    median_in_cons = torch.logical_and(less_cons, more_cons)\n",
     "    median_out_cons = torch.logical_and(torch.logical_and(bound, bound_avg), torch.logical_and(torch.logical_and(lower_cons, upper_cons), torch.logical_and(lower_exist, upper_exist)))\n",
-    "        \n",
+    "\n",
     "    return(torch.where(count_equal==0, median_out_cons, median_in_cons), median)\n"
    ]
   },
@@ -212,6 +212,17 @@
     "    return (torch.abs(torch.sum(X)-X.size()[1]*(mean))<=torch.abs(0.01*X.size()[1]*mean), mean)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scales = [1]\n",
+    "selected_columns = ['col_1', 'col_2']\n",
+    "commitment_maps = get_data_commitment_maps(data_path, scales)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 24,
@@ -257,7 +268,7 @@
     "        bool3, output_mean = mean(torch.tensor([median1, median2]).reshape(1,-1,1), self.mean)\n",
     "        return (torch.logical_and(torch.logical_and(bool1, bool2),bool3), output_mean )\n",
     "\n",
-    "    \n",
+    "\n",
     "\n",
     "verifier_define_calculation(dummy_data_path, ['col_1', 'col_2'],sel_dummy_data_path,verifier_model, verifier_model_path)"
    ]
@@ -315,9 +326,9 @@
     "        bool3, output_mean = mean(torch.tensor([median1, median2]).reshape(1,-1,1), self.mean)\n",
     "        return (torch.logical_and(torch.logical_and(bool1, bool2),bool3), output_mean )\n",
     "\n",
-    " \n",
-    "      \n",
-    "prover_gen_settings(data_path,['col_1', 'col_2'], sel_data_path, prover_model,prover_model_path, 'default', \"resources\", settings_path)"
+    "\n",
+    "\n",
+    "prover_gen_settings(data_path,['col_1', 'col_2'], sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
    ]
   },
   {
@@ -381,7 +392,7 @@
     }
    ],
    "source": [
-    "# Here verifier & prover can concurrently call setup since all params are public to get pk. \n",
+    "# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
     "# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
     "verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
     "\n",
@@ -410,7 +421,7 @@
    ],
    "source": [
     "# Verifier verifies\n",
-    "verifier_verify(proof_path, settings_path, vk_path)"
+    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
    ]
   },
   {
@@ -437,7 +448,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.12.1"
   },
   "orig_nbformat": 4
  },

examples/mean/mean.ipynb

@@ -149,6 +149,17 @@
     "theory_output = torch.mean(data_tensor)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scales = [5]\n",
+    "selected_columns = ['col_name']\n",
+    "commitment_maps = get_data_commitment_maps(data_path, scales)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -205,8 +216,8 @@
     "\n",
     "    def forward(self,X):\n",
     "        return (torch.abs(torch.sum(X)-X.size()[1]*(self.w))<=torch.abs(0.01*X.size()[1]*self.w), self.w)\n",
-    "        \n",
-    "prover_gen_settings(data_path,['col_name'], sel_data_path, prover_model,prover_model_path, [5], \"resources\", settings_path)"
+    "\n",
+    "prover_gen_settings(data_path, selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
    ]
   },
   {
@@ -301,7 +312,7 @@
    ],
    "source": [
     "# Verifier verifies\n",
-    "verifier_verify(proof_path, settings_path, vk_path)"
+    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
    ]
   }
  ],
@@ -321,7 +332,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.12.1"
   },
   "orig_nbformat": 4
  },

examples/mean/mean_OG.ipynb

@@ -137,6 +137,17 @@
     "theory_output = torch.mean(data_tensor)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scales = [15]\n",
+    "selected_columns = ['col_name']\n",
+    "commitment_maps = get_data_commitment_maps(data_path, scales)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -161,7 +172,7 @@
     "    def forward(self,X):\n",
     "        return (torch.tensor(1), torch.mean(X))\n",
     "        # return (torch.abs(torch.sum(X)-X.size()[1]*(self.w))<=torch.abs(0.01*X.size()[1]*self.w), self.w)\n",
-    "    \n",
+    "\n",
     "verifier_define_calculation(dummy_data_path, ['col_name'],sel_dummy_data_path,verifier_model, verifier_model_path)"
    ]
   },
@@ -201,7 +212,7 @@
     "    def forward(self,X):\n",
     "        return (torch.tensor(1),torch.mean(X))\n",
     "        # return (torch.abs(torch.sum(X)-X.size()[1]*(self.w))<=torch.abs(0.01*X.size()[1]*self.w), self.w)\n",
-    "prover_gen_settings(data_path,['col_name'], sel_data_path, prover_model,prover_model_path, [15], \"resources\", settings_path)"
+    "prover_gen_settings(data_path, selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
    ]
   },
   {
@@ -253,7 +264,7 @@
     }
    ],
    "source": [
-    "# Here verifier & prover can concurrently call setup since all params are public to get pk. \n",
+    "# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
     "# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
     "verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
     "\n",
@@ -282,7 +293,7 @@
    ],
    "source": [
     "# Verifier verifies\n",
-    "verifier_verify(proof_path, settings_path, vk_path)"
+    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
    ]
   },
   {

examples/median/median.ipynb

@@ -271,7 +271,7 @@
     "\n",
     "\n",
     " \n",
-    "prover_gen_settings(data_path,['col_name'], sel_data_path, prover_model,prover_model_path, 'default', \"resources\", settings_path)"
+    "prover_gen_settings(data_path, selected_columns, sel_data_path, prover_model,prover_model_path, 'default', \"resources\", settings_path)"
    ]
   },
   {
@@ -364,7 +364,7 @@
    ],
    "source": [
     "# Verifier verifies\n",
-    "verifier_verify(proof_path, settings_path, vk_path)"
+    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
    ]
   },
   {

examples/mode/mode.ipynb

@@ -160,6 +160,17 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scales = [1]\n",
+    "selected_columns = ['col_name']\n",
+    "commitment_maps = get_data_commitment_maps(data_path, scales)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 19,
@@ -195,7 +206,7 @@
     "        count_equal = torch.sum((torch.abs(X-self.w)<=torch.abs(0.01*self.w)).double())\n",
     "        result = torch.tensor([torch.sum((torch.abs(X-ele[0])<=torch.abs(0.01*ele[0])).double())<=count_equal for ele in X[0]])\n",
     "        return (torch.sum(result) == X.size()[1], self.w)\n",
-    "    \n",
+    "\n",
     "verifier_define_calculation(dummy_data_path, ['col_name'],sel_dummy_data_path,verifier_model, verifier_model_path)"
    ]
   },
@@ -245,7 +256,7 @@
     "        result = torch.tensor([torch.sum((torch.abs(X-ele[0])<=torch.abs(0.01*ele[0])).double())<=count_equal for ele in X[0]])\n",
     "        return (torch.sum(result) == X.size()[1], self.w)\n",
     "\n",
-    "prover_gen_settings(data_path,['col_name'], sel_data_path, prover_model,prover_model_path, 'default', \"resources\", settings_path)"
+    "prover_gen_settings(data_path, selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
    ]
   },
   {
@@ -309,7 +320,7 @@
     }
    ],
    "source": [
-    "# Here verifier & prover can concurrently call setup since all params are public to get pk. \n",
+    "# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
     "# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
     "verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
     "\n",
@@ -338,7 +349,7 @@
    ],
    "source": [
     "# Verifier verifies\n",
-    "verifier_verify(proof_path, settings_path, vk_path)"
+    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
    ]
   },
   {

examples/pstdev/pstdev.ipynb

@@ -133,6 +133,17 @@
     "data_mean = torch.mean(data_tensor)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scales = [2]\n",
+    "selected_columns = ['col_name']\n",
+    "commitment_maps = get_data_commitment_maps(data_path, scales)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -160,7 +171,7 @@
     "        # since we square\n",
     "        return (torch.logical_and(torch.abs(torch.sum((X-self.data_mean)*(X-self.data_mean))-self.w*self.w*X.size()[1])<=torch.abs(0.02*self.w*self.w*X.size()[1]),x_mean_cons ),self.w)\n",
     "\n",
-    "        \n",
+    "\n",
     "verifier_define_calculation(dummy_data_path, ['col_name'],sel_dummy_data_path,verifier_model, verifier_model_path)"
    ]
   },
@@ -193,7 +204,7 @@
     "        # since we square\n",
     "        return (torch.logical_and(torch.abs(torch.sum((X-self.data_mean)*(X-self.data_mean))-self.w*self.w*X.size()[1])<=torch.abs(0.02*self.w*self.w*X.size()[1]),x_mean_cons ),self.w)\n",
     "\n",
-    "prover_gen_settings(data_path,['col_name'], sel_data_path, prover_model,prover_model_path, [2], \"resources\", settings_path)"
+    "prover_gen_settings(data_path, selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
    ]
   },
   {
@@ -257,7 +268,7 @@
     }
    ],
    "source": [
-    "# Here verifier & prover can concurrently call setup since all params are public to get pk. \n",
+    "# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
     "# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
     "verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
     "\n",
@@ -286,7 +297,7 @@
    ],
    "source": [
     "# Verifier verifies\n",
-    "verifier_verify(proof_path, settings_path, vk_path)"
+    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
    ]
   },
   {

examples/pvariance/pvariance.ipynb

@@ -133,6 +133,17 @@
     "data_mean = torch.mean(data_tensor)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scales = [2]\n",
+    "selected_columns = ['col_name']\n",
+    "commitment_maps = get_data_commitment_maps(data_path, scales)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -191,7 +202,7 @@
     "        x_mean_cons = torch.abs(torch.sum(X)-X.size()[1]*(self.data_mean))<=torch.abs(0.01*X.size()[1]*self.data_mean)\n",
     "        return (torch.logical_and(torch.abs(torch.sum((X-self.data_mean)*(X-self.data_mean))-self.w*X.size()[1])<=torch.abs(0.01*self.w*X.size()[1]),x_mean_cons ),self.w)\n",
     "\n",
-    "prover_gen_settings(data_path,['col_name'], sel_data_path, prover_model,prover_model_path, [2], \"resources\", settings_path)"
+    "prover_gen_settings(data_path, selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
    ]
   },
   {
@@ -255,7 +266,7 @@
     }
    ],
    "source": [
-    "# Here verifier & prover can concurrently call setup since all params are public to get pk. \n",
+    "# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
     "# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
     "verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
     "\n",
@@ -284,7 +295,7 @@
    ],
    "source": [
     "# Verifier verifies\n",
-    "verifier_verify(proof_path, settings_path, vk_path)"
+    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
    ]
   },
   {

examples/regression/regression.ipynb

@@ -155,6 +155,17 @@
     "dummy_w_tensor = torch.tensor(dummy_w_vals).reshape(1,-1,1)\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scales = [0]\n",
+    "selected_columns = ['x1', 'x2', 'y']\n",
+    "commitment_maps = get_data_commitment_maps(data_path, scales)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 9,
@@ -176,9 +187,9 @@
     "            torch.sum(torch.abs(X_T @ X_one @ self.w - X_T @ Y)) <= 0.01 * torch.sum(torch.abs(X_T @ Y)),\n",
     "            self.w\n",
     "        )\n",
-    "    \n",
     "\n",
-    "verifier_define_calculation(dummy_data_path, ['x1','x2','y'],sel_dummy_data_path,verifier_model, verifier_model_path)"
+    "\n",
+    "verifier_define_calculation(dummy_data_path, selected_columns, sel_dummy_data_path,verifier_model, verifier_model_path)"
    ]
   },
   {
@@ -219,7 +230,7 @@
     "            self.w\n",
     "        )\n",
     "# try scale = [3] for more accuracy\n",
-    "prover_gen_settings(data_path,['x1','x2','y'], sel_data_path, prover_model,prover_model_path, [0], \"resources\", settings_path)"
+    "prover_gen_settings(data_path, selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
    ]
   },
   {
@@ -287,7 +298,7 @@
     }
    ],
    "source": [
-    "# Here verifier & prover can concurrently call setup since all params are public to get pk. \n",
+    "# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
     "# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
     "verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
     "\n",
@@ -317,7 +328,7 @@
     }
    ],
    "source": [
-    "verifier_verify(proof_path, settings_path, vk_path)"
+    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
    ]
   },
   {
@@ -344,7 +355,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.12.1"
   },
   "orig_nbformat": 4
  },

examples/stdev/stdev.ipynb

@@ -142,6 +142,17 @@
     "print(dummy_theory_output)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scales = [2]\n",
+    "selected_columns = ['col_name']\n",
+    "commitment_maps = get_data_commitment_maps(data_path, scales)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 14,
@@ -160,7 +171,7 @@
     "        x_mean_cons = torch.abs(torch.sum(X)-X.size()[1]*(self.data_mean))<=torch.abs(0.01*X.size()[1]*self.data_mean)\n",
     "        return (torch.logical_and(torch.abs(torch.sum((X-self.data_mean)*(X-self.data_mean))-self.w*self.w*(X.size()[1]-1))<=torch.abs(0.02*self.w*self.w*(X.size()[1]-1)),x_mean_cons),self.w)\n",
     "\n",
-    "verifier_define_calculation(dummy_data_path, ['col_name'],sel_dummy_data_path,verifier_model, verifier_model_path)"
+    "verifier_define_calculation(dummy_data_path, selected_columns, sel_dummy_data_path,verifier_model, verifier_model_path)"
    ]
   },
   {
@@ -192,7 +203,7 @@
     "        x_mean_cons = torch.abs(torch.sum(X)-X.size()[1]*(self.data_mean))<=torch.abs(0.01*X.size()[1]*self.data_mean)\n",
     "        return (torch.logical_and(torch.abs(torch.sum((X-self.data_mean)*(X-self.data_mean))-self.w*self.w*(X.size()[1]-1))<=torch.abs(0.02*self.w*self.w*(X.size()[1]-1)),x_mean_cons),self.w)\n",
     "\n",
-    "prover_gen_settings(data_path,['col_name'], sel_data_path, prover_model,prover_model_path, [2], \"resources\", settings_path)"
+    "prover_gen_settings(data_path, selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
    ]
   },
   {
@@ -262,7 +273,7 @@
     }
    ],
    "source": [
-    "# Here verifier & prover can concurrently call setup since all params are public to get pk. \n",
+    "# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
     "# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
     "verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
     "\n",
@@ -291,7 +302,7 @@
    ],
    "source": [
     "# Verifier verifies\n",
-    "verifier_verify(proof_path, settings_path, vk_path)"
+    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
    ]
   },
   {
@@ -318,7 +329,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.12.1"
   },
   "orig_nbformat": 4
  },

examples/variance/variance.ipynb

@@ -133,6 +133,17 @@
     "data_mean = torch.mean(data_tensor)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scales = [2]\n",
+    "selected_columns = ['col_name']\n",
+    "commitment_maps = get_data_commitment_maps(data_path, scales)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -160,7 +171,7 @@
     "        x_mean_cons = torch.abs(torch.sum(X)-X.size()[1]*(self.data_mean))<=torch.abs(0.01*X.size()[1]*self.data_mean)\n",
     "        return (torch.logical_and(torch.abs(torch.sum((X-self.data_mean)*(X-self.data_mean))-self.w*(X.size()[1]-1))<=torch.abs(0.01*self.w*(X.size()[1]-1)),x_mean_cons ),self.w)\n",
     "\n",
-    "verifier_define_calculation(dummy_data_path, ['col_name'],sel_dummy_data_path,verifier_model, verifier_model_path)"
+    "verifier_define_calculation(dummy_data_path, selected_columns,sel_dummy_data_path,verifier_model, verifier_model_path)"
    ]
   },
   {
@@ -193,7 +204,7 @@
     "        return (torch.logical_and(torch.abs(torch.sum((X-self.data_mean)*(X-self.data_mean))-self.w*(X.size()[1]-1))<=torch.abs(0.01*self.w*(X.size()[1]-1)),x_mean_cons ),self.w)\n",
     "\n",
     "\n",
-    "prover_gen_settings(data_path,['col_name'], sel_data_path, prover_model,prover_model_path, [2], \"resources\", settings_path)"
+    "prover_gen_settings(data_path, selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
    ]
   },
   {
@@ -257,7 +268,7 @@
     }
    ],
    "source": [
-    "# Here verifier & prover can concurrently call setup since all params are public to get pk. \n",
+    "# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
     "# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
     "verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
     "\n",
@@ -286,7 +297,7 @@
    ],
    "source": [
     "# Verifier verifies\n",
-    "verifier_verify(proof_path, settings_path, vk_path)"
+    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
    ]
   },
   {

examples/where+geomean/where+geomean.ipynb

@@ -139,6 +139,17 @@
     "theory_output = torch.exp(torch.mean(torch.log(gt20_data_tensor)))\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scales = [8]\n",
+    "selected_columns = ['col_name']\n",
+    "commitment_maps = get_data_commitment_maps(data_path, scales)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 18,
@@ -167,8 +178,8 @@
     "        num_fil_X = torch.sum(filter.double())\n",
     "        fil_X = torch.where(filter, X, 1.0)\n",
     "        return (torch.abs((torch.log(self.w)*num_fil_X)-torch.sum(torch.log(fil_X)))<=num_fil_X*torch.log(torch.tensor(1.01)), self.w)\n",
-    "  \n",
-    "verifier_define_calculation(dummy_data_path, ['col_name'],sel_dummy_data_path,verifier_model, verifier_model_path)"
+    "\n",
+    "verifier_define_calculation(dummy_data_path, selected_columns, sel_dummy_data_path,verifier_model, verifier_model_path)"
    ]
   },
   {
@@ -205,7 +216,7 @@
     "        super(prover_model, self).__init__()\n",
     "        # w represents mean in this case\n",
     "        self.w = nn.Parameter(data = theory_output, requires_grad = False)\n",
-    "        \n",
+    "\n",
     "    def forward(self,X):\n",
     "        # where part\n",
     "        filter = (X>20)\n",
@@ -214,8 +225,8 @@
     "        print('self w: ', self.w)\n",
     "        return (torch.abs((torch.log(self.w)*num_fil_X)-torch.sum(torch.log(fil_X)))<=num_fil_X*torch.log(torch.tensor(1.01)), self.w)\n",
     "\n",
-    "    \n",
-    "prover_gen_settings(data_path,['col_name'], sel_data_path, prover_model,prover_model_path, [8], \"resources\", settings_path)"
+    "\n",
+    "prover_gen_settings(data_path, selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
    ]
   },
   {
@@ -279,7 +290,7 @@
     }
    ],
    "source": [
-    "# Here verifier & prover can concurrently call setup since all params are public to get pk. \n",
+    "# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
     "# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
     "verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
     "\n",
@@ -314,7 +325,7 @@
    ],
    "source": [
     "# Verifier verifies\n",
-    "verifier_verify(proof_path, settings_path, vk_path)"
+    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
    ]
   },
   {

examples/where+mean/where+mean.ipynb

@@ -128,7 +128,7 @@
     "\n",
     "data = np.array(json.loads(open(data_path, \"r\").read())['col_name'])\n",
     "data_tensor = torch.reshape(torch.tensor(data),(1,-1, 1))\n",
-    "# remember that our create_dummy creates random numbers in range 1 - 30, so need to make sure this example the filter doesnt \n",
+    "# remember that our create_dummy creates random numbers in range 1 - 30, so need to make sure this example the filter doesnt\n",
     "# get filter out all data\n",
     "create_dummy(data_path, dummy_data_path)\n",
     "dummy_data = np.array(json.loads(open(dummy_data_path, \"r\").read())['col_name'])\n",
@@ -142,6 +142,17 @@
     "theory_output = torch.mean(gt20_data_tensor)\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scales = [2]\n",
+    "selected_columns = ['col_name']\n",
+    "commitment_maps = get_data_commitment_maps(data_path, scales)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 36,
@@ -161,9 +172,9 @@
     "        num_fil_X = torch.sum(filter.double())\n",
     "        fil_X = torch.where(filter, X, 0.0)\n",
     "        return (torch.abs(torch.sum(fil_X)-num_fil_X*self.w)<=torch.abs(0.01*num_fil_X*self.w), self.w)\n",
-    "    \n",
     "\n",
-    "verifier_define_calculation(dummy_data_path, ['col_name'],sel_dummy_data_path,verifier_model, verifier_model_path)"
+    "\n",
+    "verifier_define_calculation(dummy_data_path, selected_columns,sel_dummy_data_path,verifier_model, verifier_model_path)"
    ]
   },
   {
@@ -192,7 +203,7 @@
     "        super(prover_model, self).__init__()\n",
     "        # w represents mean in this case\n",
     "        self.w = nn.Parameter(data = theory_output, requires_grad = False)\n",
-    "        \n",
+    "\n",
     "    def forward(self,X):\n",
     "        # where part\n",
     "        filter = (X>20)\n",
@@ -200,9 +211,9 @@
     "        fil_X = torch.where(filter, X, 0.0)\n",
     "\n",
     "        return (torch.abs(torch.sum(fil_X)-num_fil_X*self.w)<=torch.abs(0.01*num_fil_X*self.w), self.w)\n",
-    "    \n",
     "\n",
-    "prover_gen_settings(data_path,['col_name'], sel_data_path, prover_model,prover_model_path, [2], \"resources\", settings_path)"
+    "\n",
+    "prover_gen_settings(data_path, selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
    ]
   },
   {
@@ -266,7 +277,7 @@
     }
    ],
    "source": [
-    "# Here verifier & prover can concurrently call setup since all params are public to get pk. \n",
+    "# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
     "# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
     "verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
     "\n",
@@ -295,7 +306,7 @@
    ],
    "source": [
     "# Verifier verifies\n",
-    "verifier_verify(proof_path, settings_path, vk_path)"
+    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
    ]
   },
   {

examples/where+median/where+median.ipynb

@@ -140,6 +140,17 @@
     "upper_to_median = torch.tensor(np.sort(data[data<50])[int(len(data[data<50])/2)])\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scales = [2]\n",
+    "selected_columns = ['col_name']\n",
+    "commitment_maps = get_data_commitment_maps(data_path, scales)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 39,
@@ -173,7 +184,7 @@
     "        num_fil_X = X.size()[1]-num_lowest\n",
     "        min_X = torch.min(X)\n",
     "        fil_X = torch.where(filter, X, min_X-1)\n",
-    "        \n",
+    "\n",
     "        count_less = torch.sum((fil_X < 0.99*self.w).double()) - num_lowest\n",
     "        count_equal = torch.sum((torch.abs(fil_X-self.w)<=torch.abs(0.01*self.w)).double())\n",
     "        half_len = torch.floor(torch.div(num_fil_X, 2))\n",
@@ -193,11 +204,11 @@
     "\n",
     "        median_in_cons = torch.logical_and(less_cons, more_cons)\n",
     "        median_out_cons = torch.logical_and(torch.logical_and(bound, bound_avg), torch.logical_and(torch.logical_and(lower_cons, upper_cons), torch.logical_and(lower_exist, upper_exist)))\n",
-    "        \n",
+    "\n",
     "        return(torch.where(count_equal==0, median_out_cons, median_in_cons), self.w)\n",
     "\n",
     "\n",
-    "verifier_define_calculation(dummy_data_path, ['col_name'],sel_dummy_data_path,verifier_model, verifier_model_path)"
+    "verifier_define_calculation(dummy_data_path, selected_columns,sel_dummy_data_path,verifier_model, verifier_model_path)"
    ]
   },
   {
@@ -234,7 +245,7 @@
     "        num_fil_X = X.size()[1]-num_lowest\n",
     "        min_X = torch.min(X)\n",
     "        fil_X = torch.where(filter, X, min_X-1)\n",
-    "        \n",
+    "\n",
     "        count_less = torch.sum((fil_X < 0.99*self.w).double()) - num_lowest\n",
     "        count_equal = torch.sum((torch.abs(fil_X-self.w)<=torch.abs(0.01*self.w)).double())\n",
     "        half_len = torch.floor(torch.div(num_fil_X, 2))\n",
@@ -258,7 +269,7 @@
     "        return(torch.where(count_equal==0, median_out_cons, median_in_cons), self.w)\n",
     "\n",
     "\n",
-    "prover_gen_settings(data_path,['col_name'], sel_data_path, prover_model,prover_model_path, [2], \"resources\", settings_path)"
+    "prover_gen_settings(data_path,selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
    ]
   },
   {
@@ -322,7 +333,7 @@
     }
    ],
    "source": [
-    "# Here verifier & prover can concurrently call setup since all params are public to get pk. \n",
+    "# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
     "# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
     "verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
     "\n",
@@ -351,7 +362,7 @@
    ],
    "source": [
     "# Verifier verifies\n",
-    "verifier_verify(proof_path, settings_path, vk_path)"
+    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
    ]
   },
   {

examples/where+mode/where+mode.ipynb

@@ -155,6 +155,17 @@
     "theory_output = torch.tensor(mode_within(data[data>20],1))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scales = [2]\n",
+    "selected_columns = ['col_name']\n",
+    "commitment_maps = get_data_commitment_maps(data_path, scales)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -189,7 +200,7 @@
     "        return (torch.sum(result) == X.size()[1], self.w)\n",
     "\n",
     "\n",
-    "verifier_define_calculation(dummy_data_path, ['col_name'],sel_dummy_data_path,verifier_model, verifier_model_path)"
+    "verifier_define_calculation(dummy_data_path, selected_columns,sel_dummy_data_path,verifier_model, verifier_model_path)"
    ]
   },
   {
@@ -242,7 +253,7 @@
     "        result = torch.tensor([torch.logical_or(torch.sum((torch.abs(X-ele[0])<=torch.abs(0.01*ele[0])).double())<=count_equal, torch.abs(min_X-1-ele[0])<=torch.abs(0.01*ele[0])) for ele in X[0]])\n",
     "        return (torch.sum(result) == X.size()[1], self.w)\n",
     "\n",
-    "prover_gen_settings(data_path,['col_name'], sel_data_path, prover_model,prover_model_path, [2], \"resources\", settings_path)"
+    "prover_gen_settings(data_path,selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
    ]
   },
   {
@@ -306,7 +317,7 @@
     }
    ],
    "source": [
-    "# Here verifier & prover can concurrently call setup since all params are public to get pk. \n",
+    "# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
     "# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
     "verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
     "\n",
@@ -335,7 +346,7 @@
    ],
    "source": [
     "# Verifier verifies\n",
-    "verifier_verify(proof_path, settings_path, vk_path)"
+    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
    ]
   },
   {

examples/where+regression/where+regression.ipynb

@@ -168,6 +168,17 @@
     "\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scales = [0]\n",
+    "selected_columns = ['x1', 'x2', 'y']\n",
+    "commitment_maps = get_data_commitment_maps(data_path, scales)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 9,
@@ -186,12 +197,12 @@
    ],
    "source": [
     "# Verifier/ data consumer side:\n",
-    "# Want to calculate regression of y over \n",
+    "# Want to calculate regression of y over\n",
     "class verifier_model(nn.Module):\n",
     "    def __init__(self):\n",
     "        super(verifier_model, self).__init__()\n",
     "        self.w = nn.Parameter(data = lt20_dummy_w_tensor, requires_grad = False)\n",
-    "        \n",
+    "\n",
     "    def forward(self, *args):\n",
     "        # infer Y from the last parameter\n",
     "        Y = args[-1]\n",
@@ -206,7 +217,7 @@
     "            self.w\n",
     "        )\n",
     "\n",
-    "verifier_define_calculation(dummy_data_path, ['x1','x2','y'],sel_dummy_data_path,verifier_model, verifier_model_path)"
+    "verifier_define_calculation(dummy_data_path, selected_columns,sel_dummy_data_path,verifier_model, verifier_model_path)"
    ]
   },
   {
@@ -258,8 +269,8 @@
     "            torch.sum(torch.abs(fil_X_T @ fil_X_one @ self.w - fil_X_T @ fil_Y)) <= 0.01 * torch.sum(torch.abs(fil_X_T @ fil_Y)),\n",
     "            self.w\n",
     "        )\n",
-    "    \n",
-    "prover_gen_settings(data_path,['x1','x2','y'], sel_data_path, prover_model,prover_model_path, [0], \"resources\", settings_path)"
+    "\n",
+    "prover_gen_settings(data_path,selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
    ]
   },
   {
@@ -333,7 +344,7 @@
     }
    ],
    "source": [
-    "# Here verifier & prover can concurrently call setup since all params are public to get pk. \n",
+    "# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
     "# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
     "verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
     "\n",
@@ -363,7 +374,7 @@
     }
    ],
    "source": [
-    "verifier_verify(proof_path, settings_path, vk_path)"
+    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
    ]
   },
   {
@@ -390,7 +401,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.12.1"
   },
   "orig_nbformat": 4
  },

examples/where+stdev/where+stdev.ipynb

@@ -136,7 +136,7 @@
     "# Note, here we make\n",
     "gt5_data_tensor = data_tensor[data_tensor > 5].reshape(1,-1,1)\n",
     "\n",
-    "create_dummy(data_path, dummy_data_path) \n",
+    "create_dummy(data_path, dummy_data_path)\n",
     "dummy_data = np.array(json.loads(open(dummy_data_path, \"r\").read())['col_name'])\n",
     "\n",
     "dummy_data_tensor = torch.reshape(torch.tensor(dummy_data), (1,-1,1))\n",
@@ -152,6 +152,17 @@
     "print(data_mean)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scales = [2]\n",
+    "selected_columns = ['col_name']\n",
+    "commitment_maps = get_data_commitment_maps(data_path, scales)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 82,
@@ -186,8 +197,8 @@
     "        fil_std_X = torch.where(filter, X, self.data_mean)\n",
     "        print(\"bool: \",torch.logical_and(torch.abs(torch.sum((fil_std_X-self.data_mean)*(fil_std_X-self.data_mean))-self.w*self.w*(num_fil_X-1))<=torch.abs(0.02*self.w*self.w*(num_fil_X-1)),x_mean_cons) )\n",
     "        return (torch.logical_and(torch.abs(torch.sum((fil_std_X-self.data_mean)*(fil_std_X-self.data_mean))-self.w*self.w*(num_fil_X-1))<=torch.abs(0.02*self.w*self.w*(num_fil_X-1)),x_mean_cons),self.w)\n",
-    "    \n",
-    "verifier_define_calculation(dummy_data_path, ['col_name'],sel_dummy_data_path,verifier_model, verifier_model_path)"
+    "\n",
+    "verifier_define_calculation(dummy_data_path, selected_columns,sel_dummy_data_path,verifier_model, verifier_model_path)"
    ]
   },
   {
@@ -216,7 +227,7 @@
     "        # w represents mean in this case\n",
     "        self.w = nn.Parameter(data = theory_output, requires_grad = False)\n",
     "        self.data_mean = nn.Parameter(data = data_mean, requires_grad = False)\n",
-    "        \n",
+    "\n",
     "    def forward(self,X):\n",
     "        filter = (X>5)\n",
     "        num_fil_X = torch.sum(filter.double())\n",
@@ -227,7 +238,7 @@
     "        return (torch.logical_and(torch.abs(torch.sum((fil_std_X-self.data_mean)*(fil_std_X-self.data_mean))-self.w*self.w*(num_fil_X-1))<=torch.abs(0.02*self.w*self.w*(num_fil_X-1)),x_mean_cons),self.w)\n",
     "\n",
     "\n",
-    "prover_gen_settings(data_path,['col_name'], sel_data_path, prover_model,prover_model_path, [2], \"resources\", settings_path)"
+    "prover_gen_settings(data_path, selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
    ]
   },
   {
@@ -291,7 +302,7 @@
     }
    ],
    "source": [
-    "# Here verifier & prover can concurrently call setup since all params are public to get pk. \n",
+    "# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
     "# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
     "verifier_setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
     "\n",
@@ -320,7 +331,7 @@
    ],
    "source": [
     "# Verifier verifies\n",
-    "verifier_verify(proof_path, settings_path, vk_path)"
+    "verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
    ]
   },
   {