plotting fitted lines

src/HowBadIsMyBatch.ipynb

@@ -705,15 +705,6 @@
     "clusters, lines = MultiLineFitter.fitPointsByLines(points, consensusThreshold = 0.001)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clusters"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -724,141 +715,28 @@
     "from skspatial.objects import Line\n",
     "\n",
     "_, ax = plt.subplots()\n",
-    "for line in lines:\n",
-    "    line.plot_2d(ax, label = \"line\")\n",
-    "for cluster in clusters:\n",
-    "    plt.scatter(\n",
-    "        [x for (x, _) in cluster],\n",
-    "        [y for (_, y) in cluster],\n",
-    "        marker = \".\",\n",
-    "        s = 100,\n",
-    "        label = \"Cluster\")\n",
-    "# plt.scatter(\n",
-    "#     [x for (x, _) in points],\n",
-    "#     [y for (_, y) in points],\n",
-    "#     color = \"blue\",\n",
-    "#     marker = \".\",\n",
-    "#     s = 100,\n",
-    "#     label = \"Dots\")\n",
-    "plt.xlabel(symptomX)\n",
-    "plt.ylabel(symptomY)\n",
-    "plt.legend(loc=\"lower right\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Fit line using all data\n",
-    "lr = linear_model.LinearRegression()\n",
-    "lr.fit(X, y)\n",
-    "\n",
-    "# Robustly fit linear model with RANSAC algorithm\n",
-    "ransac = linear_model.RANSACRegressor(random_state = 0)\n",
-    "ransac.fit(X, y)\n",
-    "inlier_mask = ransac.inlier_mask_\n",
-    "outlier_mask = np.logical_not(inlier_mask)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X2 = X[outlier_mask]\n",
-    "y2 = y[outlier_mask]\n",
-    "ransac2 = linear_model.RANSACRegressor(random_state = 0)\n",
-    "ransac2.fit(X2, y2)\n",
-    "inlier_mask2 = ransac2.inlier_mask_\n",
-    "outlier_mask2 = np.logical_not(inlier_mask2)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X3 = X2[outlier_mask2]\n",
-    "y3 = y2[outlier_mask2]\n",
-    "ransac3 = linear_model.RANSACRegressor(random_state = 0)\n",
-    "ransac3.fit(X3, y3)\n",
-    "inlier_mask3 = ransac3.inlier_mask_\n",
-    "outlier_mask3 = np.logical_not(inlier_mask3)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_ransac_list = [(X, ransac), (X2, ransac2), (X3, ransac3)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib.pyplot import figure\n",
-    "figure(figsize=(8, 6), dpi=80)\n",
-    "\n",
-    "def plotRANSACResult(X, y, X_ransac_list):\n",
-    "    figure(figsize=(8, 6), dpi=80)\n",
-    "    plt.scatter(X, y, color=\"yellowgreen\", marker=\".\", label=\"Dots\")\n",
-    "    for (X, ransac) in X_ransac_list:\n",
-    "        line_X = np.arange(X.min(), X.max())[:, np.newaxis]\n",
-    "        line_y_ransac = ransac.predict(line_X)\n",
-    "        plt.plot(\n",
-    "            line_X,\n",
-    "            line_y_ransac,\n",
-    "            color = \"cornflowerblue\",\n",
-    "            linewidth = 2,\n",
-    "            label = \"RANSAC regressor\")\n",
-    "        plt.legend(loc=\"lower right\")\n",
-    "    plt.xlabel(symptomX)\n",
-    "    plt.ylabel(symptomY)\n",
-    "    plt.show()\n",
-    "\n",
-    "# Predict data of estimated models\n",
-    "line_X = np.arange(X.min(), X.max())[:, np.newaxis]\n",
-    "line_y = lr.predict(line_X)\n",
-    "line_y_ransac = ransac.predict(line_X)\n",
-    "\n",
-    "# Compare estimated coefficients\n",
-    "print(\"Estimated coefficients (true, linear regression, RANSAC):\")\n",
-    "print(lr.coef_, ransac.estimator_.coef_)\n",
-    "\n",
-    "lw = 2\n",
-    "plt.scatter(X, y, color=\"blue\", marker=\".\", label=\"Inliers\")\n",
-    "#plt.plot(line_X, line_y, color=\"navy\", linewidth=lw, label=\"Linear regressor\")\n",
-    "#plt.plot(\n",
-    "#    line_X,\n",
-    "#    line_y_ransac,\n",
-    "#    color=\"cornflowerblue\",\n",
-    "#    linewidth=lw,\n",
-    "#    label=\"RANSAC regressor\")\n",
-    "plt.legend(loc=\"lower right\")\n",
+    "plt.scatter(\n",
+    "    [x for (x, _) in points],\n",
+    "    [y for (_, y) in points],\n",
+    "    color = \"blue\",\n",
+    "    marker = \".\",\n",
+    "    s = 100,\n",
+    "    label = \"Dots\")\n",
+    "for cluster, line in zip(clusters, lines):\n",
+    "    if(len(cluster) > 2):\n",
+    "        coords = line.transform_points(cluster)\n",
+    "        magnitude = line.direction.norm()\n",
+    "        line.plot_2d(ax, t_1 = min(coords) / magnitude, t_2 = max(coords) / magnitude)\n",
+    "        plt.scatter(\n",
+    "            [x for (x, _) in cluster],\n",
+    "            [y for (_, y) in cluster],\n",
+    "            marker = \".\",\n",
+    "            s = 100)\n",
     "plt.xlabel(symptomX)\n",
     "plt.ylabel(symptomY)\n",
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plotRANSACResult(X, y, X_ransac_list)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},