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@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\n\n\ndef plot_samples(S, axis_list=None):\n    plt.scatter(\n        S[:, 0], S[:, 1], s=2, marker=\"o\", zorder=10, color=\"steelblue\", alpha=0.5\n    )\n    if axis_list is not None:\n        colors = [\"orange\", \"red\"]\n        for color, axis in zip(colors, axis_list):\n            axis /= axis.std()\n            x_axis, y_axis = axis\n            # Trick to get legend to work\n            plt.plot(0.1 * x_axis, 0.1 * y_axis, linewidth=2, color=color)\n            plt.quiver(\n                (0, 0),\n                (0, 0),\n                x_axis,\n                y_axis,\n                zorder=11,\n                width=0.01,\n                scale=6,\n                color=color,\n            )\n\n    plt.hlines(0, -3, 3)\n    plt.vlines(0, -3, 3)\n    plt.xlim(-3, 3)\n    plt.ylim(-3, 3)\n    plt.xlabel(\"x\")\n    plt.ylabel(\"y\")\n\n\nplt.figure()\nplt.subplot(2, 2, 1)\nplot_samples(S / S.std())\nplt.title(\"True Independent Sources\")\n\naxis_list = [pca.components_.T, ica.mixing_]\nplt.subplot(2, 2, 2)\nplot_samples(X / np.std(X), axis_list=axis_list)\nlegend = plt.legend([\"PCA\", \"ICA\"], loc=\"upper right\")\nlegend.set_zorder(100)\n\nplt.title(\"Observations\")\n\nplt.subplot(2, 2, 3)\nplot_samples(S_pca_ / np.std(S_pca_, axis=0))\nplt.title(\"PCA recovered signals\")\n\nplt.subplot(2, 2, 4)\nplot_samples(S_ica_ / np.std(S_ica_))\nplt.title(\"ICA recovered signals\")\n\nplt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.36)\nplt.show()"
+        "import matplotlib.pyplot as plt\n\n\ndef plot_samples(S, axis_list=None):\n    plt.scatter(\n        S[:, 0], S[:, 1], s=2, marker=\"o\", zorder=10, color=\"steelblue\", alpha=0.5\n    )\n    if axis_list is not None:\n        for axis, color, label in axis_list:\n            axis /= axis.std()\n            x_axis, y_axis = axis\n            plt.quiver(\n                (0, 0),\n                (0, 0),\n                x_axis,\n                y_axis,\n                zorder=11,\n                width=0.01,\n                scale=6,\n                color=color,\n                label=label,\n            )\n\n    plt.hlines(0, -3, 3)\n    plt.vlines(0, -3, 3)\n    plt.xlim(-3, 3)\n    plt.ylim(-3, 3)\n    plt.xlabel(\"x\")\n    plt.ylabel(\"y\")\n\n\nplt.figure()\nplt.subplot(2, 2, 1)\nplot_samples(S / S.std())\nplt.title(\"True Independent Sources\")\n\naxis_list = [(pca.components_.T, \"orange\", \"PCA\"), (ica.mixing_, \"red\", \"ICA\")]\nplt.subplot(2, 2, 2)\nplot_samples(X / np.std(X), axis_list=axis_list)\nlegend = plt.legend(loc=\"lower right\")\nlegend.set_zorder(100)\n\nplt.title(\"Observations\")\n\nplt.subplot(2, 2, 3)\nplot_samples(S_pca_ / np.std(S_pca_, axis=0))\nplt.title(\"PCA recovered signals\")\n\nplt.subplot(2, 2, 4)\nplot_samples(S_ica_ / np.std(S_ica_))\nplt.title(\"ICA recovered signals\")\n\nplt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.36)\nplt.show()"
       ]
     }
   ],
 
@@ -68,12 +68,9 @@ def plot_samples(S, axis_list=None):
         S[:, 0], S[:, 1], s=2, marker="o", zorder=10, color="steelblue", alpha=0.5
     )
     if axis_list is not None:
-        colors = ["orange", "red"]
-        for color, axis in zip(colors, axis_list):
+        for axis, color, label in axis_list:
             axis /= axis.std()
             x_axis, y_axis = axis
-            # Trick to get legend to work
-            plt.plot(0.1 * x_axis, 0.1 * y_axis, linewidth=2, color=color)
             plt.quiver(
                 (0, 0),
                 (0, 0),
@@ -83,6 +80,7 @@ def plot_samples(S, axis_list=None):
                 width=0.01,
                 scale=6,
                 color=color,
+                label=label,
             )
 
     plt.hlines(0, -3, 3)
@@ -98,10 +96,10 @@ def plot_samples(S, axis_list=None):
 plot_samples(S / S.std())
 plt.title("True Independent Sources")
 
-axis_list = [pca.components_.T, ica.mixing_]
+axis_list = [(pca.components_.T, "orange", "PCA"), (ica.mixing_, "red", "ICA")]
 plt.subplot(2, 2, 2)
 plot_samples(X / np.std(X), axis_list=axis_list)
-legend = plt.legend(["PCA", "ICA"], loc="upper right")
+legend = plt.legend(loc="lower right")
 legend.set_zorder(100)
 
 plt.title("Observations")
Original file line number	Diff line number	Diff line change
`@@ -62,7 +62,7 @@`
`62`	`62`	`},`
`63`	`63`	`"outputs": [],`
`64`	`64`	`"source": [`
`65`		- "import matplotlib.pyplot as plt\n\n\ndef plot_samples(S, axis_list=None):\n plt.scatter(\n S[:, 0], S[:, 1], s=2, marker=\"o\", zorder=10, color=\"steelblue\", alpha=0.5\n )\n if axis_list is not None:\n colors = [\"orange\", \"red\"]\n for color, axis in zip(colors, axis_list):\n axis /= axis.std()\n x_axis, y_axis = axis\n # Trick to get legend to work\n plt.plot(0.1 * x_axis, 0.1 * y_axis, linewidth=2, color=color)\n plt.quiver(\n (0, 0),\n (0, 0),\n x_axis,\n y_axis,\n zorder=11,\n width=0.01,\n scale=6,\n color=color,\n )\n\n plt.hlines(0, -3, 3)\n plt.vlines(0, -3, 3)\n plt.xlim(-3, 3)\n plt.ylim(-3, 3)\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n\n\nplt.figure()\nplt.subplot(2, 2, 1)\nplot_samples(S / S.std())\nplt.title(\"True Independent Sources\")\n\naxis_list = [pca.components_.T, ica.mixing_]\nplt.subplot(2, 2, 2)\nplot_samples(X / np.std(X), axis_list=axis_list)\nlegend = plt.legend([\"PCA\", \"ICA\"], loc=\"upper right\")\nlegend.set_zorder(100)\n\nplt.title(\"Observations\")\n\nplt.subplot(2, 2, 3)\nplot_samples(S_pca_ / np.std(S_pca_, axis=0))\nplt.title(\"PCA recovered signals\")\n\nplt.subplot(2, 2, 4)\nplot_samples(S_ica_ / np.std(S_ica_))\nplt.title(\"ICA recovered signals\")\n\nplt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.36)\nplt.show()"
	`65`	+ "import matplotlib.pyplot as plt\n\n\ndef plot_samples(S, axis_list=None):\n plt.scatter(\n S[:, 0], S[:, 1], s=2, marker=\"o\", zorder=10, color=\"steelblue\", alpha=0.5\n )\n if axis_list is not None:\n for axis, color, label in axis_list:\n axis /= axis.std()\n x_axis, y_axis = axis\n plt.quiver(\n (0, 0),\n (0, 0),\n x_axis,\n y_axis,\n zorder=11,\n width=0.01,\n scale=6,\n color=color,\n label=label,\n )\n\n plt.hlines(0, -3, 3)\n plt.vlines(0, -3, 3)\n plt.xlim(-3, 3)\n plt.ylim(-3, 3)\n plt.xlabel(\"x\")\n plt.ylabel(\"y\")\n\n\nplt.figure()\nplt.subplot(2, 2, 1)\nplot_samples(S / S.std())\nplt.title(\"True Independent Sources\")\n\naxis_list = [(pca.components_.T, \"orange\", \"PCA\"), (ica.mixing_, \"red\", \"ICA\")]\nplt.subplot(2, 2, 2)\nplot_samples(X / np.std(X), axis_list=axis_list)\nlegend = plt.legend(loc=\"lower right\")\nlegend.set_zorder(100)\n\nplt.title(\"Observations\")\n\nplt.subplot(2, 2, 3)\nplot_samples(S_pca_ / np.std(S_pca_, axis=0))\nplt.title(\"PCA recovered signals\")\n\nplt.subplot(2, 2, 4)\nplot_samples(S_ica_ / np.std(S_ica_))\nplt.title(\"ICA recovered signals\")\n\nplt.subplots_adjust(0.09, 0.04, 0.94, 0.94, 0.26, 0.36)\nplt.show()"
`66`	`66`	`]`
`67`	`67`	`}`
`68`	`68`	`],`