},

"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()"

]

}

],

S[:,0],S[:,1],s=2,marker="o",zorder=10,color="steelblue",alpha=0.5

)

ifaxis_listisnotNone:

colors= ["orange","red"]

forcolor,axisinzip(colors,axis_list):

foraxis,color,labelinaxis_list:

axis/=axis.std()

x_axis,y_axis=axis

plt.plot(0.1*x_axis,0.1*y_axis,linewidth=2,color=color)

plt.quiver(

(0,0),

(0,0),

width=0.01,

scale=6,

color=color,

label=label,

)

plt.hlines(0,-3,3)

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")