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"metadata": {},
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"metadata": {},
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"source": [
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"source": [
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"<div align=\"right\">Peter Norvig</div>\n",
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"<div align=\"right\">Peter Norvig</div>\n",
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"\n",
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"# A Chaos Game with Triangles\n",
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"# A Chaos Game with Triangles\n",
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"\n",
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"\n",
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"John D. Cook [proposed](https://www.johndcook.com/blog/2017/07/08/the-chaos-game-and-the-sierpinski-triangle/) an interesting \"game\" from the book *[Chaos and Fractals](https://smile.amazon.com/Chaos-Fractals-New-Frontiers-Science/dp/0387202293)*: start at a vertex of an equilateral triangle. Then move to a new point halfway between the current point and one of the three vertexes of the triangle, chosen at random. Repeat to create *N* points, and plot them. What do you get? \n",
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"John D. Cook [proposed](https://www.johndcook.com/blog/2017/07/08/the-chaos-game-and-the-sierpinski-triangle/) an interesting \"game\" from the book *[Chaos and Fractals](https://smile.amazon.com/Chaos-Fractals-New-Frontiers-Science/dp/0387202293)*: start at a vertex of an equilateral triangle. Then move to a new point halfway between the current point and one of the three vertexes of the triangle, chosen at random. Repeat to create *N* points, and plot them. What do you get? \n",
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@@ -33,7 +34,7 @@
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" Xs, Ys = transpose(random_walk(vertexes, N))\n",
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" Xs, Ys = transpose(random_walk(vertexes, N))\n",
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" Xv, Yv = transpose(vertexes)\n",
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" Xv, Yv = transpose(vertexes)\n",
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" plt.plot(Xs, Ys, 'r.')\n",
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" plt.plot(Xs, Ys, 'r.')\n",
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" plt.plot(Xv, Yv, 'bs')\n",
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" plt.plot(Xv, Yv, 'bs', clip_on=False)\n",
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" plt.gca().set_aspect('equal')\n",
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" plt.gca().set_aspect('equal')\n",
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" plt.gcf().set_size_inches(9, 9)\n",
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" plt.gcf().set_size_inches(9, 9)\n",
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" plt.axis('off')\n",
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" plt.axis('off')\n",
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@@ -53,12 +54,14 @@
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"outputs": [
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"outputs": [
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{
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{
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"data": {
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAiAAAAHlCAYAAADMREeGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
|
"image/png": "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\n",
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"text/plain": [
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"text/plain": [
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"<matplotlib.figure.Figure at 0x1087f3748>"
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"<Figure size 648x648 with 1 Axes>"
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]
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]
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},
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},
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"metadata": {},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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"output_type": "display_data"
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}
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}
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],
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],
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@@ -80,12 +83,14 @@
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"outputs": [
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"outputs": [
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{
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{
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"data": {
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"data": {
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||||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAiAAAAHlCAYAAADMREeGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
"image/png": "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 truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<matplotlib.figure.Figure at 0x10879e710>"
|
"<Figure size 648x648 with 1 Axes>"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"metadata": {},
|
"metadata": {
|
||||||
|
"needs_background": "light"
|
||||||
|
},
|
||||||
"output_type": "display_data"
|
"output_type": "display_data"
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -108,12 +113,14 @@
|
|||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
|
"image/png": 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truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<matplotlib.figure.Figure at 0x111077438>"
|
"<Figure size 648x648 with 1 Axes>"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"metadata": {},
|
"metadata": {
|
||||||
|
"needs_background": "light"
|
||||||
|
},
|
||||||
"output_type": "display_data"
|
"output_type": "display_data"
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -137,12 +144,14 @@
|
|||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
|
"image/png": 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truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<matplotlib.figure.Figure at 0x10d934320>"
|
"<Figure size 648x648 with 1 Axes>"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"metadata": {},
|
"metadata": {
|
||||||
|
"needs_background": "light"
|
||||||
|
},
|
||||||
"output_type": "display_data"
|
"output_type": "display_data"
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -164,12 +173,14 @@
|
|||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
|
"image/png": 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truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<matplotlib.figure.Figure at 0x110e3e048>"
|
"<Figure size 648x648 with 1 Axes>"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"metadata": {},
|
"metadata": {
|
||||||
|
"needs_background": "light"
|
||||||
|
},
|
||||||
"output_type": "display_data"
|
"output_type": "display_data"
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -193,12 +204,14 @@
|
|||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
|
"image/png": 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truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<matplotlib.figure.Figure at 0x110f7e588>"
|
"<Figure size 648x648 with 1 Axes>"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"metadata": {},
|
"metadata": {
|
||||||
|
"needs_background": "light"
|
||||||
|
},
|
||||||
"output_type": "display_data"
|
"output_type": "display_data"
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -222,12 +235,14 @@
|
|||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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|
"image/png": 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truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<matplotlib.figure.Figure at 0x111093048>"
|
"<Figure size 648x648 with 1 Axes>"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"metadata": {},
|
"metadata": {
|
||||||
|
"needs_background": "light"
|
||||||
|
},
|
||||||
"output_type": "display_data"
|
"output_type": "display_data"
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -242,12 +257,14 @@
|
|||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
|
"image/png": 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truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<matplotlib.figure.Figure at 0x111001160>"
|
"<Figure size 648x648 with 1 Axes>"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"metadata": {},
|
"metadata": {
|
||||||
|
"needs_background": "light"
|
||||||
|
},
|
||||||
"output_type": "display_data"
|
"output_type": "display_data"
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -269,12 +286,14 @@
|
|||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
|
"image/png": 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truncated
|
||||||
"text/plain": [
|
"text/plain": [
|
||||||
"<matplotlib.figure.Figure at 0x1111368d0>"
|
"<Figure size 648x648 with 1 Axes>"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"metadata": {},
|
"metadata": {
|
||||||
|
"needs_background": "light"
|
||||||
|
},
|
||||||
"output_type": "display_data"
|
"output_type": "display_data"
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -291,4 +310,48 @@
|
|||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"data": {
|
"data": {
|
||||||
"image/png": 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truncated
|
"image/png": 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truncated
|
||||||
|
"text/plain": [
|
||||||
|
"<Figure size 648x648 with 1 Axes>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"metadata": {
|
||||||
|
"needs_background": "light"
|
||||||
|
},
|
||||||
|
"output_type": "display_data"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"show_walk(hexagon, 20000)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"You can see a little of the six-fold symmetry, but it is not as clear as the triangle and pentagon."
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.5.3"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 2
|
||||||
|
}
|
||||||
Reference in new issue
Block a user