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@va2577
Created September 15, 2018 14:27
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Python SciPy ランダムウォーク
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ランダムウォーク"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## コインの表・裏"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"s = pd.Series(np.random.choice([1, -1], size=10000))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f41bee9b2e8>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"s2 = s.cumsum()\n",
"s2.plot()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n, bins, patches = plt.hist(s, 100)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.4973"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s[s > 0].count() / s.count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ランダムなデータ"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"s11 = pd.Series(np.random.randn(10000))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f41bea42b00>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"s12 = s11.cumsum() + 110\n",
"s12.plot()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n, bins, patches = plt.hist(s11, 100)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ヒストグラムの参考\n",
"\n",
"* [matplotlib.pyplot.hist — Matplotlib 2.2.2 documentation](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html\n",
")\n",
"* [Pyplot Text — Matplotlib 2.2.2 documentation](https://matplotlib.org/gallery/pyplots/pyplot_text.html#sphx-glr-gallery-pyplots-pyplot-text-py)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Fixing random state for reproducibility\n",
"np.random.seed(19680801)\n",
"\n",
"mu, sigma = 100, 15\n",
"x = mu + sigma * np.random.randn(10000)\n",
"\n",
"# the histogram of the data\n",
"n, bins, patches = plt.hist(x, 50, density=True, facecolor='g', alpha=0.75)\n",
"\n",
"\n",
"plt.xlabel('Smarts')\n",
"plt.ylabel('Probability')\n",
"plt.title('Histogram of IQ')\n",
"plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
"plt.axis([40, 160, 0, 0.03])\n",
"plt.grid(True)\n",
"plt.show()"
]
}
],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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