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-rw-r--r--statistic/try_HMC.ipynb137
1 files changed, 74 insertions, 63 deletions
diff --git a/statistic/try_HMC.ipynb b/statistic/try_HMC.ipynb
index bb11871..2609f9d 100644
--- a/statistic/try_HMC.ipynb
+++ b/statistic/try_HMC.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -17,7 +17,7 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
@@ -26,67 +26,85 @@
"B = 1\n",
"N = 100000 + B\n",
"epsilon = 0.01\n",
- "L = 100"
+ "L = 100\n",
+ "initial = np.full(1, 2.5)"
]
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
- "def h(x):\n",
- " return (_lambda * x) - ((alpha - 1) * np.log(x)) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "metadata": {},
- "outputs": [],
- "source": [
- "def h_prime(x):\n",
- " return _lambda - ((alpha - 1) / x)"
+ "def leapfrog(theta, p, h_prime):\n",
+ " half_next_p = p - (epsilon * h_prime(theta)) / 2\n",
+ " next_theta = theta + epsilon * half_next_p\n",
+ " next_p = half_next_p - (epsilon * h_prime(next_theta)) / 2\n",
+ " return (next_p, next_theta)"
]
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
- "def leapfrog(theta, p):\n",
- " half_next_p = p - (epsilon / 2) * h_prime(theta)\n",
- " next_theta = theta + epsilon * half_next_p\n",
- " next_p = half_next_p - (epsilon / 2) * h_prime(next_theta)\n",
- " return (next_p, next_theta)"
+ "def hamiltonian(theta, p, h):\n",
+ " return (\n",
+ " h(theta)\n",
+ " + (\n",
+ " np.sum(p ** 2) # 各次元がもつ運動エネルギーの足し合わせ\n",
+ " / 2\n",
+ " )\n",
+ " )"
]
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
- "def hamiltonian(theta, p):\n",
- " return h(theta) + ((p ** 2) / 2)"
+ "def r(theta1, p1, theta2, p2, h):\n",
+ " return (\n",
+ " np.exp(hamiltonian(theta1, p1, h) - hamiltonian(theta2, p2, h))\n",
+ " )"
]
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
- "def r(theta1, p1, theta2, p2):\n",
- " return (\n",
- " np.exp(hamiltonian(theta1, p1) - hamiltonian(theta2, p2))\n",
- " )"
+ "def hmc(h, h_prime):\n",
+ " data = np.empty((N, len(initial)))\n",
+ " data[0] = initial\n",
+ " accept_count = 1 # data[0] は受容とみなす\n",
+ " for i in range(1, N):\n",
+ " if not i % 1000: # 進捗用\n",
+ " sys.stdout.write(\"%s / %s %s %% \\r\" % (i, N, np.round(100 * (i / N), decimals=2)))\n",
+ " \n",
+ " start_p = p = np.random.normal(0, 1, len(initial))\n",
+ " start_theta = theta = data[i - 1]\n",
+ "\n",
+ " for _ in range(L):\n",
+ " p, theta = leapfrog(theta, p, h_prime)\n",
+ "\n",
+ " if np.random.rand() < r(start_theta, start_p, theta, p, h):\n",
+ " data[i] = theta\n",
+ " accept_count = accept_count + 1\n",
+ " else:\n",
+ " data[i] = start_theta\n",
+ " \n",
+ " print(\"\\nacceptance ratio: %s \" % str(accept_count / N))\n",
+ " \n",
+ " return data"
]
},
{
"cell_type": "code",
- "execution_count": 56,
+ "execution_count": 26,
"metadata": {},
"outputs": [
{
@@ -94,54 +112,47 @@
"output_type": "stream",
"text": [
"100000 / 100001 100.0 % \n",
- "acceptance ratio: 0.999990000099999 \n"
+ "acceptance ratio: 0.999810001899981 \n"
]
}
],
"source": [
- "last_theta = 2.5\n",
- "data = deque([last_theta])\n",
- "accept_count = 1\n",
- "\n",
- "for i in range(2, N):\n",
- " if not i % 1000: # 進捗用\n",
- " sys.stdout.write(\"%s / %s %s %% \\r\" % (i, N, np.round(100 * (i / N), decimals=2)))\n",
+ "def h(x):\n",
+ " \"\"\"\n",
+ " 目標分布(事後確率)関数の対数関数のマイナス。必ずスカラー値を返却させる\n",
+ " \"\"\"\n",
+ " return (_lambda * x) - ((alpha - 1) * np.log(x))\n",
"\n",
- " start_p = p = np.random.normal()\n",
- " start_theta = theta = data[len(data) - 1]\n",
- " \n",
- " for j in range(L):\n",
- " p, theta = leapfrog(theta, p)\n",
- " \n",
- " if np.random.rand() < r(start_theta, start_p, theta, p):\n",
- " data.append(theta)\n",
- " accept_count = accept_count + 1\n",
- " else:\n",
- " data.append(start_theta)\n",
+ "def h_prime(x):\n",
+ " \"\"\"\n",
+ " 目標分布関数の対数関数の微分のマイナス\n",
+ " \"\"\"\n",
+ " return _lambda - ((alpha - 1) / x)\n",
"\n",
- " accept_count = accept_count + 1\n",
- " \n",
- "print(\"\\nacceptance ratio: %s \" % str(accept_count / N))"
+ "data = hmc(\n",
+ " h,\n",
+ " h_prime\n",
+ ")"
]
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.axes._subplots.AxesSubplot at 0x7fb932d0d510>"
+ "<matplotlib.axes._subplots.AxesSubplot at 0x7fbac21509d0>"
]
},
- "execution_count": 44,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -153,27 +164,27 @@
}
],
"source": [
- "pd.Series(data).plot()"
+ "pd.DataFrame(data).plot()"
]
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.axes._subplots.AxesSubplot at 0x7fb933aad190>"
+ "<matplotlib.axes._subplots.AxesSubplot at 0x7fbac1e6be50>"
]
},
- "execution_count": 46,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -187,7 +198,7 @@
"source": [
"fig = plt.figure()\n",
"ax = fig.add_subplot(1,1,1)\n",
- "sns.distplot(list(data)[B:],bins=100, label='HMC', ax=ax)\n",
+ "sns.distplot(data[B:],bins=100, label='HMC', ax=ax)\n",
"arr = np.arange(0, 2.5, 0.01)\n",
"sns.lineplot(\n",
" data=pd.DataFrame(\n",