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colehaus/condition-truncate.ipynb Secret

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]
},
{
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"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: numpyro in /usr/local/lib/python3.7/dist-packages (0.10.0)\n",
"Requirement already satisfied: jaxlib>=0.1.65 in /usr/local/lib/python3.7/dist-packages (from numpyro) (0.3.14+cuda11.cudnn805)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from numpyro) (4.64.0)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from numpyro) (1.21.6)\n",
"Requirement already satisfied: multipledispatch in /usr/local/lib/python3.7/dist-packages (from numpyro) (0.6.0)\n",
"Requirement already satisfied: jax>=0.2.13 in /usr/local/lib/python3.7/dist-packages (from numpyro) (0.3.14)\n",
"Requirement already satisfied: opt-einsum in /usr/local/lib/python3.7/dist-packages (from jax>=0.2.13->numpyro) (3.3.0)\n",
"Requirement already satisfied: scipy>=1.5 in /usr/local/lib/python3.7/dist-packages (from jax>=0.2.13->numpyro) (1.7.3)\n",
"Requirement already satisfied: etils[epath] in /usr/local/lib/python3.7/dist-packages (from jax>=0.2.13->numpyro) (0.6.0)\n",
"Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from jax>=0.2.13->numpyro) (4.1.1)\n",
"Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from jax>=0.2.13->numpyro) (1.2.0)\n",
"Requirement already satisfied: flatbuffers<3.0,>=1.12 in /usr/local/lib/python3.7/dist-packages (from jaxlib>=0.1.65->numpyro) (2.0)\n",
"Requirement already satisfied: importlib_resources in /usr/local/lib/python3.7/dist-packages (from etils[epath]->jax>=0.2.13->numpyro) (5.9.0)\n",
"Requirement already satisfied: zipp in /usr/local/lib/python3.7/dist-packages (from etils[epath]->jax>=0.2.13->numpyro) (3.8.1)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from multipledispatch->numpyro) (1.15.0)\n"
]
}
],
"source": [
"!pip install numpyro"
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import numpyro as ny\n",
"import numpyro.distributions as dist\n",
"import jax.numpy as jnp\n",
"from jax.random import PRNGKey"
],
"metadata": {
"id": "FT51M1F_tIoU"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"ny.util.set_host_device_count(4)"
],
"metadata": {
"id": "3A8UWTguuHQ1"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def model1():\n",
" normal = ny.sample(\"normal\", dist.Normal(loc=np.array([-1, 1]), scale=1))\n",
" print(\"normal\", normal.shape)\n",
" forecast = ny.deterministic(\"forecast\", normal[-1, np.newaxis])\n",
" print(\"forecast\", forecast.shape)\n",
"def model2():\n",
" normal_dist = dist.Normal(loc=np.array([-1, 1]), scale=1) \n",
" with ny.handlers.condition(data={\"normal\": np.array([3])}):\n",
" normal = ny.sample(\"normal\", normal_dist)\n",
" print(\"normal\", normal.shape)\n",
"def model3():\n",
" normal_dist = dist.Normal(loc=np.array([-1, 1]), scale=1) \n",
" with ny.handlers.condition(data={\"normal\": np.array([3])}):\n",
" normal = ny.sample(\"normal\", normal_dist)\n",
" print(\"normal\", normal.shape)\n",
" forecast = ny.sample(\"forecast\", dist.Normal(loc=normal_dist.loc[-1], scale=normal_dist.scale))\n",
" print(\"forecast\", forecast.shape)\n"
],
"metadata": {
"id": "zeTYtwl4tRZt"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"mcmc = ny.infer.MCMC(ny.infer.NUTS(model1), num_warmup=200, num_samples=500, num_chains=4)\n",
"mcmc.run(rng_key=PRNGKey(0))\n",
"mcmc.print_summary(exclude_deterministic=False)"
],
"metadata": {
"colab": {
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{
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"text": [
"normal (2,)\n",
"forecast (1,)\n",
"normal (2,)\n",
"forecast (1,)\n"
]
},
{
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" 0%| | 0/700 [00:00<?, ?it/s]"
],
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" 0%| | 0/700 [00:00<?, ?it/s]"
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{
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"name": "stdout",
"text": [
"normal (2,)\n",
"forecast (1,)\n",
"normal (2,)\n",
"forecast (1,)\n",
"\n",
" mean std median 5.0% 95.0% n_eff r_hat\n",
"forecast[0] 0.98 0.98 1.00 -0.50 2.72 1647.16 1.00\n",
" normal[0] -1.04 0.99 -1.05 -2.63 0.64 1362.00 1.00\n",
" normal[1] 0.98 0.98 1.00 -0.50 2.72 1647.16 1.00\n",
"\n",
"Number of divergences: 0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"mcmc = ny.infer.MCMC(ny.infer.NUTS(model3), num_warmup=200, num_samples=500, num_chains=4)\n",
"mcmc.run(rng_key=PRNGKey(0))\n",
"mcmc.print_summary()"
],
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"colab": {
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"height": 336,
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"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"normal (1,)\n",
"forecast (1,)\n",
"normal (1,)\n",
"forecast (1,)\n"
]
},
{
"output_type": "display_data",
"data": {
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" 0%| | 0/700 [00:00<?, ?it/s]"
],
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},
{
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" 0%| | 0/700 [00:00<?, ?it/s]"
],
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}
},
"metadata": {}
},
{
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"data": {
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" 0%| | 0/700 [00:00<?, ?it/s]"
],
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}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"normal (1,)\n",
"forecast (1,)\n",
"\n",
" mean std median 5.0% 95.0% n_eff r_hat\n",
"forecast[0] 1.01 1.02 1.01 -0.74 2.62 727.01 1.00\n",
"\n",
"Number of divergences: 0\n"
]
}
]
}
]
}
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