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| "text": [ | |
| "Collecting numpyro\n", | |
| " Downloading numpyro-0.9.0-py3-none-any.whl (282 kB)\n", | |
| "\u001b[?25l\r\u001b[K |█▏ | 10 kB 16.9 MB/s eta 0:00:01\r\u001b[K |██▎ | 20 kB 21.7 MB/s eta 0:00:01\r\u001b[K |███▌ | 30 kB 27.1 MB/s eta 0:00:01\r\u001b[K |████▋ | 40 kB 29.9 MB/s eta 0:00:01\r\u001b[K |█████▉ | 51 kB 33.0 MB/s eta 0:00:01\r\u001b[K |███████ | 61 kB 24.4 MB/s eta 0:00:01\r\u001b[K |████████▏ | 71 kB 20.7 MB/s eta 0:00:01\r\u001b[K |█████████▎ | 81 kB 21.2 MB/s eta 0:00:01\r\u001b[K |██████████▍ | 92 kB 21.7 MB/s eta 0:00:01\r\u001b[K |███████████▋ | 102 kB 23.3 MB/s eta 0:00:01\r\u001b[K |████████████▊ | 112 kB 23.3 MB/s eta 0:00:01\r\u001b[K |██████████████ | 122 kB 23.3 MB/s eta 0:00:01\r\u001b[K |███████████████ | 133 kB 23.3 MB/s eta 0:00:01\r\u001b[K |████████████████▎ | 143 kB 23.3 MB/s eta 0:00:01\r\u001b[K |█████████████████▍ | 153 kB 23.3 MB/s eta 0:00:01\r\u001b[K |██████████████████▌ | 163 kB 23.3 MB/s eta 0:00:01\r\u001b[K |███████████████████▊ | 174 kB 23.3 MB/s eta 0:00:01\r\u001b[K |████████████████████▉ | 184 kB 23.3 MB/s eta 0:00:01\r\u001b[K |██████████████████████ | 194 kB 23.3 MB/s eta 0:00:01\r\u001b[K |███████████████████████▏ | 204 kB 23.3 MB/s eta 0:00:01\r\u001b[K |████████████████████████▍ | 215 kB 23.3 MB/s eta 0:00:01\r\u001b[K |█████████████████████████▌ | 225 kB 23.3 MB/s eta 0:00:01\r\u001b[K |██████████████████████████▋ | 235 kB 23.3 MB/s eta 0:00:01\r\u001b[K |███████████████████████████▉ | 245 kB 23.3 MB/s eta 0:00:01\r\u001b[K |█████████████████████████████ | 256 kB 23.3 MB/s eta 0:00:01\r\u001b[K |██████████████████████████████▏ | 266 kB 23.3 MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▎| 276 kB 23.3 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 282 kB 23.3 MB/s \n", | |
| "\u001b[?25hCollecting multipledispatch\n", | |
| " Downloading multipledispatch-0.6.0-py3-none-any.whl (11 kB)\n", | |
| "Requirement already satisfied: jax>=0.2.13 in /usr/local/lib/python3.7/dist-packages (from numpyro) (0.2.25)\n", | |
| "Requirement already satisfied: jaxlib>=0.1.65 in /usr/local/lib/python3.7/dist-packages (from numpyro) (0.1.71+cuda111)\n", | |
| "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from numpyro) (1.19.5)\n", | |
| "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from numpyro) (4.62.3)\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>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from jax>=0.2.13->numpyro) (1.4.1)\n", | |
| "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from jax>=0.2.13->numpyro) (1.0.0)\n", | |
| "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from jax>=0.2.13->numpyro) (3.10.0.2)\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: six in /usr/local/lib/python3.7/dist-packages (from absl-py->jax>=0.2.13->numpyro) (1.15.0)\n", | |
| "Installing collected packages: multipledispatch, numpyro\n", | |
| "Successfully installed multipledispatch-0.6.0 numpyro-0.9.0\n", | |
| "(2,)\n", | |
| "(200, 2)\n", | |
| "(200, 2)\n", | |
| "(200, 2)\n" | |
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| "(200, 2)\n", | |
| "\n", | |
| " mean std median 5.0% 95.0% n_eff r_hat\n", | |
| " mus[0] 0.76 0.07 0.76 0.64 0.87 2093.63 1.00\n", | |
| " mus[1] -0.22 0.07 -0.22 -0.35 -0.12 1900.43 1.00\n", | |
| "scale_tril[0,0] 1.00 0.00 1.00 1.00 1.00 nan nan\n", | |
| "scale_tril[0,1] 0.00 0.00 0.00 0.00 0.00 nan nan\n", | |
| "scale_tril[1,0] 0.61 0.04 0.61 0.55 0.67 3006.51 1.00\n", | |
| "scale_tril[1,1] 0.79 0.03 0.79 0.74 0.84 3061.30 1.00\n", | |
| "\n", | |
| "Number of divergences: 0\n", | |
| "(200, 2)\n", | |
| "(200, 2)\n" | |
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| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "(200, 2)\n", | |
| "\n", | |
| " mean std median 5.0% 95.0% n_eff r_hat\n", | |
| " mus[0] 0.76 0.07 0.76 0.64 0.87 2093.63 1.00\n", | |
| " mus[1] -0.22 0.07 -0.22 -0.35 -0.12 1900.43 1.00\n", | |
| "scale_tril[0,0] 1.00 0.00 1.00 1.00 1.00 nan nan\n", | |
| "scale_tril[0,1] 0.00 0.00 0.00 0.00 0.00 nan nan\n", | |
| "scale_tril[1,0] 0.61 0.04 0.61 0.55 0.67 3006.51 1.00\n", | |
| "scale_tril[1,1] 0.79 0.03 0.79 0.74 0.84 3061.30 1.00\n", | |
| "\n", | |
| "Number of divergences: 0\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "%pip install numpyro\n", | |
| "\n", | |
| "import jax\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np\n", | |
| "import numpyro as ny\n", | |
| "import numpyro.distributions as dist\n", | |
| "from numpyro.infer import MCMC, NUTS, Predictive\n", | |
| "from typing import Optional\n", | |
| "\n", | |
| "ny.util.set_host_device_count(2)\n", | |
| "\n", | |
| "\n", | |
| "def model1(data: Optional[\"np.ndarray[float]\"] = None) -> None:\n", | |
| " mus = ny.sample(\"mus\", dist.Normal(0, 1).expand((2,)))\n", | |
| " scale_tril = ny.sample(\"scale_tril\", dist.LKJCholesky(2, concentration=1))\n", | |
| " xs = ny.sample(\n", | |
| " \"xs\", dist.MultivariateNormal(loc=mus, scale_tril=scale_tril), obs=data\n", | |
| " )\n", | |
| " print(xs.shape)\n", | |
| "\n", | |
| "def model2(data: \"np.ndarray[float]\") -> None:\n", | |
| " mus = ny.sample(\"mus\", dist.Normal(0, 1).expand((2,)))\n", | |
| " scale_tril = ny.sample(\"scale_tril\", dist.LKJCholesky(2, concentration=1))\n", | |
| " with ny.plate(\"obs\", data.shape[0], dim=-1):\n", | |
| " xs = ny.sample(\n", | |
| " \"xs\", dist.MultivariateNormal(loc=mus, scale_tril=scale_tril), obs=data\n", | |
| " )\n", | |
| " print(xs.shape)\n", | |
| "\n", | |
| "xs = Predictive(\n", | |
| " model1,\n", | |
| " num_samples=200,\n", | |
| " params={\"mus\": np.array([0.8, -0.2]), \"scale_tril\": np.array([[1, 0], [0.6, 0.8]])},\n", | |
| ")(jax.random.PRNGKey(2))[\"xs\"]\n", | |
| "print(xs.shape)\n", | |
| "\n", | |
| "# plt.scatter(xs_missing[:,0], xs_missing[:,1], alpha=0.3)\n", | |
| "\n", | |
| "mcmc = MCMC(NUTS(model1), num_warmup=500, num_samples=2000, num_chains=2)\n", | |
| "mcmc.run(jax.random.PRNGKey(0), data=xs)\n", | |
| "mcmc.print_summary()\n", | |
| "\n", | |
| "mcmc = MCMC(NUTS(model2), num_warmup=500, num_samples=2000, num_chains=2)\n", | |
| "mcmc.run(jax.random.PRNGKey(0), data=xs)\n", | |
| "mcmc.print_summary()\n" | |
| ] | |
| } | |
| ] | |
| } |
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