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import os | |
os.environ['THEANO_FLAGS'] = 'device=cuda,floatX=float64' | |
import pymc3 as pm | |
with pm.Model(): | |
x = pm.Normal('x', shape=1000) | |
trace = pm.sample(chains=1, cores=1) |
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import theano | |
from theano import function, config, shared, tensor as tt | |
import numpy | |
import time | |
vlen = 10 * 30 * 768 # 10 x #cores x # threads per core | |
iters = 1000 | |
rng = numpy.random.RandomState(22) | |
x = shared(numpy.asarray(rng.rand(vlen), config.floatX)) |
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import pymc3 as pm | |
import numpy as np | |
import matplotlib.pyplot as plt | |
import arviz as az | |
from pymc3.step_methods.arraystep import BlockedStep | |
b = [2,1.5] | |
sigma = 2 |
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import arviz as az | |
import matplotlib.pyplot as plt | |
import numpy as np | |
import pymc3 as pm | |
from pymc3.step_methods.arraystep import BlockedStep | |
# ### Create simulated data under linear regression model |
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Y = xr.DataArray(Y_raw, dims=('observed_columns','rows')) | |
Y = Y.assign_coords({'observed_columns':np.arange(d),'rows':np.arange(n)}) | |
def post_hoc_fun(W, psi, Y, rng=None): | |
''' | |
Recovers latent variables given observation Y and single draw of W, psi | |
from the posterior distribution. | |
''' | |
k, d = W.shape[-2:] | |
_, n = Y.shape |
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import pandas as pd | |
# Run this as a notebook so that the bash cell magic ! works | |
import pandas as pd | |
! wget https://aqs.epa.gov/aqsweb/airdata/daily_44201_2020.zip | |
filepath = './daily_44201_2020.csv' | |
df = pd.read_csv(filepath) |
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import enum | |
import numpy as np | |
from mesa import Agent, Model | |
from mesa.time import RandomActivation | |
from mesa.space import MultiGrid | |
from mesa.datacollection import DataCollector | |
from tqdm import tqdm | |
class InfectionModel(Model): |
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colors = [ | |
'tab:blue', | |
'tab:orange', | |
'tab:green', | |
'tab:red', | |
'tab:purple', | |
'tab:brown', | |
'tab:pink', | |
'tab:gray', | |
'tab:olive', |
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--------------------------------------------------------------------------- | |
AttributeError Traceback (most recent call last) | |
<ipython-input-5-6e14f40a5c7d> in <module> | |
5 p = pm.Uniform("p", 0, 1) | |
6 pm.Binomial("w", p=p, n=2, observed=1) | |
----> 7 inference_data = pm.sample(500, chains=2, return_inferencedata=True) | |
8 | |
9 assert inference_data | |
~/anaconda3/envs/pymc3-dev-py39/lib/python3.9/site-packages/pymc3-3.11.1-py3.9.egg/pymc3/sampling.py in sample(draws, step, init, n_init, start, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, jitter_max_retries, return_inferencedata, idata_kwargs, mp_ctx, pickle_backend, **kwargs) |
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## Combined model | |
c_comb = np.asarray([[16,29,4], | |
[16,29,6], | |
[14,30,4], | |
[16,29,3], | |
[16,31,5], | |
[13,29,5], | |
[15,32,5], | |
[15,29,6], | |
[17,31,6], |