ckrapu

def vector_randint(p, axis=1):
    u = np.random.uniform(size=p.shape)
    v = u*p
    return np.argmax(v, axis=axis)

def batch_gibbs_sample(parents, children, point, parent_factors, is_observed, extra_factors=None):
    
    for var in parents.keys():
        
        should_update = is_observed[:, var]

ckrapu

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc3 as pm

# Define number of entities
p = 4

# Define number of obs. per entity
n = 6

ckrapu

import pymc3 as pm

with pm.Model() as model:
    x = pm.Normal('x', shape=2)
    step = pm.NUTS(x)
    gen = pm.iter_sample(5, step, tune=5, streaming=True)

    for trace in gen:
        print(trace)

ckrapu

from pymc3 import (
    NUTS,
    Deterministic,
    HalfCauchy,
    Model,
    MvNormal,
    find_MAP,
    sample,
    summary,
    traceplot,

ckrapu

import numpy as np
import aesara.tensor as at
import pymc3 as pm

np.random.seed(20090425)

n = 1
p = 10
k = 5
t = 200

ckrapu

## 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],

ckrapu

---------------------------------------------------------------------------
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)

ckrapu

colors = [
    'tab:blue',
'tab:orange',
'tab:green',
'tab:red',
'tab:purple',
'tab:brown',
'tab:pink',
'tab:gray',
'tab:olive',

ckrapu

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):

ckrapu

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)