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

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

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

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

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 numpy as np
import pymc3 as pm

N, T = 20, 10

min_lat, max_lat   = 35, 40
min_long, max_long = 30, 35 

lat_interval  = max_lat - min_lat
long_interval = max_long - min_long

ckrapu

import geopandas as gpd
import numpy as np
import pandas as pd
import requests

save_directory = '../data/'

osm_api_url_base = 'http://overpass.openstreetmap.ru/cgi/xapi_meta?'

regions = {

ckrapu

import numpy as np
import pymc3 as pm
from pymc3.distributions.transforms import StickBreaking

genus_counts = np.random.multinomial(10, np.ones(5)/5, 20)

with pm.Model() as model:
    k = 3
    n, p = genus_counts.shape
    profile_gamma = pm.Gamma('profile_gamma', alpha=np.ones((k, p)), beta=np.ones((k, p)), shape=(k,p))

ckrapu

from pymc3.step_methods.arraystep import BlockedStep 
from pymc3.distributions.transforms import stick_breaking
from pymc3.model import modelcontext

import pymc3 as pm
import numpy as np

def sample_dirichlet(c):
    gamma = np.random.gamma(c)
    p = gamma/gamma.sum(axis=-1, keepdims=True)

ckrapu

import pymc3 as pm
from time import time

traces = []

t1 = time()
with pm.Model() as model:
    RVS = []
    for i in range(20):
        RVS.append(pm.Normal('var_{0}'.format(i)))