ckrapu

import theano
import theano.tensor as tt
import numpy as np
import pymc3 as pm

print(theano.__version__)

x = np.asarray([0,1])
print(x.dtype)

ckrapu

import pymc3 as pm
import numpy as np

def sigmoid(x):
    return np.exp(x) / (1. + np.exp(x))

# Create toy dataset
n = 100
n_components = 2
p = 2

ckrapu

import numpy as np
import pymc3 as pm

n = 4

X_2D_train = np.random.randn(n,2)
X_2D_star  = np.random.randn(n-1, 2)
y          = np.random.randn(n,1)

with pm.Model() as model:

ckrapu

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from theano import shared
import scipy.stats as stats
from scipy.stats import gamma, norm
import pymc3 as pm
import theano.tensor as tt
import arviz as az

ckrapu

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from theano import shared
import scipy.stats as stats
from scipy.stats import gamma, norm
import pymc3 as pm
import theano.tensor as tt
import arviz as az

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)))

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 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

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

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