ckrapu

INSERT INTO USER_SANDBOX.CHRIS_KRAPU.DOMGEO_DAILY_TEST_FOR_SALE (
    RDC_VISITOR_ID
    , MEMBER_ID
    , DOMINANT_GEO
    , MEAN_DIST_KM
    , DG_VIEWS
    , SUM_VIEWS_ACROSS_TOP
    , DG_VIEWS_PCT
    , TOP_VIEWED_GEOS
    , SPATIAL_DISPERSION_INDEX

ckrapu

import pymc as pm
import numpy as np

with pm.Model() as model:

  x = pm.Normal('x', shape=2)
  x_2d = pm.Normal('x_2d', shape=(3,4))

  # Takes variable of shape (2,) and extends it to shape (3,)
  y = pm.Deterministic('y',pm.math.concatenate([x, [0]], axis=0))

ckrapu

{"1": "Corn", "2": "Cotton", "3": "Rice", "4": "Sorghum", "5": "Soybeans", "6": "Sunflower", "10": "Peanuts", "11": "Tobacco", "12": "Sweet Corn", "13": "Pop or Orn Corn", "14": "Mint", "21": "Barley", "22": "Durum Wheat", "23": "Spring Wheat", "24": "Winter Wheat", "25": "Other Small Grains", "26": "Dbl Crop WinWht/Soybeans", "27": "Rye", "28": "Oats", "29": "Millet", "30": "Speltz", "31": "Canola", "32": "Flaxseed", "33": "Safflower", "34": "Rape Seed", "35": "Mustard", "36": "Alfalfa", "37": "Other Hay/Non Alfalfa", "38": "Camelina", "39": "Buckwheat", "41": "Sugarbeets", "42": "Dry Beans", "43": "Potatoes", "44": "Other Crops", "45": "Sugarcane", "46": "Sweet Potatoes", "47": "Misc Vegs & Fruits", "48": "Watermelons", "49": "Onions", "50": "Cucumbers", "51": "Chick Peas", "52": "Lentils", "53": "Peas", "54": "Tomatoes", "55": "Caneberries", "56": "Hops", "57": "Herbs", "58": "Clover/Wildflowers", "59": "Sod/Grass Seed", "60": "Switchgrass", "61": "Fallow/Idle Cropland", "62": "Pasture/Grass", "63": "Fores

ckrapu

import pymc as pm
import numpy as np
import pandas as pd
import arviz as az
import matplotlib.pyplot as plt
import xarray as xr
import aesara

trainSize,testSize = 1000,400
group_num_all = 7

ckrapu

log.ts:301  INFO [remote-connection][ExtensionHost][c4c80…][reconnect] received socket timeout event (unacknowledgedMsgCount: 9776, timeSinceOldestUnacknowledgedMsg: 20002, timeSinceLastReceivedSomeData: 20003).
11:43:34.029 log.ts:301  INFO [remote-connection][ExtensionHost][c4c80…][reconnect] starting reconnecting loop. You can get more information with the trace log level.
11:43:34.029 log.ts:301  INFO [remote-connection][ExtensionHost][c4c80…][reconnect] resolving connection...
11:43:34.030 log.ts:301  INFO [remote-connection][ExtensionHost][c4c80…][reconnect] connecting to 127.0.0.1:44341...
11:43:34.030 log.ts:289 TRACE [remote-connection][ExtensionHost][c4c80…][reconnect][127.0.0.1:44341] 1/6. invoking socketFactory.connect().
11:43:40.202 log.ts:289 TRACE [remote-connection][ExtensionHost][c4c80…][reconnect][127.0.0.1:44341] 2/6. socketFactory.connect() was successful.
11:43:40.203 log.ts:289 TRACE [remote-connection][ExtensionHost][c4c80…][reconnect][127.0.0.1:44341] 3/6. sending AuthRequest control 

ckrapu

with pm.Model() as bnn:
        x_data = pm.Data("x_data", x_input)
        y_data = pm.Data("y_data", y_input)

        #weights and bias prior
        w_1 = pm.Normal("w_1", 0, sigma=1, shape=(layer_in, layer_nodes[0]))
        b_1 = pm.Normal("b_1", 0, sigma=3, shape=1)
        #w_2 = pm.Normal("w_2", 0, sigma=1, shape=(layer_nodes[0], layer_nodes[1]))
        #b_2 = pm.Normal("b_2", 0, sigma=3, shape=1)
        w_out = pm.Normal("w_out", 0, sigma=1, shape=(layer_nodes[1], ))

ckrapu

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

def frobenius_norm(X):
    return tt.sum(tt.nlinalg.trace(A@A.T))**0.5

# Create simulated data via forward evolution of system
K        = 3
T        = 10

ckrapu

class Sampling(layers.Layer):
    """Uses (z_mean, z_log_var) to sample z, the vector encoding a digit."""

    def call(self, inputs):
        z_mean, z_log_var = inputs
        batch = tf.shape(z_mean)[0]
        dim = tf.shape(z_mean)[1]
        epsilon = tf.keras.backend.random_normal(shape=(batch, dim))
        return z_mean + tf.exp(0.5 * z_log_var) * epsilon

ckrapu

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)

ckrapu

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))