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Markov Chains

A while back I wrote a blog post explaining Markov chains and demonstrating different ways of finding their steady-state distribution in R. Now, I want to play with Markov chains as a graph. I’m going to pull examples from around the internet and answer the same questions in Cypher as the authors do with matrices. This gives me the opportunity to explore more advanced Cypher queries while working with a topic I enjoy very much (stochastic processes and Markov chains). So this is officially just for funsies.

I found three Markov chains online that I’m going to showcase, and they involve the following topics:

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A new design for a radar chart in D3.js. You can read more about in on the blog I wrote "A different look for the D3 radar chart"

An older version of a radar chart that I adjusted two years ago when I was just starting to learn D3.js can be found here

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os

def computeCost(X, y, theta):
    inner = np.power(((X * theta.T) - y), 2)
    return np.sum(inner) / (2 * len(X))

def gradientDescent(X, y, theta, alpha, iters):

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import tensorflow as tf


tf.add(1, 2)
# 3

tf.sub(2, 1)
# 1

tf.mul(2, 2)

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import tensorflow as tf

# create a constant 2X2 matrix
tensor_1 = tf.constant([[1., 2.], [3.,4]])

tensor_2 = tf.constant([[5.,6.],[7.,8.]])

# create a matrix multiplication operation
output_tensor = tf.matmul(tensor_1, tensor_2)

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WITH monthly_usage AS (
    SELECT
      user_id,
      date_part('month', age(created_at, '1970-01-01')) +
      12 * date_part('year', age(created_at, '1970-01-01')) AS time_period
    FROM orders
    WHERE order_state = 'completed'
    GROUP BY 1, 2
    ORDER BY 1, 2)

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import numpy as np
import cv2

# cv2.getGaborKernel(ksize, sigma, theta, lambda, gamma, psi, ktype)
# ksize - size of gabor filter (n, n)
# sigma - standard deviation of the gaussian function
# theta - orientation of the normal to the parallel stripes
# lambda - wavelength of the sunusoidal factor
# gamma - spatial aspect ratio
# psi - phase offset

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"""Functions for downloading and reading MNIST data."""
from __future__ import print_function
import gzip
import os
import urllib
import numpy
SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'


def maybe_download(filename, work_directory):