jdmoore7

def traverse(g, node, bfs=True):
  s = Staq()
  s.insert(node)
  scheduled = set([node])
  
  while s.size > 0:
    node = s.pop(head=bfs, val=True)
    print(node)
    
    neighbors = g[node]

jdmoore7

class StaqNode:
  def __init__(self, val=None, left=None, right=None):
    self.val = val
    self.left = left
    self.right = right 

class Staq:
  def __init__(self):
    self.head = None 
    self.tail = None 

jdmoore7

import pymc3 as pm
from theano import tensor as tt
import arviz as az
import numpy as np

scores = np.array([1,1,1,0,0,0
    ]).flatten()

games = [(0,1), (0,2), (0,3),
         (1,2), (1,3),

jdmoore7

import pymc3 as pm
from theano import tensor as tt
import arviz as az
import numpy as np

# Binary, correct answer array
scores = np.array([1,1,1,0,0,0
    ]).flatten()

# (student:question) tuples

jdmoore7

import pymc3 as pm
from theano import tensor as tt
import arviz as az
import numpy as np

# Binary, correct answer array
scores = np.array([1,1,1,0,0,0
    ]).flatten()

# (student:question) tuples

jdmoore7

import pymc3 as pm
from theano import tensor as tt
import arviz as az
import numpy as np

# Binary, correct answer array
scores = np.array([1,1,1,0,0,0
    ]).flatten()

# (student:question) tuples

jdmoore7

def rev_posterior(samples=100, size=len(X)):
  for s in range(samples):
    idx = random.choice(range(size))
    m = trace.get_values('m')[idx]
    b = trace.get_values('b')[idx]
    rev = X * np.exp(m*X +b)
    plt.plot(X, rev)
  return 

# rev_posterior()  

jdmoore7

import random
def post_plot(trace_obj=trace,samples=100,size=len(X)):
  for itr in range(samples):
    idx = random.choice(range(size))
    m = trace_obj.get_values('m')[idx]
    b = trace_obj.get_values('b')[idx]
    Y_hat = np.exp(m*X + b)
    plt.plot(X,Y_hat)
  plt.scatter(X,Y)

jdmoore7

import pymc3 as pm
with pm.Model() as model:
  m = pm.Normal('m',mu=0, sd=2)
  b = pm.Normal('b',mu=0, sd=2)
  s = pm.Exponential('s',lam=1)
  y_hat = pm.math.dot(m, X) + b
  lik = pm.Normal('lik', mu=y_hat, observed=pm.math.log(Y), sigma=s)
  trace = pm.sample(chains=4)

jdmoore7

import numpy as np
sd = 0.5
m,b = -0.25, 5
X = np.linspace(0,20,100)
Y = np.exp(np.random.normal(loc=m*X+b, scale=sd))

import matplotlib.pyplot as plt
plt.scatter(X,Y)
Y_hat = np.exp(m*X+b)
plt.plot(X,Y_hat, color='r')