def mc_reject(a, b, x):
top = np.amax(fn(x)) # get max of function to integrate
nAccept = 0
nTotal = 0
# accepted values
Xaccept = []
Yaccept = []
# reject values
Xreject = []
Yreject = []
# sample number
isample = []
# calculated values of the integral
calcInt = []
random = Random()
idraw = max(1, int(Nsteps) / 100000)
#print(idraw)
for i in range(0, Nsteps):
X = random.rand() # leave as is since x range is [0,1]
Y = top * random.rand(
) # transform this to sample from the max value of the function
#print(top)
nTotal += 1
if (Y < np.sin(np.pi * X) + 1.0): #accept if inside
nAccept += 1
if (i % idraw == 0):
Xaccept.append(X)
Yaccept.append(Y)
else: # reject if outside
if (i % idraw == 0):
Xreject.append(X)
Yreject.append(Y)
if (i % idraw == 0):
isample.append(nTotal)
calcInt.append(top * nAccept / nTotal)
return (isample, calcInt)
def __init__(self, seed=5555):
self.m_random = Random(seed)
p = sys.argv.index('-Nmeas')
Nt = int(sys.argv[p + 1])
if Nt > 0:
Nmeas = Nt
if '-Nexp' in sys.argv:
p = sys.argv.index('-Nexp')
Ne = int(sys.argv[p + 1])
if Ne > 0:
Nexp = Ne
if '-output' in sys.argv:
p = sys.argv.index('-output')
OutputFileName = sys.argv[p + 1]
doOutputFile = True
# class instance of our Random class using seed
random = Random(seed)
if doOutputFile:
outfile = open(OutputFileName, 'w')
outfile.write(str(rate) + " \n")
for e in range(0, Nexp):
for t in range(0, Nmeas):
outfile.write(str(random.Exponential(rate)) + " ")
outfile.write(" \n")
outfile.close()
else:
print(rate)
for e in range(0, Nexp):
for t in range(0, Nmeas):
print(random.Exponential(rate), end=' ')
print(" ")
Nskip = Ns
if '--model0' in sys.argv:
model = 0
elif '--model1' in sys.argv:
model = 1
else:
print("You must specify a model.")
sys.exit(1)
if '-output' in sys.argv:
p = sys.argv.index('-output')
OutputFileName = sys.argv[p + 1]
doOutputFile = True
# class instance of our Random class using seed
random = Random(seed)
def getObservation():
# model 0
# alpha and beta for our gamma distribution
alpha = 2.7
beta = 1.57
if model == 1: # model 1
alpha, beta = random.PriorRand(alpha, beta)
# get rate for given parameters
rate = np.random.gamma(alpha, beta)
return np.random.poisson(rate)
class MySort:
"""A crappy sorting class"""
# initialization method for Random class
def __init__(self, seed=5555):
self.m_random = Random(seed)
# sorts array using bubble sort
def BubbleSort(self, array):
n = len(array)
for i in range(n):
# Create a flag that will allow the function to
# terminate early if there's nothing left to sort
already_sorted = True
# Start looking at each item of the list one by one,
# comparing it with its adjacent value. With each
# iteration, the portion of the array that you look at
# shrinks because the remaining items have already been
# sorted.
for j in range(n - i - 1):
if array[j] > array[j + 1]:
# If the item you're looking at is greater than its
# adjacent value, then swap them
array[j], array[j + 1] = array[j + 1], array[j]
# Since you had to swap two elements,
# set the `already_sorted` flag to `False` so the
# algorithm doesn't finish prematurely
already_sorted = False
# If there were no swaps during the last iteration,
# the array is already sorted, and you can terminate
if already_sorted:
break
return array
# sorts array using insertion sort
def InsertionSort(self, array):
# Loop from the second element of the array until
# the last element
for i in range(1, len(array)):
# This is the element we want to position in its
# correct place
key_item = array[i]
# Initialize the variable that will be used to
# find the correct position of the element referenced
# by `key_item`
j = i - 1
# Run through the list of items (the left
# portion of the array) and find the correct position
# of the element referenced by `key_item`. Do this only
# if `key_item` is smaller than its adjacent values.
while j >= 0 and array[j] > key_item:
# Shift the value one position to the left
# and reposition j to point to the next element
# (from right to left)
array[j + 1] = array[j]
j -= 1
# When you finish shifting the elements, you can position
# `key_item` in its correct location
array[j + 1] = key_item
return array
# sorts array using quicksort
def QuickSort(self, array):
# If the input array contains fewer than two elements,
# then return it as the result of the function
if len(array) < 2:
return array
low, same, high = [], [], []
# Select your `pivot` element randomly
pivot = array[int(self.m_random.rand() * len(array))]
for item in array:
# Elements that are smaller than the `pivot` go to
# the `low` list. Elements that are larger than
# `pivot` go to the `high` list. Elements that are
# equal to `pivot` go to the `same` list.
if item < pivot:
low.append(item)
elif item == pivot:
same.append(item)
elif item > pivot:
high.append(item)
# The final result combines the sorted `low` list
# with the `same` list and the sorted `high` list
return self.QuickSort(low) + same + self.QuickSort(high)
# sorts array using default Python sort
def DefaultSort(self, array):
array.sort()
return array
nTotal = 0
# accepted values
Xaccept = []
Yaccept = []
# reject values
Xreject = []
Yreject = []
# sample number
isample = []
# calculated values of Pi (per sample)
calcPi = []
random = Random()
idraw = max(1, int(Nsample) / 100000)
for i in range(0, Nsample):
X = random.rand()
Y = random.rand()
nTotal += 1
if (X * X + Y * Y <= 1): #accept if inside
nAccept += 1
if (i % idraw == 0):
Xaccept.append(X)
Yaccept.append(Y)
else: # reject if outside
if (i % idraw == 0):
Xreject.append(X)
if '-Ntoss' in sys.argv:
p = sys.argv.index('-Ntoss')
Nt = int(sys.argv[p + 1])
if Nt > 0:
Ntoss = Nt
if '-Nexp' in sys.argv:
p = sys.argv.index('-Nexp')
Ne = int(sys.argv[p + 1])
if Ne > 0:
Nexp = Ne
if '-output' in sys.argv:
p = sys.argv.index('-output')
OutputFileName = sys.argv[p + 1]
doOutputFile = True
# class instance of our Random class using seed
random = Random(seed)
if doOutputFile:
outfile = open(OutputFileName, 'w')
for e in range(0, Nexp):
for t in range(0, Ntoss):
outfile.write(str(random.Categorical(prob1, prob2)) + " ")
outfile.write(" \n")
outfile.close()
else:
for e in range(0, Nexp):
for t in range(0, Ntoss):
print(random.Categorical(prob1, prob2), end=' ')
print(" ")
import numpy as np
import sys
import matplotlib.pyplot as plt
sys.path.append(".")
from python.Random import Random
#global variables
bin_width = 0.
Xmin = -3.
Xmax = 2.
random = Random()
# Normal distribution with mean zero and sigma = 1
# Note: multiply by bin width to match histogram
def Gaus(x):
return (1. / np.sqrt(2. * np.arccos(-1))) * np.exp(-x * x / 2.)
def PlotGaus(x, bin_width):
return bin_width * Gaus(x)
# Uniform (flat) distribution scaled to Gaussian max
# Note: multiply by bin width to match histogram
def Flat(x):
return 1. / np.sqrt(2 * np.arccos(-1.))
def PlotFlat(x, bin_width):
p = sys.argv.index('-Ndice')
Nd = int(sys.argv[p + 1])
if Nd > 0:
Ndice = Nd
if '-Nexp' in sys.argv:
p = sys.argv.index('-Nexp')
Ne = int(sys.argv[p + 1])
if Ne > 0:
Nexp = Ne
if '-output' in sys.argv:
p = sys.argv.index('-output')
OutputFileName = sys.argv[p + 1]
doOutputFile = True
# class instance of our Random class using seed
random = Random(seed)
if doOutputFile:
outfile = open(OutputFileName, 'w')
for e in range(0, Nexp):
for t in range(0, Ndice):
outfile.write(
str(random.Categorical(prob1, prob2, prob3, prob4, prob5))
+ " ")
outfile.write(" \n")
outfile.close()
else:
for e in range(0, Nexp):
for t in range(0, Ndice):
print(random.Categorical(prob1, prob2, prob3, prob4, prob5),
end=' ')
if '-Ntoss' in sys.argv:
p = sys.argv.index('-Ntoss')
Nt = int(sys.argv[p + 1])
if Nt > 0:
Ntoss = Nt
if '-Nexp' in sys.argv:
p = sys.argv.index('-Nexp')
Ne = int(sys.argv[p + 1])
if Ne > 0:
Nexp = Ne
if '-output' in sys.argv:
p = sys.argv.index('-output')
OutputFileName = sys.argv[p + 1]
doOutputFile = True
# class instance of our Random class using seed
random = Random(seed)
if doOutputFile:
outfile = open(OutputFileName, 'w')
for e in range(0, Nexp):
for t in range(0, Ntoss):
outfile.write(str(random.Bernoulli(prob)) + " ")
outfile.write(" \n")
outfile.close()
else:
for e in range(0, Nexp):
for t in range(0, Ntoss):
print(random.Bernoulli(prob), end=' ')
print(" ")