def request(self):
'''The method that determines the actual ping that will be
made by the pinger object.'''
randy = uniform(0, 1)
ping_cdf = h.cdf(self.E)
ping = h.get_index(randy, ping_cdf)
return ping
def next_action(self):
randy = uniform(0, 1) # Throwback to Archer.
index = 0 # Worst case select the first action.
# print("The p is: " + str(self.p))
probs = self.p / sum(self.p)
cdf = h.cdf(probs)
# print("The cdf is: " + str(cdf))
index = h.get_index(randy, cdf)
return index
def next_action(self):
'''Pick the next action of the learning automata based on the
probability vector, self.p, of the LRI automata. At the first
time instant all action probabilities are equally likely.'''
# Pick a uniformly distributed random number to be tested against
# the CDF of self.p. Used to determine the action of the
# automaton.
randy = uniform(0, 1) # Throwback to Archer.
# On catastrophic failure pick the first action.
index = 0 # Worst case select the first action.
# print("The p is: " + str(self.p)) # Debug the change in self.p.
cdf = h.cdf(self.p)
# print("The cdf is: " + str(cdf)) # Debug the selected action.
# index is the index of the CDF corresponding to the random
# action. This value is the next action the automaton will choose.
index = h.get_index(randy, cdf)
return index
def get_cdf(self):
cdf_counts = self.Nitems * 0.5**np.arange(1, self.Ncat + 1)
cdf = helpers.cdf(self.Ncat)
return np.vstack((cdf_counts, cdf))
from scipy import optimize, stats
import numpy as np
import helpers
# Run once to compile the functions
print("Compiling the functions...")
print(helpers.one_trial_div2(20, 60))
print(helpers.get_tree_fudge_multi(20, 60, 2))
print(helpers.Ptree(20, 10))
print(helpers.f_empty(20, 10))
print(helpers.n_filled(20, 10))
print(helpers.cdf(20))
print(helpers.get_mc(20, 60, 2, 10))
class Counts(object):
"""Class that reads a counts filename and holds
all the operations we want to do"""
def __init__(self, fname):
"""Read a file of (sorted) counts"""
self.counts = np.sort(np.loadtxt(fname))[::-1]
self.Ncat = len(self.counts)
self.Nitems = np.sum(self.counts)
self.frequencies = self.counts / self.Nitems
self.ranks = np.arange(1, self.Ncat + 1)
def get_cdf(self):
cdf_counts = self.Nitems * 0.5**np.arange(1, self.Ncat + 1)
cdf = helpers.cdf(self.Ncat)
return np.vstack((cdf_counts, cdf))
def get_cdf(self):
cdf_counts = self.Nitems * 0.5**np.arange(1, self.Ncat+1)
cdf = helpers.cdf(self.Ncat)
return np.vstack((cdf_counts, cdf))
from scipy import optimize, stats
import numpy as np
import helpers
# Run once to compile the functions
print("Compiling the functions...")
print(helpers.one_trial_div2(20, 60))
print(helpers.get_tree_fudge_multi(20, 60, 2))
print(helpers.Ptree(20, 10))
print(helpers.f_empty(20, 10))
print(helpers.n_filled(20, 10))
print(helpers.cdf(20))
print(helpers.get_mc(20, 60, 2, 10))
class Counts(object):
"""Class that reads a counts filename and holds
all the operations we want to do"""
def __init__(self, fname):
"""Read a file of (sorted) counts"""
self.counts = np.sort(np.loadtxt(fname))[::-1]
self.Ncat = len(self.counts)
self.Nitems = np.sum(self.counts)
self.frequencies = self.counts/self.Nitems
self.ranks = np.arange(1, self.Ncat+1)
def get_cdf(self):
cdf_counts = self.Nitems * 0.5**np.arange(1, self.Ncat+1)
cdf = helpers.cdf(self.Ncat)
