def get_factor(self):
"""Gives the expected Q/K. It should always converge."""
res = optimize.fsolve(lambda x: helpers.n_filled(
int(x * self.Ncat), self.Nitems) - self.Ncat,
1.1,
xtol=0.001,
full_output=True)
return res[0][0]
def get_factor(self):
"""Gives the expected Q/K. It should always converge."""
res = optimize.fsolve(lambda x: helpers.n_filled(int(x*self.Ncat), self.Nitems) - self.Ncat,
1.1, xtol=0.001, full_output=True)
return res[0][0]
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))
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)
