from time import process_time
import matplotlib.pyplot as plt
from fitter import Fitter
from scipy.stats import kstest
from collatz.v1.misc import get_fullpath, get_data, logmsg
CLOS_FILE = get_fullpath('data/collatz-clos2-100.csv')
def get_miss(stop_file):
data = get_data(stop_file, cols=['miss'])
return data['miss']
def fit_exp(miss):
dist = ['expon']
f = Fitter(miss, distributions=dist, timeout=600)
f.fit()
params = f.fitted_param['expon']
logmsg('fitted params exp = %s', str(params))
f.summary()
plt.show()
return params
def stat_test(x, params):
start = process_time()
_, pvalue = kstest(rvs=x, cdf='expon', args=params, mode='asymp')
end = process_time()
from time import process_time
import matplotlib.pyplot as plt
from fitter import Fitter
from scipy.stats import kstest
from collatz.v1.misc import get_fullpath, get_data, logmsg
CLOS_FILE = get_fullpath('data/collatz-clos1-824559.csv')
def get_miss(stop_file):
data = get_data(stop_file, cols=['miss'])
return data['miss']
def fit_exp(miss):
dist = ['expon']
f = Fitter(miss, distributions=dist, timeout=600)
f.fit()
params = f.fitted_param['expon']
logmsg('fitted params exp = %s', str(params))
f.summary()
plt.show()
return params
def stat_test(x, params):
start = process_time()
_, pvalue = kstest(rvs=x, cdf='expon', args=params, mode='asymp')
end = process_time()
import sys
import warnings
import matplotlib.pyplot as plt
from fitter import Fitter
from collatz.v1.misc import get_fullpath, get_data, logmsg
CLOS_FILE = get_fullpath('data/collatz-stop.csv')
def get_col(stop_file, col):
data = get_data(stop_file, cols=[col])
return data[col]
def fit_all(miss):
# dist = get_distributions()
# dist = ['genpareto', 'betaprima', 'lomax', 'f', 'ncf']
# dist = ['genpareto', 'lomax', 'f', 'ncf']
# dist = ['genpareto', 'lomax']
# dist = ['expon', 'lomax']
dist = ['expon']
f = Fitter(miss, timeout=600, distributions=dist)
f.fit()
print(f.df_errors.sort_values('sumsquare_error'))
logmsg('best fit = %s', str(f.get_best()))
f.summary()
plt.show()
# computes collatz stop function up to certain limit
import pandas as pd
from collatz.v1.misc import get_fullpath, logmsg
MAX_N = 100
STOP_FILE = get_fullpath('data/collatz-stop-%d.csv' % MAX_N)
COLS = ['n', 'stop', 'miss']
cache = dict()
def collatz_stop(n):
orig_n = n
stop = 0
miss = 0
while n != 1:
if n in cache:
stop = stop + cache[n]
break
else:
miss += 1
if n % 2 == 0:
n = n // 2
else:
n = 3 * n + 1
stop += 1
cache[orig_n] = stop
return stop, miss
# computes collatz closures with method 1
import pandas as pd
from sortedcontainers import SortedSet
from collatz.v1.misc import get_fullpath, logmsg
START_N = 100
CLOS_FILE = get_fullpath('data/collatz-clos2-%d.csv' % START_N)
COLS = ['n', 'stop', 'miss']
def collatz_stop(n, cache, pend):
orig_n = n
stop = 0
miss = 0
while n != 1:
if n in cache:
stop = stop + cache[n][0]
break
else:
miss += 1
pend.add(n)
if n % 2 == 0:
n = n // 2
else:
n = 3 * n + 1
stop += 1
cache[orig_n] = stop, miss
# TODO: fitting stopping time to a dist seems harder, better see miss seq
import matplotlib.pyplot as plt
from fitter import Fitter
from scipy.stats import normaltest
from collatz.v1.misc import get_fullpath, get_data, logmsg
STOP_FILE = get_fullpath('data/collatz-stop-100.csv')
stop = get_data(STOP_FILE)['stop']
plt.hist(stop, density=True, bins=100)
plt.show()
_, pvalue = normaltest(stop)
logmsg('Normality test p-value = %.20f', pvalue)
f = Fitter(stop.sample(300000), timeout=60)
f.fit()
f.summary()