Python chi2_contingency示例

示例#1

文件： sum_exp.py 项目： AkiraKane/Cloudera-Data-Scientist-Challenge-3

	else:
		total2.append(sum(total[keys].values()))

exp1_val1 = test1[0]
exp1_val2 = total1[0]-test1[0]
exp2_val1 = test1[1]
exp2_val2 = total1[1]-test1[1]
exp3_val1 = test2[0]
exp3_val2 = total2[0]-test2[0]
exp4_val1 = test2[1]
exp4_val2 = total2[1]-test2[1]
exp1_2_total = exp1_val1 + exp2_val1
observations1 = np.array([[exp1_val1,exp1_val2],[exp2_val1,exp2_val2]])
observations2 = np.array([[exp3_val1,exp3_val2],[exp4_val1,exp4_val2]])

test1_result,p1,dof1,expected1 = pl.chi2_contingency(observations1,lambda_="log-likelihood")
test2_result,p2,dof2,expected2 = pl.chi2_contingency(observations2,lambda_="log-likelihood")

#result["performance_of_experiment_1_versus_2"] = test1_result[1]
#result["performance_of_experiment_3_versus_4"] = test2_result[1]
result["performance_of_experiment_1_versus_2"] = p1
result["performance_of_experiment_3_versus_4"] = p2

def get_days(population_size):
  Z = 2.57
  p = 0.5
  e = .01
  N = population_size
  n_0 = 0.0
  n = 0.0

示例#2

文件： testcont.py 项目： AkiraKane/Cloudera-Data-Scientist-Challenge-3

#!/usr/bin/python
from __future__ import division, print_function, absolute_import
import sys
sys.path.append('.')
from functools import reduce
import numpy as np
from scipy import special
import port_lib as pl
#1:3,4 2:3,4

#obs = np.array([[45151,44199],[46812,42258]])
obs = np.array([[45151,636240],[44199,635592]])
g, p, dof, expctd = pl.chi2_contingency(obs, lambda_="log-likelihood")
print(g)
print(p)
print(dof)
print(expctd)
print
print 
obs = np.array([[46812,632901],[42258,639211]])
g, p, dof, expctd = pl.chi2_contingency(obs, lambda_="log-likelihood")
print(g)
print(p)
print(dof)
print(expctd)

示例#3

文件： testcont.py 项目： azuranop/Cloudera-Data-Scientist-Challenge-3

#!/usr/bin/python
from __future__ import division, print_function, absolute_import
import sys
sys.path.append('.')
from functools import reduce
import numpy as np
from scipy import special
import port_lib as pl
#1:3,4 2:3,4

#obs = np.array([[45151,44199],[46812,42258]])
obs = np.array([[45151, 636240], [44199, 635592]])
g, p, dof, expctd = pl.chi2_contingency(obs, lambda_="log-likelihood")
print(g)
print(p)
print(dof)
print(expctd)
print
print
obs = np.array([[46812, 632901], [42258, 639211]])
g, p, dof, expctd = pl.chi2_contingency(obs, lambda_="log-likelihood")
print(g)
print(p)
print(dof)
print(expctd)

示例#4

文件： sum_exp.py 项目： azuranop/Cloudera-Data-Scientist-Challenge-3

    else:
        total2.append(sum(total[keys].values()))

exp1_val1 = test1[0]
exp1_val2 = total1[0] - test1[0]
exp2_val1 = test1[1]
exp2_val2 = total1[1] - test1[1]
exp3_val1 = test2[0]
exp3_val2 = total2[0] - test2[0]
exp4_val1 = test2[1]
exp4_val2 = total2[1] - test2[1]
exp1_2_total = exp1_val1 + exp2_val1
observations1 = np.array([[exp1_val1, exp1_val2], [exp2_val1, exp2_val2]])
observations2 = np.array([[exp3_val1, exp3_val2], [exp4_val1, exp4_val2]])

test1_result, p1, dof1, expected1 = pl.chi2_contingency(
    observations1, lambda_="log-likelihood")
test2_result, p2, dof2, expected2 = pl.chi2_contingency(
    observations2, lambda_="log-likelihood")

#result["performance_of_experiment_1_versus_2"] = test1_result[1]
#result["performance_of_experiment_3_versus_4"] = test2_result[1]
result["performance_of_experiment_1_versus_2"] = p1
result["performance_of_experiment_3_versus_4"] = p2


def get_days(population_size):
    Z = 2.57
    p = 0.5
    e = .01
    N = population_size
    n_0 = 0.0