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Verify distribution uniformity/Chi-squared test: Difference between revisions
Verify distribution uniformity/Chi-squared test (view source)
Revision as of 02:56, 28 April 2014
, 10 years agoAdded Ruby
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</lang>
=={{header|Ruby}}==
{{trans|Python}}
<lang ruby>def gammaInc_Q(a, x)
a1, a2 = a-1, a-2
f0 = lambda {|t| t**a1 * Math.exp(-t)}
df0 = lambda {|t| (a1-t) * t**a2 * Math.exp(-t)}
y = a1
y += 0.3 while f0[y]*(x-y) > 2.0e-8 and y < x
y = x if y > x
h = 3.0e-4
n = (y/h).to_i
h = y/n
hh = 0.5 * h
sum = 0
(n-1).step(0, -1) do |j|
t = h * j
sum += f0[t] + hh * df0[t]
end
h * sum / gamma_spounge(a)
end
A = 12
k1_factrl = 1.0
coef = [Math.sqrt(2.0*Math::PI)]
COEF = (1...A).each_with_object(coef) do |k,c|
c << Math.exp(A-k) * (A-k)**(k-0.5) / k1_factrl
k1_factrl *= -k
end
def gamma_spounge(z)
accm = (1...A).inject(COEF[0]){|res,k| res += COEF[k] / (z+k)}
accm * Math.exp(-(z+A)) * (z+A)**(z+0.5) / z
end
def chi2UniformDistance(dataSet)
expected = dataSet.inject(:+).to_f / dataSet.size
dataSet.map{|d|(d-expected)**2}.inject(:+) / expected
end
def chi2Probability(dof, distance)
1.0 - gammaInc_Q(0.5*dof, 0.5*distance)
end
def chi2IsUniform(dataSet, significance=0.05)
dof = dataSet.size - 1
dist = chi2UniformDistance(dataSet)
chi2Probability(dof, dist) > significance
end
dsets = [ [ 199809, 200665, 199607, 200270, 199649 ],
[ 522573, 244456, 139979, 71531, 21461 ] ]
for ds in dsets
puts "Data set:#{ds}"
dof = ds.size - 1
puts " degrees of freedom: %d" % dof
distance = chi2UniformDistance(ds)
puts " distance: %.4f" % distance
puts " probability: %.4f" % chi2Probability(dof, distance)
puts " uniform? %s" % (chi2IsUniform(ds) ? "Yes" : "No")
end</lang>
{{out}}
<pre>
Data set:[199809, 200665, 199607, 200270, 199649]
degrees of freedom: 4
distance: 4.1463
probability: 0.3866
uniform? Yes
Data set:[522573, 244456, 139979, 71531, 21461]
degrees of freedom: 4
distance: 790063.2759
probability: -0.0000
uniform? No
</pre>
=={{header|Tcl}}==
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