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# This code is in the public domain.
# choose_without_replacement is copyright (2007) Anne Archibald
"""
Utility functions
choose_without_replacement(m,n,repeats=None)
sample from a set without replacement
runlength(v)
return the values and lengths of runs in a vector
countunique(A)
return the element and frequency of each unique value in an array
"""
import numpy as np
# Author: Anne Archibald
# Re: [Numpy-discussion] Generating random samples without repeats
# Fri, 19 Sep 2008 12:19:22 -0700
def choose_without_replacement(m,n,repeats=None):
"""
Choose n nonnegative integers less than m without replacement
Returns an array of shape n, or (n,repeats).
"""
if repeats is None:
r = 1
else:
r = repeats
if n>m:
raise ValueError("Cannot find %d nonnegative integers less than %d" %(n,m))
elif n>m/2:
res = np.sort(np.random.rand(m,r).argsort(axis=0)[:n,:],axis=0)
else:
res = np.random.random_integers(0,m-1,size=(n,r))
while True:
res = np.sort(res,axis=0)
w = np.nonzero(np.diff(res,axis=0)==0)
nr = len(w[0])
if nr==0:
break
res[w] = np.random.random_integers(0,m-1,size=nr)
if repeats is None:
return res[:,0]
else:
return res
def runlength(v):
"""
Return the run lengths for repeated values in a vector v.
See also countunique.
"""
if len(v) == 0: return [],[]
diffs = np.diff(v)
steps = np.where(diffs!=0)[0]+1
ends = np.hstack([[0],steps,[len(v)]])
vals = v[ends[:-1]]
lens = np.diff(ends)
return vals, lens
def countunique(A):
"""
Returns the unique elements in an array and their frequency.
"""
return runlength(np.sort(A.flatten()))
def zscore(A, axis=None):
"""
Convert an array of data to zscores.
Use *axis* to limit the calculation of mean and standard deviation to
a particular axis.
"""
return (A - np.mean(A,axis=axis)) / np.std(A,axis=axis,ddof=1)
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